Real-time disease outbreak phylogenetic analysis

Abstract

To infer transmission links using within-host variation, Applicants first developed a statistical model of minor variant frequencies, which Applicants fit to a dataset of 134,682 SARS- CoV-2 genomes from Massachusetts, USA. Applicants then combined this model with a stochastic epidemic process to develop a hierarchical Bayesian statistical model of viral outbreaks. After validating the approach on both synthetic and real outbreak datasets, Applicants applied the tool to 5,692 outbreak clusters among the 134,682 Massachusetts genomes. Applicants found that informative sub-consensus variants are relatively rare in outbreaks, but that when they do occur, they significantly help resolve transmission networks. Applicants methods and results demonstrate the utility of within-host variation for transmission inference of SARS-CoV-2 and other pathogens, stressing the importance of pathogen sequencing in epidemiology and public health.

Description

REAL-TIME DISEASE OUTBREAK PHYLOGENETIC ANALYSIS STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0001] This invention was made with government support under Grant Nos. GM007753, GM144273, AI110818, and AI151812 awarded by the National Institutes of Health, Grant No. 75D30120C09605 awarded by the Centers for Disease Control and Prevention, and Grant Nos. CCF-2101140, CNS-2107078, and DMS-2023528 awarded by the National Science Foundation. The government has certain rights in the invention. CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 543,719, filed October 11, 2023, and U.S. Provisional Application No.63 / 650,250, filed May 21, 2024. The entire contents of the above-identified applications are hereby fully incorporated herein by reference. TECHNICAL FIELD

[0003] The subject matter disclosed herein is generally directed to reconstructing pathogen outbreaks and constructing phylogenetic tress using nucleic acid sequences and machine processing models. BACKGROUND

[0004] Epidemiological investigations have increasingly relied on genomic sequencing to identify pathways of infectious disease transmission. Most existing methods of transmission inference involve comparing SNV distances between consensus genomes, neglecting within-host variation. Within-host variants can serve as key genetic markers of transmission events in outbreaks because they may be passed from one host to another via an incomplete transmission bottleneck.

[0005] The identification of transmission events in viral outbreaks based on various forms of data, including sequenced patient samples, known times of testing or symptom onset, and available contact tracing data, remains a challenge [1]. Inferred transmission networks provide insight into how pathogens spread, quantifying key parameters such as the effective reproductive number.They also permit evaluation of the effectiveness of mitigation strategies, e.g., by measuring the effect of vaccination on the number of transmissions [2], or by analyzing transmission rates in different settings such as colleges, households, and workplaces [3].

[0006] Numerous studies have proposed algorithms for reconstructing outbreaks based on genomic and epidemiological data, such as viral genomes, sample collection dates, symptom onset dates, and contact tracing [1,4–10]. Many current approaches, however, face critical methodological limitations. The majority of existing models take only consensus genomes as inputs, using single nucleotide variant (SNV) distance as the only genetic signal to inform transmission probability. While a lower SNV distance is indeed a stronger indicator of transmission than a higher one, this approach oversimplifies the true underlying biological process in a way that loses critical information [11,12]. There are two plausible ways in which a transmission event may result in a SNV distance greater than 0: in one scenario, a sub-consensus- level mutation arises in the donor and is transmitted to the recipient, within whom it reaches consensus frequency; and in the other scenario a mutation arises de novo in the recipient and rises to consensus frequency. Most consensus-based outbreak reconstruction methods fail to account for these possibilities.

[0007] Several studies have addressed the challenge of incorporating within-host variation by taking a phylogenetic approach to outbreak reconstruction [13–15]. This approach infers a phylogenetic tree that connects all variants (consensus level and within-host) across all hosts in such a way that accounts for the constraints imposed by transmission bottlenecks (The transmission bottleneck for a donor-recipient pair is the collection of virions that exit the donor and found a persistent viral population in the recipient). Theoretically, this method properly incorporates within-host variance into the inference of transmission links, but requires both enormous computational power and a highly-efficient algorithmic implementation to yield accurate results if implemented in a Bayesian context. And while a maximum-likelihood methods may yield results more efficiently, they fail to capture the different probabilities associated with different transmission networks, as well as the distributions of key epidemic parameters such as the mutation rate and serial interval. BadTrIP, the only of these aforementioned tools to incorporate read-level data in a fully-Bayesian context, takes 1.8 hours to reconstruct an outbreak of 10 people, compared to only several minutes for the consensus-based method outbreaker2 [4,13].

[0008] In this work, Applicants developed, validated, and applied an outbreak reconstruction tool that models the composition of the viral population within each host over time. This approach has previously been used to infer transmission bottleneck sizes [16–18], but never entire transmission networks. To extend existing transmission bottleneck inference methods to transmission link inference, Applicants introduced a probability distribution for intrahost single nucleotide variant (iSNV) frequencies (Figure 1). Applicants validated the use of this distribution by fitting it to a dataset of 134,682 Massachusetts SARS-CoV-2 genomes sequenced at a single institution. Applicants then tested the outbreak reconstruction model on synthetic outbreak datasets, generated in accordance with the known reproduction number, overdispersion parameter, and mutation rate of SARS-CoV-2. Applicants also implemented contact tracing into the algorithm, demonstrating an increase in sensitivity. Next, Applicants validated the model using a nosocomial SARS-CoV-2 outbreak in South Africa with a number of confirmed transmission links [19,20], as well as a controlled outbreak experiment among cows with bovine respiratory syncytial virus (BRSV)

[0021] . Finally, Applicants applied the model to a dataset of 5,692 outbreak clusters among the 134,682 Massachusetts genomes, identifying genetic signatures of transmission links. The findings improved upon existing methods in outbreak reconstruction while underscoring the usefulness of sequencing data in epidemiological investigations.

[0009] Typically, the phylogenetic analysis of a disease outbreak suffers from an insufficient amount of data and slow processing. Many methods of phylogenetic analysis of disease outbreaks require many sampled DNA sequences from infected hosts and rely on simplifying assumptions, which may result in poor results. For example, many such models make statistical inferences based only on consensus-level genetic information, neglecting within-host variants detected by deep sequencing. As a result, they tend to yield high levels of uncertainty in terms of the directionality and structure of transmission networks among cases with identical or nearly-identical consensus genomes. In many instances, the conclusion remains unresolved and more data from the disease outbreak is needed.

[0010] Conventional systems are not configured for real-time disease outbreak reconstruction, both genetically and vectors of transmission, and predicting disease spread. Typically, conventional systems cannot generate pathogen transmission pathways fast enough during an outbreak. Conventional systems do not facilitate real-time linking of a plurality of nucleic acidsequences to pathogen transmission pathways. Previous systems do not provide solutions in a manner that is quick and effective for users to likely acknowledge.

[0011] Further, conventional systems configure pathogen transmission pathways based on human assessments of a large group of plurality of nucleic acid sequences over a long period of time. Human systems are unable to capture vast amounts of nucleic acid sequences data in real time. Unlike an outbreak transmission network, systems that rely on humans are unable to draw the subtle conclusions required to identify pathogen transmission pathways. Human systems are unable to create predictive models based on combined data collected from, for example, a plurality of samples from a plurality of.

[0012] In addition, usually, phylogenetic construction suffers from an insufficient amount of data and slow processing. Many methods of phylogenetic construction require many sampled sequences and rely on simplifying assumptions, which may result in poor results. In many instances, the conclusion remains unresolved and more data for the phylogenetic construction is needed.

[0013] Conventional systems are not configured for rapid, real-time phylogenetic construction, and suffer from the incapability of parallel analysis. Typically, conventional systems cannot generate phylogenetic trees fast enough for real-time analysis. Conventional systems do not facilitate real-time linking of a plurality of nucleic acid sequences to one or more phylogenetic trees. Previous systems do not provide solutions in a manner that is quick and effective for users to likely acknowledge.

[0014] Further, conventional systems configure one or more phylogenetic trees based on human assessments of a large group of plurality of nucleic acid sequences over a long period of time. Human systems are unable to capture vast amounts of nucleic acid sequences data in real time. Unlike a phylogenetic construction network, systems that rely on humans are unable to draw the subtle conclusions required to identify one or more phylogenetic trees. Human systems are unable to create predictive models based on combined data collected from, for example, a plurality of samples of a plurality of nucleic acid sequences.

[0015] Citation or identification of any document in this application is not an admission that such a document is available as prior art to the present invention.SUMMARY

[0016] In one aspect, a method of transmission reconstruction is provided including: (a) receiving, by one or more computing devices, a plurality of nucleic acid sequences; (b) processing, by one or more computing devices, the plurality of nucleic acid sequences with an outbreak transmission network, based at least in part on minor variant relationships and determines transmission links, in part, by identifying the frequency of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence; and (c) generating, by the outbreak transmission network, pathogen transmission pathways.

[0017] In an embodiment, the method further includes: (d) transmitting, by the one or more computing devices, the pathogen transmission pathways to a user device associated with a user.

[0018] In an embodiment, the method further includes: (i) collecting a plurality of samples from a plurality of subjects including the plurality of nucleic acid sequences and / or (ii) sequencing the plurality of nucleic acid sequences from the plurality of samples from the plurality of subjects.

[0019] In an embodiment, the minor variant relationships include of intra-host single nucleotide variants (iSNVs).

[0020] In an embodiment, processing includes determining the distribution of iSNV frequencies.

[0021] In an embodiment, the method further includes masking iSNV calls that appear to exhibit strong selective advantage due to the number of genomes in which the iSNV calls are present.

[0022] In an embodiment, the outbreak transmission network includes of computing the likelihood of minor variant / allele frequencies.

[0023] In an embodiment, computing the likelihood of minor variant / allele frequencies includes of a probability density function.

[0024] In an embodiment, the outbreak transmission network comprises of a ^-coalescent.

[0025] In an embodiment, the ^-coalescent comprises of a non-disjoint or disjoint ^- coalescent.

[0026] In an embodiment, the outbreak transmission network comprises of one or more model tree data structures.

[0027] In an embodiment, the one or more model tree data structures are independently selected to be fully-labeled or partially-labeled.

[0028] In an embodiment, the outbreak transmission network comprises of an epidemiological likelihood function.

[0029] In an embodiment, the outbreak transmission network comprises of a genomic likelihood function.

[0030] In an embodiment, transmission links are determined in parallel.

[0031] In an embodiment, the outbreak transmission network includes of a within-host viral replication process, the within-host viral replication process includes of computing the proportion of within-host nucleic acid sequences to contain a nucleotide at a given site.

[0032] In an embodiment, the within-host viral replication process of a pure-birth process.

[0033] In an embodiment, the pure-birth process includes of modeling birth events of a pathogen as randomly occurring over time.

[0034] In an embodiment, birth events include of one or more offspring.

[0035] In an embodiment, the birth even occurs instantaneously.

[0036] In an embodiment, the rate of a birth is dependent on the population of the pathogen.

[0037] In an embodiment, the rate of birth includes of a Poisson process.

[0038] In an embodiment, the population of the pathogen only increases.

[0039] In an embodiment, the birth event includes of, independently, one or more mutations of one or more nucleic acids of the one or more offspring from one or more nucleic acids in a parent pathogen sequence.

[0040] In an embodiment, the mutation occurs with a set probability.

[0041] In an embodiment, the within-host viral replication process includes of a Pólya Urn model.

[0042] In an embodiment, the outbreak transmission network includes of one or more genetic phases.

[0043] In an embodiment, the outbreak transmission network includes of two genetic phases.

[0044] In an embodiment, the two genetic phases include of an exponential growth phase and a quiescent phase.

[0045] In an embodiment, the quiescent phase includes of a Jukes-Cantor model.

[0046] In an embodiment, the outbreak transmission network includes of Markov Chain Monte Carlo.

[0047] In an embodiment, the outbreak transmission network includes of Metropolis-Hastings algorithm for Markov Chain Monte Carlo.

[0048] In an embodiment, the viral transmission pathway includes of network graph.

[0049] In an embodiment, the nucleic acid sequences include of read-level data, genomes, portions of genomes, or combination thereof.

[0050] In an embodiment, the genomes include of consensus genomes.

[0051] In an embodiment, the plurality of nucleic acid sequences is derived from deep sequencing methods.

[0052] In an embodiment, the network graph includes of nodes and edges, a node corresponds to a nucleic acid sequence from the plurality of nucleic acid sequences and an edge corresponds to a SNV distance.

[0053] In an embodiment, generating the pathogen transmission pathways is based at least in part on per-site, per-cycle mutation rate.

[0054] In an embodiment, generating the pathogen transmission pathways includes detecting minor allele transmissions and split bottlenecks.

[0055] In an embodiment, generating the pathogen transmission pathways includes putative detecting donor-recipient pairs with genomic signatures of transmission.

[0056] In an embodiment, generating the pathogen transmission pathways includes detecting reproductive number, overdispersion parameter, attack rate by vaccine status, the origin of a cluster, viral transmission pathways in nosocomial settings, or any combination thereof.

[0057] In an embodiment, the plurality of nucleic acid sequences is associated with a timestamp.

[0058] In an embodiment, the outbreak transmission network is trained on the frequency of iSNVs.

[0059] In an embodiment, identifying the frequency of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence occurs at each nucleic acid in the sequence.

[0060] In an embodiment, the determining the distribution of iSNV frequencies occurs at each site on the nucleic acid sequence.

[0061] In an embodiment, the pathogen of the pathogen transmission network is a virus, bacteria, fungi, or protozoa.

[0062] In an embodiment, the pathogen is a virus.

[0063] In another aspect, a system of transmission reconstruction is provided, including: a storage device; and a processor communicatively coupled to the storage device, wherein the processor executes application code instructions that are stored in the storage device to cause the system to: (a) receive, by one or more computing device, a plurality of nucleic acid sequences; (b) process the plurality of nucleic acid sequences with an outbreak transmission network, based at least in part on minor variant relationships and determines transmission links, in part, by identifying the proportion of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence; and (c) generate, outbreak transmission network, pathogen transmission pathways.

[0064] In an embodiment, the system further includes: d) transmit, by the one or more computing devices, the pathogen transmission pathways to a user device associated with a user.

[0065] In an embodiment, the minor variant relationships include of intra-host single nucleotide variants (iSNVs).

[0066] In an embodiment, processing includes determining the distribution of iSNV frequencies.

[0067] In an embodiment, the system further includes masking iSNV calls that appear to exhibit strong selective advantage due to the number of genomes in which the iSNV calls are present.

[0068] In an embodiment, the outbreak transmission network includes of computing the likelihood of minor variant / allele frequencies.

[0069] In an embodiment, computing the likelihood of minor variant / allele frequencies includes of a probability density function.

[0070] In an embodiment, the outbreak transmission network comprises of a ^-coalescent.

[0071] In an embodiment, the ^-coalescent comprises of a non-disjoint or disjoint ^- coalescent.

[0072] In an embodiment, the outbreak transmission network comprises of one or more model tree data structures.

[0073] In an embodiment, the one or more model tree data structures are independently selected to be fully-labeled or partially-labeled.

[0074] In an embodiment, the outbreak transmission network comprises of an epidemiological likelihood function.

[0075] In an embodiment, the outbreak transmission network comprises of a genomic likelihood function.

[0076] In an embodiment, transmission links are determined in parallel.

[0077] In an embodiment, the outbreak transmission network includes of a within-host viral replication process, the within-host viral replication process includes of computing the proportion of within-host nucleic acid sequences to contain a nucleotide at a given site.

[0078] In an embodiment, the within-host viral replication process of a pure-birth process.

[0079] In an embodiment, the pure-birth process includes of modeling birth events of a pathogen as randomly occurring over time.

[0080] In an embodiment, birth events include of one or more offspring.

[0081] In an embodiment, the birth even occurs instantaneously.

[0082] In an embodiment, the rate of a birth is dependent on the population of the pathogen.

[0083] In an embodiment, the rate of birth includes of a Poisson process.

[0084] In an embodiment, the population of the pathogen only increases.

[0085] In an embodiment, the birth event includes of, independently, one or more mutations of one or more nucleic acids of the one or more offspring from one or more nucleic acids in a parent pathogen sequence.

[0086] In an embodiment, the mutation occurs with a set probability.

[0087] In an embodiment, the within-host viral replication process includes of a Pólya Urn model.

[0088] In an embodiment, the outbreak transmission network includes of one or more genetic phases.

[0089] In an embodiment, the outbreak transmission network includes of two genetic phases.

[0090] In an embodiment, the two genetic phases include of an exponential growth phase and a quiescent phase.

[0091] In an embodiment, the quiescent phase includes of a Jukes-Cantor model.

[0092] In an embodiment, the outbreak transmission network includes of Markov Chain Monte Carlo.

[0093] In an embodiment, the outbreak transmission network includes of Metropolis-Hastings algorithm for Markov Chain Monte Carlo.

[0094] In an embodiment, the viral transmission pathway includes of network graph.

[0095] In an embodiment, the nucleic acid sequences include of read-level data, genomes, portions of genomes, or combination thereof.

[0096] In an embodiment, the genomes include of consensus genomes.

[0097] In an embodiment, the plurality of nucleic acid sequences is derived from deep sequencing methods.

[0098] In an embodiment, the network graph includes of nodes and edges, a node corresponds to a nucleic acid sequence from the plurality of nucleic acid sequences and an edge corresponds to a SNV distance.

[0099] In an embodiment, generating the pathogen transmission pathways is based at least in part on per-site, per-cycle mutation rate.

[0100] In an embodiment, generating the pathogen transmission pathways includes detecting minor allele transmissions and split bottlenecks.

[0101] In an embodiment, generating the pathogen transmission pathways includes putative detecting donor-recipient pairs with genomic signatures of transmission.

[0102] In an embodiment, generating the pathogen transmission pathways includes detecting reproductive number, overdispersion parameter, attack rate by vaccine status, the origin of a cluster, viral transmission pathways in nosocomial settings, or any combination thereof.

[0103] In an embodiment, the plurality of nucleic acid sequences is associated with a timestamp.

[0104] In an embodiment, the outbreak transmission network is trained on the frequency of iSNVs.

[0105] In an embodiment, identifying the frequency of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence occurs at each nucleic acid in the sequence.

[0106] In an embodiment, the determining the distribution of iSNV frequencies occurs at each site on the nucleic acid sequence.

[0107] In an embodiment, the pathogen of the pathogen transmission network is a virus, bacteria, fungi, or protozoa.

[0108] In an embodiment, the pathogen is a virus.

[0109] In some aspects, the techniques described herein relate to a computer program product, including: a non-transitory computer-readable storage device having computer-executable program instructions embodied thereon that when executed by a computer cause the computer to perform transmission reconstruction, the computer-executable program instructions including: (a) computer-executable program instructions to receive, by the one or more computing devices, a plurality of nucleic acid sequences; (b) computer-executable program instructions to process the plurality of nucleic acid sequences with an outbreak transmission network, based at least in part on minor variant relationships and determines transmission links, in part, by identifying the proportion of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence; and (c) computer- executable program instructions to generate, by the outbreak transmission network, a pathogen transmission pathways.

[0110] In an embodiment, the product further includes: d) transmit, by the one or more computing devices, the pathogen transmission pathways to a user device associated with a user.

[0111] In an embodiment, the minor variant relationships include of intra-host single nucleotide variants (iSNVs).

[0112] In an embodiment, processing includes determining the distribution of iSNV frequencies.

[0113] In an embodiment, the computer program product further includes masking iSNV calls that appear to exhibit strong selective advantage due to the number of genomes in which the iSNV calls are present.

[0114] In an embodiment, the outbreak transmission network includes of computing the likelihood of minor variant / allele frequencies.

[0115] In an embodiment, computing the likelihood of minor variant / allele frequencies includes of a probability density function.

[0116] In an embodiment, the outbreak transmission network comprises of a ^-coalescent.

[0117] In an embodiment, the ^-coalescent comprises of a non-disjoint or disjoint ^- coalescent.

[0118] In an embodiment, the outbreak transmission network comprises of one or more model tree data structures.

[0119] In an embodiment, the one or more model tree data structures are independently selected to be fully-labeled or partially-labeled.

[0120] In an embodiment, the outbreak transmission network comprises of an epidemiological likelihood function.

[0121] In an embodiment, the outbreak transmission network comprises of a genomic likelihood function.

[0122] In an embodiment, transmission links are determined in parallel.

[0123] In an embodiment, the outbreak transmission network includes of a within-host viral replication process, the within-host viral replication process includes of computing the proportion of within-host nucleic acid sequences to contain a nucleotide at a given site.

[0124] In an embodiment, the within-host viral replication process of a pure-birth process.

[0125] In an embodiment, the pure-birth process includes of modeling birth events of a pathogen as randomly occurring over time.

[0126] In an embodiment, birth events include of one or more offspring.

[0127] In an embodiment, the birth even occurs instantaneously.

[0128] In an embodiment, the rate of a birth is dependent on the population of the pathogen.

[0129] In an embodiment, the rate of birth includes of a Poisson process.

[0130] In an embodiment, the population of the pathogen only increases.

[0131] In an embodiment, the birth event includes of, independently, one or more mutations of one or more nucleic acids of the one or more offspring from one or more nucleic acids in a parent pathogen sequence.

[0132] In an embodiment, the mutation occurs with a set probability.

[0133] In an embodiment, the within-host viral replication process includes of a Pólya Urn model.

[0134] In an embodiment, the outbreak transmission network includes of one or more genetic phases.

[0135] In an embodiment, the outbreak transmission network includes of two genetic phases.

[0136] In an embodiment, the two genetic phases include of an exponential growth phase and a quiescent phase.

[0137] In an embodiment, the quiescent phase includes of a Jukes-Cantor model.

[0138] In an embodiment, the outbreak transmission network includes of Markov Chain Monte Carlo.

[0139] In an embodiment, the outbreak transmission network includes of Metropolis-Hastings algorithm for Markov Chain Monte Carlo.

[0140] In an embodiment, the viral transmission pathway includes of network graph.

[0141] In an embodiment, the nucleic acid sequences include of read-level data, genomes, portions of genomes, or combination thereof.

[0142] In an embodiment, the genomes include of consensus genomes.

[0143] In an embodiment, the plurality of nucleic acid sequences is derived from deep sequencing methods.

[0144] In an embodiment, the network graph includes of nodes and edges, a node corresponds to a nucleic acid sequence from the plurality of nucleic acid sequences and an edge corresponds to a SNV distance.

[0145] In an embodiment, generating the pathogen transmission pathways is based at least in part on per-site, per-cycle mutation rate.

[0146] In an embodiment, generating the pathogen transmission pathways includes detecting minor allele transmissions and split bottlenecks.

[0147] In an embodiment, generating the pathogen transmission pathways includes putative detecting donor-recipient pairs with genomic signatures of transmission.

[0148] In an embodiment, generating the pathogen transmission pathways includes detecting reproductive number, overdispersion parameter, attack rate by vaccine status, the origin of a cluster, viral transmission pathways in nosocomial settings, or any combination thereof.

[0149] In an embodiment, the plurality of nucleic acid sequences is associated with a timestamp.

[0150] In an embodiment, the outbreak transmission network is trained on the frequency of iSNVs.

[0151] In an embodiment, identifying the frequency of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence occurs at each nucleic acid in the sequence.

[0152] In an embodiment, the determining the distribution of iSNV frequencies occurs at each site on the nucleic acid sequence.

[0153] In an embodiment, the pathogen of the pathogen transmission network is a virus, bacteria, fungi, or protozoa.

[0154] In an embodiment, the pathogen is a virus.

[0155] In some aspects, the techniques described herein relate to a method of constructing a phylogenetic tree from a plurality of nucleic acid sequences including: (a) receiving, by one or more computing devices, the plurality of nucleic acid sequences, wherein the nucleic acid sequences are associated with a time stamp; (b) processing, by one or more computing devices, the plurality of nucleic acid sequences with a phylogenetic construction network; and (c) generating, by the phylogenetic construction network, one or more phylogenetic trees.

[0156] In an embodiment, the method further includes: (e) transmitting, by the one or more computing devices, the one or more phylogenetic trees to a user device associated with a user.

[0157] In an embodiment, the method further includes: (i) collecting a plurality of samples from a plurality of subjects including the plurality of nucleic acid sequences and / or (ii) sequencing the plurality of nucleic acid sequences from the plurality of samples from the plurality of subjects.

[0158] In an embodiment, the phylogenetic construction network is trained to reduce tree size and reduce mutations.

[0159] In an embodiment, the plurality of nucleic acid sequences is embedded.

[0160] In an embodiment, embedding includes encoding the plurality of nucleic acid sequences into one or more vectors.

[0161] In an embodiment, the one or more vectors are weight vectors.

[0162] In an embodiment, the phylogenetic construction network includes of one or more model tree data structures.

[0163] In an embodiment, weights, of the weight vectors, encode the probability of a nucleic acid sequence at a node of the model tree.

[0164] In an embodiment, the weights are trained to encode the probability.

[0165] In an embodiment, the one or more model trees include of mutation-annotated trees.

[0166] In an embodiment, the mutation-annotated trees include of explicit mutation-annotated tree (EMAT).

[0167] In an embodiment, the EMAT is a binary or an n-array tree data structure.

[0168] In an embodiment, the EMAT include of one or more point-mutations per branch of the model tree.

[0169] In an embodiment, the time stamp is associated with the one or more point-mutations on the branches of the model tree.

[0170] In an embodiment, one or more branch of the model tree includes of two or more point- mutations and the phylogenetic construction network merges the two or more point-mutations.

[0171] In an embodiment, the nodes of the model trees include of a nucleic acid sequence or portion thereof corresponding to the plurality of nucleic acid sequences.

[0172] In an embodiment, the time stamp corresponds to the nucleic acid sequence or portion thereof on the nodes of the model tree.

[0173] In an embodiment, the phylogenetic construction network includes of Bayesian inference.

[0174] In an embodiment, the phylogenetic construction network includes of a Jukes-Cantor model.

[0175] In an embodiment, the Jukes-Cantor model does not include of rate heterogeneity.

[0176] In an embodiment, the phylogenetic construction network includes of Maximum- Clade-Credibility tree.

[0177] In an embodiment, the phylogenetic construction network includes of Felsenstein pruning.

[0178] In an embodiment, the phylogenetic construction network includes of Markov Chain Monte Carlo.

[0179] In an embodiment, the Markov Chain Monte Carlo includes of the Metropolis-Hastings criterion.

[0180] In an embodiment, the phylogenetic construction network comprises of an annealing factor.

[0181] In an embodiment, the phylogenetic construction network includes of one or more cluster moves.

[0182] In an embodiment, the one or more cluster moves include of one or more changes to one or more neighboring branches of the model tree.

[0183] In an embodiment, the phylogenetic construction network includes of two or more cluster moves and the two or more cluster moves are performed in parallel.

[0184] In an embodiment, the two or more cluster moves performed in parallel includes of separating the neighboring branches from the model tree and fixing a boundary node that is a parent node to the neighboring branches.

[0185] In an embodiment, the one or more clusters are selected randomly.

[0186] In an embodiment, the separated neighboring branches are reassembled into a global model tree.

[0187] In an embodiment, the phylogenetic construction network includes of parsimony.

[0188] In an embodiment, the phylogenetic construction network includes of maximum likelihood.

[0189] In an embodiment, the phylogenetic construction network includes of coalescent prior.

[0190] In an embodiment, the coalescent prior includes of augmented ancestral prior.

[0191] In an embodiment, the phylogenetic construction network includes recombination, reassortment, or both.

[0192] In an embodiment, the phylogenetic construction network includes of an uncorrelated relaxed molecular clock.

[0193] In an embodiment, the method further includes unifying genome alignments.

[0194] In an embodiment, the EMAT encodes one or more missing nucleic acids in the plurality of nucleic acid sequences with a missation.

[0195] In an embodiment, the phylogenetic construction network includes of N-pruning.

[0196] In an embodiment, N-pruning includes of removing branches whose tips include of one or more missing nucleic acids.

[0197] In an embodiment, the phylogenetic construction network includes of subtree pruning and / or regrating (SPR).

[0198] In an embodiment, wherein the phylogenetic construction network comprises of mutation directed-SPR.

[0199] In an embodiment, the phylogenetic construction network includes of subtree slide, Wilson-Balding move, inner node displacement, branch reform, junction reform, or any combination thereof.

[0200] In an embodiment, the plurality of nucleic acid sequences includes partial genomic sequences, whole genomic sequences, or a combination thereof.

[0201] In an embodiment, the phylogenetic construction network includes of relative selective advantages, the relative selective advantage includes of coupling the coalescent prior and the plurality of nucleic acid sequences at each point in a model tree

[0202] In an embodiment, the phylogenetic construction network includes of an evolution model, the evolution model includes of mutation rates trained on the whole sequence of the plurality of nucleic acid sequences.

[0203] In an embodiment, the phylogenetic construction network performs the processing step through the web browser on a user's computing device.

[0204] In an embodiment, the processing step is visualized through a graphical user interface, the graphical user interface is presented on a user's computing device, and the graphical user interface is accessed by a user computing device on a website or on an application of the service provider.

[0205] In an embodiment, one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof are visualized.

[0206] In an embodiment, the processing step can be terminated by a user visualizing one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof.

[0207] In an embodiment, the method further includes of mean lineage prevalence charts, distribution of times at which a node arose, a listing of the mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof wherein mean lineage prevalence charts, distribution of times at which a node arose, a listing of the mutations that intervened between pair of nodes, the distribution of the time at which mutationsarose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof can be performed during or after the processing step.

[0208] In an embodiment, the visualization includes of labeling the one or more phylogenetic trees.

[0209] In an embodiment, labeling the one or more phylogenetic trees includes of labeling branches of the one or more phylogenetic trees.

[0210] In an embodiment, labeling branches includes of labeling branches with an adjustable credibility threshold, wherein the adjustable credibility threshold includes of confidence and uncertainty of a branch.

[0211] In an embodiment, the phylogenetic construction network includes of computing and identifying a most recent common ancestor (MRCA) of at least two nodes on the one or more phylogenetic trees selected by a user through the graphical user interface.

[0212] In an embodiment, the method further includes generating a summarized representation of the one or more phylogenetic trees, the summarized representation may further be visualized through a graphical user interface.

[0213] In an embodiment, the summarized representation of the one or more phylogenetic trees includes of one or more relationships between the root node and a user selected one or more lineages, one or more MCRA, or a combination thereof.

[0214] In an embodiment, the summarized representation includes of one or more mutations in the one or more phylogenetic trees, wherein the summary of one or more mutations includes of a number or percentage of trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which include of the one or more mutations, or any combination thereof.

[0215] In an embodiment, visualization allows a user to search for and select one or more mutations, the one or more mutations may include mutations labeled by a number or percentage of one or more phylogenetic trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which include of the one or more mutations, or any combination thereof.

[0216] In an embodiment, the method further includes generating a pruned one or more phylogenetic trees.

[0217] In an embodiment, the pruned one or more phylogenetic trees include of every inner node including a mutation even immediately upstream in at least one of the one or more phylogenetic trees.

[0218] In an embodiment, the pruned one or more phylogenetic trees are generated by: (i) tagging one or more inner nodes including a mutation immediately upstream of the one or more inner nodes and identifying a MRCA for the one or more inner nodes and (ii) removing inner nodes not tagged in step (i) and connecting the children nodes of the removed inner nodes to a most recent tagged ancestor node.

[0219] In an embodiment, the plurality of nucleic acid sequences includes of a pathogen nucleic acid sequence.

[0220] In an embodiment, the pathogen includes of a virus, bacteria, fungi, or protozoa.

[0221] In an embodiment, the pathogen is a virus.

[0222] In another aspect, a system of constructing a phylogenetic tree from a plurality of nucleic acid sequences is provided, including: a storage device; and a processor communicatively coupled to the storage device, wherein the processor executes application code instructions that are stored in the storage device to cause the system to: (a) receive, by one or more computing device, the plurality of nucleic acid sequences, wherein the nucleic acid sequences are associated with a time stamp; (b) process the plurality of nucleic acid sequences and associated time stamps with a phylogenetic construction network; and (c) generate, by the phylogenetic construction network, one or more phylogenetic trees.

[0223] In an embodiment, the system further includes: d) transmit, by the one or more computing devices, the one or more phylogenetic trees to a user device associated with a user.

[0224] In an embodiment, the phylogenetic construction network is trained to reduce tree size and reduce mutations.

[0225] In an embodiment, the plurality of nucleic acid sequences is embedded.

[0226] In an embodiment, embedding includes encoding the plurality of nucleic acid sequences into one or more vectors.

[0227] In an embodiment, the one or more vectors are weight vectors.

[0228] In an embodiment, the phylogenetic construction network includes of one or more model tree data structures.

[0229] In an embodiment, weights, of the weight vectors, encode the probability of a nucleic acid sequence at a node of the model tree.

[0230] In an embodiment, the weights are trained to encode the probability.

[0231] In an embodiment, the one or more model trees include of mutation-annotated trees.

[0232] In an embodiment, the mutation-annotated trees include of explicit mutation-annotated tree (EMAT).

[0233] In an embodiment, the EMAT is a binary or an n-array tree data structure.

[0234] In an embodiment, the EMAT include of one or more point-mutations per branch of the model tree.

[0235] In an embodiment, the time stamp is associated with the one or more point-mutations on the branches of the model tree.

[0236] In an embodiment, one or more branch of the model tree includes of two or more point- mutations and the phylogenetic construction network merges the two or more point-mutations.

[0237] In an embodiment, the nodes of the model trees include of a nucleic acid sequence or portion thereof corresponding to the plurality of nucleic acid sequences.

[0238] In an embodiment, the time stamp corresponds to the nucleic acid sequence or portion thereof on the nodes of the model tree.

[0239] In an embodiment, the phylogenetic construction network includes of Bayesian inference.

[0240] In an embodiment, the phylogenetic construction network includes of a Jukes-Cantor model.

[0241] In an embodiment, the Jukes-Cantor model does not include of rate heterogeneity.

[0242] In an embodiment, the phylogenetic construction network includes of Maximum- Clade-Credibility tree.

[0243] In an embodiment, the phylogenetic construction network includes of Felsenstein pruning.

[0244] In an embodiment, the phylogenetic construction network includes of Markov Chain Monte Carlo.

[0245] In an embodiment, the Markov Chain Monte Carlo includes of the Metropolis-Hastings criterion.

[0246] In an embodiment, the phylogenetic construction network comprises of an annealing factor.

[0247] In an embodiment, the phylogenetic construction network includes of one or more cluster moves.

[0248] In an embodiment, the one or more cluster moves include of one or more changes to one or more neighboring branches of the model tree.

[0249] In an embodiment, the phylogenetic construction network includes of two or more cluster moves and the two or more cluster moves are performed in parallel.

[0250] In an embodiment, the two or more cluster moves performed in parallel includes of separating the neighboring branches from the model tree and fixing a boundary node that is a parent node to the neighboring branches.

[0251] In an embodiment, the one or more clusters are selected randomly.

[0252] In an embodiment, the separated neighboring branches are reassembled into a global model tree.

[0253] In an embodiment, the phylogenetic construction network includes of parsimony.

[0254] In an embodiment, the phylogenetic construction network includes of maximum likelihood.

[0255] In an embodiment, the phylogenetic construction network includes of coalescent prior.

[0256] In an embodiment, the coalescent prior includes of augmented ancestral prior.

[0257] In an embodiment, the phylogenetic construction network includes recombination, reassortment, or both.

[0258] In an embodiment, the phylogenetic construction network includes of an uncorrelated relaxed molecular clock.

[0259] In an embodiment, the system further includes unifying genome alignments.

[0260] In an embodiment, the EMAT encodes one or more missing nucleic acids in the plurality of nucleic acid sequences with a missation.

[0261] In an embodiment, the phylogenetic construction network includes of N-pruning.

[0262] In an embodiment, N-pruning includes of removing branches whose tips include of one or more missing nucleic acids.

[0263] In an embodiment, the phylogenetic construction network includes of subtree pruning and / or regrating (SPR).

[0264] In an embodiment, wherein the phylogenetic construction network comprises of mutation directed-SPR.

[0265] In an embodiment, the phylogenetic construction network includes of subtree slide, Wilson-Balding move, inner node displacement, branch reform, junction reform, or any combination thereof.

[0266] In an embodiment, the plurality of nucleic acid sequences includes partial genomic sequences, whole genomic sequences, or a combination thereof.

[0267] In an embodiment, the phylogenetic construction network includes of relative selective advantages, the relative selective advantage includes of coupling the coalescent prior and the plurality of nucleic acid sequences at each point in a model tree.

[0268] In an embodiment, the phylogenetic construction network includes of an evolution model, the evolution model includes of mutation rates trained on the whole sequence of the plurality of nucleic acid sequences.

[0269] In an embodiment, the phylogenetic construction network performs the processing step through the web browser on a user's computing device.

[0270] In an embodiment, the processing step is visualized through a graphical user interface, the graphical user interface is presented on a user's computing device, and the graphical user interface is accessed by a user computing device on a website or on an application of the service provider.

[0271] In an embodiment, one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof are visualized.

[0272] In an embodiment, the processing step can be terminated by a user visualizing one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof.

[0273] In an embodiment, the system further includes of mean lineage prevalence charts, distribution of times at which a node arose, a listing of the mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of theprevalence of a mutation over time and / or with associated uncertainty, or a combination thereof wherein mean lineage prevalence charts, distribution of times at which a node arose, a listing of the mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof can be performed during or after the processing step.

[0274] In an embodiment, the visualization includes of labeling the one or more phylogenetic trees.

[0275] In an embodiment, labeling the one or more phylogenetic trees includes of labeling branches of the one or more phylogenetic trees.

[0276] In an embodiment, labeling branches includes of labeling branches with an adjustable credibility threshold, wherein the adjustable credibility threshold includes of confidence and uncertainty of a branch.

[0277] In an embodiment, the phylogenetic construction network includes of computing and identifying a most recent common ancestor (MRCA) of at least two nodes on the one or more phylogenetic trees selected by a user through the graphical user interface.

[0278] In an embodiment, the system further includes generating a summarized representation of the one or more phylogenetic trees, the summarized representation may further be visualized through a graphical user interface.

[0279] In an embodiment, the summarized representation of the one or more phylogenetic trees includes of one or more relationships between the root node and a user selected one or more lineages, one or more MCRA, or a combination thereof.

[0280] In an embodiment, the summarized representation includes of one or more mutations in the one or more phylogenetic trees, wherein the summary of one or more mutations includes of a number or percentage of trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which include of the one or more mutations, or any combination thereof.

[0281] In an embodiment, visualization allows a user to search for and select one or more mutations, the one or more mutations may include mutations labeled by a number or percentage of one or more phylogenetic trees containing the one or more mutations, a number of times ofintroduction of the one or more mutations, a most common one or more inner nodes which include of the one or more mutations, or any combination thereof.

[0282] In an embodiment, the system further includes generating a pruned one or more phylogenetic trees.

[0283] In an embodiment, the pruned one or more phylogenetic trees include of every inner node including a mutation even immediately upstream in at least one of the one or more phylogenetic trees.

[0284] In an embodiment, the pruned one or more phylogenetic trees are generated by: (iii) tagging one or more inner nodes including a mutation immediately upstream of the one or more inner nodes and identifying a MRCA for the one or more inner nodes and (iv) removing inner nodes not tagged in step (i) and connecting the children nodes of the removed inner nodes to a most recent tagged ancestor node.

[0285] In an embodiment, the plurality of nucleic acid sequences includes of a pathogen nucleic acid sequence.

[0286] In an embodiment, the pathogen includes of a virus, bacteria, fungi, or protozoa.

[0287] In an embodiment, the pathogen is a virus.

[0288] In some aspects, the techniques described herein relate to a computer program product, including: a non-transitory computer-readable storage device having computer-executable program instructions embodied thereon that when executed by a computer cause the computer to construct a phylogenetic tree from a plurality of nucleic acid sequences, the computer-executable program instructions including: (a) computer-executable program instructions to receive, by the one or more computing devices, the plurality of nucleic acid sequences, wherein the nucleic acid sequences are associated with a time stamp; (b) computer-executable program instructions to process the plurality of nucleic acid sequences and associated time stamps with a phylogenetic construction network; and (c) computer-executable program instructions to generate, by the phylogenetic construction network, one or more phylogenetic trees.

[0289] In an embodiment, the computer program product further includes: (f) transmit, by the one or more computing devices, the one or more phylogenetic trees to a user device associated with a user.

[0290] In an embodiment, the phylogenetic construction network is trained to reduce tree size and reduce mutations.

[0291] In an embodiment, the plurality of nucleic acid sequences is embedded.

[0292] In an embodiment, embedding includes encoding the plurality of nucleic acid sequences into one or more vectors.

[0293] In an embodiment, the one or more vectors are weight vectors.

[0294] In an embodiment, the phylogenetic construction network includes of one or more model tree data structures.

[0295] In an embodiment, weights, of the weight vectors, encode the probability of a nucleic acid sequence at a node of the model tree.

[0296] In an embodiment, the weights are trained to encode the probability.

[0297] In an embodiment, the one or more model trees include of mutation-annotated trees.

[0298] In an embodiment, the mutation-annotated trees include of explicit mutation-annotated tree (EMAT).

[0299] In an embodiment, the EMAT is a binary or an n-array tree data structure.

[0300] In an embodiment, the EMAT include of one or more point-mutations per branch of the model tree.

[0301] In an embodiment, the time stamp is associated with the one or more point-mutations on the branches of the model tree.

[0302] In an embodiment, one or more branch of the model tree includes of two or more point- mutations and the phylogenetic construction network merges the two or more point-mutations.

[0303] In an embodiment, the nodes of the model trees include of a nucleic acid sequence or portion thereof corresponding to the plurality of nucleic acid sequences.

[0304] In an embodiment, the time stamp corresponds to the nucleic acid sequence or portion thereof on the nodes of the model tree.

[0305] In an embodiment, the phylogenetic construction network includes of Bayesian inference.

[0306] In an embodiment, the phylogenetic construction network includes of a Jukes-Cantor model.

[0307] In an embodiment, the Jukes-Cantor model does not include of rate heterogeneity.

[0308] In an embodiment, the phylogenetic construction network includes of Maximum- Clade-Credibility tree.

[0309] In an embodiment, the phylogenetic construction network includes of Felsenstein pruning.

[0310] In an embodiment, the phylogenetic construction network includes of Markov Chain Monte Carlo.

[0311] In an embodiment, the Markov Chain Monte Carlo includes of the Metropolis-Hastings criterion.

[0312] In an embodiment, the phylogenetic construction network comprises of an annealing factor.

[0313] In an embodiment, the phylogenetic construction network includes of one or more cluster moves.

[0314] In an embodiment, the one or more cluster moves include of one or more changes to one or more neighboring branches of the model tree.

[0315] In an embodiment, the phylogenetic construction network includes of two or more cluster moves and the two or more cluster moves are performed in parallel.

[0316] In an embodiment, the two or more cluster moves performed in parallel includes of separating the neighboring branches from the model tree and fixing a boundary node that is a parent node to the neighboring branches.

[0317] In an embodiment, the one or more clusters are selected randomly.

[0318] In an embodiment, the separated neighboring branches are reassembled into a global model tree.

[0319] In an embodiment, the phylogenetic construction network includes of parsimony.

[0320] In an embodiment, the phylogenetic construction network includes of maximum likelihood.

[0321] In an embodiment, the phylogenetic construction network includes of coalescent prior.

[0322] In an embodiment, the coalescent prior includes of augmented ancestral prior.

[0323] In an embodiment, the phylogenetic construction network includes recombination, reassortment, or both.

[0324] In an embodiment, the phylogenetic construction network includes of an uncorrelated relaxed molecular clock.

[0325] In an embodiment, the computer program product further includes unifying genome alignments.

[0326] In an embodiment, the EMAT encodes one or more missing nucleic acids in the plurality of nucleic acid sequences with a missation.

[0327] In an embodiment, the phylogenetic construction network includes of N-pruning.

[0328] In an embodiment, N-pruning includes of removing branches whose tips include of one or more missing nucleic acids.

[0329] In an embodiment, the phylogenetic construction network includes of subtree pruning and / or regrating (SPR).

[0330] In an embodiment, wherein the phylogenetic construction network comprises of mutation directed-SPR.

[0331] In an embodiment, the phylogenetic construction network includes of subtree slide, Wilson-Balding move, inner node displacement, branch reform, junction reform, or any combination thereof.

[0332] In an embodiment, the plurality of nucleic acid sequences includes partial genomic sequences, whole genomic sequences, or a combination thereof.

[0333] In an embodiment, the phylogenetic construction network includes of relative selective advantages, the relative selective advantage includes of coupling the coalescent prior and the plurality of nucleic acid sequences at each point in a model tree

[0334] In an embodiment, the phylogenetic construction network includes of an evolution model, the evolution model includes of mutation rates trained on the whole sequence of the plurality of nucleic acid sequences.

[0335] In an embodiment, the phylogenetic construction network performs the processing step through the web browser on a user's computing device.

[0336] In an embodiment, the processing step is visualized through a graphical user interface, the graphical user interface is presented on a user's computing device, and the graphical user interface is accessed by a user computing device on a website or on an application of the service provider.

[0337] In an embodiment, one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof are visualized.

[0338] In an embodiment, the processing step can be terminated by a user visualizing one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof.

[0339] In an embodiment, the computer program product further includes of mean lineage prevalence charts, distribution of times at which a node arose, a listing of the mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof wherein mean lineage prevalence charts, distribution of times at which a node arose, a listing of the mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof can be performed during or after the processing step.

[0340] In an embodiment, the visualization includes of labeling the one or more phylogenetic trees.

[0341] In an embodiment, labeling the one or more phylogenetic trees includes of labeling branches of the one or more phylogenetic trees.

[0342] In an embodiment, labeling branches includes of labeling branches with an adjustable credibility threshold, wherein the adjustable credibility threshold includes of confidence and uncertainty of a branch.

[0343] In an embodiment, the phylogenetic construction network includes of computing and identifying a most recent common ancestor (MRCA) of at least two nodes on the one or more phylogenetic trees selected by a user through the graphical user interface.

[0344] In an embodiment, the computer program product further includes generating a summarized representation of the one or more phylogenetic trees, the summarized representation may further be visualized through a graphical user interface.

[0345] In an embodiment, the summarized representation of the one or more phylogenetic trees includes of one or more relationships between the root node and a user selected one or more lineages, one or more MCRA, or a combination thereof.

[0346] In an embodiment, the summarized representation includes of one or more mutations in the one or more phylogenetic trees, wherein the summary of one or more mutations includes of a number or percentage of trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which include of the one or more mutations, or any combination thereof.

[0347] In an embodiment, visualization allows a user to search for and select one or more mutations, the one or more mutations may include mutations labeled by a number or percentage of one or more phylogenetic trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which include of the one or more mutations, or any combination thereof.

[0348] In an embodiment, the computer program product further includes generating a pruned one or more phylogenetic trees.

[0349] In an embodiment, the pruned one or more phylogenetic trees include of every inner node including a mutation even immediately upstream in at least one of the one or more phylogenetic trees.

[0350] In an embodiment, the pruned one or more phylogenetic trees are generated by: (v) tagging one or more inner nodes including a mutation immediately upstream of the one or more inner nodes and identifying a MRCA for the one or more inner nodes and (vi) removing inner nodes not tagged in step (i) and connecting the children nodes of the removed inner nodes to a most recent tagged ancestor node.

[0351] In an embodiment, the plurality of nucleic acid sequences includes of a pathogen nucleic acid sequence.

[0352] In an embodiment, the pathogen includes of a virus, bacteria, fungi, or protozoa.

[0353] In an embodiment, the pathogen is a virus.

[0354] These and other aspects, objects, features, and advantages of the example embodiments will become apparent to those having ordinary skill in the art upon consideration of the following detailed description of example embodiments.BRIEF DESCRIPTION OF THE DRAWINGS

[0355] An understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention may be utilized, and the accompanying drawings of which:

[0356] FIG. 1A-1F – Schematic representations of six possible genetic profiles of viral transmission. In all diagrams, an orange circle represents a virion with adenine at a given site k on the viral genome, and a blue circle represents a virion with thymine at site k. The branching process diagrams represent the early phase of infection (labeled Phase 1), when the viral population remains small, and mutation events, although rare, significantly impact the resulting minor allele frequency. The bar charts represent the proportion of virions exhibiting the major and minor allele at site k at the end of Phase 2, in which the viral population grows to its maximum size of approximately 109to 1011particles

[0022] . The vertical gray arrows represent the virions passed from donor to recipient through the transmission bottleneck. In (1A), no iSNVs are observed at site k in the donor or recipient, with both exhibiting the same allele. In (1B), an iSNV arises at site k in the donor, and the major allele is passed to the recipient through a bottleneck of one particle. In (1C), an iSNV arises at site k in the donor, and the minor allele is passed to the recipient through a bottleneck of one particle, resulting in a change of consensus genome. In (1E), an iSNV arises at site k in the recipient, where it rises to consensus frequency, resulting in a change of consensus genome. In (1F), an iSNV arises at site k in the donor, and both alleles are passed to the recipient through a split bottleneck of multiple particles; as a result, the donor and recipient share the same within-host variant. Note: this list does not represent scenarios in which multiple particles with the same allele are transmitted, which is modeled in similar fashion to the passage of only one allele.

[0357] FIG.2A-2C – (2A) Histogram of minor allele frequencies from the dataset of 134,682 genomes (gray bars) and modeled probability density function (blue dashed line); (2B) Empirical cumulative density function (orange solid line) and modeled cumulative density function (blue dashed line). A minor allele frequency filter of at least 3% was applied for both figures; (2C) Log- log plot of empirical (gray) and theoretical density functions.

[0358] FIG.3A-3J – (3A) Epidemiology of an example synthetic outbreak, showing the latent (orange) and infectious (red) stages for each case, as well as transmission events (black linesoriginating from circles). (3B) Genetic profile of a synthetic outbreak, showing iSNVs and fixed SNVs for each case (mutations appearing in at most one case not shown). (3C), (3D) Transmission networks for the example synthetic outbreak as inferred by our model and by outbreaker2 (2018), respectively, with the opacity of each transmission link corresponding to its posterior probability. A green arrow indicates a correct transmission link, i.e., one that exists in the synthetic outbreak, and an orange arrow indicates an incorrect transmission link. Nodes 12 and 13 were not part of the same cluster as nodes 1–11. (3E), (3F), (3G) Boxplots of accuracy, coverage probability, and specificity for our model, outbreaker2, our model using only consensus genomes, and BadTrIP. (3H), (3I), (3J) Boxplots of accuracy, coverage probability, and specificity for our model and outbreaker2 under eight parameter combinations for contact tracing, where ν is the true positive rate and ξ is the false positive rate.

[0359] FIG. 4A-4J – (4A) Transmission network for the BRSV outbreak. (4B), (4C), (4D), (4E) Inferred transmission networks for the BRSV outbreak by Applicants’ tool, BadTrIP, outbreaker2, and Applicants’ tool with only consensus genomes, with a green arrow representing a correctly-inferred transmission link and a gray arrow representing an incorrectly-inferred transmission link. The opacity of each arrow corresponds to its posterior probability. (4F) Epidemiologically-confirmed transmissions in the nosocomial SARS-CoV-2 outbreak in South Africa. (4G), (4H), (4I), (4J) Inferred transmission networks for the BRSV outbreak by our tool, BadTrIP, outbreaker2, and Applicants’ tool with only consensus genomes, with a green arrow representing an epidemiologically-confirmed transmission link, a blue arrow representing a plausible transmission link based on the location histories of each patient, and a gray arrow representing a transmission link with neither form of supporting evidence. The opacity of each arrow corresponds to its posterior probability. Nodes are color coded by facility: orange represent the medical intensive care unit, red represents a medical ward, green represents a nursing home, blue represents the cardiac intensive care unit, purple represents the surgical intensive care unit, yellow represents the neurology ward, and gray represents patients who traveled between different wards.

[0360] FIG.5A-5F – Inferred transmission networks for the three clusters with high-levels of informative within-host diversity, using the outbreak reconstruction tool with read-level data in (5A), (5C), (5E) and with only consensus-level data in (5B), (5D), (5F). Each node represents onecase, each arrow represents an inferred transmission, and the opacity of an arrow corresponds to its posterior probability.

[0361] FIG.6A-6C – Inferred transmission matrices for the three clusters with high levels of informative within-host diversity. The x-axis represents the proposed donor, the y-axis represents the proposed recipient, and the size of the circle represents the posterior probability. Green, purple, and yellow circles correspond to the three iSNV signatures: a consensus-level variant in the recipient being detected as a minor variant in the donor, a consensus-level variant in the donor being detected as an iSNV in the recipient, and a split bottleneck, respectively. A gray circle represents an index case, a black circle represents a consensus change with no supporting iSNV information, and a blue circle represents no consensus change. Pairs of cases satisfying both the green and purple conditions are labeled as green circles; pairs satisfying the yellow condition and any other conditions are labeled as yellow circles.

[0362] FIG. 7 – PMF of the minor allele fraction where mutations may occur both when creating the template and copying the template to produce offspring virions (orange, “bidirectional”), versus only when creating the template (blue, “unidirectional”). Note: probabilities are plotted on a log scale.

[0363] FIG.8 – The problem with current outbreak reconstruction methods.

[0364] FIG.9 – How do iSNVs inform transmission. Major allele -> major allele (boring).

[0365] FIG. 10 – How do iSNVs inform transmission. Major allele -> major allele (interesting).

[0366] FIG.11 – How do iSNVs inform transmission. Major allele -> major allele; incomplete bottleneck.

[0367] FIG.12 – Our approach: tracking the viral population composition over time, without phylogenetic reconstruction.

[0368] FIG. 13 – De novo iSNV frequency model. Density / histogram and CDF / eCDF of minor allele frequencies.

[0369] FIG. 14 – Outbreak reconstruction model. Modeling de novo iSNVs is central to outbreak reconstruction.

[0370] FIG.15 – Validation on synthetic outbreaks. Epidemiology and genetics of a synthetic outbreak.

[0371] FIG.16 – Validation on synthetic outbreaks. Left: our model; Right: outbreaker2.

[0372] FIG. 17 – Validation on synthetic outbreaks. Accuracy and 90%-coverage of four outbreak reconstruction methods.

[0373] FIG.18 – Application to 100k genomes data. Three signatures of transmission are only detectable in iSNV data: 1) minor allele -> fixed 2) fixed -> minor allele 3) incomplete bottleneck. 8.84% of all putative links in 100k genomes dataset exhibit one of the three signatures but these signatures are disproportionately useful for reconstruction. For links with SNP distance = 0: 30.4% mean posterior probability, incomplete bottleneck; 5.88% mean posterior probability, complete bottleneck. For links with SNP distance > 0: 21.3% exhibit one of the three signatures.

[0374] FIG.19 – Example clusters for certain outbreaks, iSNVs offer significantly improved resolution.

[0375] FIG. 20 – Validation on real data. CMU peacock outbreak reconstruction. Green: contact tracing; blue: wifi. Applicants tested the probability of transmission by contact tracing and wifi. Mann-Whitney U Test: p<0.002 for wifi; p<1e-6 for contact tracing.

[0376] FIG. 21 – Structure of an explicit mutation-annotated tree (EMAT). All features associated with node i and branch i are labeled. See text for details.

[0377] FIG.22 – Schematic of our parallelization scheme.

[0378] FIG.23 – Illustrated example of pruning a single branch ending in an N tip.

[0379] FIG. 24 – N-pruning: A tree with “missations” decomposes into site trees, each of which has had its subtrees ending in N-tips pruned off. The genetic prior for the whole tree equals the product of site genetic priors for the pruned site trees. the label on the third tip on the left is NCG. The equation at the bottom may also read G(T |θ) = G(1)(T |θ) × G(2)(T |θ) × G(3)(T |θ).

[0380] FIG. 25 – Schematic of “general” SPR move: node X is detached from its parent branch, G–S, and reattached to a different branch, G′–S′.

[0381] FIG. 26 – When tips have missing data, an SPR move may cause a precarious path hanging off of X to shrink in the old configuration, and a new one to grow towards X in the new configuration.

[0382] FIG. 27 – When P is the root in the old or new configuration, some of the four key branches are above the root, and must be treated specially.

[0383] FIG. 28 – When an SPR move is really a slide up or down a path, some previously distinct branches coincide and must be treated specially.

[0384] FIG.29 – A maximally degenerate SPR move simply slides the attachment point of a node along its parent branch, so many branches that are distinct in the general case instead coincide.

[0385] FIG.30 – This ability to have a real-time view of the run allow users to access the state of their run This transparency is also seen in the ability to scrub through all previously sampled trees of a Delphy run, while simultaneously viewing the MCC tree, so that a clear picture of how the MCC tree has been constructed is available at all times.

[0386] FIG. 31 – Through its exploratory views, Delphy also emphasizes the percentage of trees in which any given lineage or mutation appears, drawing focus to areas in which researchers can be more or less confident in their conclusions. In this way, users can begin to narrow in on the degree to which any conclusions drawn from their data can be trusted right from the outset of their investigation.

[0387] FIG. 32 – Delphy automatically generates a summary sketch of the relationships between the root node, MCRA, and selected lineages so that users can see at a glance how many mutations occur between their set of lineages and get a general sense of how they relate to one another within the broader context of the full MCC tree.

[0388] FIG.33 – These nodes are also highlighted on the full MCC tree alongside a detailed breakdown of the possible time ranges of when these lineages were most likely to have emerged and information on the mutations that occurred between them. By immediately drawing focus to the MCRA, Delphy is able to guide users towards information that will most likely be relevant to a broad range of research objectives more efficiently than has previously been possible.

[0389] FIG.34 – Detailed views of each mutation show the possible times of introduction, the number of sequences bearing the mutation, and the most common inner nodes to first contain the mutation.

[0390] FIG. 35 – Though the “Features of Interest” section is meant to guide users towards the mutations that are most likely to be noteworthy, exploration is not limited to this set of identified mutations. Users are able to search for any mutation within their dataset and any numberof mutations can be selected or deselected for a detailed summary so that users can quickly view mutations relevant to their research interests in direct comparison to one another.

[0391] FIG. 36 – A non-binary MCC tree generated to reduce visual noise in the generated trees.

[0392] FIG. 37A-37B – A block diagram depicting a portion of a communications and processing architecture of a typical system to 37A) acquire a plurality of nucleic acid sequences from a user or database and perform outbreak reconstruction resulting in pathogen transmission pathways, in accordance with certain examples of the technology disclosed herein and 37B) acquire a plurality of nucleic acid sequences from a user or database and perform phylogenetic construction resulting in one or more phylogenetic trees, in accordance with certain examples of the technology disclosed herein.

[0393] FIG. 38A-38B – A block flow diagram depicting methods to 38A) reconstruct an outbreak or 38B) construct a phylogenetic tree, in accordance with certain examples of the technology disclosed herein.

[0394] FIG.39 – A block diagram depicting a computing machine and modules, in accordance with certain examples of the technology disclosed herein.

[0395] FIG. 40 – An Explicit Mutation-Annotated Tree (EMAT). The nodes a, b and c are dated tips, whose sequences were determined experimentally. The node x is an inner node, the most recent common ancestor of a and b. The root node r is the most recent common ancestor of all the tips. An EMAT explicitly encodes the reference sequence above the root, the mutation events (red crosses) and the missations (red parentheses). From this representation, the sequence of the virions represented at any point in the tree is determined implicitly (shown in parentheses in blue below every node).

[0396] FIG. 41 – Setup for a mutation-directed SPR move. A subtree rooted at X will be detached from the remaining tree, where it is currently grafted at P. The sequence at X is AAA, while the sequence at P is CCC. Each region k (see text) is annotated with its sequence (blue) and the number of differences Nkbetween that sequence and the sequence at X (in red, in parentheses): this is the quantity Nk.

[0397] FIG. 42 – Cartoon of grafting site probabilities calculated by mutation-directed SPR move (red) vs. distribution implied by the posterior (blue). Note that probabilities are broadlysimilar. The parsimonious region with sequence AAA, where attaching X necessitates no mutations on the P′–X branch, is clearly the most likely choice, but all other regions are possible, and their combined weight need not be negligible.

[0398] FIG.43 – Diagram demonstrating commutation for SL(R,G)(M, [N]) as the set of triples (S, H, B). Note that it no longer matters whether φ ◦ ι is the identity on L, or even whether R is a subpopulation of S. The only condition required now is that the labels assigned to ι(L) and its image φ ◦ ι(L) are the same.

[0399] FIG. 44 – In the following tree, assigning labels (e.g.) 3 and 4 to nodes r11 and r12, respectively, is different from assigning labels 4 and 3 to nodes r11and r12, respectively. The reason is that r11is the unique node in generation 1 that is an ancestor of the node with label A(r21) = 1, and r12 is the unique node in generation 1 that is an ancestor of the node with label A(r22) = 2. If r21 and r22 were unlabeled, the two proposed assignments of the labels of r11 and r12 would be indistinguishable, and we would face an overcounting issue.

[0400] FIG.45 – Graphical representation of a label function as described in examples herein.

[0401] FIG.46 - Consider the above tree, where a • denotes a node in L and a ◦ denotes a node not in L. All tracked nodes have been given the designator of an integer, positioned above the node. The vector h corresponding to this tree is (NA, 1, 1, 2, 2, 3, 5, 5). We derived this from the fact that the ith entry hiis the most recent tracked ancestor of the node with designator i. The vector w corresponding to this tree is (NA, 0, 1, 0, 1, 1, 0, 0). This comes from the fact that wiequals the number of generations back we need to go from node i to reach node hi, minus one.

[0402] FIG. 47 – An overlapping partition with two subtrees. A • denotes a node in one subtree; a □ denotes a node in the other subtree, and a ◦ denotes a node in both.

[0403] FIG.48A-48O – Posterior density plots of b for experiments A through O.

[0404] FIG.49A-49O – Posterior density plots of μ for experiments A through O.

[0405] FIG.50A-50O – Posterior density plots of p for experiments A through O.

[0406] FIG.51A-50O – Histogram of change in posterior transmission probability (direct or indirect) between observed hosts for experiments A through O. Values equal to 0 omitted for visual clarity.

[0407] FIG. 52A-52D – Boxplots of summary statistics across 100 reconstructed epidemic simulations, under the correctly specified model (52A) and three incorrectly specified models (52B), (52C), (52D).

[0408] FIG.53A-53C – Posterior densities of μ, p, and b.

[0409] FIG. 54 – A single inferred transmission network among our dataset of 45,260 genomes, colored by lineage.

[0410] FIG.55A-55C – Modeling minor allele frequencies. (55A) Histogram of minor allele frequencies from the dataset of 134,682 genomes (gray bars) and modeled probability density function (dashed line); (55B) Empirical cumulative density function (solid line) and modeled cumulative density function (dashed line). A minor allele frequency filter of at least 3% was applied for both figures; (55C) Log-log plot of empirical (gray) and theoretical density functions.

[0411] FIG.56 – Parameters of each experiment, where μg and σg2are the mean and variance of the Gamma-distributed generation interval, respectively; μsand σs2are the mean and variance of the Gamma-distributed sojourn interval, respectively; μ is the evolution rate in substitutions per site per day; Ne is the growth rate of the within-host viral population growth rate (i.e. the within- host effective population size t days after inoculation is exp(Net); π is the probability that a case is sampled and sequenced; ρs(if specified) is the probability that a case is sampled and sequenced given that its infector is sampled and sequenced; R is the basic reproductive number, ψ is the second parameter of the Negative Binomial offspring distribution; and γ is the sequencing coverage rate. Values differing from those used in experiment 1 (i.e. the default) are highlighted in orange.

[0412] FIG. 57A-57L – Assessing JUNIPER’s performance on simulated data. (57A–57D) Posterior density of the (57A) mutation rate, (57B) sampling rate, (57C) reproductive number, and (57D) epidemic start date inferred by JUNIPER in gray shading with the true parameter value shown as a diamond. (57E–57H) Posterior probabilities assigned to each correct direct or indirect transmission link (gray boxplots), accuracy (circles), coverage (squares), and hit rate (triangles) for (57E) JUNIPER with perfectly-specified input parameters, (57F) JUNIPER with mis-specified input parameters, (57G) JUNIPER without access to within-host variation data, and (57H) outbreaker2. (57I–57J) Accuracy, coverage, and hit rate computed based on (57I) direct and indirect transmission links and (57J) direct transmissions only for JUNIPER with perfectly-specified input parameters (left in each column), JUNIPER with mis-specified input parameters (second to left in each column), JUNIPER without access to within-host variation data (second to right in each column), and outbreaker2 (right in each column). (57K–57L) Posterior density of the (57K) mutation rate and (57L) sampling rate inferred by outbreaker2 in gray shading with the true parameter value shown in as a diamond.

[0413] FIG.58 – Let boxes represent hosts and arrows represent transmissions. Here, hiand i are explicit hosts, and the two unlabeled hosts along the transmission chain from hito i are referred as the implicit hosts leading to i.

[0414] FIG. 59 –An Explicit Mutation-Annotated Tree (EMAT). The nodes a, b and c are dated tips, whose sequences were determined experimentally. The node x is an inner node, the most recent common ancestor of a and b. The root node r is the most recent common ancestor of all the tips. An EMAT explicitly encodes the reference sequence above the root, the mutation events (crosses) and the missations (parentheses). From this representation, the sequence of the species represented at any point in the tree is determined implicitly (shown in parentheses below every node).

[0415] FIG. 60 – N-pruning: Integrating over all possible histories of branch C–A is equivalent to pruning it off.

[0416] FIG. 61 – N-pruning: A tree with “missations” decomposes into site trees, each of which has had its subtrees ending in N-tips pruned off. The genetic prior for the whole tree equals the product of site genetic priors for the pruned site trees.

[0417] FIG.62 – N-pruning permits large-scale rearrangements with only local changes: By changing the single missation at the beginning of branch X from A2N to C2N, the implied sequences downstream of X all change from A to C.

[0418] FIG.63 – A generic SPR move: a subtree rooted at X is attached to the rest of the tree at point P. And SPR move proposes detaching this subtree and reattaching it at a different point P′. The move includes a proposal for a new mutational history, at the very least covering the P′–X branch.

[0419] FIG.64 – An SPR move that changes the root. Whereas the mutational history along the A–B path can remain fixed, a new mutational history must be proposed for the entire X–P′–S′ path.

[0420] FIG.65 – The pruning step of an SPR move in the presence of missations. The paths labeled 1 through 4 all need to be treated differently. See Example 7 for details.

[0421] FIG.66 – The situation preceding the pruning step of an SPR move when the pruned subtree is a direct of the root and there are missations around the root. The paths labeled S − P − X, P − X and P − S all need to be treated differently. See Example 7 for details.

[0422] FIG. 67 – Setup for an SPR move. A subtree rooted at X will be detached from the remaining tree, where it is currently grafted at P. The sequence at X is AAA, while the sequence at P is CCC. Each branch is divided by its mutations into distinct regions, here annotated with their sequence and the number of differences between that sequence and the sequence at X (in parentheses).

[0423] FIG. 68 – Cartoon of grafting site probabilities calculated by mutation-directed SPR move vs. distribution implied by the posterior. In this example, the real posterior has an HKY substitution model with κ = 5.25, π = [0.29,0.18,0.19,0.33], which is representative of the SARS- CoV-2 data in

[0010] . Note that probabilities are broadly similar. The parsimonious region with sequence AAA, where attaching X necessitates no mutations on the P′–X branch, is clearly the most likely choice, but all other regions are possible, and their combined weight need not be negligible.

[0424] FIG. 69 – Schematic of Delphy’s scaling strategy. In each cycle, the sequence and times of the boundary nodes (filled) is fixed, while the sequence, times and topology of the remaining nodes (unfilled) can change. Within each shaded subtree, local MCMC moves are confined to that subtree. To ensure ergodicity, the fixed boundary nodes on successive cycles do not overlap.

[0425] The figures herein are for illustrative purposes only and are not necessarily drawn to scale. DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS General Definitions

[0426] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Definitions of common terms and techniques in molecular biology may be found inMolecular Cloning: A Laboratory Manual, 2ndedition (1989) (Sambrook, Fritsch, and Maniatis); Molecular Cloning: A Laboratory Manual, 4thedition (2012) (Green and Sambrook); Current Protocols in Molecular Biology (1987) (F.M. Ausubel et al. eds.); the series Methods in Enzymology (Academic Press, Inc.): PCR 2: A Practical Approach (1995) (M.J. MacPherson, B.D. Hames, and G.R. Taylor eds.): Antibodies, A Laboratory Manual (1988) (Harlow and Lane, eds.): Antibodies A Laboratory Manual, 2ndedition 2013 (E.A. Greenfield ed.); Animal Cell Culture (1987) (R.I. Freshney, ed.); Benjamin Lewin, Genes IX, published by Jones and Bartlet, 2008 (ISBN 0763752223); Kendrew et al. (eds.), The Encyclopedia of Molecular Biology, published by Blackwell Science Ltd., 1994 (ISBN 0632021829); Robert A. Meyers (ed.), Molecular Biology and Biotechnology: a Comprehensive Desk Reference, published by VCH Publishers, Inc., 1995 (ISBN 9780471185710); Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, N.Y.1992); and Marten H. Hofker and Jan van Deursen, Transgenic Mouse Methods and Protocols, 2ndedition (2011).

[0427] As used herein, the singular forms “a”, “an”, and “the” include both singular and plural referents unless the context clearly dictates otherwise.

[0428] The term “optional” or “optionally” means that the subsequent described event, circumstance or substituent may or may not occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.

[0429] The recitation of numerical ranges by endpoints includes all numbers and fractions subsumed within the respective ranges, as well as the recited endpoints.

[0430] The terms “about” or “approximately” as used herein when referring to a measurable value such as a parameter, an amount, a temporal duration, and the like, are meant to encompass variations of and from the specified value, such as variations of + / -10% or less, + / -5% or less, + / - 1% or less, and + / -0.1% or less of and from the specified value, insofar such variations are appropriate to perform in the disclosed invention. It is to be understood that the value to which the modifier “about” or “approximately” refers is itself also specifically, and preferably, disclosed.

[0431] As used herein, a “biological sample” may contain whole cells and / or live cells and / or cell debris. The biological sample may contain (or be derived from) a “bodily fluid”. The present invention encompasses embodiments wherein the bodily fluid is selected from amniotic fluid,aqueous humour, vitreous humour, bile, blood serum, breast milk, cerebrospinal fluid, cerumen (earwax), chyle, chyme, endolymph, perilymph, exudates, feces, female ejaculate, gastric acid, gastric juice, lymph, mucus (including nasal drainage and phlegm), pericardial fluid, peritoneal fluid, pleural fluid, pus, rheum, saliva, sebum (skin oil), semen, sputum, synovial fluid, sweat, tears, urine, vaginal secretion, vomit and mixtures of one or more thereof. Biological samples include cell cultures, bodily fluids, cell cultures from bodily fluids. Bodily fluids may be obtained from a mammal organism, for example by puncture, or other collecting or sampling procedures.

[0432] The terms “subject,” “individual,” and “patient” are used interchangeably herein to refer to a vertebrate, preferably a mammal, more preferably a human. Mammals include, but are not limited to, murines, simians, humans, farm animals, sport animals, and pets. Tissues, cells and their progeny of a biological entity obtained in vivo or cultured in vitro are also encompassed.

[0433] Various embodiments are described hereinafter. It should be noted that the specific embodiments are not intended as an exhaustive description or as a limitation to the broader aspects discussed herein. One aspect described in conjunction with a particular embodiment is not necessarily limited to that embodiment and can be practiced with any other embodiment(s). Reference throughout this specification to “one embodiment”, “an embodiment,” “an example embodiment,” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” or “an example embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to a person skilled in the art from this disclosure, in one or more embodiments. Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention. For example, in the appended claims, any of the claimed embodiments can be used in any combination.

[0434] All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as though each individual publication, published patent document, or patent application was specifically and individually indicated as being incorporated by reference.OVERVIEW

[0435] The embodiments disclosed herein address the aforementioned limitations in transmission network reconstruction by determining a joint transmission network (or pathway) and phylogenetic inference that combines the additional explanatory power of intra-host variants with the computational efficiency of consensus sequence-based methods, while modeling unsampled intermediates and offspring distributions. In addition, the embodiments disclosed herein provides methods that are parallelizable allowing the methods to scale to large datasets, making epidemic- scale reconstructions possible.

[0436] The outbreak transmission network takes advantage of minor variant relationships and identifies a frequency of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence to form relationships from a pool of samples and create one or more pathogen transmission pathways. In addition, the outbreak transmission network can model unsampled intermediates (i.e., samples that are not provided for reasons such as incubation time, slow laboratory assays, or lack of access) and create novel transmission pathways other models need to account.

[0437] Similarly, there are many difficulties to discovering and describing the dynamics of ancient and modern biological populations as well as the emergence and spread of infectious diseases. These may include evolutionary or epidemiological barriers, which can include mutation rates, selection, reassortment, and recombination and stochastic population dynamics, host population structure, and different patterns among various levels of inter- and intra-host relationships, respectively. Furthermore, as biological populations expand and infectious diseases spread, many methods fail to scale with the increase in data. While improvements in sequencing methods have improved aspects of phylogenetics, sequencing alone cannot provide phylogenetic trees. The methods and products described herein provide fast, confident, and higher resolution phylogenetic trees that overcome many of these issues.

[0438] The phylogenetic construction network and corresponding products described herein, take advantage various computational methods to create one or more one or more phylogenetic trees. A combination of various data structures, data types, and computational techniques overcome many of the evolutionary and epidemiological hurdles. For example, the network may include stochastic pure-birth processes or particular mutation models. In addition, the phylogenetic construction network may use, for example, explicit mutation-annotated tree or parallel treeoperations, which maintaining efficient computational times and more reasonable scaling, which other models fail to accomplish.

[0439] Standard techniques related to making and using aspects of the invention may or may not be described in detail herein. Various aspects of computing systems and specific computer programs to implement the various technical features described herein are well known. Example System Architectures

[0440] Turning now to the drawings, in which like numerals represent like (but not necessarily identical) elements throughout the figures, example embodiments are described in detail.

[0441] FIG.37 is a block diagram depicting a system 100 to reconstruct a pathogen outbreak or construct a phylogenetic tree and perform machine processing on a plurality of nucleic acid sequences. In one example embodiment, a user 101 associated with a user computing device 110 must install an application, and or make a feature selection to obtain the benefits of the techniques described herein.

[0442] As depicted in FIG.37, the system 100 includes network computing devices / systems 110, 120, and 130 that are configured to communicate with one another via one or more networks 105 or via any suitable communication technology.

[0443] Each network 105 includes a wired or wireless telecommunication means by which network devices / systems (including devices 110, 120, and 130) can exchange data. For example, each network 105 can include any of those described herein such as the network 2080 described in FIG.39 or any combination thereof or any other appropriate architecture or system that facilitates the communication of signals and data. Throughout the discussion of example embodiments, it should be understood that the terms “data” and “information” are used interchangeably herein to refer to text, images, audio, video, or any other form of information that can exist in a computer- based environment. The communication technology utilized by the devices / systems 110, 120, and 130 may be similar networks to network 105 or an alternative communication technology.

[0444] Each network computing device / system 110, 120, and 130 includes a computing device having a communication module capable of transmitting and receiving data over the network 105 or a similar network. For example, each network device / system 110, 120, and 130 can include any computing machine 2000 described herein and found in FIG. 39 or any other wired or wireless, processor-driven device. In the example embodiment depicted in FIG. 37, the networkdevices / systems 110, 120, and 130 are operated by user 101, data acquisition system operators, and outbreak transmission network or phylogenetic construction network operators, respectively.

[0445] The user computing device 110 includes a user interface 114. The user interface 114 may be used to display a graphical user interface and other information to the user 101 to allow the user 101 to interact with the data acquisition system 120, the outbreak transmission network or phylogenetic construction network 130, and others. The user interface 114 receives user input for data acquisition and / or machine processing and displays results to user 101. In another example embodiment, the user interface 114 may be provided with a graphical user interface by the data acquisition system 120 and / or the outbreak transmission network or phylogenetic construction network 130. The user interface 114 may be accessed by the processor of the user computing device 110. The user interface may display 114 may display a webpage associate with the data acquisition system 120 and / or the outbreak transmission network or phylogenetic construction network 130. The user interface 114 may be used to provide input, configuration data, and other display direction by the webpage of the data acquisition system 120 and / or the outbreak transmission network or phylogenetic construction network 130. In another example embodiment, the user interface 114 may be managed by the data acquisition system 120, the outbreak transmission network or phylogenetic construction network 130, or others. In another example embodiment, the user interface 114 may be managed by the user computing device 110 and be prepared and displayed to the user 101 based on the operations of the user computing device 110.

[0446] The user 101 can use the communication application 112 on the user computing device 110, which may be, for example, a web browser application or a stand-alone application, to view, download, upload, or otherwise access documents or web pages through the user interface 114 via the network 105. The user computing device 110 can interact with the web servers or other computing devices connected to the network, including the data acquisition server 125 of the data acquisition system 120 and the outbreak transmission server or phylogenetic construction server 135 of the outbreak transmission network or phylogenetic construction network 130. In another example embodiment, the user computing device 110 communicates with devices in the data acquisition system 120 and / or the outbreak transmission network or phylogenetic construction network 130 via any other suitable technology, including the example computing system described below.

[0447] The user computing device 110 also includes a data storage unit 113 accessible by the user interface 114, the communication application 112, or other applications. The example data storage unit 113 can include one or more tangible computer-readable storage devices. The data storage unit 113 can be stored on the user computing device 110 or can be logically coupled to the user computing device 110. For example, the data storage unit 113 can include on-board flash memory and / or one or more removable memory accounts or removable flash memory. In another example embodiments, the data storage unit 113 may reside in a cloud-based computing system.

[0448] An example data acquisition system 120 comprises a data storage unit 123 and an acquisition server 125. The data storage unit 123 can include any local or remote data storage structure accessible to the data acquisition system 120 suitable for storing information. The data storage unit 123 can include one or more tangible computer-readable storage devices, or the data storage unit 123 may be a separate system, such as a different physical or virtual machine or a cloud-based storage service.

[0449] In one aspect, the data acquisition server 125 communicates with the user computing device 110 and / or the outbreak transmission network or phylogenetic construction network 130 to transmit requested data. The data may include a plurality of nucleic acid sequences.

[0450] An example outbreak transmission network or phylogenetic construction network 130 comprises an outbreak transmission system or phylogenetic construction system 133, an outbreak transmission server or phylogenetic construction server 135, and a data storage unit 137. The outbreak transmission server or phylogenetic construction server 135 communicates with the user computing device 110 and / or the data acquisition system 120 to request and receive data. The data may comprise the data types previously described in reference to the data acquisition server 125.

[0451] The outbreak transmission system or phylogenetic construction system 133 receives an input of data from the outbreak transmission server or phylogenetic construction server 135. The outbreak transmission system or phylogenetic construction system 133 can comprise one or more functions to implement any of the mentioned training methods to learn transmission pathways of a pathogen or phylogenetic tree, respectively.

[0452] In a preferred embodiment, the machine processing program may comprise a Markov Chain Monte Carlo. In an example embodiment, the Markov Chain Monte Carlo may comprise a Metropolis-Hastings algorithm for Markov Chain Monte Carlo. In another example embodiment,the outbreak transmission network may comprise intra-host single nucleotide variants (iSNVs). Any suitable architecture may be applied to learn pathogen transmission pathways from a plurality of nucleic acid sequences.

[0453] In a preferred embodiment, the machine processing program may comprise an explicit mutation-annotated tree (EMAT). In an example embodiment, the EMAT may comprise Bayesian inference. In another example embodiment, the EMAT may comprise Markov Chain Monte Carlo. Any suitable architecture may be applied to learn one or more phylogenetic trees from a plurality of nucleic acid sequences.

[0454] The data storage unit 137 can include any local or remote data storage structure accessible to the outbreak transmission network or phylogenetic construction network 130 suitable for storing information. The data storage unit 137 can include one or more tangible computer- readable storage devices, or the data storage unit 137 may be a separate system, such as a different physical or virtual machine or a cloud-based storage service.

[0455] In an alternate embodiment, the functions of either or both of the data acquisition system 120 and the outbreak transmission network or phylogenetic construction network 130 may be performed by the user computing device 110.

[0456] It will be appreciated that the network connections shown are examples, and other means of establishing a communications link between the computers and devices can be used. Moreover, those having ordinary skill in the art having the benefit of the present disclosure will appreciate that the user computing device 110, data acquisition system 120, and the outbreak transmission network or phylogenetic construction network 130 illustrated in FIG. 37 can have any of several other suitable computer system configurations. For example, a user computing device 110 embodied as a mobile phone or handheld computer may not include all the components described above.

[0457] In an embodiment, the network computing devices and any other computing machines associated with the technology presented herein may be any type of computing machine such as, but not limited to, those discussed in more detail with respect to FIG.39. Furthermore, any modules associated with any of these computing machines, such as modules described herein, or any other modules (scripts, web content, software, firmware, or hardware) associated with the technology presented herein may by any of the modules discussed in more detail with respect to FIG.39. Thecomputing machines discussed herein may communicate with one another as well as other computer machines or communication systems over one or more networks, such as network 105. The network 105 may include any type of data or communications network, including any of the network technology discussed with respect to FIG.39. Example Processes

[0458] The example methods illustrated in FIG.38 is described hereinafter with respect to the components of the example architecture 100. The example methods also can be performed with other systems and in other architectures including similar elements.

[0459] Referring to FIG.38A, and continuing to refer to FIG.37A for context, a block flow diagram illustrates methods 200 to reconstruct an outbreak, in accordance with certain examples of the technology disclosed herein. Outbreak Reconstruction

[0460] In block 210, the outbreak transmission network 130 receives sequencing data. The sequencing data is obtained from patient samples that were infected with the pathogen(s) for which the transmission network is being reconstructed. The sequencing data may be obtained using any known sequencing method. The sequences may contain whole or partial genome sequences of the pathogen(s). In an embodiment, the genomic sequences may be aligned to a reference genome. Non-reference sequences may exhibit any level of coverage. In an embodiment, all nucleotides in the reference sequence are known. For reconstructing transmission networks during an outbreak, the sequence data set may be large. In an embodiment, the sequence data set comprises at least 1,000, at least 2,000, at least 3,000, at least 4,000, at least 5,000, at least 6,000, at least 7,000, at least 8,000, at least 9,000, at least 10,000, at least 20,000, at least 30,000, at least 40,000, at least 50,000, at least 60,000, at least 70,000, at least 80,000, at least 90,000, at least 100,000, at least 200,000, at least 300,000, at least 400,000, at least 500,000, at least 600,000, at least 700,000, at least 800,000, at least 900,000, at least 1,000,000. In addition to sequence data, the outbreak transmission network 130 may receive a file comprising a time stamp for each non-reference sequence indicating the date and time at which a sample from which the sequence was derived was sampled or collected. For ease of reference, the sequencing data will refer to both the actual sequences and corresponding time stamp data. In an example embodiment, the sequencing data may further comprise other information such as location of the subject and vaccination status orother therapeutic or non-therapeutic intervention strategies a subject received. The outbreak transmission network 130 may receive the sequencing data from the user computing device 110, the data acquisition system 120, or any other suitable source of nucleic acid sequences via the network 105 to the outbreak transmission network 130, discussed in more detail in other sections herein. The acquisition engine comprises any software or hardware individually or in combination described herein that is capable of communicating with a user device, such as fetching, receiving, or sending information, thereby allowing access to the plurality of nucleic acid sequences or transmission pathways by the outbreak transmission network 130 or the data acquisition system 120. Sampling Posterior Distribution

[0461] In block 220, the outbreak transmission system 133 processes the sequence data to generate a transmission network. In an embodiment, the sequencing data is processed using one or more machine processing methods described herein. The processing of the sequencing data with an outbreak transmission network 130 is performed by the machine processing algorithm based on data collected by the data acquisition system 120. Accordingly, human analysis or cataloging is not required. The process is performed automatically by the outbreak transmission network 130 without human intervention, as described below. The data typically collected includes thousands, tens of thousands, to millions of sequences. Human intervention is not useful or required because the data is too great. A team of humans would not be able to catalog or analyze the data in any useful manner. Moreover, a human cannot access a plurality of nucleic acid sequences and, from that data, predict pathogen transmission pathways in the necessary time to avert a serious outbreak, such as a pandemic.

[0462] In an embodiment, processing the sequencing data comprises defining a posterior distribution representing the combined uncertainty and probabilistic model over all the parameters of a pathogen transmission network, the evolutionary dynamics, and the pathogen’s phylogenies. The posterior distribution represents the combined knowledge of a pathogen's transmission and evolution based on prior information and observed sequencing data. A sampling algorithm may be used to sample from this posterior distribution to explore the entire parameter space. By generating a set of parameter samples that approximate the posterior distribution, the sampling algorithm provides insights into likely configurations of the transmission network, the phylogenies, andwithin-host dynamics. In an embodiment, the sampling algorithm may be a Markov Chain Monte Carlo (MCMC) algorithm, described in further detail below in the Implementation section and Working Examples.

[0463] In an embodiment, the posterior distribution may include multiple components. In an embodiment, the posterior distribution includes transmission network parameters, phylogenetic relationships, mutation and evolutionary parameters, and intrahost variants. The transmission network parameters capture possible configurations of the transmission network, such as which host infected which other host and the timings of those infection events. The phylogenetic relationships include different possible evolutionary trees (phylogenies) representing the relationship between the pathogen sequences sampled from different hosts. These phylogenies illustrate how a pathogen evolved through a population. The mutation and evolutionary parameters include parameters that govern within-host viral evolution such as mutation rates, effective population sizes and other factors that determine the rate and nature of mutations within a host.

[0464] Reconstructing both the underlying transmission network and the evolutionary history of a pathogen requires a statistical model of each component, as well as how they interact with one another. As used herein the “transmission network” refers to the tree of all host-to-host pairs, the times at which each host was infected, and the times at which each host was sampled if they were sampled. As used herein, “evolutionary history” refers to the topology of the phylogenetic tree, which links the samples and the times at which mutation events occurred. In an embodiment, determining the posterior distribution comprises a transmission model, a global evolutionary model, and an intrahost evolutionary model. Transmission Model

[0465] In an embodiment, transmission networks are modeled as random branching processes with randomly drawn generation intervals. In an embodiment, the number of offspring (number of cases infected by a given case) is modeled as a negative binomial, an over-dispersed probability distribution that can capture superspreading. In an embodiment, generation intervals are taken to be gamma-distributed. This distribution allows flexibility regarding its mean and variance to capture a wide range of pathogen dynamics. Given a transmission network, each host being sampled (i.e., tested and sequenced and part of the sequencing data) is modeled with some probability ^, independently across hosts. The time period between a sampled host being infectedand tested is again modeled as being gamma-distributed for analogous reasons. In an embodiment, the transmission model used may be TransPhylo. However, in contrast to TransPhylo, the present method infers both phylogenies and transmission networks and employs an intrahost evolutionary model, whereas TransPhylo treats phylogeny as fixed and does not account for intrahost evolution. Global Evolutionary Model

[0466] In an embodiment, transmission networks and the shape of the underlying phylogenetic tree are placed in a one-to-one correspondence by modeling two parameters. The first is that transmission bottlenecks are modeled as consisting of a single pathogen, which ensures that exactly one lineage on the underlying phylogeny infects each host. The second is that all coalescence events within a given host occur at the time of infection for said host. This follows from the fact that within-host pathogen replication immediately following infection exhibits rapid exponential growth, making earlier coalescent events exponentially more likely than later ones

[0467] In an embodiment, given a phylogenetic tree topology, molecular evolution may be modeled as a continuous-time Markov process, in which the probability of a given substitution at a given site per unit time is parametrized. In one embodiment, a Jukes-Cantor model is used where all possible substitutions have the same probability, and substitutions occur independently across different sites on a genome. Intrahost Evolutionary Model

[0468] In an embodiment, an intrahost evolutionary models helps define the mutation and evolutionary parameters mentioned above. The intra-host evolutionary also models the distribution of minor allele frequencies within hosts. In an embodiment, the minor variant relationships comprise intra-host single nucleotide variants (iSNVs). An iSNV may comprise an allele different from a reference allele at the same genomic position within the sample. In an embodiment, the processing determines the distribution of iSNV frequencies. A variant, e.g., iSNV, frequencies require quantitative sequencing such that the number of reads is proportional to the amount of the corresponding sequence in the sample of interest. The distribution of variant frequencies may comprise the collection of frequencies for each variant measured. In an embodiment, determining the distribution of iSNV frequencies occurs at each site on the nucleic acid sequence.

[0469] In an embodiment, the method further comprises masking iSNV calls that exhibit a strong selective advantage due to the number of genomes in which the iSNV calls are present.Masking may comprise removing (e.g., not mapping / discarding) parts of the reference sequence (i.e., iSNV calls) wherein the reference sequence is split into separate sequences according to the original reference sequence. This may be useful, for example, when iSNV calls exhibit a strong selective advantage due to the number of genomes in which the iSNV calls are present. In general, the outbreak transmission network will still base its output on the full reference. However, particular aspects of the outbreak transmission network may ignore masked regions.

[0470] In an embodiment, the probability density function of an iSNV exhibiting a certain frequency is modeled as a pure-birth process. For example, a pure birth process does not consider, factor, or count deaths within the population. In an embodiment, the pure-birth process comprises modeling the birth events of a pathogen as randomly occurring over time. For example, the birth events occur at a rate described by an exponential random variable. A birth event may comprise an addition to the population, wherein a birth event of, for example, 0 means there were no additions to the population. For example, a birth event for a pathogen may comprise the addition of another pathogen to the population of pathogens. Birth events may comprise one or more offspring. For example, a birth event resulting in one or more offspring may be the offspring of a pathogen such as a virus. Therefore, a birth even comprising offspring may result in more virions. Birth events may also occur instantaneously. In an embodiment, the birth rate depends on the pathogen's population. In an embodiment, the rate of birth comprises a Poisson process. A Poisson process may refer to the change in space by random objects that occur independently; the birth event comprises independent one or more mutations of one or more nucleic acids of the one or more offspring from one or more nucleic acids in a parent pathogen sequence. In an embodiment, the mutation occurs within a set probability. For example, suppose a birth event modeling pathogen population growth occurs. In that case, the offspring may comprise the same nucleic acid sequence or a similar nucleic acid sequence wherein the only differences are mutations computed to occur.

[0471] In an embodiment, a pure birth process is parameterized by the generation interval ^ and growth rate of ^ of pathogen offspring per generation. Under a Jukes-Cantor model, the number of pathogens in the population at the time of the first mutation event at a given site on the genome may be approximately distributed as exponential with mean ^ ^ ^ ^ ^. Then, conditional on a pathogen population of one pathogen with a given mutation and n pathogens without it, the proportion of sequencing reads exhibiting the mutation is modeled following a Beta(1, n)distribution. This model results from modeling the number of descendants of each virion in a population as independent and identically distributed exponential random values. Further details as to the derivation and calculation of the corresponding probability density function are described in the Working Example section. Birth Processes A Pólya Urn model

[0472] In an embodiment, the outbreak transmission network comprises of a Pólya Urn model. A Pólya Urn model may refer to a type of system that can be represented as one or more urns containing various objects. Objects may be removed and / or added according to a particular scheme. In particular, for a Pólya Urn model, the objects in the urn are colored balls which are sampled from the urn at random and, depending on the color of the ball, are replaced in the urn. For example, a Pólya Urn model, applied to the concepts described herein, may be used to compute the birth event at a given time from a particular parent. In a particular example, a Pólya Urn model may compute how a population of pathogens grow. For example, a Pólya Urn model may be used to compute how a virus mutates over time, wherein the model generates children from a parent during a birth event with or without a particular mutation. See e.g., Mahmoud, H., 2008. Pólya urn models. CRC press. Genetic Phases

[0473] In an embodiment, the outbreak transmission network comprises of one or more genetic phases. The one or more genetic phases are used to compute mutations during the infection process. For example, the one or more genetic phases are used to compute the likelihood of iSNVs during the infection process. The one or more genetic phases may describe or model any of the steps of viral replication, such as entry, uncoating, transcription and genome replication, assembly, maturation and release. In an embodiment, the two genetic phases comprise an exponential growth phase and a quiescent phase. The exponential growth phase may represent anyone or more of the viral replication steps up to but not including maturation. Therein the exponential phase captures the largest impact to mutation rate and, In an embodiment, can capture the appearance of iSNVs. The quiescent phase may represent the maturation and / or release steps. Therein the quiescent phase may capture the genetic drift of a pathogen after the viral population is no longer exponentially growing.Jukes-Cantor Model

[0474] In example embodiments, the quiescent phase comprises a Jukes-Cantor model. A Jukes-Cantor model computes the evolution of system, for example, using a continuous-time, stationary Markov process. The individual states, and accordingly their evolution (i.e., mutations), occur with equal probability at the root, and the transition rates on any edge of the tree are the same for each possible substitution. Wherein a quiescent phase comprises of a Jukes-Cantor model, the outbreak transmission network computes the genetic drift (e.g., evolution / mutations) under neutral evolution. Markov Chain Monte Carlo

[0475] In example embodiments, the outbreak transmission network comprises of Markov Chain Monte Carlo. In example embodiments, the outbreak transmission network comprises of Metropolis-Hastings algorithm for Markov Chain Monte Carlo. The second method is Markov chain Monte Carlo (MCMC), which can be implemented when the sample size is large. MCMC is a well-known class of sample distribution algorithms and will not be discussed in detail here. Implementation

[0476] As previously noted, an MCMC algorithm is used to explore different configurations of the transmission network, the phylogenies, and the evolutionary parameters that describe the spread and evolution of a pathogen to generate a transmission network. In an embodiment, a Metropolis-Hastings MCMC sampler is used. In an embodiment, a moveset is defined. A moveset refers to the set of rules or operations used to propose transitions from the current state of the Markov chain to a new state. These “moves” determine how the MCMC explores the parameter space of the posterior distribution. In an embodiment, the moves may be separated into two categories: global moves, which affect every node’s contribution to the likelihood function, and local moves, which only affect the contributions of a few nodes. In an embodiment, the global moves are: 1. Adjust the value of the mutation rate. 2. Adjust the value of the sampling rate. 3. Adjust the value of the reproductive number. In an embodiment, the local moves may be defined as: 4. Resample the time of infection along a branch.5. Resample the times of infection and mutation along a branch. 6. Resample the times of infection for a host i and all hosts along the branches connected to i. 7. Resample the times of infection for a host i, its ancestors, and all hosts along the branches connected to i and its ancestors. 8. Update the genotype of a host i. 9. Resample the ancestor hiof i to be either the ancestor or an offspring of hi.10. Resample the ancestor hi of i to be anyone infected before i. 11. Resample the ancestor hiof i to be anyone infected before i, but bias the choice such that hosts with genotypic similarities to i are proposed more often. 12. Pick a pair of hosts i, j where i infects j, and rearrange the transmission networks such that j infects i. 13. Do the same as the previous move, except also set the offspring of i to be infected by j, and set the offspring of j to be infected by i. 14. With probability ½, pick a host h and create a new unobserved host i that is infected by h. Then, take some of the offspring of h and make them into offspring of i. With probability ½, delete an unobserved host i, and update the ancestor of i’s offspring to be hi. 15. With probability ½, pick a host j and create a new unobserved host i that infects j. Then take some of the offspring of j and make them into offspring of i. With probability ½, delete an unobserved host i, and update the ancestor of i’s offspring to be j, one of the former offspring of i. 16. With probability ½, pick a mutation that occurs twice on the global phylogeny, create a new unobserved host I with this mutation, and rearrange the phylogeny such that this mutation occur only once. With probability ½, delete an unobserved host i and resample the ancestors of the former children of i from the descendants of hi. It is to be understood that the order of the global and local moves may be varied from the example sequence provided above.

[0477] In an embodiment the sampling algorithm is run until a desired endpoint is reached. The overall goal is to ensure that the samples adequately represent the posterior distribution. Different methodologies may be used to determine the appropriate endpoint, including trace plots, Gelman-Rubin statistic (R-hat) and effective sample size. By visualizing trace plots of parameter values over iterations, a user can assess if the chain has “mixed” well and reached a stationary distribution. A stable trace plot without trends indicates that the chain may have converged. When running multiple chains, the Gelman-Rubin diagnostic can be used to measure convergence. If the R-hat value for each parameter is close to 1 (typically below 1.1), this indicates that the chains have converged to the sample distribution, indicating sufficient sampling. In another embodiment a fixed number of iterations may be defined. The number of iterations will depend on the size of the sample space. In an embodiment the chain may be run for at least 1,000, at least 2,000, at least 3,000, at least 4,000, at least 5,000, at least 6,000, at least 7,000, at least 8,000, at least 9,000, at least 10,000, at least 20,000, at least 30,000, at least 40,000, at least 50,000, at least 60,000, at least 70,000, at least 80,000, at least 90,000, at least 100,000, at least 200,000, at least 300,000, at least 400,000, at least 500,000, at least 600,000, at least 700,000, at least 800,000, at least 900,000, at least 1,000,000 Parallelization

[0478] The sampling algorithm may be run in parallel over subtrees partitioning the transmission network. This is because the likelihood function computed on the entire tree equals the product of the likelihood function computed on each subtree. The only moves that require an update of the likelihood on all parts of the tree at once are the global moves. In an embodiment, the algorithm can be parallelized by randomly partitioning the tree into subtrees after completing the global moves, then joining the subtrees back together to perform the next cycle of global moves and repeating. Viral transmission pathways

[0479] In block 230, the outbreak transmission network 130 generates viral transmission pathways. In an embodiment, pathogen transmission pathways are transmitted back to the user via the network 105. In an embodiment, the resulting user information is stored on the data storage unit 137. In an embodiment, the resulting user information is immediately transmitted to the user’s device. In an embodiment, the resulting user information is transmitted across the network 105 tothe data acquisition system for subsequent access by the user associated device 110 or outbreak transmission network 130.

[0480] In an embodiment, the viral transmission pathway comprises a network graph. A network graph, which may also be referred to as a link chart; a node-link diagram; or a network map, is a data structure comprising vertices or nodes connected by edges. For example, a network graph data structure comprises of a matrix or vector wherein the row and / or column represent the nodes and the values of the cells of the matrix or vector represent the edges connecting the nodes. In some instances, the graph data structure is a directed graph data structure or an undirected graph data structure. In some instances, the graph data structure comprises of linked lists. In an embodiment, the network graph comprises of nodes and edges, a node corresponds to a nucleic acid sequence from the plurality of nucleic acid sequences and an edge corresponds to a SNV / iSNV distance.

[0481] In an embodiment, generating the pathogen transmission pathways comprises of per- site, per-cycle mutation rate. In an embodiment, generating the pathogen transmission pathways comprises detecting minor allele transmissions and split bottlenecks. A transmission bottleneck may comprise modeling pathogen infection wherein a limited subset of the pathogen diversity (e.g., variation among pathogen nucleic acid sequences) is transmitted to a new subject. A split bottleneck may model a pathogen infection wherein the major and minor allele (wherein the pathogen diversity describes the major and minor alleles throughout the nucleic acid sequences) at a polymorphic site are both present in the infected subject.

[0482] In an embodiment, generating the pathogen transmission pathways comprises putative detection of donor-recipient pairs with genomic signatures of transmission. In an embodiment, generating the pathogen transmission pathways comprises detecting reproductive number, overdispersion (e.g., high individual-level variation) parameter, attack rate by vaccine status, the origin of a cluster, viral transmission pathways in nosocomial settings, or any combination thereof. PHYLOGENETIC CONSTRUCTION

[0483] Referring to FIG. 38B, and continuing to refer to FIG. 37B for context, a block flow diagram illustrates methods 200 to construct a phylogenetic tree, in accordance with certain examples of the technology disclosed herein.

[0484] In block 210, the phylogenetic construction network 130 receives an input of a plurality of nucleic acid sequences. The phylogenetic construction network 130 may receive the plurality of nucleic acid sequences from the user computing device 110, the data acquisition system 120, or any other suitable source of the plurality of nucleic acid sequences via the network 105 to the phylogenetic construction network 130, discussed in more detail in other sections herein. The acquisition engine comprises any software or hardware individually or in combination described herein that is capable of communicating with a user device, such as fetching, receiving, or sending information, thereby allowing access to the plurality of nucleic acid sequences or one or more phylogenetic trees by the phylogenetic construction network 130 or the data acquisition system 120.

[0485] In an embodiment, the method further comprises: i) collecting a plurality of samples from a plurality of subjects comprising the plurality of nucleic acid sequences and / or i) sequencing the plurality of nucleic acid sequences from the plurality of samples from the plurality of subjects. Methods for sequencing nucleic acid sequences from a collected sample are described elsewhere herein. In an embodiment, the plurality of nucleic acid sequences comprises partial genomic sequences, whole genomic sequences, or a combination thereof.

[0486] In block 220, the phylogenetic construction system 133 processes the data of the plurality of nucleic acid sequences. In an embodiment, the plurality of nucleic acid sequences is processed with one or more of the machine processing methods described herein. The processing of the plurality of nucleic acid sequences is performed by the machine processing algorithm based on data collected by the data acquisition system 120. Accordingly, human analysis or cataloging is not required. The process is performed automatically by the machine processing network 130 without human intervention, as described in the machine processing section herein. The amount of data typically collected includes thousands to tens of thousands to millions of data items for plurality of nucleic acid sequences. The total number of users may include all users accessing the system or a portion of users using a particular aspect of the system (e.g., the portion of users using the mobile application as opposed to those using a web-browser portal). Human intervention in the process is not useful or required because the amount of data is too great. A team of humans would not be able to catalog or analyze the data in any useful manner. Moreover, a human cannotaccess the plurality of nucleic acid sequences and from that data predict one or more phylogenetic trees in the necessary time to identify the genetic origin of, for example, a pathogen. Mutation-Annotated Trees (MATs)

[0487] Computational bottlenecks may limit or prevent real-time analysis of disease outbreaks. In order to increase computational efficiency and rapid disease outbreak analysis, the analysis networks described herein may comprise mutation-annotated trees (MATs). In general, a MAT is a computational data structure designed to store a tree diagram and annotate the branches of the tree diagram. MAT data structures also allow new information, such as nucleic acid sequences, to be quickly inserted, even when the MAT is very large, and the new information needs to be organized within the MAT. Typically, the new information is organized using maximum parsimony, which places the new information into the tree such that it minimizes the number of changes to the tree. See e.g., McBroome, J. et al. A Daily-Updated Database and Tools for Comprehensive SARS-CoV-2 Mutation-Annotated Trees, 2021. In an embodiment, an analysis network comprises mutation-annotated trees. In an embodiment, the phylogenic tree network comprises mutation-annotated trees. Succinct Tree Sequences (STS)

[0488] Alternatively, succinct tree sequence (STS) data structures may be used. STSs are rooted in ancestral recombination graphs (ARGs) and comprise constructing multiple trees encoding correlated mutations along a genome. In some instances, redundant mutations are added to the STS. However, the redundant mutations can be automatically removed and in effect increase the efficiency of the data structure. See e.g., Haller, BC, et al. Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes. Mol Ecol Resour.2019; 19: 552– 566. Explicit Mutation-Annotated Tree

[0489] In an embodiment, the mutation-annotated trees comprise explicit mutation-annotated tree (EMAT). EMATs further annotate (or decorate) a MAT data structure with time information corresponding to the sequence information in the tree data structure. For example, the time information (e.g., time stamp) may include the time (e.g., year, month, day, hour, minute, second, or any combination thereof) the nucleic acid sequence was recovered from a sample and / or subject. In addition, for example, the time information may comprise the time a subject was infected and / orwhen the nucleic acid sequence was taken from the subject. In an embodiment, the time stamp is associated with the one or more point-mutations on the branches of the model tree. The time information may comprise the time in which a mutation was computed to occur. In an embodiment, the time stamp corresponds to the nucleic acid sequence or portion thereof on the nodes of the model tree. In an embodiment, the EMAT encodes one or more missing nucleic acids in the plurality of nucleic acid sequences with a missation. A missation may refer a point on the tree below which (e.g., the children nodes) all sequences are missing a nucleic acid at a particular site.

[0490] In an embodiment, the EMAT is a binary or an n-array tree data structure. In an embodiment, the EMAT comprise one or more point-mutations per branch of the model tree. In an embodiment, one or more branch of the model tree comprises of two or more point-mutations and the phylogenetic construction network merges the two or more point-mutations. In an embodiment, the nodes of the model trees comprise a nucleic acid sequence or portion thereof corresponding to the plurality of nucleic acid sequences. For example, a node may comprise just the portion of the nucleic acid sequence that changes between one time point and another. EMATs are further described herein. Additional Features

[0491] In an embodiment, the phylogenetic construction network comprises of Maximum- Clade-Credibility (MCC) tree. A MCC tree comprises of a tree resulting from one or more phylogenetic trees with the largest sum or product of posterior probabilities across its constituent parts. Further details are discussed herein.

[0492] In an embodiment, the phylogenetic construction network comprises of Felsenstein pruning. Felsenstein pruning is a type of dynamic programming, wherein a complex problem, such as constructing phylogenetic trees, is reduced into simpler steps. This dynamic programming reuses computations efficiently and reduces the time required to make calculations. Felsenstein pruning is a well-known class of likelihood sampling algorithms and will not be discussed in detail here but is further described herein.

[0493] In an embodiment, the phylogenetic construction network comprises of Markov Chain Monte Carlo. In an embodiment, wherein the Markov Chain Monte Carlo comprises of the Metropolis-Hastings criterion. As noted above, MCMC is a well-known class of sample distribution algorithms and will not be discussed in detail here bit is further described herein.

[0494] In an embodiment, the phylogenetic construction network comprises of one or more cluster moves. A cluster may refer to two or more branches in a tree. In some instance, the two or more branches are neighboring branches connected by the same parent node or ancestor node (an ancestor node can be any node from which a child node is connected to the root node). Accordingly, a cluster move may refer to moving (e.g., removing, adding, substituting, swapping, or any combination thereof) a cluster in a tree. In an embodiment, the one or more cluster moves comprise one or more changes to one or more neighboring branches of the model tree. For example, the one more changes may comprise removing one or more neighboring branches, adding one or more neighboring branches, substituting one or more neighboring branches, swapping one or more neighboring branches, or any combination thereof. In an embodiment, the phylogenetic construction network comprises of two or more cluster moves and the two or more cluster moves are performed in parallel. For example, one or more trees may be divided into clusters and each cluster (or a group of clusters) are operated on at the same time. This is accomplished by dividing a processing unit or units and assigning a task with its corresponding cluster to the divided parts. Parallel processing is well-known in the art and will not be discussed in detail here. In an embodiment, the two or more cluster moves performed in parallel comprises of separating the neighboring branches from the model tree and fixing a boundary node that is a parent node to the neighboring branches. In an embodiment, the one or more clusters are selected randomly. In an embodiment, the separated neighboring branches are reassembled into a global model tree. A global model tree can refer to a fully recombined tree, wherein a recombined tree has undergone some kind of transformation, such as cluster move. In addition, wherein a cluster move comprises of two or more branches in a tree (and sometimes neighboring branches), a global move refers to moving (e.g., removing, adding, substituting, swapping, or any combination thereof) larger, potentially non-neighboring, clusters within the tree.

[0495] In an embodiment, the phylogenetic construction network comprises of parsimony. Parsimony refers to a programmatic approach to finding the shortest line between two points. For example, parsimony may refer to identifying the smallest number of changes (e.g., mutations) required to connect a root node to any one of its leaves. In general, parsimony uses a matrix data structure of discrete phylogenetic characters and states to compute one or more optimal / minimalphylogenetic trees. Parsimony is a well-known class of character-based tree estimation methods and will not be discussed in detail here but is further described herein.

[0496] In an embodiment, the phylogenetic construction network comprises of maximum likelihood. Maximum likelihood models genome rearrangements as well as duplications, insertions, and losses to maximize the probability of a root node producing the eventual leaf nodes. In some instances, maximum likelihood models comprise substitution models (i.e., a type of Markov model). Maximum likelihood, in some instances, can modify rates of evolution across both lineages and sites. In some instances, the maximum likelihood method models mutations at different sites and / or along different lineages as statistically independent. Maximum likelihood is further described herein.

[0497] In an embodiment, the phylogenetic construction network comprises of coalescent prior. A coalescent prior model treats the children nodes as random variables and therefore treats each mutation neutrally. In an embodiment, the coalescent prior comprises of augmented ancestral prior. In an embodiment, the phylogenetic construction network comprises recombination, reassortment, or both.

[0498] In an embodiment, the phylogenetic construction network comprises of an uncorrelated relaxed molecular clock. Molecular clocks model sequences as evolving at a rate that is constant over time. Different types of molecular clocks may be used in these methods such as a strict clock, fixed clock, uncorrelated relaxed clock, random local clock, or a combination thereof. A strict clock models every branch in a tree as evolving at the same rate. A fixed clock models one or more clades in the tree do not evolve according to a global rate. An uncorrelated relaxed clock models each branch of a tree as having its own evolutionary rate. A random local clock models the variation in evolutionary rate across a tree which is, typically, more than the strict clock but, typically, less than the relaxed clock.

[0499] Trees modeled using coalescent prior may be modeled in terms of time. For example, the model may comprise a fixed mutation rate, and coalescent analyses therefore assume a molecular clock. In an embodiment, the phylogenetic construction network comprises of relative selective advantages, the relative selective advantage comprises of coupling the coalescent prior and the plurality of nucleic acid sequences at each point in a model tree. Coalescent prior is further described herein.

[0500] In an embodiment, wherein the method further comprises unifying genome alignments.

[0501] In an embodiment, wherein the phylogenetic construction network comprises of N- pruning. In an embodiment, N-pruning comprises of removing branches whose tips comprise one or more missing nucleic acids. In an embodiment, the phylogenetic construction network comprises of subtree pruning and / or regrating. N-pruning “prunes” (i.e., removes) one or more branches of a tree, generally, those branches that lead to sequences with data missing at the node associated with each branch. N-pruning is further described herein.

[0502] In an embodiment, the phylogenetic construction network comprises of subtree slide, Wilson-Balding move, inner node displacement, branch reform, junction reform, or any combination thereof. These methods are further described herein. For example, a subtree slide may include moving the position of a node up or down along a branch. A subtree slide can place a node above a root thereby making the node the new root. A subtree slide down may include decisions into which branch to slide down into. For example, a Wilson-Balding move may include detaching a subtree and placing it somewhere else in the tree. Wilson-Balding move may be random.

[0503] In an embodiment, the phylogenetic construction network comprises of an evolution model, the evolution model comprises of mutation rates trained on the whole sequence of the plurality of nucleic acid sequences. An evolutional model may comprise multiple site partitions with linked or unlinked mutation rates, with or without site rate heterogeneity, with more or fewer free exchangeabilities, with some proportion of invariant sites, or any combination thereof. In addition, the evolution model may comprise more elaborate schemes such as uncorrelated relaxed molecular clocks. Posterior distribution

[0504] In an embodiment, the phylogenetic construction network comprises a posterior distribution. In an embodiment, the posterior distribution includes one or more factors. The one or more factors may include four factors. In an embodiment, the four factors may include: a parameter prior that is a product of priors on associated inference parameters (e.g., a mutation rate); an ancestry prior on the genealogy of a tree; a genetic prior for the exact root sequence and mutations layered onto the genealogy of the tree ; and a likelihood of the data given the tree.Likelihood

[0505] In an embodiment, the likelihood factor is either 1 if the tree agrees with the data, or 0 otherwise. In an embodiment, the likelihood may be a product of weight vectors at every tip and site. In an embodiment, a starting tree and MCMC moves always preserve the compatibility of the model with the plurality of nucleic acid sequences. Genetic prior

[0506] In an embodiment, each site evolves independently according to a continuous-time Markov chain with a transition rate matrix. In an embodiment, the transition rate matrix is the negative of the diagonal elements. In an embodiment, there is a stationary distribution of a Markov chain at a site. In an embodiment, the total mutation rate is the rate at which mutations happen at a point on the tree on any site in. In an embodiment, all possible genetic histories below any given missation are not allowed. In an embodiment, the tree likelihood is calculated by Felsenstein pruning, optionally then summed over all possible values of those states.

[0507] In an embodiment, local MCMC moves on EMATs are implemented in time proportional to the number of mutations and missation blocks in nearby branches. In an embodiment, local MCMC moves on EMATs are not the number of variable sites in the entire dataset. In an embodiment, local moves acting on different parts of the tree are decoupled. In an embodiment, local moves are performed in parallel. In an embodiment, trees have no missing data. In an embodiment, trees specialize to a Jukes-Cantor model of evolution with no site rate heterogeneity, optionally whereby all possible mutations occur at the same rate at every site.

[0508] In an embodiment, the calculation of the genetic prior occur in terms of the contributions made by each branch of a tree. In an embodiment, the contribution of the reference sequence for the sites that are missing in every input sequence are factored out. In an embodiment, leftover factors are from mutations above the root. In an embodiment, only the factors arising from mutations above the root survive when taking ratios of branch genetic priors for the root branch. In an embodiment, a path and subtree genetic priors are products of the relevant branch genetic priors. In an embodiment, the computation is split according to the contributions made by each site. In an embodiment, mutations are sorted by ascending time. In an embodiment, mutations are labeled by their identities. In an embodiment, there is a recurrence relation between the total mutation rate at a node i and that at its ancestor:Ancestry prior

[0509] In an embodiment, any ancestry prior suitable for Bayesian phylogenetics can be used in the phylogenetic construction network. In an embodiment, the phylogenetic construction network includes the Kingman coalescent prior. Parameter priors

[0510] In an embodiment, the genetic prior is defined in terms of the site transition rate matrices. In an embodiment, the phylogenetic construction network can include all common choices for these, e.g., multiple site partitions with linked or unlinked mutation rates, with or without site rate heterogeneity, with more (GTR) or fewer (HKY) free exchangeabilities, with some proportion of invariant sites, or any combination thereof. In an embodiment, more elaborate schemes can also be included (e.g., uncorrelated relaxed molecular clocks). In an embodiment, the ancestral prior is defined in terms of the population curve. In an embodiment, all models in common use can be applied (e.g., a fixed flat population, a parametrized exponential growth, or more flexible schemes such as a Skygrid prior).

[0511] In an embodiment, the phylogenetic construction network includes an evolution model consisting of a single-partition HKY model. In an embodiment, the single-partition HKY model incudes site-rate heterogeneity. In an embodiment, the single-partition HKY model incudes no invariant sites. In an embodiment, the single- partition HKY model incudes a population model representing exponential growth. In an embodiment, the phylogenetic construction network includes a two-partition models.

[0512] In an embodiment, the mutation rate is a uniform, improper prior (e.g., = 1). In an embodiment, site relative mutation rates are 1 for all sites (e.g., no site rate heterogeneity). In an embodiment, site relative mutation rates are Gamma distributed with mean 1 and shape parameter. In an embodiment, site relative mutation rate parameters are explicit, independent, continuous, or a combination thereof. In an embodiment, site relative mutation rate parameters are not restricted to discrete categories. In an embodiment, the site rate heterogeneity parameter includes an additional latent parameter. In an embodiment, the site rate heterogeneity parameter is connected to a prior distribution. In an embodiment, the prior distribution is exponential. In an embodiment, the phylogenetic construction network includes an evolution model. In an embodiment, thephylogenetic construction network includes a concrete population model. In an embodiment, the concrete population model includes exponential growth. Genetic prior vs. tree likelihood

[0513] In an embodiment, the genetic prior is integrated over all possible EMATs, optionallypruning. In an embodiment, the phylogenetic construction network samples over explicit topologies and genetic histories using a simple posterior. In an embodiment, the phylogenetic construction network does not integrate out the genetic histories analytically and, optionally, then samples only over topologies using the resulting more computationally demanding posterior. In an embodiment, site genetic histories to encompass mutations at any site along a single branch. In an embodiment, a branch genetic history can be partitioned into individual site genetic histories. In an embodiment, genetic histories cover the entire tree. In an embodiment, a tree genetic history includes a union of branch genetic histories. In an embodiment, the union of branch genetic histories include each branch except the root branch. In an embodiment, the phylogenetic construction network includes site weight vectors at the tips and / or non-strict clocks. Dealing with missing data: N-pruning

[0514] In an embodiment, the product of the genetic prior and the likelihood over all genetic histories are integrated. In an embodiment, the product of the genetic prior and the likelihood are integrated over all genetic histories. In an embodiment, the integration is computed statistically or analytically. In an embodiment, the pruning process is repeated recursively. In an embodiment, missations are the pruning points at particular sites for each of the site trees that make up the total tree. In an embodiment, an EMAT with missations are all trees resulting all possible genetic histories below each missation. In an embodiment, the space of all possible EMATs without missations is partitioned by EMATs with missations into sets. In an embodiment, the partitions are according first to topology, then to genetic histories above the missations. In an embodiment, the posterior probability of an EMAT with missations is the sum of the posterior probabilities of the contained EMATs without missations. In an embodiment, any EMAT without missations is contained in exactly one EMAT with missations.

[0515] In an embodiment, MCMC moves are local for EMATs, optionally with tracked sites and the tips are densely sampled. In an embodiment, N-pruning is applied to tips with missingdata. In an embodiment, partially ambiguous sites are converted into totally ambiguous ones. In an embodiment, the phylogenetic construction network includes implicit tracking. In an embodiment, implicit tracking includes encoding large-scale rearrangements with small structural changes. In an embodiment, the phylogenetic construction network includes explicit tracking. In an embodiment, the missations at the start of a branch include a set of disjoint sorted intervals of sites that have no informative tips downstream, coupled with a sparse map from a site to a state for sites where this state differs from the reference state. In an embodiment, the phylogenetic construction network includes parsimony and / or maximum likelihood approaches that include N- pruning. Local MCMC moves

[0516] In an embodiment, a tree is rearranged while keeping all the associated inference parameters fixed. In an embodiment, a probability is computed according to the Metropolis- Hastings criterion. In an embodiment, the ratio of the posterior involves only ratios of the genetic and ancestral priors. Subtree Pruning and Regrafting (SPR) moves

[0517] In an embodiment, the phylogenetic construction network includes subtree pruning and regrafting. In an embodiment, the grafting point is made general. In an embodiment, the mutation proposal is adapted for efficiency for large genomes, optionally with few mutations per branch. In an embodiment, the move is generalized to arbitrary evolution models. In an embodiment, the move efficiently handles tip sequences with missing data. In an embodiment, a new mutational history for the entire tree is created via stochastic mapping. In an embodiment, the normalization constant is the Felsenstein pruning tree likelihood. In an embodiment, the phylogenetic construction network includes a Jukes-Cantor model. In an embodiment, the Jukes-Cantor model includes a fictitious mutation rate and no site-rate heterogeneity. Efficient Jukes-Cantor stochastic mapping in short branches

[0518] In an embodiment, the phylogenetic construction network includes a stochastic mapping on a branch of a length with a Jukes-Cantor model having a mutation rate, and where the starting and ending state at all sites. In an embodiment, the number of mutations on the trajectory is drawn from a Poisson distribution. In an embodiment, the Poisson distribution includes a mean, with times distributed uniformly, with identities chosen at random, or a combination thereof. In anembodiment, the phylogenetic construction network includes a Poisson-distributed number of mutations, optionally subject to the number of mutations are ≥ 1. In an embodiment, the mutations produce random mutation identities and times. In an embodiment, a mutation is accepted if the start and end states agree with the actual states. In an embodiment, the phylogenetic construction network includes cyclically rotating the states on the proposed mutational history until the start and end states match the known state. SPR moves involving root changes but no missing data

[0519] In an embodiment, sample sequences have no missing data. In an embodiment, a ratio of genetic priors in the acceptance probability includes a ratio of root priors between the old and new configurations. SPR moves involving missing data but no root changes

[0520] In an embodiment, for every path the sequences and mutational histories of the sites are fixed everywhere except in the interior of a path. In an embodiment, new mutational histories along the entirety of the path are created, with a length, subject to fixed end states, with a fake mutation rate. In an embodiment, a ratio of ratios of the genetic priors between each configuration is computed. In an embodiment, a SPR mutational history includes a simple open-ended trajectory. In an embodiment, the trajectory is computed backwards. In an embodiment, computing the trajectory backwards includes starting at a time and repeatedly drawing exponentially distributed times between successive mutations at a rate until the next mutation time precedes the root time. Concrete SPR moves

[0521] In an embodiment, the phylogenetic construction network includes a subtree slide. In an embodiment, the phylogenetic construction network includes Wilson-Balding. In an embodiment, the phylogenetic construction network includes mutation-directed SPR (mdSPR). Mutation-directed SPR moves (mdSPR)

[0522] In an embodiment, mutation-directed SPR (mdSPR) includes skipping over one or more mutations. In an embodiment, mutation-directed SPR (mdSPR) includes limiting the walk on a tree when the walk would cross a mutation. In an embodiment, mutation-directed SPR (mdSPR) includes truncating a walk and backtrack. In an embodiment, mutation-directed SPR (mdSPR) includes a dampened p.d.f. for a grafting point. In an embodiment, mutation-directed SPR (mdSPR) includes an annealing factor.Other local moves

[0523] In an embodiment, the phylogenetic construction network includes inner node displacement moves, branch reform moves, junction reform moves, narrow and wide exchange moves, cluster moves, or a combination thereof. Global MCMC moves

[0524] In an embodiment, the phylogenetic construction network includes global MCMC moves. In an embodiment, the global MCMC moves include keeping the topology and genetic history of the tree fixed but vary the associated parameters. Mutation rate

[0525] In an embodiment, the phylogenetic construction network includes a mutation rate. Evolution model parameters

[0526] In an embodiment, the mutation rate includes a posterior distribution for the variables that specify the evolution model parameters. In an embodiment, each transition rate is inferred independently under a Gamma-distributed prior. In an embodiment, the evolution model parameters’ conditional posterior is a Dirichlet-multinomial distribution. Population model

[0527] In an embodiment, the conditional posterior distribution for the parameters that specify the population curve depends only on the coalescent prior and parameter priors. In an embodiment, the coalescent prior depends on the tree topology only through (a) a function that counts the number of active branches at a time; (b) the times of all the inner nodes; or (c) a combination thereof. In an embodiment, the phylogenetic construction network includes a fine-grained piecewise-constant approximation. Site-rate heterogeneity parameters

[0528] In an embodiment, the Gamma distribution for the site relative rates is computed by a discrete distribution with categories. In an embodiment, the integration is approximated by evaluations of the tree likelihood at evenly distributed quantiles.

[0529] In an embodiment, computing the total branch length of every subtree rooted at inner node includes post-order traversal of the tree. In an embodiment, computing an initial value for each site includes using the reference sequence and the total branch length at the root node. In anembodiment, the phylogenetic construction network includes walking the tree and adjusting for each observed mutation and missation at a site. Scaling strategy

[0530] In an embodiment, the search for a parsimonious tree is parallelized by two or more SPR moves in different portions of the tree. In an embodiment, a tree is partitioned into non- overlapping subtrees and independently SPR moves are processed in each subtree in parallel. Parallelizable coalescent prior

[0531] In an embodiment, the phylogenetic construction network includes augmented coalescent prior. In an embodiment, the phylogenetic construction network includes splitting a tree into partitions. In an embodiment, the phylogenetic construction network includes parallelizing the coalescent prior. In an embodiment, parallelizing the coalescent prior includes cutting up a tree into partitions. In an embodiment, parallelizing the coalescent prior includes for every cell sample computing random Gaussian fields for every partition. In an embodiment, parallelizing the coalescent prior includes distributing the partitions in parallel. In an embodiment, parallelizing the coalescent prior includes computing the local MCMC moves using the augmented coalescent prior in parallel. In an embodiment, the modified subtrees are reassembled into a global tree. In an embodiment, global MCMC moves are computed on the global tree. In an embodiment, global MCMC moves include changes in mutation rates and / or evolution model parameters. In an embodiment, the global MCMC moves are computed with the discretized coalescent prior. In an embodiment, the subtrees are reassembled to be repartitioned. Partitioning a tree into partitions

[0532] In an embodiment, parallelization includes partitioning the tree into partitions. In an embodiment, parallelization includes computing local moves in all partitions in parallel. In an embodiment, parallelization includes identifying a series of nodes. In an embodiment, the series of nodes include cut points. In an embodiment, a cut point is where one subtree ends and another begins. In an embodiment, each subtree is rooted at either the root of another subtree or one of the cut points, and optionally extends as far as the closest descendant cut points or the tips of the original tree, optionally whichever is closets. In an embodiment, the subtrees are of about equal size (e.g., within 10%).

[0533] In an embodiment, parallelization includes post-order traversal of the tree. In an embodiment, post-order traversal of the tree includes descending from a node to its children in random order. In an embodiment, parallelization includes choosing cutpoints uniformly at random from the inner nodes of the tree, subject to one of these being the root.

[0534] In an embodiment, parallelization includes producing distinct stencils. In an embodiment, each stencil consists of node identifiers of all the cutpoints, so any topology of a tree is partitioned according to a particular stencil. In an embodiment, the stencils are sampled uniformly and use it to partition. In an embodiment, partitioning is computed iteratively.

[0535] In an embodiment, repeatedly partition stencils. In an embodiment, run a fixed schedule of global moves. In an embodiment, partition the tree into subtrees according to one of the stencils chosen at random. In an embodiment, run a fixed number of independent local moves in each subtree in parallel. In an embodiment, reassemble the tree from the modified subtrees. In an embodiment, compute the number of active branches in each partition and cell, and use them to Gibbs sample values for of the augmented coalescent prior as well as their sums. In an embodiment, parallelize the partitions. In an embodiment, each subtree, values, sums, and copies of the global model parameters are independently computed. In an embodiment, the subtree is locally equilibrated conditional on the global parameters and the fixed times and sequences of the boundary nodes between it and its ancestral and descendant subtrees. In an embodiment, each local move is chosen at random. In an embodiment, the random move is selected from inner node displacement move, branch reform move, subtree slide move, mutation-directed SPR moves, or a combination thereof. In an embodiment, the partitions are reassembled so that at the end of each iteration of the sampling of a complete configuration of the tree and the associated global parameters. In an embodiment, the reference sequence is changed to match the current root sequence. Fast MCC trees, approximate incremental MCC trees

[0536] In an embodiment, the phylogenetic construction network includes a web interface. In an embodiment, the web interface automatically re-derives an MCC every time a new posterior tree is sampled.

[0537] In an embodiment, all its clades in a tree are enumerated. In an embodiment, a clade’s posterior support includes any clade present in a fraction of the posterior trees. In an embodiment,a MCC is defined as a tree with: (a) the topology of a posterior tree with the highest product of posterior support over all its nodes; (b) node times that summarize those of the corresponding nodes in the posterior trees; or (c) a combination thereof. In an embodiment, these node times are the average (or median) of the times of the inner node for the same clade in each posterior tree.

[0538] In an embodiment, a clade of a node as a fixed-size C-bit fingerprint. In an embodiment, completely random fingerprints are assigned to tips. In an embodiment, the fingerprint of a clade that is the union of two smaller and disjoint clades is calculated as the exclusive-or (XOR) of those two clade’s fingerprints. In an embodiment, the fingerprints are computed with a post-order traversal of the tree. In an embodiment, the fingerprint for an arbitrary inner node is the XOR of the fingerprints of all its descendant tips.

[0539] In an embodiment, the MCC includes building a map of clade fingerprints to lists of tuples. In an embodiment, the MCC includes calculating the log-credibility of each clade. In an embodiment, the MCC includes computing for each posterior tree the sum of the log-clade- credibilities of each inner node, and, optionally, recording the tree with the highest sum. In an embodiment, the MCC includes iterating over its nodes. In an embodiment, the MCC includes iterating over the corresponding nodes in the posterior trees and calculating the mean (or median) node time of each MCC node. Incremental calculation of MCCs

[0540] In an embodiment, the phylogenetic construction network includes incremental MCC. In an embodiment, a MCC is created with a beam search. In an embodiment, when a new posterior tree sample is added, its inner node clade fingerprints is computed and, optionally, updates the map of clade fingerprints to pointers to posterior tree nodes. In an embodiment, the highest-clade- credibility tree in the beam for the MCC topology is selected. In an embodiment, a parameter controls the lag between changes in the real MCC topology and that of this incremental MCC. Bayesian Inference

[0541] In an embodiment, the phylogenetic construction network comprises of Bayesian inference. A Bayesian inference can make predictions from probabilistic graphical models. In Bayesian inference, for example, input features are mapped onto a directed acyclic graph forming the nodes of the graph. The edges connecting the nodes contain the conditional dependencies between nodes to form a predicative model. For each connected node the probability of the inputfeatures resulting in the connected node is learned and forms the predictive mechanism. The nodes may comprise the same, similar or different probability functions to determine movement from one node to another. The nodes of a Bayesian inference are conditionally independent of its non- descendants given its parents thus satisfying a local Markov property. This property affords reduced computations in larger networks by simplifying the joint distribution.

[0542] There are multiple methods to evaluate the inference, or predictability, in a Bayesian inference but only two are mentioned for demonstrative purposes. The first method involves computing the joint probability of a particular assignment of values for each variable. The joint probability can be considered the product of each conditional probability and, in some instances, comprises the logarithm of that product. The second method is Markov chain Monte Carlo (MCMC), which can be implemented when the sample size is large. MCMC is a well-known class of sample distribution algorithms and will not be discussed in detail here. In an embodiment, a plurality of nucleic acid sequences is mapped and are used to estimate one or more phylogenetic trees.

[0543] In an embodiment, the phylogenetic construction network comprises of a Jukes-Cantor model. The Jukes-Cantor model is described above herein. In an embodiment, the Jukes-Cantor model does not comprise rate heterogeneity. Rate heterogeneity may refer to the different rates at which mutations occur at individual sites in a nucleic acid sequence. In some instances, the Jukes- Cantor model describes the individual states, and accordingly their evolution (i.e., mutations), as occurring with equal probability at the root, and the transition rates on any edge of the tree are the same for each possible substitution, Accordingly, the phylogenetic construction network may not comprise rate heterogeneity because the mutation rate is modeled as homogeneous. Phylogenetic Trees

[0544] In block 230, the phylogenetic construction network 130 generates one or more phylogenetic trees. In an embodiment, the one or more phylogenetic trees is transmitted back to the user via the network 105. In an embodiment, the resulting user information is stored on the data storage unit 137. In an embodiment, the resulting user information is immediately transmitted to the user’s device. In an embodiment, the resulting user information is transmitted across the network 105 to the data acquisition system for subsequent access by the user associated device 110 or phylogenetic construction network 130.

[0545] Generally, nucleic acid sequences are mapped to discover the origin, transmission, and evolution of a disease. Tree diagrams, such as phylogenetic trees or phylogeographic trees, are generally used to map these aspects of a disease outbreak. These tree diagrams are typically rooted binary weighted trees and are operated on, for example, as connected graph data structures. See e.g., Mau, B., et al. Biometrics 55.1 (1999): 1-12.

[0546] In an embodiment, the phylogenetic construction network comprises of one or more model tree data structures. Computationally, a tree (i.e., model tree data structure) is a widely used hierarchical data structure comprising of connected nodes. The nodes in the tree can be connected to one or more children. A child node, however, is connected to one parent. The tree data structure also comprises of a root node, which has no parent and is the top-most node in the tree hierarchy.

[0547] The structure of the tree, typically, results in absence of cycles or “loops” such that a node cannot be its own ancestor. In addition, the tree data structure can be divided into subtrees wherein a child node can be treated like the root node of its own subtree. The ability to divide a tree makes it possible to operate on a branch or group of branches of a tree in parallel as well as making possible recursion techniques for tree traversal.

[0548] The tree data structure may comprise a binary tree, which limit the maximum number of children to two for each parent. A binary tree data structure corresponds to an ordered tree and benefit from techniques offered by ordered trees. A node may comprise an indicator (e.g., a value or pointer) to other data in the tree and / or with the leaf nodes (i.e., nodes with no children nodes).

[0549] In an embodiment, the model tree data structure is may be fully-labeled or partially- labeled. A fully-labeled model tree data structure may include labeling each node, each branch, or a combination thereof. A partially-labeled model tree data structure may include partially-labeled the nodes, partially-labeled the branches, or partially-labeled nodes and branches. In an embodiment, partially-labeled can include labeling less than 100% (<100%), <99%, <98%, <97%, <96%, <95%, <94%, <93%, <92%, <91%, <90%, <89%, <88%, <87%, <86%, <85%, <84%, <83%, <82%, <81%, <80%, <79%, <78%, <77%, <76%, <75%, <74%, <73%, <72%, <71%, <70%, <69%, <68%, <67%, <66%, <65%, <64%, <63%, <62%, <61%, <60%, <59%, <58%, <57%, <56%, <55%, <54%, <53%, <52%, <51%, <50%, <49%, <48%, <47%, <46%, <45%, <44%, <43%, <42%, <41%, <40%, <39%, <38%, <37%, <36%, <35%, <34%, <33%, <32%, <31%, <30%, <29%, <28%, <27%, <26%, <25%, <24%, <23%, <22%, <21%, <20%, <19%,<18%, <17%, <16%, <15%, <14%, <13%, <12%, <11%, <10%, <9%, <8%, <7%, <6%, <5%, <4%, <3%, <2%, or <1%. In an embodiment, partially-labeled can include labeling <100%, <95%, <90%, <85%, <80%, <75%, <70%, <65%, <60%, <55%, <50%, <45%, <40%, <35%, <30%, <25%, <20%, <15%, <10%, or <5%. Phylogenetic GUI

[0550] In an embodiment, the phylogenetic construction network performs the processing step through the web browser on a user’s computing device. In an embodiment, the processing step is visualized through a graphical user interface, the graphical user interface is presented on a user’s computing device, and the graphical user interface is accessed by a user computing device on a website or on an application of the service provider.

[0551] The user computing device 110 may view the application via the network 105 on a webpage of the service provider system or an application associated with the service provider system. In some instances, the service provider system is the phylogenetic construction network 130.

[0552] The service provider system represents any device, computing system, organization, cloud computing system, or other device or system that is suitable to manage a user account and provide services to a client including services to construct a phylogenetic tree by using pre-built, secure, and pre-tested standardized connectors. A service provider server operating on the service provider system hosts a connection portal and performs any other suitable computing or communication functions. The connection portal allows users to perform certain ones of the functions described herein by providing a graphical user interface to the user. The graphical user interface may be displayed on a client computing device 110 when the service provider system is accessed, such as by a webpage or an application.

[0553] The service provider system determines a set of one or more systems 133 that have been pre-built and are recommended for the client computing device 110. The set may be all pre- built systems 133, or the set may be based on the type of organization or person that is operating the client computing device 110. The service provider system displays the set of systems 130 on the graphical user interface 122 to allow a user to select a system 130 with which to connect.

[0554] An illustration of the display is provided with more details in Figures 30-36.

[0555] The example Figures illustrates a display of the phylogenetic construction network. If process or result was selected for display based on a search by the user or based on any other criteria for selecting the process or result to display, then the user is presented with the interface object for selection by the user. The user may click on an interface object or otherwise select a process or result for connection. Any other number of suitable processes or results described herein may be displayed on the graphical user interface.

[0556] In an embodiment, the one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, or any combination thereof are visualized. In an embodiment, the processing step can be terminated by a user visualizing the convergence of the one or more phylogenetic trees. Convergence refers the minimization of the change to the one or more phylogenetic trees over time. For example, a user observing the visualization of the process of generating the one or more phylogenetic trees and determine the one or more phylogenetic trees are not significantly changing (e.g., branches are not moving, probabilities are stabilizing, nodes are consistent). The user may than stop the process, thereby generating the one or more phylogenetic trees.

[0557] In an embodiment, the method further comprises of mean lineage prevalence charts, distribution of times at which a node arose, a listing of mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof wherein mean lineage prevalence charts, distribution of times at which a node arose, a listing of the mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof can be performed during or after the processing step.

[0558] For example, a user can interactively choose the point in the run beyond which all trees contribute to the MCC, so a user can see the effect of changing that threshold; (2) the mean lineage prevalence charts, where a user can define a lineage interactively as "the descendants of node X on the MCC", and a user can be immediately shown an estimate of the fraction of the population over time that is descended from this node, with associated uncertainties; (3) for any highlighted node (the root is always highlighted), a user can get the full distribution of plausible times at which this node arose (technically called the "tMRCA = time of Most Recent Common Ancestor"); (4)for every pair of nearest-neighbor highlighted nodes, a user can get a listing of the mutations that intervened between them, as well as the distribution of the time at which those mutations probably arose; (5) for each mutation that a user selects, a user can immediately get a plot of the prevalence of that mutation over time, with associated uncertainty; (6) if a user can annotate each "tip" of the tree with additional metadata (free-form, geographical location such as state, or membership of an institution, or presence of an unusual symptom), then those annotations get propagated up the MCC via parsimony, so a user can get a sense of how the ancestral relationships between the tips led to the present data (e.g., a user can more or less date *when* a pathogen entered a certain geographical area).

[0559] In an embodiment, the visualization comprises of labeling the one or more phylogenetic trees. In an embodiment, labeling the one or more phylogenetic trees comprises of labeling branches of the one or more phylogenetic trees. In an embodiment, labeling branches comprises of labeling branches with an adjustable credibility threshold, wherein the adjustable credibility threshold comprises of confidence and uncertainty of a branch. For example, a the one or more phylogenetic trees observed by the user may comprise labels on or near the branches or nodes. The labels on the one or more phylogenetic trees may comprise an adjustable credibility threshold, wherein the adjustable credibility threshold comprises of confidence and uncertainty of a branch. The credibility threshold can refer to the reliability of a branch between nodes. This is computed as a confidence or uncertainty variable. These variables are labeled on the branches. The credibility threshold is adjustable such that a user may change the threshold of credibility. For example, the threshold may be greater than 99%, 95%, 90%, 85%, 80%, 75%, 70%, 65%, 60%. The threshold may be any number selected by a user.

[0560] In an embodiment, the phylogenetic construction network comprises of computing and identifying a most recent common ancestor (MRCA) of at least two nodes on the one or more phylogenetic trees selected by a user through the graphical user interface. The MRCA refers to a node on the tree that provides the shortest connection between two selected nodes. For example, the MRCA may be the parent node of two children nodes directly branching from the parent node. In another example, the MRCA may be the root node of the two descendent node selected by the user.

[0561] In an embodiment, the method further comprises generating a summary of the one or more phylogenetic trees. The summary generated by the phylogenetic construction network may comprise any data produced by the phylogenetic construction network during the processing step. Furthermore, the summary may be provided in any number of methods. For example, the summary may be adjacent to the one or more phylogenetic trees, on the one or more phylogenetic trees wherein the summary data is located next to the portion of the one or more trees the data describes, or a combination thereof. Alternatively or additionally, the data may be provided on a separate webpage. In an embodiment, the summary of the one or more phylogenetic trees comprises of one or more relationships between the root node and a user selected lineage, one or more MCRA, or a combination thereof. One or more relationships between the root node and a user selected lineage may comprise branch data, child node data, and / or confidence data associated with the root node and user selected lineage. In an embodiment, the summary comprises of one or more mutations in the one or more phylogenetic trees, wherein the summary of one or more mutations comprises of a number or percentage of trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which comprise the one or more mutations, or any combination thereof. In an embodiment, visualization allows a user to search for and select one or more mutations. Any known search method in the art can accomplish the search described herein.

[0562] In an embodiment, the method further comprises generating a pruned one or more phylogenetic trees. A pruned one or more phylogenetic trees may refer to a generated one or more phylogenetic tree wherein one or more branches have been removed. The one or more removed branches may be removed for any reason. For example, a user may want to visualize the one or more phylogenetic trees with the least significant mutations removed. In another example, a user may want to visualize the one or more phylogenetic trees with the least significant lineages removed. In both examples, removing the selected branches results in a pruned phylogenetic tree. In an embodiment, the pruned one or more phylogenetic trees comprise every inner node comprising a mutation even immediately upstream in at least one of the one or more phylogenetic trees. In an embodiment, the pruned one or more phylogenetic trees are generated by: (i) tagging one or more inner nodes comprising a mutation immediately upstream of the one or more inner nodes and identifying a MRCA for the one or more inner nodes and (ii) removing inner nodes nottagged in step (i) and connecting the children nodes of the removed inner nodes to a most recent tagged ancestor node. Embedding

[0563] In one example, the machine processing module may use embedding to provide a lower dimensional representation, such as a vector, of features to organize them based off respective similarities. In some situations, these vectors can become massive. In the case of massive vectors, particular values may become very sparse among a large number of values (e.g., a single instance of a value among 50,000 values). Because such vectors are difficult to work with, reducing the size of the vectors, in some instances, is necessary. A machine processing module can learn the embeddings along with the model parameters. In an embodiment, features such as a plurality of nucleic acid sequences can be mapped to vectors implemented in embedding methods. In an embodiment, embedded semantic meanings are utilized. Embedded semantic meanings are values of respective similarity. For example, the distance between two vectors, in vector space, may imply two values located elsewhere with the same distance are categorically similar. Embedded semantic meanings can be used with similarity analysis to rapidly return similar values. In an embodiment, a plurality of nucleic acid sequences is embedded. For example, a sequence can be embedded into a vector wherein the individual nucleic acids are assigned to a cell corresponding to their position in the sequence. The individual cells may also encode various allele information. In some instances, the plurality of nucleic acid sequences is embedded into a matrix where each vector of the matrix embeds one of the nucleic acid sequences. In an embodiment, the methods herein are developed to identify meaningful portions of the vector and extract semantic meanings between that space.

[0564] In an embodiment, embedding comprises encoding the plurality of nucleic acid sequences into one or more vectors. In an embodiment, the one or more vectors are weight vectors. For example, the vectors encoding the plurality of nucleic acid sequences are associated with weights such as a corresponding vector or matrix of weights. The corresponding vector can be applied to the vector encoding the plurality of nucleic acid sequences. In an embodiment, wherein weights, of the weight vectors, encode the probability of a nucleic acid sequence at a node of the model tree.Training Methods

[0565] In an embodiment, the outbreak transmission network is trained on the frequency of iSNVs. Training typically occurs after selection and development of a machine processing module and before the machine processing module is operably in use. In one aspect, the training data, used to teach the machine processing module, can comprise input data such as a plurality of nucleic acid sequences and the respective target output data such as a pathogen transmission pathways. For example, the outbreak transmission network can be trained on known nucleic acid sequences from a known pathogen transmission pathway to learn the frequency of iSNVs.

[0566] In an embodiment, the weights are trained to encode the probability. The phylogenetic construction network can be trained with known phylogenetic data. For example, the known phylogenetic lineage of pathogen can be used to train the phylogenetic construction network weights to encode the probability. In this example, only a subset of children, such as the terminal children, and an ancestor, such as the oldest ancestor, are used for training. The model then operates on the subset of children and the weights encoding the probability are modified to arrive at the ancestor according to the entire known date set.

[0567] In an embodiment, the phylogenetic construction network is trained to reduce tree size and reduce mutations. Similar to preceding paragraph, the phylogenetic construction network can be trained with known phylogenetic data. For example, the known phylogenetic lineage of pathogen can be used to train the phylogenetic construction network to reduce the tree size and mutations. In this example, only a subset of children, such as the terminal children, and an ancestor, such as the oldest ancestor, are used for training. The model then operates on the subset of children, corresponding tree size, and corresponding mutations, to arrive at the ancestor according to the entire known date set.

[0568] The ladder diagrams, scenarios, flowcharts and block diagrams in the figures and discussed herein illustrate architecture, functionality, and operation of example embodiments and various aspects of systems, methods, and computer program products of the present invention. Each block in the flowchart or block diagrams can represent the processing of information and / or transmission of information corresponding to circuitry that can be configured to execute the logical functions of the present techniques. Each block in the flowchart or block diagrams can represent a module, segment, or portion of one or more executable instructions for implementing the specifiedoperation or step. In an embodiment, the functions / acts in a block can occur out of the order shown in the figures and nothing requires that the operations be performed in the order illustrated. For example, two blocks shown in succession can executed concurrently or essentially concurrently. In another example, blocks can be executed in the reverse order. Furthermore, variations, modifications, substitutions, additions, or reduction in blocks and / or functions may be used with any of the ladder diagrams, scenarios, flow charts and block diagrams discussed herein, all of which are explicitly contemplated herein.

[0569] The ladder diagrams, scenarios, flow charts and block diagrams may be combined with one another, in part or in whole. Coordination will depend upon the required functionality. Each block of the block diagrams and / or flowchart illustration as well as combinations of blocks in the block diagrams and / or flowchart illustrations can be implemented by special purpose hardware- based systems that perform the aforementioned functions / acts or carry out combinations of special purpose hardware and computer instructions. Moreover, a block may represent one or more information transmissions and may correspond to information transmissions among software and / or hardware modules in the same physical device and / or hardware modules in different physical devices.

[0570] The present techniques can be implemented as a system, a method, a computer program product, digital electronic circuitry, and / or in computer hardware, firmware, software, or in combinations of them. The system may comprise distinct software modules embodied on a computer readable storage medium; the modules can include, for example, any or all of the appropriate elements depicted in the block diagrams and / or described herein; by way of example and not limitation, any one, some, or all of the modules / blocks and or sub-modules / sub-blocks described. The method steps can then be carried out using the distinct software modules and / or sub-modules of the system, as described above, executing on one or more hardware processors such as CPU or GPU.

[0571] The computer program product can include a program tangibly embodied in an information carrier (e.g., computer readable storage medium or media) having computer readable program instructions thereon for execution by, or to control the operation of, data processing apparatus (e.g., a processor) to carry out aspects of one or more embodiments of the present invention. It will be understood that each block of the flowchart illustrations and / or blockdiagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable program instructions.

[0572] The computer readable program instructions can be performed on general purpose computing device, special purpose computing device, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the functions / acts specified in the flowchart and / or block diagram block or blocks. The processors, either: temporarily or permanently; or partially configured, may comprise processor-implemented modules. The present techniques referred to herein may, In an embodiment, comprise processor-implemented modules. Functions / acts of the processor- implemented modules may be distributed among the one or more processors. Moreover, the functions / acts of the processor-implements modules may be deployed across a number of machines, where the machines may be located in a single geographical location or distributed across a number of geographical locations.

[0573] The computer readable program instructions can also be stored in a computer readable storage medium that can direct one or more computer devices, programmable data processing apparatuses, and / or other devices to carry out the function / acts of the processor-implemented modules. The computer readable storage medium containing all or partial processor-implemented modules stored therein, comprises an article of manufacture including instructions which implement aspects, operations, or steps to be performed of the function / act specified in the flowchart and / or block diagram block or blocks.

[0574] Computer readable program instructions described herein can be downloaded to a computer readable storage medium within a respective computing / processing devices from a computer readable storage medium. Optionally, the computer readable program instructions can be downloaded to an external computer device or external storage device via a network. A network adapter card or network interface in each computing / processing device can receive computer readable program instructions from the network and forward the computer readable program instructions for permanent or temporary storage in a computer readable storage medium within the respective computing / processing device.

[0575] Computer readable program instructions described herein can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code. The computer readable program instructions can be written in any programming language such as compiled or interpreted languages. In addition, the programming language can be object-oriented programming language (e.g., “C++”) or conventional procedural programming languages (e.g., “C”) or any combination thereof may be used to as computer readable program instructions. The computer readable program instructions can be distributed in any form, for example as a stand-alone program, module, subroutine, or other unit suitable for use in a computing environment. The computer readable program instructions can execute entirely on one computer or on multiple computers at one site or across multiple sites connected by a communication network, for example on user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on a remote computer or server. If the computer readable program instructions are executed entirely remote, then the remote computer can be connected to the user's computer through any type of network or the connection can be made to an external computer. In examples embodiments, electronic circuitry including, but not limited to, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions. Electronic circuitry can utilize state information of the computer readable program instructions to personalize the electronic circuitry, to execute functions / acts of one or more embodiments of the present invention.

[0576] Example embodiments described herein include logic or a number of components, modules, or mechanisms. Modules may comprise either software modules or hardware- implemented modules. A software module may be code embodied on a non-transitory machine- readable medium or in a transmission signal. A hardware-implemented module is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In an embodiment, one or more computer systems (e.g., a standalone, client or server computer system) or one or more processors may be configured by software (e.g., an application or application portion) as a hardware-implemented module that operates to perform certain operations as described herein.

[0577] In an embodiment, a hardware-implemented module may be implemented mechanically or electronically. In an embodiment, hardware-implemented modules may comprise permanently configured dedicated circuitry or logic to execute certain functions / acts such as a special-purpose processor or logic circuitry (e.g., a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)). In an embodiment, hardware-implemented modules may comprise temporary programmable logic or circuitry to perform certain functions / acts. For example, a general-purpose processor or other programmable processor.

[0578] The term “hardware-implemented module” encompasses a tangible entity. A tangible entity may be physically constructed, permanently configured, or temporarily or transitorily configured to operate in a certain manner and / or to perform certain functions / acts described herein. Hardware-implemented modules that are temporarily configured need not be configured or instantiated at any one time. For example, if the hardware-implemented modules comprise a general-purpose processor configured using software, then the general-purpose processor may be configured as different hardware-implemented modules at different times.

[0579] Hardware-implemented modules can provide, receive, and / or exchange information from / with other hardware-implemented modules. The hardware-implemented modules herein may be communicatively coupled. Multiple hardware-implemented modules operating concurrently, may communicate through signal transmission, for instance appropriate circuits and buses that connect the hardware-implemented modules. Multiple hardware-implemented modules configured or instantiated at different times may communicate through temporarily or permanently archived information, for instance the storage and retrieval of information in memory structures to which the multiple hardware-implemented modules have access. For example, one hardware- implemented module may perform an operation, and store the output of that operation in a memory device to which it is communicatively coupled. Consequently, another hardware-implemented module may, at some time later, access the memory device to retrieve and process the stored information. Hardware-implemented modules may also initiate communications with input or output devices, and can operate on information from the input or output devices.

[0580] In an embodiment, the present techniques can be at least partially implemented in a cloud or virtual machine environment.Example Computing Device

[0581] FIG.39 depicts a block diagram of a computing machine 2000 and a module 2050 in accordance with certain examples. The computing machine 2000 may comprise, but are not limited to, remote devices, work stations, servers, computers, general purpose computers, Internet / web appliances, hand-held devices, wireless devices, portable devices, wearable computers, cellular or mobile phones, personal digital assistants (PDAs), smart phones, smart watches, tablets, ultrabooks, netbooks, laptops, desktops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, set-top boxes, network PCs, mini-computers, and any machine capable of executing the instructions.The module 2050 may comprise one or more hardware or software elements configured to facilitate the computing machine 2000 in performing the various methods and processing functions presented herein. The computing machine 2000 may include various internal or attached components such as a processor 2010, system bus 2020, system memory 2030, storage media 2040, input / output interface 2060, and a network interface 2070 for communicating with a network 2080.

[0582] The computing machine 2000 may be implemented as a conventional computer system, an embedded controller, a laptop, a server, a mobile device, a smartphone, a set-top box, a kiosk, a router or other network node, a vehicular information system, one or more processors associated with a television, a customized machine, any other hardware platform, or any combination or multiplicity thereof. The computing machine 2000 may be a distributed system configured to function using multiple computing machines interconnected via a data network or bus system.

[0583] The one or more processor 2010 may be configured to execute code or instructions to perform the operations and functionality described herein, manage request flow and address mappings, and to perform calculations and generate commands. Such code or instructions could include, but is not limited to, firmware, resident software, microcode, and the like. The processor 2010 may be configured to monitor and control the operation of the components in the computing machine 2000. The processor 2010 may be a general purpose processor, a processor core, a multiprocessor, a reconfigurable processor, a microcontroller, a digital signal processor (“DSP”), an application specific integrated circuit (“ASIC”), tensor processing units (TPUs), a graphics processing unit (“GPU”), a field programmable gate array (“FPGA”), a programmable logic device (“PLD”), a radio-frequency integrated circuit (RFIC), a controller, a state machine, gated logic,discrete hardware components, any other processing unit, or any combination or multiplicity thereof. In an embodiment, each processor 2010 can include a reduced instruction set computer (RISC) microprocessor. The processor 2010 may be a single processing unit, multiple processing units, a single processing core, multiple processing cores, special purpose processing cores, co- processors, or any combination thereof. According to certain examples, the processor 2010 along with other components of the computing machine 2000 may be a virtualized computing machine executing within one or more other computing machines. Processors 2010 are coupled to system memory and various other components via a system bus 2020.

[0584] The system memory 2030 may include non-volatile memories such as read-only memory (“ROM”), programmable read-only memory (“PROM”), erasable programmable read- only memory (“EPROM”), flash memory, or any other device capable of storing program instructions or data with or without applied power. The system memory 2030 may also include volatile memories such as random-access memory (“RAM”), static random-access memory (“SRAM”), dynamic random-access memory (“DRAM”), and synchronous dynamic random- access memory (“SDRAM”). Other types of RAM also may be used to implement the system memory 2030. The system memory 2030 may be implemented using a single memory module or multiple memory modules. While the system memory 2030 is depicted as being part of the computing machine 2000, one skilled in the art will recognize that the system memory 2030 may be separate from the computing machine 2000 without departing from the scope of the subject technology. It should also be appreciated that the system memory 2030 is coupled to system bus 2020 and can include a basic input / output system (BIOS), which controls certain basic functions of the processor 2010 and / or operate in conjunction with, a non-volatile storage device such as the storage media 2040.

[0585] In an embodiment, the computing device 2000 includes a graphics processing unit (GPU) 2090. Graphics processing unit 2090 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. In general, a graphics processing unit 2090 is efficient at manipulating computer graphics and image processing and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.

[0586] The storage media 2040 may include a hard disk, a floppy disk, a compact disc read only memory (“CD-ROM”), a digital versatile disc (“DVD”), a Blu-ray disc, a magnetic tape, a flash memory, other non-volatile memory device, a solid state drive (“SSD”), any magnetic storage device, any optical storage device, any electrical storage device, any electromagnetic storage device, any semiconductor storage device, any physical-based storage device, any removable and non-removable media, any other data storage device, or any combination or multiplicity thereof. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read- only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any other data storage device, or any combination or multiplicity thereof. The storage media 2040 may store one or more operating systems, application programs and program modules such as module 2050, data, or any other information. The storage media 2040 may be part of, or connected to, the computing machine 2000. The storage media 2040 may also be part of one or more other computing machines that are in communication with the computing machine 2000 such as servers, database servers, cloud storage, network attached storage, and so forth. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0587] The module 2050 may comprise one or more hardware or software elements, as well as an operating system, configured to facilitate the computing machine 2000 with performing the various methods and processing functions presented herein. The module 2050 may include one or more sequences of instructions stored as software or firmware in association with the system memory 2030, the storage media 2040, or both. The storage media 2040 may therefore represent examples of machine or computer readable media on which instructions or code may be stored for execution by the processor 2010. Machine or computer readable media may generally refer to any medium or media used to provide instructions to the processor 2010. Such machine or computerreadable media associated with the module 2050 may comprise a computer software product. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. It should be appreciated that a computer software product comprising the module 2050 may also be associated with one or more processes or methods for delivering the module 2050 to the computing machine 2000 via the network 2080, any signal-bearing medium, or any other communication or delivery technology. The module 2050 may also comprise hardware circuits or information for configuring hardware circuits such as microcode or configuration information for an FPGA or other PLD.

[0588] The input / output (“I / O”) interface 2060 may be configured to couple to one or more external devices, to receive data from the one or more external devices, and to send data to the one or more external devices. Such external devices along with the various internal devices may also be known as peripheral devices. The I / O interface 2060 may include both electrical and physical connections for coupling in operation the various peripheral devices to the computing machine 2000 or the processor 2010. The I / O interface 2060 may be configured to communicate data, addresses, and control signals between the peripheral devices, the computing machine 2000, or the processor 2010. The I / O interface 2060 may be configured to implement any standard interface, such as small computer system interface (“SCSI”), serial-attached SCSI (“SAS”), fiber channel, peripheral component interconnect (“PCI”), PCI express (PCIe), serial bus, parallel bus, advanced technology attached (“ATA”), serial ATA (“SATA”), universal serial bus (“USB”), Thunderbolt, FireWire, various video buses, and the like. The I / O interface 2060 may be configured to implement only one interface or bus technology. Alternatively, the I / O interface 2060 may be configured to implement multiple interfaces or bus technologies. The I / O interface 2060 may be configured as part of, all of, or to operate in conjunction with, the system bus 2020. The I / O interface 2060 may include one or more buffers for buffering transmissions between one or more external devices, internal devices, the computing machine 2000, or the processor 2010.

[0589] The I / O interface 2060 may couple the computing machine 2000 to various input devices including cursor control devices, touch-screens, scanners, electronic digitizers, sensors, receivers, touchpads, trackballs, cameras, microphones, alphanumeric input devices, any other pointing devices, or any combinations thereof. The I / O interface 2060 may couple the computingmachine 2000 to various output devices including video displays (The computing device 2000 may further include a graphics display, for example, a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, a cathode ray tube (CRT), or any other display capable of displaying graphics or video), audio generation device, printers, projectors, tactile feedback devices, automation control, robotic components, actuators, motors, fans, solenoids, valves, pumps, transmitters, signal emitters, lights, and so forth. The I / O interface 2060 may couple the computing device 2000 to various devices capable of input and out, such as a storage unit. The devices can be interconnected to the system bus 2020 via a user interface adapter, which can include, for example, a Super I / O chip integrating multiple device adapters into a single integrated circuit.

[0590] The computing machine 2000 may operate in a networked environment using logical connections through the network interface 2070 to one or more other systems or computing machines across the network 2080. The network 2080 may include a local area network (“LAN”), a wide area network (“WAN”), an intranet, an Internet, a mobile telephone network, storage area network (“SAN”), personal area network (“PAN”), a metropolitan area network (“MAN”), a wireless network (“Wi-Fi;”), wireless access networks, a wireless local area network (“WLAN”), a virtual private network (“VPN”), a cellular or other mobile communication network, Bluetooth, near field communication (“NFC”), ultra-wideband, wired networks, telephone networks, optical networks, copper transmission cables, or combinations thereof or any other appropriate architecture or system that facilitates the communication of signals and data. The network 2080 may be packet switched, circuit switched, of any topology, and may use any communication protocol. The network 2080 may comprise routers, firewalls, switches, gateway computers and / or edge servers. Communication links within the network 2080 may involve various digital or analog communication media such as fiber optic cables, free-space optics, waveguides, electrical conductors, wireless links, antennas, radio-frequency communications, and so forth.

[0591] Information for facilitating reliable communications can be provided, for example, as packet / message sequencing information, encapsulation headers and / or footers, size / time information, and transmission verification information such as cyclic redundancy check (CRC) and / or parity check values. Communications can be made encoded / encrypted, or otherwise made secure, and / or decrypted / decoded using one or more cryptographic protocols and / or algorithms,such as, but not limited to, Data Encryption Standard (DES), Advanced Encryption Standard (AES), a Rivest-Shamir-Adelman (RSA) algorithm, a Diffie-Hellman algorithm, a secure sockets protocol such as Secure Sockets Layer (SSL) or Transport Layer Security (TLS), and / or Digital Signature Algorithm (DSA). Other cryptographic protocols and / or algorithms can be used as well or in addition to those listed herein to secure and then decrypt / decode communications.

[0592] The processor 2010 may be connected to the other elements of the computing machine 2000 or the various peripherals discussed herein through the system bus 2020. The system bus 2020 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. For example, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus. It should be appreciated that the system bus 2020 may be within the processor 2010, outside the processor 2010, or both. According to certain examples, any of the processor 2010, the other elements of the computing machine 2000, or the various peripherals discussed herein may be integrated into a single device such as a system on chip (“SOC”), system on package (“SOP”), or ASIC device.

[0593] Examples may comprise a computer program that embodies the functions described and illustrated herein, wherein the computer program is implemented in a computer system that comprises instructions stored in a machine-readable medium and a processor that executes the instructions. However, it should be apparent that there could be many different ways of implementing examples in computer programming, and the examples should not be construed as limited to any one set of computer program instructions. Further, a skilled programmer would be able to write such a computer program to implement an example of the disclosed examples based on the appended flow charts and associated description in the application text. Therefore, disclosure of a particular set of program code instructions is not considered necessary for an adequate understanding of how to make and use examples. Further, those ordinarily skilled in the art will appreciate that one or more aspects of examples described herein may be performed by hardware, software, or a combination thereof, as may be embodied in one or more computingsystems. Moreover, any reference to an act being performed by a computer should not be construed as being performed by a single computer as more than one computer may perform the act.

[0594] The examples described herein can be used with computer hardware and software that perform the methods and processing functions described herein. The systems, methods, and procedures described herein can be embodied in a programmable computer, computer-executable software, or digital circuitry. The software can be stored on computer-readable media. For example, computer-readable media can include a floppy disk, RAM, ROM, hard disk, removable media, flash memory, memory stick, optical media, magneto-optical media, CD-ROM, etc. Digital circuitry can include integrated circuits, gate arrays, building block logic, field programmable gate arrays (FPGA), etc.

[0595] A “server” may comprise a physical data processing system (for example, the computing device 2000 as shown in FIG.39) running a server program. A physical server may or may not include a display and keyboard. A physical server may be connected, for example by a network, to other computing devices. Servers connected via a network may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a distributed (e.g., peer-to-peer) network environment. The computing device 2000 can include clients’ servers. For example, a client and server can be remote from each other and interact through a network. The relationship of client and server arises by virtue of computer programs in communication with each other, running on the respective computers.

[0596] Any two or more devices, two or more software / programs, and any two or more portions of a device or software / program, for simplicity referred to as technology, may be described herein as operably linked. Operably linked may be defined as at least one technology can mediate a function exerted upon at least one other technology such that the two or more technologies function normally. In general, operably linked refers to the ability for at least one technology to communicate with at least one other technology.

[0597] The example systems, methods, and acts described in the examples and described in the figures presented previously are illustrative, not intended to be exhaustive, and not meant to be limiting. In alternative examples, certain acts can be performed in a different order, in parallel with one another, omitted entirely, and / or combined between different examples, and / or certain additional acts can be performed, without departing from the scope and spirit of various examples.Plural instances may implement components, operations, or structures described as a single instance. Structures and functionality that may appear as separate In an embodiment may be implemented as a combined structure or component. Similarly, structures and functionality that may appear as a single component may be implemented as separate components. Accordingly, such alternative examples are included in the scope of the following claims, which are to be accorded the broadest interpretation to encompass such alternate examples. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

[0598] Further embodiments are illustrated in the following Examples which are given for illustrative purposes only and are not intended to limit the scope of the invention. Example Pathogens

[0599] In an embodiment, the plurality of nucleic acid sequences of the present intention disclosed herein may comprise bacteria, fungus, protozoa, parasite and / or virus nucleic acid sequences. Exemplary pathogens are now described. Viruses

[0600] The virus may be a DNA virus, a RNA virus, or a retrovirus. Non-limiting example of viruses useful with the present invention include, but are not limited to Ebola, measles, SARS, Chikungunya, hepatitis, Marburg, yellow fever, MERS, Dengue, Lassa, influenza, rhabdovirus or HIV. A hepatitis virus may include hepatitis A, hepatitis B, or hepatitis C. An influenza virus may include, for example, influenza A or influenza B. An HIV may include HIV 1 or HIV 2. In an embodiment, the viral sequence may be a human respiratory syncytial virus, Sudan Ebola virus, Bundibugyo virus, Tai Forest Ebola virus, Reston Ebola virus, Achimota, Aedes flavivirus, Aguacate virus, Akabane virus, Alethinophid reptarenavirus, Allpahuayo mammarenavirus, Amapari mmarenavirus, Andes virus, Apoi virus, Aravan virus, Aroa virus, Arumwot virus, Atlantic salmon paramyoxivirus, Australian bat lyssavirus, Avian bornavirus, Avian metapneumovirus, Avian paramyoxviruses, penguin or Falkland Islandsvirus, BK polyomavirus, Bagaza virus, Banna virus, Bat hepevirus, Bat sapovirus, Bear Canon mammarenavirus, Beilong virus, Betacoronoavirus, Betapapillomavirus 1-6, Bhanja virus, Bokeloh bat lyssavirus, Borna disease virus, Bourbon virus, Bovine hepacivirus, Bovine parainfluenza virus 3, Bovine respiratorysyncytial virus, Brazoran virus, Bunyamwere virus, Caliciviridae virus. California encephalitis virus, Candiru virus, Canine distemper virus, Canaine pneumovirus, Cedar virus, Cell fusing agent virus, Cetacean morbillivirus, Chandipura virus, Chaoyang virus, Chapare mammarenavirus, Chikungunya virus, Colobus monkey papillomavirus, Colorado tick fever virus, Cowpox virus, Crimean-Congo hemorrhagic fever virus, Culex flavivirus, Cupixi mammarenavirus, Dengue virus, Dobrava-Belgrade virus, Donggang virus, Dugbe virus, Duvenhage virus, Eastern equine encephalitis virus, Entebbe bat virus, Enterovirus A-D, European bat lyssavirus 1-2, Eyach virus, Feline morbillivirus, Fer-de-Lance paramyxovirus, Fitzroy River virus, Flaviviridae virus, Flexal mammarenavirus, GB virus C, Gairo virus, Gemycircularvirus, Goose paramyoxiviurs SF02, Great Island virus, Guanarito mammarenavirus, Hantaan virus, Hantavirus Z10, Heartland virus, Hendra virus, Hepatitis A / B / C / E, Hepatitis delta virus, Human bocavirus, Human coronavirus, Human endogenous retrovirus K, Human enteric coronavirus, Human gential-associated circular DNA virus-1, Human herpesvirus 1-8, Human immunodeficiency virus 1 / 2, Huan mastadenovirus A-G, Human papillomavirus, Human parainfluenza virus 1-4, Human paraechovirus, Human picobirnavirus, Human smacovirus, Ikoma lyssavirus, Ilheus virus, Influenza A-C, Ippy mammarenavirus, Irkut virus, J-virus, JC polyomavirus, Japanses encephalitis virus, Junin mammarenavirus, KI polyomavirus, Kadipiro virus, Kamiti River virus, Kedougou virus, Khujand virus, Kokobera virus, Kyasanur forest disease virus, Lagos bat virus, Langat virus, Lassa mammarenavirus, Latino mammarenavirus, Leopards Hill virus, Liao ning virus, Ljungan virus, Lloviu virus, Louping ill virus, Lujo mammarenavirus, Luna mammarenavirus, Lunk virus, Lymphocytic choriomeningitis mammarenavirus, Lyssavirus Ozernoe, MSSI2\.225 virus, Machupo mammarenavirus, Mamastrovirus 1, Manzanilla virus, Mapuera virus, Marburg virus, Mayaro virus, Measles virus, Menangle virus, Mercadeo virus, Merkel cell polyomavirus, Middle East respiratory syndrome coronavirus, Mobala mammarenavirus, Modoc virus, Moijang virus, Mokolo virus, Monkeypox virus, Montana myotis leukoenchalitis virus, Mopeia lassa virus reassortant 29, Mopeia mammarenavirus, Morogoro virus, Mossman virus, Mumps virus, Murine pneumonia virus, Murray Valley encephalitis virus, Nariva virus, Newcastle disease virus, Nipah virus, Norwalk virus, Norway rat hepacivirus, Ntaya virus, O’nyong-nyong virus, Oliveros mammarenavirus, Omsk hemorrhagic fever virus, Oropouche virus, Parainfluenza virus 5, Parana mammarenavirus, Parramatta River virus, Peste-des-petits-ruminants virus, Pichandemammarenavirus, Picornaviridae virus, Pirital mammarenavirus, Piscihepevirus A, Procine parainfluenza virus 1, porcine rubulavirus, Powassan virus, Primate T-lymphotropic virus 1-2, Primate erythroparvovirus 1, Punta Toro virus, Puumala virus, Quang Binh virus, Rabies virus, Razdan virus, Reptile bornavirus 1, Rhinovirus A-B, Rift Valley fever virus, Rinderpest virus, Rio Bravo virus, Rodent Torque Teno virus, Rodent hepacivirus, Ross River virus, Rotavirus A-I, Royal Farm virus, Rubella virus, Sabia mammarenavirus, Salem virus, Sandfly fever Naples virus, Sandfly fever Sicilian virus, Sapporo virus, Sathuperi virus, Seal anellovirus, Semliki Forest virus, Sendai virus, Seoul virus, Sepik virus, Severe acute respiratory syndrome-related coronavirus, Severe fever with thrombocytopenia syndrome virus, Shamonda virus, Shimoni bat virus, Shuni virus, Simbu virus, Simian torque teno virus, Simian virus 40-41, Sin Nombre virus, Sindbis virus, Small anellovirus, Sosuga virus, Spanish goat encephalitis virus, Spondweni virus, St. Louis encephalitis virus, Sunshine virus, TTV-like mini virus, Tacaribe mammarenavirus, Taila virus, Tamana bat virus, Tamiami mammarenavirus, Tembusu virus, Thogoto virus, Thottapalayam virus, Tick-borne encephalitis virus, Tioman virus, Togaviridae virus, Torque teno canis virus, Torque teno douroucouli virus, Torque teno felis virus, Torque teno midi virus, Torque teno sus virus, Torque teno tamarin virus, Torque teno virus, Torque teno zalophus virus, Tuhoko virus, Tula virus, Tupaia paramyxovirus, Usutu virus, Uukuniemi virus, Vaccinia virus, Variola virus, Venezuelan equine encephalitis virus, Vesicular stomatitis Indiana virus, WU Polyomavirus, Wesselsbron virus, West Caucasian bat virus, West Nile virus, Western equine encephalitis virus, Whitewater Arroyo mammarenavirus, Yellow fever virus, Yokose virus, Yug Bogdanovac virus, Zaire ebolavirus, Zika virus, or Zygosaccharomyces bailii virus Z viral sequence. Examples of RNA viruses that may be detected include one or more of (or any combination of) Coronaviridae virus, a Picornaviridae virus, a Caliciviridae virus, a Flaviviridae virus, a Togaviridae virus, a Bornaviridae, a Filoviridae, a Paramyxoviridae, a Pneumoviridae, a Rhabdoviridae, an Arenaviridae, a Bunyaviridae, an Orthomyxoviridae, or a Deltavirus. In an embodiment, the virus is Coronavirus, SARS, Poliovirus, Rhinovirus, Hepatitis A, Norwalk virus, Yellow fever virus, West Nile virus, Hepatitis C virus, Dengue fever virus, Zika virus, Rubella virus, Ross River virus, Sindbis virus, Chikungunya virus, Borna disease virus, Ebola virus, Marburg virus, Measles virus, Mumps virus, Nipah virus, Hendra virus, Newcastle disease virus, Human respiratory syncytialvirus, Rabies virus, Lassa virus, Hantavirus, Crimean-Congo hemorrhagic fever virus, Influenza, or Hepatitis D virus.

[0601] In an embodiment, the virus may be a plant virus selected from the group comprising Tobacco mosaic virus (TMV), Tomato spotted wilt virus (TSWV), Cucumber mosaic virus (CMV), Potato virus Y (PVY), the RT virus Cauliflower mosaic virus (CaMV), Plum pox virus (PPV), Brome mosaic virus (BMV), Potato virus X (PVX), Citrus tristeza virus (CTV), Barley yellow dwarf virus (BYDV), Potato leafroll virus (PLRV), Tomato bushy stunt virus (TBSV), rice tungro spherical virus (RTSV), rice yellow mottle virus (RYMV), rice hoja blanca virus (RHBV), maize rayado fino virus (MRFV), maize dwarf mosaic virus (MDMV), sugarcane mosaic virus (SCMV), Sweet potato feathery mottle virus (SPFMV), sweet potato sunken vein closterovirus (SPSVV), Grapevine fanleaf virus (GFLV), Grapevine virus A (GVA), Grapevine virus B (GVB), Grapevine fleck virus (GFkV), Grapevine leafroll-associated virus-1, -2, and -3, (GLRaV-1, -2, and -3), Arabis mosaic virus (ArMV), or Rupestris stem pitting-associated virus (RSPaV). In a preferred embodiment, the target RNA molecule is part of said pathogen or transcribed from a DNA molecule of said pathogen.

[0602] In an embodiment, the virus may be a retrovirus. Example retroviruses that may be detected using the embodiments disclosed herein include one or more of or any combination of viruses of the Genus Alpharetrovirus, Betaretrovirus, Gammaretrovirus, Deltaretrovirus, Epsilonretrovirus, Lentivirus, Spumavirus, or the Family Metaviridae, Pseudoviridae, and Retroviridae (including HIV), Hepadnaviridae (including Hepatitis B virus), and Caulimoviridae (including Cauliflower mosaic virus).

[0603] In an embodiment, the virus is a DNA virus. Example DNA viruses that may be detected using the embodiments disclosed herein include one or more of (or any combination of) viruses from the Family Myoviridae, Podoviridae, Siphoviridae, Alloherpesviridae, Herpesviridae (including human herpes virus, and Varicella Zozter virus), Malocoherpesviridae, Lipothrixviridae, Rudiviridae, Adenoviridae, Ampullaviridae, Ascoviridae, Asfarviridae (including African swine fever virus), Baculoviridae, Cicaudaviridae, Clavaviridae, Corticoviridae, Fuselloviridae, Globuloviridae, Guttaviridae, Hytrosaviridae, Iridoviridae, Maseilleviridae, Mimiviridae, Nudiviridae, Nimaviridae, Pandoraviridae, Papillomaviridae, Phycodnaviridae, Plasmaviridae, Polydnaviruses, Polyomaviridae (including Simian virus 40, JCvirus, BK virus), Poxviridae (including Cowpox and smallpox), Sphaerolipoviridae, Tectiviridae, Turriviridae, Dinodnavirus, Salterprovirus, Rhizidovirus, among others. In some embodiments, a method of diagnosing a species-specific bacterial infection in a subject suspected of having a bacterial infection is described as obtaining a sample comprising bacterial ribosomal ribonucleic acid from the subject; contacting the sample with one or more of the probes described, and detecting hybridization between the bacterial ribosomal ribonucleic acid sequence present in the sample and the probe, wherein the detection of hybridization indicates that the subject is infected with Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, Acinetobacter baumannii, Candida albicans, Enterobacter cloacae, Enterococcus faecalis, Enterococcus faecium, Proteus mirabilis, Staphylococcus agalactiae, or Staphylococcus maltophilia or a combination thereof.

[0604] In an embodiment, the infectious agent is a virus. In an embodiment, the virus is a DNA virus or an RNA virus. In an embodiment, the virus is a double stranded DNA virus, single stranded DNA virus, double-stranded RNA virus, a positive sense RNA virus, a negative sense RNA virus, or a retrovirus (which is inclusive of lentiviruses). In some embodiments the virus is a Group I, Group II, Group III, Group IV, Group V, Group VI, or Group VII virus according to the Baltimore classification system.

[0605] In some embodiments, the virus is an RNA virus.

[0606] In some embodiments the RNA virus can infect a human and / or non-human vertebrates and is in the family of Birnaviridae, Arteriviridae, Bornaviridae, Nodaviridae, Picobirnaviridae, Reoviridae, Coronaviridae, Astroviridaee, Caliciviridae, Flaviviridae, Hepeviridae, Matonaviridae, Picornaviridae, Togaviridae, Filoviridae, Paramyxoviridae, Pneumoviridae, Rhabdoviridae, Arenaviridae, Hantaviridae, Nairoviridae, Peribunyaviridae, Phenuiviridae, or Orthomyxoviridae.

[0607] In some embodiments the RNA virus can infect a human and / or non-human vertebrates and is in the genus Aquabirnavirus, Avibirnavirus, Blosnavirus, Picobirnavirus, Aquareovirus, Coltivirus, Orthoreovirus, Orbivirus, Rotavirus, Seadornavirus, Orthohepevirus, Piscihepevirus, Alphaartervirus, Lambdaartervirus, Deltavirus, Etaaterivirus, Epsilonaterivirus, Iotaarterivirus, Thetaartereivirus, Zetaartervirius, Betaarterivirus, Gammaatervirus, Kappaarterivirus, Alphacoronavirus, Betacoronavirus, Gammacoronavirus, Deltacoronavirus, Torovirus,Bafinivirus, Ailurivirus, Ampivirus, Aphtovirus, Aquamavirus, Avihepatovirus, Avisivirus, Cardiovirus, Cosavirus, Crohivirus, Dicipivirus, Enterovirus, Erbovirus, Gallivirus, Harkavirus, Hepatovirus, Hunnivirus, Kobuvirus, Kunsagivirus, Limnipivirus, Megrivirus, Mosavirus, Oscivirus, Parechovirus, Pasivirus, Passerivirus, Potamipvirus, Rabovirus, Rosavirus, Sakobuvirus, Salivirus, Sapelovirus, Senecavirus, Sicinivirus, Teschovirus, Torchivirus, Tremovirus, Avastrovirus, Mamastrovirus, Lagovirus, Nebovirus, Norovirus, Sapovirus, Vesivirus, Flavivirus, Hepacivirus, Pegivirus, Pestivirus, Rubivirus, Alphanodavirus, Betanodivirus, Alphavirus, Orthobornavirus, Carbovirus, Nyavirus, Ephemerovirus, Ephemerovirus, Hapavirus, Ledantevirus, Perhabdovirus, Sprivivirus, Tibrovirus, Tupavirus, Vesiculovirus, Cuevavirus, Ebolavirus, Marburgvirus, Aquaparamyxovirus, Avulavirus, Ferlavirus, Henipavirus, Morbillivirus, Respirovirus, Rubulavirus, Metapneumonvirus, Orthopneumonvirus, Hartmanivirus, Mammarenvirus, Reptarenavirus, Orthohantavirus, Orthonairovirus, Phlebovirus, Alphainfluenzavirus, Betainfluenzavirus, Gammainfluenzavirus, Deltainfluenzavirus, Thogotovirus, Isavirus, Quaranjavirus, Orthobunyavirus, Sunshinevirus, Tilapinevirus, or Deltavirus.

[0608] In some embodiments the RNA virus can infect a plant and is in the family Amalgaviridae, Endornaviridae, Partitiviridae, Reoviridae, Secoviridae, Alpha-flexiviridae, Beta- flexiviridae, Tymoviridae, Virgaviridae, Bromoviridae, Closteroviridae, Luteoviridae, Potyviridae, Solemoviridae, Tombusviridae, Benyviridae, Rhabdoviridae, Fimoviridae, Phenuiviridae, Tospoviridae, Aspiviridae, Avsunviroidae, or Pospiviroidae.

[0609] In some embodiments the RNA virus can infect a plant and is in the genus Amalgavirus, Alphaendoma, Alphapartitivirus, Betapartitivrus, Deltapartitivirus, Fijivirus, Oryzavirus, Phytoreovirus, Cheravirus, Comovirus, FAbavirus, Nepovirus, Sadwavirus, Sequivirus, Torradovirus, Waikavirus, Allexivirus, Mandarivirus, Platpuvirus, Potexivirus, Lolavirus, Capillovirus, Carlavirus, Chordovirus, Citrivirus, Divavirus, Foveavirus, Prunevirus, Robigovirus, Tepovirus, Trichovirus, Vitivirus, Maculavirus, Marafivirus, Tymovirus, Furovirus, Goravirurs, Hordeivirus, Pecluvirus, Pmovirus, Tobamovirus, Tobravirus, Alfamovirus. Anulavirus, Bromovirus, Cucumovirus, Ilarviurs, Oleavirus, Ampelovirus, Closterovirus, Crinivirus, Velarivirus, Enamovirus, Leutovirus, Polerovirus, Bevemovirus. Brambyvirus, Bymovirus, Ipomovirus, Macluravirus, Poacevirus, Potyvirus, Roymovirus, Rymovirus, Tritimovirus,Polemovirus, Sobmovirus, Alphacamovirus, Aplhanecrovirus, Aureusvirus, Avenavirus, Betavarmovirus, Betanecrovirus, Dianthovirus, Gallantivirus, Gamma-carmovirus, Macanavirus, Machlomovirus, Panicovirus, Pelarspovirus, Umbravirus, Zeavirus, Benyvirus, Albetovirus, Aumavirus, Blunervirus, Cilevirus, Higrevirus, Idaeovirus, Ourmiavirus, Papanivirus, Sinavirus, Virtovirus, Cytorhabdovirus, Dichorhavirus, Nucleorhabdo-virus, Varicosavirus, Emaravirus, Tenuivirus, Orthotospovirus, Ophiovirus, Avsunvirioid, Elaviroid, Pelamoviroid, Apscaviroid, Cocadviroid, Coleviroid, Hostuviroid, or Pospiviroid.

[0610] In some embodiments, the virus is a DNA virus.

[0611] In some embodiments, the DNA virus can infect humans and / or non-human vertebrates and is in the family Herpesviridae, Alloherpesviridae, Adenoviridae, Papillomaviridae, Polomaviridae, Asfarviridae, Iridoviridae, Poxviridae, Anelloviridae, Circoviridae, Genomoviridae, or Parvoviridae.

[0612] In some embodiments, the DNA virus can infect humans and / or non-human vertebrates and is in the genus Simplexvirus, Varicellovirus, Mardivirus, Scutavirus, Iltovirus, Cytomegalovirus, Muromegalovirus, Roseolivirus, Proboscivirus, Lymphocrypto-virus, Rhadinovirus, Macavirus, Percavirus, Batrachovirus, Cyprinivirus, Ictalurivirus, Salmonivirus, Mastadenovirus, Aviadenovirus, Atadenovirus, Ichtadenovirus, Siadenovirus, Alphapapillomavirus, Betapapillomavirus, Chipapillomavirus, Deltapapillomavirus, Dyochipapillomavirus, Dyodeltapapillomavirus, Dyoepsilonpapillomavirus, Dyoetapapillomavirus, Dyiotapapillomavirus, Dyokappapapillomavirus, Dyolambdapapillomavirus, Dyomupapillomavirus, Dyonupapillomavirus, Dyoomegapapillomavirus, Dyoomikronpapillomavirus, Dyophipapillomavirus, Dyopipapillomavirus, Dyopsipapillomavirus, Dyorhopapillomavirus, Dyosigmapapillomavirus, Dyotaupapillomavirus, Dyothetapapillomavirus, Dyoupsilonpapillomavirus, Dyoxipapillomavirus, Dyozetapapillomavirus, Epsilonpapillomavirus, Etapapillomavirus, Gammapapillomavirus, Iotapapillomavirus, Kappapapillomavirus, Lambdapapillomavirus, Mupapillomavirus, Nupapillomavirus, Omegapapillomavirus, Omikronpapillomavirus, Phipapillomavirus, Psipapillomavirus, Rhopapillomavirus, Sigmapapillomavirus, Taupapillomavirus, Thetapapillomavirus, Treisdeltapapillomavirus, Treisiotapapillomavirus, Treisepsilonpapilomavirus, Treiskappapapillomavirus, Treisthetapapillomavirus,Treiszetapapillomavirus, Treiszetapapillomavirus, Upsilonpapillomavirus, Xipapillomavirus, Zetapapillomavirus, Alefpapillomavirus, Alpha-polyomavirus, Beta-polyomavirus, Gamma- polyomavirus, Delta-polyomavirus, Asfivirus, Lymphocystivirus, Megalocytivirus, Ranavirus, Avipoxvirus, Capripoxvirus, Cervidopoxvirus, Crocodylidpoxvirus, Leporipoxvirus, Molluscopoxvirus, Orthopoxvirus, Parpoxvirus, Suipoxvirus, Yatapoxvirus, Alphatorquevirus, Betatorquevirus, Gammatorquevirus, Deltatorquevirus, Epsilontorquevirus, Lambdatorquevirus, Kappatorquevirus, Zetatorquevirus, Etatorquevirus, Thetatorquevirus, Iotatorquevirus, Gyrovirus, Circovirus, Cyclovirus, Gemycicular-virus, Gemygorvirus, Gemykibivirus, Gemykolovirus, Gemykrogvirus, Gemykroznavirus, Gemytondvirus, Gemyvongvirus, Amdoparvovirus, Aveparvovirus, Protoparvovirus, Copiparvoirus, Erythroparvovirus, Dependoparvovirus, Tetraparvovirus, or Bocaparvovirus.

[0613] In some embodiments, the virus is a retrovirus. Exemplary retroviruses include, but are not limited to, any of those of the genus Alpharetrovirus, Betaretrovirus, Gammaretrovirus, Deltaretrovirus, Epsilonretrovirus, Lentivirus, Spumavirus, or the Family Metaviridae, Pseudoviridae, and Retroviridae (including HIV), Hepadnaviridae (including Hepatitis B virus), and Caulimoviridae (including Cauliflower mosaic virus).

[0614] In an embodiment, the virus is a coronavirus, an Ebola virus, measles, SARS, Chikungunya virus, Marburg, MERS, Dengue, Lassa, influenza, rhabdovirus, HIV, a hepatitis virus (including hepatitis A, B, C, D, or E), an influenza virus (including an influenza A or influenza B), a human respiratory syncytial virus, Sudan Ebola virus, Bundibugyo virus, Tai Forest Ebola virus, Reston Ebola virus, Achimota virus, Aedes flavivirus, Aguacate virus, Akabane virus, Alethinophid reptarenavirus, Allpahuayo mammarenavirus, Amapari mmarenavirus, Andes virus, Apoi virus, Aravan virus, Aroa virus, Arumwot virus, Atlantic salmon paramyxovirus, Australian bat lyssavirus, Avian bornavirus, Avian metapneumovirus, Avian paramyxoviruses, penguin or Falkland Islandsvirus, BK polyomavirus, Bagaza virus, Banna virus, Bat herpesvirus, Bat sapovirus, Bear Canon mammarenavirus, Beilong virus, Betacoronavirus, Betapapillomavirus 1-6, Bhanja virus, Bokeloh bat lyssavirus, Borna disease virus, Bourbon virus, Bovine hepacivirus, Bovine parainfluenza virus 3, Bovine respiratory syncytial virus, Brazoran virus, Bunyamwera virus, Caliciviridae virus. California encephalitis virus, Candiru virus, Canine distemper virus, Canine pneumovirus, Cedar virus, Cell fusing agent virus, Cetacean morbillivirus,Chandipura virus, Chaoyang virus, Chapare mammarenavirus, Chikungunya virus, Colobus monkey papillomavirus, Colorado tick fever virus, Cowpox virus, Crimean-Congo hemorrhagic fever virus, Culex flavivirus, Cupixi mammarenavirus, Dengue virus, Dobrava-Belgrade virus, Donggang virus, Dugbe virus, Duvenhage virus, Eastern equine encephalitis virus, Entebbe bat virus, Enterovirus A-D, European bat lyssavirus 1-2, Eyach virus, Feline morbillivirus, Fer-de- Lance paramyxovirus, Fitzroy River virus, Flaviviridae virus, Flexal mammarenavirus, GB virus C, Gairo virus, Gemycircularvirus, Goose paramyxovirus SF02, Great Island virus, Guanarito mammarenavirus, Hantaan virus, Hantavirus Z10, Heartland virus, Hendra virus, Hepatitis A / B / C / E, Hepatitis delta virus, Human bocavirus, Human coronavirus, Human endogenous retrovirus K, Human enteric coronavirus, Human genital-associated circular DNA virus- 1, Human herpesvirus 1-8, Human mastadenovirus A-G, Human papillomavirus, Human parainfluenza virus 1-4, Human paraechovirus, Human picornavirus, Human smacovirus, Ikoma lyssavirus, Ilheus virus, Influenza A-C, Ippy mammarenavirus, Irkut virus, J-virus, JC polyomavirus, Japanese encephalitis virus, Junin mammarenavirus, KI polyomavirus, Kadipiro virus, Kamiti River virus, Kedougou virus, Khujand virus, Kokobera virus, Kyasanur forest disease virus, Lagos bat virus, Langat virus, Lassa mammarenavirus, Latino mammarenavirus, Leopards Hill virus, Liao ning virus, Ljungan virus, Lloviu virus, Louping ill virus, Lujo mammarenavirus, Luna mammarenavirus, Lunk virus, Lymphocytic choriomeningitis mammarenavirus, Lyssavirus Ozernoe, MSSI2Y225 virus, Machupo mammarenavirus, Mamastrovirus 1, Manzanilla virus, Mapuera virus, Marburg virus, Mayaro virus, Measles virus, Menangle virus, Mercadeo virus, Merkel cell polyomavirus, Middle East respiratory syndrome coronavirus, Mobala mammarenavirus, Modoc virus, Moijang virus, Mokolo virus, Monkeypox virus, Montana myotis leukoenchalitis virus, Mopeia lassa virus reassortant 29, Mopeia mammarenavirus, Morogoro virus, Mossman virus, Mumps virus, Murine pneumonia virus, Murray Valley encephalitis virus, Nariva virus, Newcastle disease virus, Nipah virus, Norwalk virus, Norway rat hepacivirus, Ntaya virus, O'nyong-nyong virus, Oliveros mammarenavirus, Omsk hemorrhagic fever virus, Oropouche virus, Parainfluenza virus 5, Parana mammarenavirus, Parramatta River virus, Peste- des-petits- ruminants virus, Pichande mammarenavirus, Picornaviridae virus, Pirital mammarenavirus, Piscihepevirus A, Porcine parainfluenza virus 1, porcine rubulavirus, Powassan virus, Primate T-lymphotropic virus 1-2, Primate erythroparvovirus 1, Punta Toro virus, Puumalavirus, Quang Binh virus, Rabies virus, Razdan virus, Reptile bornavirus 1, Rhinovirus A-B, Rift Valley fever virus, Rinderpest virus, Rio Bravo virus, Rodent Torque Teno virus, Rodent hepacivirus, Ross River virus, Rotavirus A-I, Royal Farm virus, Rubella virus, Sabia mammarenavirus, Salem virus, Sandfly fever Naples virus, Sandfly fever Sicilian virus, Sapporo virus, Sathuperi virus, Seal anellovirus, Semliki Forest virus, Sendai virus, Seoul virus, Sepik virus, Severe acute respiratory syndrome-related coronavirus, Severe fever with thrombocytopenia syndrome virus, Shamonda virus, Shimoni bat virus, Shuni virus, Simbu virus, Simian torque teno virus, Simian virus 40-41, Sin Nombre virus, Sindbis virus, Small anellovirus, Sosuga virus, Spanish goat encephalitis virus, Spondweni virus, St. Louis encephalitis virus, Sunshine virus, TTV-like mini virus, Tacaribe mammarenavirus, Taila virus, Tamana bat virus, Tamiami mammarenavirus, Tembusu virus, Thogoto virus, Thottapalayam virus, Tick-borne encephalitis virus, Tioman virus, Togaviridae virus, Torque teno canis virus, Torque teno douroucouli virus, Torque teno felis virus, Torque teno midi virus, Torque teno sus virus, Torque teno tamarin virus, Torque teno virus, Torque teno zalophus virus, Tuhoko virus, Tula virus, Tupaia paramyxovirus, Usutu virus, Uukuniemi virus, Vaccinia virus, Variola virus, Venezuelan Vesicular stomatitis Indiana virus, WU Polyomavirus, Wesselsbron virus, West Caucasian bat virus, West Nile virus, Western equine encephalitis virus, Whitewater Arroyo mammarenavirus, Yellow fever virus, Yokose virus, Yug Bogdanovac virus, Zaire ebolavirus, Zika virus, or Zygosaccharomyces bailii virus Z viral sequence, or a combination thereof.

[0615] In an embodiment, the virus is a coronavirus. In an embodiment, the virus is SARS- CoV-2. In some embodiments, the SARS-CoV-2 is strain G, strain GR, strain GH, stain L, strain V, or strain S, or a variant thereof, or a mutant thereof (see e.g. Daniele Mercatelli, Federico M. Giorgi. Geographic and Genomic Distribution of SARS-CoV-2 Mutations. Frontiers in Microbiology, 2020; 11 DOI: 10.3389 / fmicb.2020.01800, particularly at e.g. Tables 1 and 2, Supplementary Files 6-7 and 9). Bacteria

[0616] In an embodiment, the pathogen is a bacterium. Examples of bacteria that can be detected in accordance with the disclosed methods include without limitation any one or more of (or any combination of) Acinetobacter baumanii, Actinobacillus sp., Actinomycetes, Actinomyces sp. (such as Actinomyces israelii and Actinomyces naeslundii), Aeromonas sp. (such as Aeromonashydrophila, Aeromonas veronii biovar sobria (Aeromonas sobria), and Aeromonas caviae), Anaplasma phagocytophilum, Anaplasma marginale Alcaligenes xylosoxidans, Acinetobacter baumanii, Actinobacillus actinomycetemcomitans, Bacillus sp. (such as Bacillus anthracis, Bacillus cereus, Bacillus subtilis, Bacillus thuringiensis, and Bacillus stearothermophilus), Bacteroides sp. (such as Bacteroides fragilis), Bartonella sp. (such as Bartonella bacilliformis and Bartonella henselae, Bifidobacterium sp., Bordetella sp. ( such as Bordetella pertussis, Bordetella parapertussis, and Bordetella bronchiseptica), Borrelia sp. (such as Borrelia recurrentis, and Borrelia burgdorferi), Brucella sp. (such as Brucella abortus, Brucella canis, Brucella melintensis and Brucella suis), Burkholderia sp. (such as Burkholderia pseudomallei and Burkholderia cepacia), Campylobacter sp. (such as Campylobacter jejuni, Campylobacter coli, Campylobacter lari and Campylobacter fetus), Capnocytophaga sp., Cardiobacterium hominis, Chlamydia trachomatis, Chlamydophila pneumoniae, Chlamydophila psittaci, Citrobacter sp. Coxiella burnetii, Corynebacterium sp. (such as, Corynebacterium diphtheriae, Corynebacterium jeikeum and Corynebacterium), Clostridium sp. (such as Clostridium perfringens, Clostridium difficile, Clostridium botulinum and Clostridium tetani), Eikenella corrodens, Enterobacter sp. (such as Enterobacter aerogenes, Enterobacter agglomerans, Enterobacter cloacae and Escherichia coli, including opportunistic Escherichia coli, such as enterotoxigenic E. coli, enteroinvasive E. coli, enteropathogenic E. coli, enterohemorrhagic E. coli, enteroaggregative E. coli and uropathogenic E. coli) Enterococcus sp. (such as Enterococcus faecalis and Enterococcus faecium) Ehrlichia sp. (such as Ehrlichia chafeensia and Ehrlichia canis), Epidermophyton floccosum, Erysipelothrix rhusiopathiae, Eubacterium sp., Francisella tularensis, Fusobacterium nucleatum, Gardnerella vaginalis, Gemella morbillorum, Haemophilus sp. (such as Haemophilus influenzae, Haemophilus ducreyi, Haemophilus aegyptius, Haemophilus parainfluenzae, Haemophilus haemolyticus and Haemophilus parahaemolyticus, Helicobacter sp. (such as Helicobacter pylori, Helicobacter cinaedi and Helicobacter fennelliae), Kingella kingii, Klebsiella sp. ( such as Klebsiella pneumoniae, Klebsiella granulomatis and Klebsiella oxytoca), Lactobacillus sp., Listeria monocytogenes, Leptospira interrogans, Legionella pneumophila, Leptospira interrogans, Peptostreptococcus sp., Mannheimia hemolytica, Microsporum canis, Moraxella catarrhalis, Morganella sp., Mobiluncus sp., Micrococcus sp., Mycobacterium sp. (such as Mycobacterium leprae, Mycobacterium tuberculosis, Mycobacterium paratuberculosis, Mycobacteriumintracellulare, Mycobacterium avium, Mycobacterium bovis, and Mycobacterium marinum), Mycoplasm sp. (such as Mycoplasma pneumoniae, Mycoplasma hominis, and Mycoplasma genitalium), Nocardia sp. (such as Nocardia asteroides, Nocardia cyriacigeorgica and Nocardia brasiliensis), Neisseria sp. (such as Neisseria gonorrhoeae and Neisseria meningitidis), Pasteurella multocida, Pityrosporum orbiculare (Malassezia furfur), Plesiomonas shigelloides. Prevotella sp., Porphyromonas sp., Prevotella melaninogenica, Proteus sp. (such as Proteus vulgaris and Proteus mirabilis), Providencia sp. (such as Providencia alcalifaciens, Providencia rettgeri and Providencia stuartii), Pseudomonas aeruginosa, Propionibacterium acnes, Rhodococcus equi, Rickettsia sp. (such as Rickettsia rickettsii, Rickettsia akari and Rickettsia prowazekii, Orientia tsutsugamushi (formerly: Rickettsia tsutsugamushi) and Rickettsia typhi), Rhodococcus sp., Serratia marcescens, Stenotrophomonas maltophilia, Salmonella sp. (such as Salmonella enterica, Salmonella typhi, Salmonella paratyphi, Salmonella enteritidis, Salmonella cholerasuis and Salmonella typhimurium), Serratia sp. (such as Serratia marcesans and Serratia liquifaciens), Shigella sp. (such as Shigella dysenteriae, Shigella flexneri, Shigella boydii and Shigella sonnei), Staphylococcus sp. (such as Staphylococcus aureus, Staphylococcus epidermidis, Staphylococcus hemolyticus, Staphylococcus saprophyticus), Streptococcus sp. (such as Streptococcus pneumoniae (for example chloramphenicol-resistant serotype 4 Streptococcus pneumoniae, spectinomycin-resistant serotype 6B Streptococcus pneumoniae, streptomycin- resistant serotype 9V Streptococcus pneumoniae, erythromycin-resistant serotype 14 Streptococcus pneumoniae, optochin-resistant serotype 14 Streptococcus pneumoniae, rifampicin- resistant serotype 18C Streptococcus pneumoniae, tetracycline-resistant serotype 19F Streptococcus pneumoniae, penicillin-resistant serotype 19F Streptococcus pneumoniae, and trimethoprim-resistant serotype 23F Streptococcus pneumoniae, chloramphenicol-resistant serotype 4 Streptococcus pneumoniae, spectinomycin-resistant serotype 6B Streptococcus pneumoniae, streptomycin-resistant serotype 9V Streptococcus pneumoniae, optochin-resistant serotype 14 Streptococcus pneumoniae, rifampicin-resistant serotype 18C Streptococcus pneumoniae, penicillin-resistant serotype 19F Streptococcus pneumoniae, or trimethoprim- resistant serotype 23F Streptococcus pneumoniae), Streptococcus agalactiae, Streptococcus mutans, Streptococcus pyogenes, Group A streptococci, Streptococcus pyogenes, Group B streptococci, Streptococcus agalactiae, Group C streptococci, Streptococcus anginosus,Streptococcus equismilis, Group D streptococci, Streptococcus bovis, Group F streptococci, and Streptococcus anginosus Group G streptococci), Spirillum minus, Streptobacillus moniliformi, Treponema sp. (such as Treponema carateum, Treponema petenue, Treponema pallidum and Treponema endemicum, Trichophyton rubrum, T. mentagrophytes, Tropheryma whippelii, Ureaplasma urealyticum, Veillonella sp., Vibrio sp. (such as Vibrio cholerae, Vibrio parahemolyticus, Vibrio vulnificus, Vibrio parahaemolyticus, Vibrio vulnificus, Vibrio alginolyticus, Vibrio mimicus, Vibrio hollisae, Vibrio fluvialis, Vibrio metchnikovii, Vibrio damsela and Vibrio furnisii), Yersinia sp. ( such as Yersinia enterocolitica, Yersinia pestis, and Yersinia pseudotuberculosis) and Xanthomonas maltophilia among others.

[0617] Near-real-time microbial diagnostics are needed for food, clinical, industrial, and other environmental settings (see e.g., Lu TK, Bowers J, and Koeris MS., Trends Biotechnol. 2013 Jun;31(6):325-7). In certain embodiments, the assay described herein is configured for detection of foodborne pathogens using guide RNAs specific to a pathogen (e.g., Campylobacter jejuni, Clostridium perfringens, Salmonella spp., Escherichia coli, Bacillus cereus, Listeria monocytogenes, Shigella spp., Staphylococcus aureus, Staphylococcal enteritis, Streptococcus, Vibrio cholerae, Vibrio parahaemolyticus, Vibrio vulnificus, Yersinia enterocolitica and Yersinia pseudotuberculosis, Brucella spp., Corynebacterium ulcerans, Coxiella burnetii, or Plesiomonas shigelloides). Fungi

[0618] In an embodiment, the pathogen is a fungus or a fungal species. Examples of fungi that can be detected in accordance with the disclosed methods include without limitation any one or more of (or any combination of), Aspergillus, Blastomyces, Candidiasis, Coccidiodomycosis, Cryptococcus neoformans, Cryptococcus gatti, sp. Histoplasma sp. (such as Histoplasma capsulatum), Pneumocystis sp. (such as Pneumocystis jirovecii), Stachybotrys (such as Stachybotrys chartarum), Mucroymcosis, Sporothrix, fungal eye infections ringworm, Exserohilum, Cladosporium.

[0619] In an embodiment, the fungus is a yeast. Examples of yeast that can be detected in accordance with disclosed methods include without limitation one or more of (or any combination of), Aspergillus species (such as Aspergillus fumigatus, Aspergillus flavus and Aspergillus clavatus), Cryptococcus sp. (such as Cryptococcus neoformans, Cryptococcus gattii,Cryptococcus laurentii and Cryptococcus albidus), a Geotrichum species, a Saccharomyces species, a Hansenula species, a Candida species (such as Candida albicans), a Kluyveromyces species, a Debaryomyces species, a Pichia species, or combination thereof. In an embodiment, the fungi is a mold. Example molds include, but are not limited to, a Penicillium species, a Cladosporium species, a Byssochlamys species, or a combination thereof. Protozoa

[0620] In an embodiment, the microbe is a protozoan. Examples of protozoa that can be detected in accordance with the disclosed methods and devices include without limitation any one or more of (or any combination of), Euglenozoa, Heterolobosea, Diplomonadida, Amoebozoa, Blastocystic, and Apicomplexa. Example Euglenoza include, but are not limited to, Trypanosoma cruzi (Chagas disease), T. brucei gambiense, T. brucei rhodesiense, Leishmania braziliensis, L. infantum, L. mexicana, L. major, L. tropica, and L. donovani. Example Heterolobosea include, but are not limited to, Naegleria fowleri. Example Diplomonadid include, but are not limited to, Giardia intestinalis (G. lamblia, G. duodenalis). Example Amoebozoa include, but are not limited to, Acanthamoeba castellanii, Balamuthia madrillaris, Entamoeba histolytica. Example Blastocystis include, but are not limited to, Blastocystic hominis. Example Apicomplexa include, but are not limited to, Babesia microti, Cryptosporidium parvum, Cyclospora cayetanensis, Plasmodium falciparum, P. vivax, P. ovale, P. malariae, and Toxoplasma gondii.Babesia microti, Cryptosporidium parvum, Cyclospora cayetanensis, Plasmodium falciparum, P. vivax, P. ovale, P. malariae, and Toxoplasma gondii. Parasites

[0621] In an embodiment, the microbe is a parasite. Examples of parasites that can be detected in accordance with disclosed methods include without limitation one or more of (or any combination of), an Onchocerca species and a Plasmodium species. Alternate Applications

[0622] In an embodiment, the outbreak reconstruction network may receive other forms of data as input into the outbreak reconstruction model, which may be referred to as an evolution reconstruction model, for analyzing the evolution of language and cultural phenomena (arts, literature, etc.). The evolution reconstruction model, for example, can take in data comprising grammatical representations of a language or languages and generate a reconstruction of thelanguages evolution. For example, the grammatical representations may include context-free grammar-based (CFG-based), attribute grammar-based (AG-based), Christiansen grammar-based (CG-based), fluid construction grammar-based (FCG-based), and universal grammar-based (UG- based). See e.g., D’Ulizia, A., Ferri, F. & Grifoni, P. A Survey on Modeling Language Evolution in the New Millennium. New Gener. Comput.38, 97–124 (2020) hereby incorporated by reference in its entirety.

[0623] In addition, the evolution reconstruction model, for example, can take in data comprising cultural traits and generate a reconstruction of the culture’s evolution. This data may include behaviors, ideas, technologies, geology, social learning, language evolution, patterns in material culture, and cultural dynamics within and between groups. See e.g., Creanza, N.; Kolodny, O.; Feldman, M. W. Cultural Evolutionary Theory: How Culture Evolves and Why It Matters. Proceedings of the National Academy of Sciences, 2017, 114, 7782–7789 herein incorporated by reference in its entirety.

[0624] In addition, the evolution reconstruction model, for example, can take in oncologic data comprising transcriptomic data, proteomic data, sequence data, tumor microenvironment, cellular interactions, micro-anatomical structures, or any combination thereof and generate a reconstruction of the evolution of a type of cancer. For example, the oncologic data may originate from bulk tumor samples. See e.g., Vendramin, R.; Litchfield, K.; Swanton, C. Cancer Evolution: Darwin and Beyond. The EMBO Journal, 2021, 40 herein incorporated by reference in its entirety. EXAMPLES Example 1 – Inferring Viral Transmission Pathways from Within-Host Variation Sub-Consensus Variant Frequencies Exhibit a Power-Law Distribution Applicants developed aa 2 A and

[0023] ,[24,25]). The model consists of two phases: one of exponential growth of the inoculum, followed by a quiescent phase that lasts from when the viral population reaches a sufficiently large size until recovery. Applicants model within-host viral replication during the first phase as a stochastic pure-birth process, consistent with previous methodological work

[0026] , in which each birth event may introduce a substitution with some probability p. Under the pure-birth model, mutations that emerge early on in infection constitute a larger fraction of the ultimate viral population than thosethat emerge later in infection. Once the within-host viral population size is sufficiently large (see Example 2 B for a precise mathematical definition), any substitution events that occur negligibly affect the composition of the viral population. Therefore, following the exponential growth of the pure-birth model, Applicants model the quiescent phase via the Jukes-Cantor model

[0027] of neutral evolution. The combination of these two phases yields a biologically-motivated hierarchical model for within-host variant frequencies in the absence of natural selection.

[0626] By fitting the model to all iSNVs observed across the dataset of 134,682 genomes with a 3% minor allele frequency threshold

[0028] , Applicants observed minimal discrepancy between the empirical and theoretical probability distribution, with the empirical density and empirical cumulative distribution function (eCDF) closely matching the density and CDF of the model (Figure 2). Applicants found the maximum likelihood estimate (MLE) of the mutation rate to be 3.40 × 10-6nt site-1cycle-1, comparable to existing estimates of 1–3 × 10-6nt site-1cycle-1[22,29– 31] (see Methods). Given an evolution rate of 1 × 10-3nt site-1year-1[29–31], this estimate corresponds to a within-host replication rate of 0.81 cycle day-1. Per Bar-On et al.’s estimate of a burst size of ~1,000 particles, Applicants estimate that it takes 2.48–3.72 days for a host to reach a peak viral population of 106–109virions respectively––in concordance with existing estimates of approximately 3 days

[0029] . Within-Host Variants Improve Transmission Inference Accuracy

[0627] Understanding the distribution of iSNV frequencies is central to developing a statistical model for viral outbreaks. Under the simplifying assumption that different sites along the viral genome evolve independently [4,13], Applicants use the within-host evolution model to quantify the relative likelihood of various outbreak transmission networks based on the read-level data collected from cases (see Methods). To test the reliability of the outbreak reconstruction model and compare the approach to existing ones, Applicants generated a dataset of 100 synthetic outbreaks

[0032] . Applicants modeled the generation intervals, latent period durations, and infectious period durations as a stochastic-epidemic process, with each time interval independently sampled from a pre-specified probability distribution based on existing studies of SARS-CoV-2 transmission, using epidemiological parameters inferred from studies of the Omicron variant [33,34] (see Figure 3A and Methods). Applicants simulated transmission networks by generating random trees in which the number of offspring of each case follows a negative binomialdistribution with a basic reproductive number (R0) of 2.5 and overdispersion parameter (k) of 0.1

[0035] , then extracting a random subtree of 10–15 individuals to be the cluster, discarding trees in which no such subcluster existed. Applicants also included an additional 0–5 cases from a different part of the tree in the cluster who each tested positive within 10 days of someone in the subtree, as a way to test the model’s robustness to multiple introductions. For genomic data, Applicants sampled minor allele fractions at each site for each host per the statistical model presented in this paper (Figure 2), with the probability of a given site exhibiting a SNV / iSNV proportional to the number of times Applicants detected a SNV / iSNV at said site among the 134,682 Massachusetts genomes. Based on these fractions, Applicants sampled the virions passed from donor to recipient as independent, identically distributed (i.i.d.) categorical random variables taking on one of four values corresponding to the four nucleotides, per the methods of Leonard et al.

[0026] (see Figure 3B). Applicants set the per-site per-cycle mutation rate to 3.40 × 10-6per the above results, and assumed a per-site per-year evolution rate of 1 × 10-3[29–31]. These two parameters are related to each other through the burst size and replication rate, whose values Applicants assigned based on existing literature [22,29] (see Methods).

[0628] Applicants measured the reliability of the outbreak reconstruction model using three metrics: accuracy, defined as the probability that a random true donor-recipient pair is present in a given posterior sample; coverage probability, defined as the chance that a minimal 90%-credible set (By “minimal 90%-credible set” Applicants mean the smallest set of possible donors for a given recipient whose combined probability of being the true donor exceeds 90%.) of modeled donors for a given recipient contains the correct donor (or correctly identifies the recipient as unrelated to the cluster); and specificity, defined as the probability that in a given posterior sample, a random unrelated case is correctly identified as being disconnected from the cluster. These metrics of reliability for Applicants’ model, outbreaker2, Applicants’ own method adapted to analyze only consensus genomes, and BadTrIP are summarized in Table 2 and Figure 3E–3G; see Figure 3C– D for an example of the differential performance of Applicants’ method and outbreaker2 on a single synthetic outbreak.

[0629] Finally, certain existing outbreak reconstruction models, including Applicants’ model and outbreaker2 but not BadTrIP, integrate contact tracing data into transmission inference. To assess sensitivity to contact tracing data, Applicants ran Applicants’ model and outbreaker2 on the100 synthetic datasets under eight different contact tracing parameter combinations: true positive rate ν (probability of reporting a contact, given that a transmission occurs) equal to 0%, 25%, 50%, or 75%; and false positive rate ξ (probability of reporting a contact, given that a transmission does not occur) equal to 0% or 50%. False-positive contact-traced links were sampled from all pairs of people for which a transmission did not occur. Applicants’ model significantly outperformed outbreaker2 in terms of accuracy, coverage probability, and specificity in all scenarios (see Figure 3H-3J). Model Accuracy (SD) Coverage Prob. (SD) Specificity (SD) ) ) ) ) delus ng ony consensus genomes as an npu . Animal Study and Contact-Traced Outbreak Validate Transmission Inference Method

[0630] To validate Applicants’ methods on real viral genomic data, Applicants applied Applicants’ outbreak reconstruction model to a controlled BRSV outbreak among nine cows

[0021] and a rigorously-investigated nosocomial outbreak in South Africa [19,20]. Applicants chose to reconstruct a BRSV outbreak because of its comparable substitution rate to SARS-CoV-2, estimated at 1.6 × 10-3nt site-1yr-1for synonymous mutations

[0036] . In the BRSV outbreak, a single cow was infected with the virus while co-housed with two naïve animals. Seven days later, once the two naïve cows had contracted BRSV via aerosol transmission, bronchoalveolar lavages were obtained from the two recipient animals and used to infect two more naïve animals. Another seven days later, bronchoalveolar lavages was obtained from one of the new recipient animals, and used to infect an additional four naïve animals (see Figure 4A). All cows were housed in separate pens (with the exception of the aerosol transmission step), to avoid unintended transmission pathways

[0021] . Applicants reconstructed the BRSV outbreak using Applicants’ tool, BadTrIP, outbreaker2, and Applicants’ tool with only consensus genomes (see Figure 4B–4E). The four approaches yielded accuracies of 59.3%, 45.5%, 38.9%, and 44.6%, respectively.

[0631] Applicants then applied the same four methods to reconstruct a SARS-CoV-2 outbreak at a hospital in South Africa between March and April of 2020. Applicants selected this outbreak because among the 39 infected persons, 9 transmission links were deemed highly probably by epidemiological investigation (see Table 1), and the COVID-19 prevalence was comparatively low at the time in South Africa (< 0.02% nationally

[0037] ) as to reduce the probability of undetected external introductions. In addition to epidemiologically-confirmed links, patient movement histories throughout the hospital were documented, suggesting certain transmissions to be plausible based on patients located in the same ward at the same time. Applicants’ tool, BadTrIP, outbreaker2, and Applicants’ tool with only consensus genomes, inferred the epidemiologically- confirmed transmission links with 49.5%, 35.1%, 39.1%, and 36.5%, respectively (see Figure 4F– J). Outbreak Reconstruction Identifies Genetic Signatures of Transmission

[0632] Outbreak reconstruction using read-level data provides key insights into cluster analysis by identifying putative transmission events in which a minor allele is passed from donor to recipient or arises de novo in the recipient. To extract clusters from Applicants’ 134,682 genomes, Applicants created a network graph in which each node represented one genome, and two nodes were connected by an edge if and only if their SNV distance was at most two and they tested positive at most 10 days apart. Applicants then defined a cluster as a connected component of this graph with at least two nodes. This filtering process identified 5,692 clusters with a total of 38,598 individuals. Applicants input each of these outbreaks into Applicants’ reconstruction algorithm, and tabulated all putative transmission links and their associated posterior probabilities across all clusters. Applicants found that in a random posterior sample, a randomly-selected transmission link has an 12.9% probability of exhibiting at least one of three key genetic relationships visible only in read-level data: a minor allele in the donor becomes fixed in the recipient, a fixed allele in the donor becomes the minor allele in the recipient, or the major and minor allele at a polymorphic site in the donor are both present in the recipient (split bottleneck). Throughout the rest of this paper, Applicants refer to these relationships as the three iSNV signatures. The other 81.1% of links exhibited no informative within-host diversity.

[0633] Although on average, only 12.9% of putative links have an iSNV signature, they are disproportionately useful for resolving transmission networks. Specifically, minor variantrelationships play two distinct roles in outbreak reconstruction: elucidating transmission pathways among subclusters whose cases all have identical consensus genomes, and identifying the ancestor of such subclusters. To investigate these two roles in greater detail, Applicants subdivided the dataset of putative transmission links by whether or not the proposed donor and recipient had the same consensus genome. In transmission links with the same consensus genome, the only aforementioned iSNV relationship that may occur is the split bottleneck, as the other two (a minor allele in the donor becomes fixed in the recipient or a de novo iSNV in the recipient rises to consensus frequency) imply a SNV distance greater than zero. To test the difference in posterior probability assigned to transmission links with and without split bottlenecks, Applicants first subset the data to transmission links with a posterior probability exceeding 1%, to avoid wholly implausible links contributing low posterior probabilities to the comparison. After filtering, Applicants found that on average, a transmission link with a split bottleneck and consensus distance of 0 appeared in 41.7% of posterior samples, as compared to only 18.4% of posterior samples for a transmission link without a split bottleneck and consensus distance of 0 (Mann- Whitney U test: p < 3 × 10-16). Among transmission links with nonzero SNV distance, Applicants found 22.2% to exhibit a split bottleneck, a minor allele becoming fixed, or a de novo iSNV rising to consensus frequency. This finding may be interpreted as showing that 22.2% of the time, a transmission link with nonzero SNV distance could be better inferred with read-level data as compared to consensus genomes, highlighting one central way that Applicants’ transmission reconstruction tool improves upon existing methods.

[0634] To showcase the impact of using iSNVs for transmission inference in a best-case scenario, Applicants searched the dataset of 5,692 reconstructed transmission clusters for the three with the highest probability that a randomly-chosen transmission link in a random posterior sample exhibits one of the three iSNV signatures, ignoring clusters with fewer than 10 people and clusters spanning multiple facilities. The purpose of this analysis is not to provide unbiassed summary statistics on iSNV transmission, but rather to highlight the utility of Applicants’ tool for relatively self-contained outbreaks with an abundance of informative within-host diversity. The three clusters identified by the search had 10, 13, and 12 genomes collected from a college, a congregate care facility, and a public testing provider in May 2022, January–February 2022, and October– November 2021, respectively. In these three clusters, the probability that a randomly-chosentransmission link in a random posterior sample exhibits one of the three iSNV signatures equaled 66.2%, 31.0%, and 27.8%. Applicants then reconstructed the same three clusters using Applicants’ tool with only consensus genomes instead of read-level data. As the goal was to isolate the impact of using read-level data for outbreak reconstruction, Applicants kept all other parameters the same, and did not compare against BadTrIP or outbreaker2 to avoid exogenous differences in transmission inference (see Figure 5 and Figure 6).

[0635] In general, by reconstructing these three outbreaks using read-level data as opposed to only consensus-level data, Applicants were able to narrow down the set of possible ancestors for each host. To elucidate why Applicants’ model made certain transmission inferences, Applicants analyzed several particular transmission links in greater detail. In Cluster A, the model with read- level data assigned a 100% posterior probability to either A3, A6, or A7 being the ancestor of A9, whereas the model with consensus-level data made no strong assertions about the ancestor of A9 (Figure 5A–B). By investigating the read-level data, however, Applicants found that A3, A6, A7, and A9 were the only cases to exhibit both the A3895C and G17259T SNVs, both at sub-consensus levels. This evidence suggests that A3, A6, or A7 may have infected A9 via an incomplete transmission bottleneck, which makes sense because any other possible donor for A9 would mean that either A3895C and G17259T arises twice independently in the outbreak. Considering the stringent filtering process for abundant iSNVs, Applicants expect such a scenario to be less likely than minor variant transmission via a split bottleneck.

[0636] In Cluster B, Applicants observed a very similar scenario: using read-level data, Applicants’ model assigned a 98.3% chance to the ancestor of B6 being B3, B4, B11 or B12, whereas using consensus genomes, Applicants’ model was able to make no such determination. Upon investigation of the deep-sequencing data, however, Applicants found that B3, B4, B6, B11, and B12 were the only cases to exhibit the G22207T iSNV, again providing evidence for a transmission with a split bottleneck. In Cluster C, the presence of a shared sub-consensus-level iSNV made an even more drastic impact on the inferred transmission network. Since the only two cases to exhibit the A22645T iSNV were C6 and C9, and since C6 tested positive 12 days later than C9, the model with read-level data assigned a posterior probability of 100% to a transmission from C9 to C6, while the model with only consensus-level data inferred a wider set of putative ancestors for C6.DISCUSSION

[0637] This study provides a systematic approach for using read-level data in epidemiological investigations. By determining the distribution of iSNV frequencies, Applicants quantified the likelihood of any possible viral outbreak transmission network based on the genomic and epidemiological data collected. Synthetic outbreak simulations showed that using read-level data in outbreak reconstruction significantly improved accuracy compared to using consensus genomes alone, as Applicants’ model was able to detect minor allele transmissions and split bottlenecks. Moreover, as Applicants show, while BadTrIP does offer some improvement in accuracy over consensus-based methods, accuracy can be improved much further with Applicants’ approach to modeling minor variants. Applicants’ methods, applied to three clusters with high levels of informative within-host diversity, reliably detected putative donor-recipient pairs with genomic signatures of transmission, in contrast to methods using consensus-level genomes alone.

[0638] These findings have implications across virology, epidemiology, and genetics. Applicants’ model for minor allele frequencies alone provides a reliable means of estimating the per-site per-cycle mutation rate without needing to back-calculate it from the evolution rate per- unit-time, which requires making assumptions about the viral population growth rate and the number of virions produced per cycle. Moreover, outbreak reconstruction has numerous applications throughout public health, such as estimating the reproductive number and overdispersion parameter, estimating attack rate by vaccine status, investigating the origin of a cluster, identifying likely viral transmission pathways in nosocomial settings, and more. With Applicants’ tool, Applicants aim to provide a more accurate and interpretable method of conducting these analyses.

[0639] However, Applicants’ outbreak reconstruction model faces a number of limitations in its current form. First and foremost, the relatively slow evolution rate of viruses like SARS-CoV- 2 will limit the resolution of any genomics-based outbreak reconstruction method, as in any given outbreak, Applicants would expect to see a number of cases with identical consensus genomes and no informative within-host diversity. In particular, this limits the ability to differentiate between multiple introductions of identical lineages versus within-facility transmission. Here, the accuracy was 59.9% on average (SD: 18.1%), and while Applicants outperformed existing tools, Applicants emphasize that Applicants would not expect any tool to perfectly reconstruct transmissionnetworks for any virus with a relatively low mutation rate, like SARS-CoV-2. Additionally, Applicants’ tool in its current state does not account for unsampled intermediates: for instance, the tool might reconstruct a transmission chain A → B → C where A and C are sequenced, but B is not, as A → C. This limitation could conceivably be addressed by modifying the MCMC algorithm to add and remove unsequenced cases. Finally, obtaining reliable and reproducible iSNV calls via deep sequencing remains a challenge, as the detection of within-host variants may be biased by the method of sample collection (e.g. nasopharyngeal swab, oral swab) and by the time of collection relative to symptom onset. The presence or absence of an informative iSNV in a given sample may significantly influence the output of the outbreak reconstruction tool.

[0640] Applicants’ model also makes a number of simplifying assumptions about the biological processes underlying viral infection and transmission, which is necessary to reduce computational complexity. In line with other outbreak reconstruction models, Applicants assume that mutations are neutral. This simplification fails to account for cases in which an iSNV rises to a detectable frequency due to a selective advantage, such as immune escape, or is lost due to either genetic drift or a selective disadvantage. However, Applicants do their best to minimize this possibility for SARS-CoV-2 outbreak reconstruction by quantifying the frequency of specific iSNVs across Applicants’ 100,000 genomes, and masking iSNV calls that appear to exhibit strong selective advantage due to the number of genomes in which they are present. In addition, Applicants’ model assumes that sites evolve independently, ignoring epistatic effects. Finally, in this paper, Applicants tailor their approach to single-stranded RNA viruses with minimal recombination. Extensions of Applicants’ model would be required to account for more complex pathogens, such as those with extensive recombination or horizontal gene transfer.

[0641] Despite these challenges, this work underscores the importance of read-level viral genomes collected from outbreaks and emphasizes the utility of high-quality iSNV calls. At present, within-host variant calls below 3% frequency are typically considered unreliable

[0028] , though iSNVs in that range certainly may still be transmitted and provide critical information about putative donor-recipient pairs. Moreover, this work stresses the importance of dense sampling of outbreaks, as missing genomes may lead Applicants’ model to infer additional index cases or transmissions with large SNV distances erroneously. Given dense sampling and high-resolutionsequencing, Applicants can expect their method to reconstruct outbreaks with a high level of accuracy. METHODS Ethical Approvals

[0642] The research project (Protocol #1603078) was reviewed and approved by the Massachusetts Department of Public Health (MADPH) Institutional Review Board and covered by a reliance agreement at the Broad Institute. An additional non-human subjects research and an exempt determination (EX-7080) were made by the Harvard Longwood Campus Institutional Review Board and the Broad Institute Office of Research Subject Protections, respectively, for the analysis of de-identified aggregate and publicly available data. SARS-CoV-2 Sequencing

[0643] Reads are available at SRA under BioProject XXX. Samples were demultiplexed, reads were filtered for known sequencing contaminants, and SARS-CoV-2 reads were assembled using a reference-based assembly approach with the SARS-CoV-2 isolate Wuhan-Hu-1 reference genome NC_045512.2 (sarscov2_illumina_full.wdl). Applicants processed all raw read data using a reference-based consensus calling method with the same NC_045512.2 reference genome. The workflow is publicly available on GitHub. Assembled genomes meeting the CDC criteria for submission to public repositories (unambiguous length ≥24,000 nt and successful gene annotation) were deposited in NCBI Genbank and GISAID immediately upon completion. Raw reads for all samples (including those that did not produce a successful genome) were deposited in NCBI SRA. All NCBI data were deposited under BioProject PRJNA715749. Applicants used LoFreq version 2.1.5 to call intrahost single nucleotide variants (iSNVs). Applicants then filtered out calls with < 100 read depth, < 10 alternate allele read count, p-value of Fisher’s exact test for strand bias < 0.05. In addition, as a means of masking highly-variable sites, Applicants compiled a table from a larger dataset of 172,519 SARS-CoV-2 genomes sequenced at the Broad Institute listing the number of times an iSNV rising above 3% frequency appeared at each site. From this dataset, Applicants calculated the probability of a randomly-chosen iSNV in a randomly-chosen host occurring at each site on the viral genome. Based on these probabilities, Applicants masked sites whose probability of being chosen more than once among 1,000 trials exceeded 5%. This methodwas designed to model the probability of the same iSNV arising de novo more than once in an outbreak of 1,000 people.

[0644] The sample IDs for genomes used in the cluster analyses are available in Supplementary Text, Section D. Modeling Minor Allele Frequencies

[0645] See Supplementary Text, Section A. Briefly, Applicants propose a stochastic pure- growth process to model within-host viral replication, where each birth event may introduce a given substitution with some probability. If the substitution probability is small, then Applicants can approximate the probability density function (PDF) of the proportion of mutated particles by the function 1 / x2. Outbreak Reconstruction Model

[0646] See Supplementary Text, Section B. Briefly, Applicants develop a statistical model for the proportion of the within-host viral population to exhibit a given nucleotide at a given site, for any host at any point in time. This model combines the result from Supplementary Text A with the standard Jukes-Cantor model of neutral evolution. Applicants then combine this genetic model with an epidemiological model, in which each host may be susceptible, exposed, infectious, or recovered at any given time. Together, these two components provide an overall likelihood function for viral outbreaks with read-level sequencing data. MCMC Implementation

[0647] See Supplementary Text, Section C. Briefly, Applicants propose several moves to search the space of possible transmission networks efficiently, as well as standard moves to update underlying parameter values. Synthetic Data Generation

[0648] Applicants simulated outbreaks by first generating stochastic transmission networks over the course of four generations. Applicants started with a single index case, and assumed a negative binomial offspring distribution with mean R0 = 2.5 and overdispersion parameter k = 0.1. Applicants then overlaid a stochastic-epidemic process identical to that in Applicants’ outbreak reconstruction model (see Supplementary Text, Section B.4). Next, Applicants selected a fully- connected subtree with 10–15 nodes uniformly at random, discarding iterations for which no such subtree existed. Applicants also included 0–5 nodes not in the subtree who tested positive at most10 days before or after the first or last case in the subtree, respectively, to simulate unrelated introductions in a cluster. Finally, Applicants overlaid the same evolution model as in their outbreak reconstruction algorithm (see Supplementary Text, Section B.4). Applicants assumed that the sequencing coverage for every case was 100%, with read depths ranging from 100 to 20,000, uniformly at random. All parameter values are equal to those listed in Supplementary Text, Section C.1. From To Supporting Evidence from

[0020] 1) in of s, e d ht e g d 1 g d o 4 er lytested positive on 29 March. This nurse worked directly with P4 on 23 M h h P4 hi d f d t k i l ding rk t, he to e ’s e ad it e h heCODE AND DATA AVAILABILITY

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The total number and mass of SARS-CoV-2 virions. medRxiv.2021. doi:10.1101 / 2020.11.16.2023200923. Specht I, Mitzenmacher M. Analyzing generalized Pólya Urn models using martingales, with an application to viral evolution.2023. doi:10.48550 / ARXIV.2306.17375 24. Wright S. Statistical Genetics and Evolution.1942. 25. Kimura M. Population Genetics, Molecular Evolution, and the Neutral Theory: Selected Papers. University of Chicago Press; 1994. 26. Leonard AS, Weissman DB, Greenbaum B, Ghedin E, Koelle K. Transmission Bottleneck Size Estimation from Pathogen Deep-Sequencing Data, with an Application to Human Influenza A Virus. J Virol.2017 [cited 26 Jul 2023]. doi:10.1128 / jvi.00171-17 27. Evolution of Protein Molecules. Mammalian Protein Metabolism. Academic Press; 1969. pp.21–132. 28. Grubaugh ND, Gangavarapu K, Quick J, Matteson NL, De Jesus JG, Main BJ, et al. An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar. Genome Biol.2019;20: 1–19. 29. Bar-On YM, Flamholz A, Phillips R, Milo R. SARS-CoV-2 (COVID-19) by the numbers. Elife.2020;9. doi:10.7554 / eLife.57309 30. Markov PV, Ghafari M, Beer M, Lythgoe K, Simmonds P, Stilianakis NI, et al. The evolution of SARS-CoV-2. Nat Rev Microbiol.2023;21: 361–379. 31. Amicone M, Borges V, Alves MJ, Isidro J, Zé-Zé L, Duarte S, et al. Mutation rate of SARS-CoV-2 and emergence of mutators during experimental evolution. Evol Med Public Health. 2022;10: 142–155. 32. The R Project for Statistical Computing. [cited 31 Jul 2023]. Available: www.r- project.org / 33. Wu Y, Kang L, Guo Z, Liu J, Liu M, Liang W. Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis. JAMA Netw Open. 2022;5: e2228008. 34. Wu Y, Guo Z, Yuan J, Cao G, Wang Y, Gao P, et al. Duration of viable virus shedding and polymerase chain reaction positivity of the SARS-CoV-2 Omicron variant in the upper respiratory tract: a systematic review and meta-analysis. Int J Infect Dis.2023;129: 228–235.35. Sneppen K, Nielsen BF, Taylor RJ, Simonsen L. Overdispersion in COVID-19 increases the effectiveness of limiting nonrepetitive contacts for transmission control. Proc Natl Acad Sci U S A.2021;118. doi:10.1073 / pnas.2016623118 36. Valarcher J-F, Schelcher F, Bourhy H. Evolution of Bovine Respiratory Syncytial Virus. J Virol.2000 [cited 4 Sep 2023]. doi:10.1128 / jvi.74.22.10714-10728.2000 37. WHO Coronavirus (COVID-19) Dashboard. [cited 4 Sep 2023]. Available: covid19.who.int Example 2 – Supplementary Text for: Inferring Viral Transmission Pathways from Within-Host Variation A Modeling Minor Allele Frequencies A.1 Stochastic Pure-Birth Process with Mutations

[0650] Applicants model viral RNA replication within the host as a stochastic pure-birth process. This term refers to a model of a population—here, a population of virions—in which birth events, i.e. events in which existing virions replicate to form new ones, occur randomly over time. Each birth event may result in one or more offspring, and the rate at which birth events occur may depend on the current population size. Moreover, the term pure-birth means that the population size only ever increases. Such a model is reasonable for the early days of an infection, when the viral population within a host grows much faster than it declines.

[0651] In Applicants’ model, there are a virions within a host at time 0, where a is a positive integer. Applicants then model the times at which an individual virion births new virions as following a Poisson process with rate λ. This means that if a given virion v is birthed at time T0, and virion v gives birth at times T1, T2, T3, . . . , then the intervals between the births of the offspring of virion v, i.e. T1 − T0, T2 − T1, T3 − T2, ... are independent and identically distributed Expo(λ) random variables. Applicants model birth events as instantaneous, and assume that the number of offspring of a given birth event always equals a positive integer w.

[0652] Finally, to capture mutations, Applicants add one additional layer of complexity to the model. Suppose every virion in the population can be white or black, which Applicants take to represent whether or not a virion exhibits a given mutation. Suppose that the colors of the initial a virions at time 0 are known. Then for each birth event, Applicants model the color of the w offspring virions as being the opposite color as the parent with probability p, and the same coloras the parent with probability 1 − p. All offspring for a given birth event always exhibit the same color. A.2 Biological Applicability

[0653] The above stochastic pure-birth process aptly models the process of a virus replicating within a host, under certain assumptions. The number a describes the number of viral particles that inoculate the host, also referred to as the transmission bottleneck size. The number w describes the number of new virions produced by a single virion that has infected a cell, also known as the burst size. In reality, this number may vary between cell infection events. In the model, however, Applicants assume every cell infection produces the same number of offspring virions, but leave w as an unknown parameter to be inferred by the model. Moreover, the model assumes that birth events are instantaneous as a simplifying assumption. In reality, the time between a virion entering a cell and its offspring exiting a cell typically takes approximately 7–8 hours for a SARS-like virus (Sender et al.2020).

[0654] Finally, Applicants assume that if a mutation occurs, it affects all offspring particles. When an infectious virion enters a cell, a “template strand” of RNA is synthesized, equal to the reverse complement of the virion’s genetic material. Then, numerous new copies of RNA are produced from the template, identical to the genetic material of the original virion, except possibly with some mutations. This entire process of producing a template from which to create new genetic material may only occur once, or may recur multiple times. While still an open area of biological research, there is limited evidence suggesting that for some RNA viruses such as influenza, the replication process typically involves producing one or few templates from which all offspring viral RNA is produced, as opposed to numerous templates that each produce a fraction of the overall viral RNA output from the cell (Sanjuán et al.2010). In the model, Applicants assume only one template RNA is created, from which all offspring are produced.

[0655] Under this assumption, the step at which a mutation occurs is very important. If a mutation occurs when copying the virion that enters a cell to produce a template, then approximately all offspring virions exhibit that mutation. If a mutation occurs when copying the template RNA to produce offspring virions, then Applicants would expect that mutation to appear in only one offspring. In the model, Applicants assume the latter scenario negligibly impacts the overall fraction of virions exhibiting a given mutation, since a typical burst size (w) tends to be onthe order of 101 to 103 (Sender et al. 2020). To test the validity of this assumption, Applicants simulated the genetic composition of virions within a host using two models, one in which mutations may occur when copying the template RNA to produce offspring virions, and the other in which they may not. Assuming an final viral population size of 106, an initial viral population size of 1, a burst size of 500, and a mutation rate of 3 × 10−6 substitutions per site per cycle, Applicants found the two models to exhibit very similar PMFs (see Figure 7). A.3 Distribution of Mutated Particles

[0656] If Applicants assume mutation events are rare, then it is possible to derive an approximate distribution for the fraction of virions exhibiting a given mutation after k birth events. Since birth events occur randomly in continuous time, Applicants may assign them an order. That is, Applicants may refer to the 1st, 2nd, 3rd, ... birth events to occur over time unambiguously, despite the fact that for a growing population, these events will happen closer and closer together. One very convenient property of the model is the following: Suppose Applicants freeze the pure birth process at a time in which there are m viral particles, and label the particles 1, 2, . . . , m. Then the next particle to give birth is uniformly distributed over the set of integers 1, 2, . .. , m, i.e. all possible parents are equally likely. Intuitively, this follows from the fact that the Exponential distribution is memoryless, i.e. at any point in time t, the interval between t and the next time a given virion v gives birth still follows an Expo(λ) distribution (for a rigorous proof, see Specht and Mitzenmacher (2023), Section 5).

[0657] The property that at any given time, the next particle to give birth is uniformly distributed, is important because it provides a mathematical tool to study the distribution of black and white (non-mutated and mutated) particles after some number k of birth events. This mathematical tool is called a generalized Pólya Urn model with random replacement, the details of which may be found in Specht and Mitzenmacher (2023). One useful property of the generalized Pólya Urn model is as follows: Suppose at some time t0, u + v birth events have occurred, where u of the birth events produce white particles, and v of the birth events produce black particles. Suppose at a later time t1, the viral population has increased by some amount c. Then for large c, the fraction of the viral population that consists of white particles approximately follows a Beta(u, v) distribution. This use of the Beta distribution has previously been applied to numerous studiesof within-host variation—notably Leonard et al. (2017) in their estimation of transmission bottleneck sizes.

[0658] So, let’s use this Beta distribution property to generate a mathematical model for the fraction of the viral population to exhibit a given mutation, after k birth events have occurred. Per the white-and-black particle notation, this is equivalent to modeling the fraction of virions that are colored white after k birth events. Let X be a random variable denoting this fraction, and let gk,u,v: [0, 1] → R denote its probability density function, where u and v are the numbers of black and white virions at time 0, respectively. First, Applicants note that g will depend—very significantly, in fact—on u and v. To address this, Applicants will divide the model into two cases: (1) some of the initial a particles are black, and some are white (u > 0 and v > 0); and (2) all of the a particles are black (u = 0) or all of the a particles are white (v = 0).

[0659] In the first case, where u > 0 and v > 0, the work of Leonard et al. (2017) already provides a distribution for X. The generalized Pólya Urn property tells that, approximately, the distribution of X is Beta(u, v). Let fBeta(x; u, v) denote the probability density function (PDF) of the Beta distribution with parameters u and v. Then Applicants write: gk,u,v(x) = fBeta(x; u, v) when u > 0 and v > 0.

[0660] In the second case, however, the above equation no longer applies—the reason being that if u = 0, then X is deterministically 0, and if v = 0, then X is deterministically 1 under the Leonard et al. model. In reality, even if none of the inoculating viral particles for a host exhibit a given mutation, said mutation may still arise de novo. To account for this, Applicants invoke the assumption that p, the probability of a mutation occurring in a given birth event, is small relative to 1 / k. Under this assumption, with high probability, either 0 or 1 of the first k birth events will introduce a mutation. If 0 mutations occur among the first k birth events, then X is deterministically 0 in the case u = 0,v = a and deterministically 1 in the case that u = a, v = 0. The probability of 0 mutations occurring among the first k steps is (1 − p)k.

[0661] The final remaining case, in which 1 mutation occurs among the first k birth events, requires slightly more machinery. First, Applicants approximate the probability of 1 mutation occurring as 1 − (1 − p)k, i.e. the probability that more than 0 mutations occur. Applicants then condition on the birth event M at which the mutation occurs. By the property of the generalized Pólya Urn model, if Applicants started with all black particles at time 0 (u = 0,v > 0) and the Mthbirth event produces white particles, then the distribution of X is approximately Beta(1,M), conditional on M. Symmetrically, if Applicants started with all b...

Claims

CLAIMS What is claimed is:

1. A method of transmission reconstruction comprising: a. receiving, by one or more computing devices, a plurality of nucleic acid sequences; b. processing, by one or more computing devices, the plurality of nucleic acid sequences with an outbreak transmission network, based at least in part on minor variant relationships and determines transmission links, in part, by identifying a frequency of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence; and c. generating, by the outbreak transmission network, pathogen transmission pathways.

2. The method of claim 1 further comprising: d. transmitting, by the one or more computing devices, the pathogen transmission pathways to a user device associated with a user.

3. The method of any one of the preceding claims further comprising: i. collecting a plurality of samples from a plurality of subjects comprising the plurality of nucleic acid sequences and / or ii. sequencing the plurality of nucleic acid sequences from the plurality of samples from the plurality of subjects.

4. The method of any one of the preceding claims, wherein the minor variant relationships comprise intra-host single nucleotide variants (iSNVs).

5. The method of claim 4, wherein processing comprises determining a distribution of iSNV frequencies.

6. The method of claim 4 or 5 further comprising masking iSNV calls that appear to exhibit strong selective advantage due to a number of genomes in which the iSNV calls are present.

7. The method of any one of the preceding claims, wherein the outbreak transmission network comprises of computing a likelihood of minor variant / allele frequencies.

8. The method of claim 7, wherein computing the likelihood of minor variant / allele frequencies comprises of a probability density function.

9. The method of any one of the preceding claims, wherein the outbreak transmission network comprises of a ^-coalescent.

10. The method of claim 9, wherein the ^-coalescent comprises of a non-disjoint or disjoint ^- coalescent.

11. The method of any one of the preceding claims, wherein the outbreak transmission network comprises of one or more model tree data structures.

12. The method of claim 11, wherein the one or more model tree data structures are independently selected to be fully-labeled or partially-labeled.

13. The method of any one of the preceding claims, wherein the outbreak transmission network comprises of an epidemiological likelihood function.

14. The method of any one of the preceding claims, wherein the outbreak transmission network comprises of a genomic likelihood function.

15. The method of any one of the preceding claims, wherein transmission links are determined in parallel.

16. The method of any one of the preceding claims, wherein the outbreak transmission network comprises of a within-host viral replication process, the within-host viral replication process comprises of computing a proportion of within-host nucleic acid sequences to contain a nucleotide at a given site.

17. The method of claim 16, wherein the within-host viral replication process of a pure-birth process.

18. The method of claim 17, wherein the pure-birth process comprises of modeling birth events of a pathogen as randomly occurring over time.

19. The method of claim 17 or 18, wherein birth events comprise one or more offspring.

20. The method of claim 17, 18, or 19, wherein birth events occurs instantaneously.

21. The method of claim 17-20, wherein a rate of a birth is dependent on a population of a pathogen.

22. The method of any one of claims 17-21, wherein a rate of birth comprises of a Poisson process.

23. The method of any one of claims 17-22, wherein a population of a pathogen only increases.

24. The method of any one of claims 17-23, wherein a birth event comprises of, independently, one or more mutations of one or more nucleic acids of one or more offspring from one or more nucleic acids in a parent pathogen sequence.

25. The method of claim 24, wherein the one or more mutations occurs with a set probability.

26. The method of any one of claims 17-25, wherein the within-host viral replication process comprises of a Pólya Urn model.

27. The method of any one of the preceding claims, wherein the outbreak transmission network comprises of one or more genetic phases.

28. The method of claim 27, wherein the outbreak transmission network comprises of two genetic phases.

29. The method of claim 28, wherein the two genetic phases comprise an exponential growth phase and a quiescent phase.

30. The method of claim 29, wherein the quiescent phase comprises of a Jukes-Cantor model.

31. The method of any one of the preceding claims, wherein the outbreak transmission network comprises of Markov Chain Monte Carlo.

32. The method of claim 31, wherein the outbreak transmission network comprises of Metropolis-Hastings algorithm for Markov Chain Monte Carlo.

33. The method of any one of the preceding claims, wherein the pathogen transmission pathways comprise a network graph.

34. The method of any one of the preceding claims, wherein the nucleic acid sequences comprise read-level data, genomes, portions of genomes, or combination thereof.

35. The method of claim 34, wherein the genomes comprise consensus genomes.

36. The method of any one of the preceding claims, wherein the plurality of nucleic acid sequences is derived from deep sequencing methods.

37. The method of claim 33, wherein the network graph comprises of nodes and edges, a node corresponds to a nucleic acid sequence from the plurality of nucleic acid sequences and an edge corresponds to a SNV distance.

38. The method of any one of the preceding claims, wherein generating the pathogen transmission pathways is based at least in part on per-site, per-cycle mutation rate.

39. The method of any one of the preceding claims, wherein generating the pathogen transmission pathways comprises detecting minor allele transmissions and split bottlenecks.

40. The method of any one of the preceding claims, wherein generating the pathogen transmission pathways comprises putative detecting donor-recipient pairs with genomic signatures of transmission.

41. The method of any one of the preceding claims, wherein generating the pathogen transmission pathways comprises detecting reproductive number, overdispersion parameter, attack rate by vaccine status, an origin of a cluster, viral transmission pathways in nosocomial settings, or any combination thereof.

42. The method of any one of the preceding claims, wherein the plurality of nucleic acid sequences are associated with a timestamp.

43. The method of any one of the preceding claims, wherein the outbreak transmission network is trained on the frequency of iSNVs.

44. The method of any one of the preceding claims, wherein identifying the frequency of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence occurs at each nucleic acid in the nucleic acid sequence.

45. The method of claim 5, wherein the determining the distribution of iSNV frequencies occurs at each site on the nucleic acid sequence.

46. The method of any one of the preceding claims, wherein a pathogen of the pathogen transmission network is a virus, bacteria, fungi, or protozoa.

47. The method of claim 46, wherein the pathogen is a virus.

48. A system of transmission reconstruction, comprising: a storage device; and a processor communicatively coupled to the storage device, wherein the processor executes application code instructions that are stored in the storage device to cause the system to: a) receive, by one or more computing device, a plurality of nucleic acid sequences;b) process the plurality of nucleic acid sequences with an outbreak transmission network, based at least in part on minor variant relationships and determines transmission links, in part, by identifying a frequency of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence; and c) generate, outbreak transmission network, pathogen transmission pathways.

49. The system of claim 48 further comprising: d) transmit, by the one or more computing devices, the pathogen transmission pathways to a user device associated with a user.

50. The system of any one of claims 48-49, wherein the minor variant relationships comprise intra-host single nucleotide variants (iSNVs).

51. The system of claim 50, wherein processing comprises determining a distribution of iSNV frequencies.

52. The system of claim 50 or 51 further comprising masking iSNV calls that appear to exhibit strong selective advantage due to a number of genomes in which the iSNV calls are present.

53. The system of any one of claims 48-52, wherein the outbreak transmission network comprises of computing a likelihood of minor variant / allele frequencies.

54. The system of claim 53, wherein computing the likelihood of minor variant / allele frequencies comprises of a probability density function.

55. The system of any one of claims 48-54, wherein the outbreak transmission network comprises of a ^-coalescent.

56. The system of claim 55, wherein the ^-coalescent comprises of a non-disjoint or disjoint ^-coalescent.

57. The system of any one of claims 48-56, wherein the outbreak transmission network comprises of one or more model tree data structures.

58. The system of claim 57, wherein the one or more model tree data structures are independently selected to be fully-labeled or partially-labeled.

59. The system of any one of claims 48-58, wherein the outbreak transmission network comprises of an epidemiological likelihood function.

60. The system of any one of claims 48-59, wherein the outbreak transmission network comprises of a genomic likelihood function.

61. The system of any one of claims 48-60, wherein transmission links are determined in parallel.

62. The system of any one of claims 48-54, wherein the outbreak transmission network comprises of a within-host viral replication process, the within-host viral replication process comprises of computing a proportion of within-host nucleic acid sequences to contain a nucleotide at a given site.

63. The system of claim 62, wherein the within-host viral replication process of a pure-birth process.

64. The system of claim 63, wherein the pure-birth process comprises of modeling birth events of a pathogen as randomly occurring over time.

65. The system of claim 63 or 64, wherein birth events comprise one or more offspring.

66. The system of claim 63, 64, or 65, wherein a birth event occurs instantaneously.

67. The system of claim 63-66, wherein a rate of a birth is dependent on a population of a pathogen.

68. The system of any one of claims 63-67, wherein a rate of birth comprises of a Poisson process.

69. The system of any one of claims 63-68, wherein a population of a pathogen only increases.

70. The system of any one of claims 63-69, wherein a birth event comprises of, independently, one or more mutations of one or more nucleic acids of a one or more offspring from one or more nucleic acids in a parent pathogen sequence.

71. The system of claim 70, wherein the mutation occurs with a set probability.

72. The system of any one of claims 63-71, wherein the within-host viral replication process comprises of a Pólya Urn model.

73. The system of any one of claims 48-72, wherein the outbreak transmission network comprises of one or more genetic phases.

74. The system of claim 73, wherein the outbreak transmission network comprises of two genetic phases.

75. The system of claim 74, wherein the two genetic phases comprise an exponential growth phase and a quiescent phase.

76. The system of claim 75, wherein the quiescent phase comprises of a Jukes-Cantor model.

77. The system of any one of claims 48-76, wherein the outbreak transmission network comprises of Markov Chain Monte Carlo.

78. The system of claim 77, wherein the outbreak transmission network comprises of Metropolis-Hastings algorithm for Markov Chain Monte Carlo.

79. The system of any one of claims 48-78, wherein the pathogen transmission pathway comprises of network graph.

80. The system of any one of claims 48-79, wherein the nucleic acid sequences comprise read- level data, genomes, portions of genomes, or combination thereof.

81. The system of claim 80, wherein the genomes comprise consensus genomes.

82. The system of any one of claims 48-81, wherein the plurality of nucleic acid sequences is derived from deep sequencing methods.

83. The system of claim 79, wherein the network graph comprises of nodes and edges, a node corresponds to a nucleic acid sequence from the plurality of nucleic acid sequences and an edge corresponds to a SNV distance.

84. The system of any one of claims 48-83, wherein generating the pathogen transmission pathways is based at least in part on per-site, per-cycle mutation rate.

85. The system of any one of claims 48-84, wherein generating the pathogen transmission pathways comprises detecting minor allele transmissions and split bottlenecks.

86. The system of any one of claims 48-85, wherein generating the pathogen transmission pathways comprises putative detecting donor-recipient pairs with genomic signatures of transmission.

87. The system of any one of claims 48-86, wherein generating the pathogen transmission pathways comprises detecting reproductive number, overdispersion parameter, attack rate by vaccine status, origin of a cluster, viral transmission pathways in nosocomial settings, or any combination thereof.

88. The system of any one of claims 48-87, wherein the plurality of nucleic acid sequences are associated with a timestamp.

89. The system of any one of claims 48-88, wherein the outbreak transmission network is trained on the frequency of iSNVs.

90. The system of any one of claims 48-89, wherein identifying the frequency of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence occurs at each nucleic acid in the sequence.

91. The system of claim 51, wherein the determining the distribution of iSNV frequencies occurs at each site on the nucleic acid sequence.

92. The system of any one of claims 48-91, wherein the pathogen of the pathogen transmission network is a virus, bacteria, fungi, or protozoa.

93. The system of claim 92, wherein the pathogen is a virus.

94. A computer program product, comprising: a non-transitory computer-readable storage device having computer-executable program instructions embodied thereon that when executed by a computer cause the computer to perform transmission reconstruction, the computer-executable program instructions comprising: a) computer-executable program instructions to receive, by one or more computing devices, a plurality of nucleic acid sequences; b) computer-executable program instructions to process the plurality of nucleic acid sequences with an outbreak transmission network, based at least in part on minor variant relationships and determines transmission links, in part, by identifying a frequency of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence; and c) computer-executable program instructions to generate, by the outbreak transmission network, a pathogen transmission pathways.

95. The computer program product of claim 94 further comprising: d) transmit, by the one or more computing devices, the pathogen transmission pathways to a user device associated with a user.

96. The computer program product of any one of claims 94-95, wherein the minor variant relationships comprise intra-host single nucleotide variants (iSNVs).

97. The computer program product of claim 96, wherein processing comprises determining a distribution of iSNV frequencies.

98. The computer program product of claim 96 or 97 further comprising masking iSNV calls that appear to exhibit strong selective advantage due to a number of genomes in which the iSNV calls are present.

99. The computer program product of any one of claims 94-98, wherein the outbreak transmission network comprises of computing a likelihood of minor variant / allele frequencies.

100. The computer program product of claim 99, wherein computing the likelihood of minor variant / allele frequencies comprises of a probability density function.

101. The computer program product of any one of claims 94-100, wherein the outbreak transmission network comprises of a ^-coalescent.

102. The computer program product of claim 101, wherein the ^-coalescent comprises of a non- disjoint or disjoint ^-coalescent.

103. The computer program product of any one of claims 94-102, wherein the outbreak transmission network comprises of one or more model tree data structures.

104. The computer program product of claim 103, wherein the one or more model tree data structures are independently selected to be fully-labeled or partially-labeled.

105. The computer program product of any one of claims 94-104, wherein the outbreak transmission network comprises of an epidemiological likelihood function.

106. The computer program product of any one of claims 94-105, wherein the outbreak transmission network comprises of a genomic likelihood function.

107. The computer program product of any one of claims 94-105, wherein transmission links are determined in parallel.

108. The computer program product of any one of claims 94-100, wherein the outbreak transmission network comprises of a within-host viral replication process, the within-host viral replication process comprises of computing a proportion of within-host nucleic acid sequences to contain a nucleotide at a given site.

109. The computer program product of claim 108, wherein the within-host viral replication process of a pure-birth process.

110. The computer program product of claim 109, wherein the pure-birth process comprises of modeling birth events of a pathogen as randomly occurring over time.

111. The computer program product of claim 109 or 110, wherein birth events comprise one or more offspring.

112. The computer program product of claim 109, 110, or 111, wherein a birth event occurs instantaneously.

113. The computer program product of claim 109-112, wherein a rate of a birth is dependent on a population of a pathogen.

114. The computer program product of any one of claims 109-113, wherein a rate of birth comprises of a Poisson process.

115. The computer program product of any one of claims 109-114, wherein a population of a pathogen only increases.

116. The computer program product of any one of claims 109-115, wherein a birth event comprises of, independently, one or more mutations of one or more nucleic acids of one or more offspring from one or more nucleic acids in a parent pathogen sequence.

117. The computer program product of claim 116, wherein the mutation occurs with a set probability.

118. The computer program product of any one of claims 109-117, wherein the within-host viral replication process comprises of a Pólya Urn model.

119. The computer program product of any one of claims 94-118, wherein the outbreak transmission network comprises of one or more genetic phases.

120. The computer program product of claim 119, wherein the outbreak transmission network comprises of two genetic phases.

121. The computer program product of claim 120, wherein the two genetic phases comprise an exponential growth phase and a quiescent phase.

122. The computer program product of claim 121, wherein the quiescent phase comprises of a Jukes-Cantor model.

123. The computer program product of any one of claims 94-122, wherein the outbreak transmission network comprises of Markov Chain Monte Carlo.

124. The computer program product of claim 123, wherein the outbreak transmission network comprises of Metropolis-Hastings algorithm for Markov Chain Monte Carlo.

125. The computer program product of any one of claims 94-124, wherein the pathogen transmission pathway comprises of network graph.

126. The computer program product of any one of claims 94-125, wherein the nucleic acid sequences comprise read-level data, genomes, portions of genomes, or combination thereof.

127. The computer program product of claim 126, wherein the genomes comprise consensus genomes.

128. The computer program product of any one of claims 94-127, wherein the plurality of nucleic acid sequences is derived from deep sequencing methods.

129. The computer program product of claim 125, wherein the network graph comprises of nodes and edges, a node corresponds to a nucleic acid sequence from the plurality of nucleic acid sequences and an edge corresponds to a SNV distance.

130. The computer program product of any one of claims 94-129, wherein generating the pathogen transmission pathways is based at least in part on per-site, per-cycle mutation rate.

131. The computer program product of any one of claims 94-130, wherein generating the pathogen transmission pathways comprises detecting minor allele transmissions and split bottlenecks.

132. The computer program product of any one of claims 94-131, wherein generating the pathogen transmission pathways comprises putative detecting donor-recipient pairs with genomic signatures of transmission.

133. The computer program product of any one of claims 94-132, wherein generating the pathogen transmission pathways comprises detecting reproductive number, overdispersion parameter, attack rate by vaccine status, origin of a cluster, viral transmission pathways in nosocomial settings, or any combination thereof.

134. The computer program product of any one of claims 94-133, wherein the plurality of nucleic acid sequences are associated with a timestamp.

135. The computer program product of any one of claims 94-134, wherein the outbreak transmission network is trained on the frequency of iSNVs.

136. The computer program product of any one of claims 94-135, wherein identifying the frequency of nucleotide type (e.g., A, C, G, T) for each nucleic acid sequence occurs at each nucleic acid in the sequence.

137. The computer program product of claim 97, wherein the determining the distribution of iSNV frequencies occurs at each site on the nucleic acid sequence.

138. The computer program product of any one of claims 94-137, wherein a pathogen of the pathogen transmission network is a virus, bacteria, fungi, or protozoa.

139. The computer program product of claim 138, wherein the pathogen is a virus.

140. A method of constructing a phylogenetic tree from a plurality of nucleic acid sequences comprising: a) receiving, by one or more computing devices, the plurality of nucleic acid sequences, wherein the nucleic acid sequences are associated with a time stamp; b) processing, by one or more computing devices, the plurality of nucleic acid sequences with a phylogenetic construction network; and c) generating, by the phylogenetic construction network, one or more phylogenetic trees.

141. The method of claim 140 further comprising: e. transmitting, by the one or more computing devices, the one or more phylogenetic trees to a user device associated with a user.

142. The method of any one of claims 140-141, further comprising: i. collecting a plurality of samples from a plurality of subjects comprising the plurality of nucleic acid sequences and / or ii. sequencing the plurality of nucleic acid sequences from the plurality of samples from the plurality of subjects.

143. The method of any one of claims 140-142, wherein the phylogenetic construction network is trained to reduce tree size and reduce mutations.

144. The method of any one of claims 140-143, wherein the plurality of nucleic acid sequences are embedded.

145. The method of claim 144, wherein embedding comprises encoding the plurality of nucleic acid sequences into one or more vectors.

146. The method of claim 145, wherein the one or more vectors are weight vectors.

147. The method of any one of claims 140-146, wherein the phylogenetic construction network comprises of one or more model tree data structures.

148. The method of claim 146 or 147, wherein weights of the weight vectors encode a probability of a nucleic acid sequence at a node of the model tree.

149. The method of any one of claims 146-148, wherein the weights are trained to encode a probability.

150. The method of claim 147-149, wherein the one or more model trees comprise mutation- annotated trees.

151. The method of claim 150, wherein the mutation-annotated trees comprise explicit mutation-annotated tree (EMAT).

152. The method of claim 151, wherein the EMAT is a binary or an n-array tree data structure.

153. The method of claim 151 or 152, wherein the EMAT comprise one or more point-mutations per branch of the model tree.

154. The method of claim 153, wherein the time stamp is associated with the one or more point- mutations on the branches of the model tree.

155. The method of claim 154, wherein one or more branch of the model tree comprises of two or more point-mutations and the phylogenetic construction network merges the two or more point- mutations.

156. The method of any one of claims 147-155, wherein nodes of the model trees comprise a nucleic acid sequence or portion thereof corresponding to the plurality of nucleic acid sequences.

157. The method of claim 147-156, wherein the time stamp corresponds to the nucleic acid sequence or portion thereof on nodes of the model tree.

158. The method of any one of claims 140-157, wherein the phylogenetic construction network comprises of Bayesian inference.

159. The method of any one of claims 140-158, wherein the phylogenetic construction network comprises of a Jukes-Cantor model.

160. The method of claim 159, wherein the Jukes-Cantor model does not comprise rate heterogeneity.

161. The method of any one of claims 140-160, wherein the phylogenetic construction network comprises of Maximum-Clade-Credibility tree.

162. The method of any one of claims 140-161, wherein the phylogenetic construction network comprises of Felsenstein pruning.

163. The method of any one of claims 147-162, wherein the phylogenetic construction network comprises of Markov Chain Monte Carlo.

164. The method of claim 163, wherein the Markov Chain Monte Carlo comprises of a Metropolis-Hastings criterion.

165. The method of any one of claims 147-164, wherein the phylogenetic construction network comprises of an annealing factor.

166. The method of any one of claims 147-164, wherein the phylogenetic construction network comprises of one or more cluster moves.

167. The method of claim 166, wherein the one or more cluster moves comprise one or more changes to one or more neighboring branches of the model tree.

168. The method of claim 166 or 167, wherein the phylogenetic construction network comprises of two or more cluster moves and the two or more cluster moves are performed in parallel.

169. The method of claim 168, wherein the two or more cluster moves performed in parallel comprises of separating neighboring branches from the model tree and fixing a boundary node that is a parent node to the neighboring branches.

170. The method of any one of claims 166-169, wherein the one or more clusters are selected randomly.

171. The method of claim 169, wherein the separated neighboring branches are reassembled into a global model tree.

172. The method of any one of claims 140-171, wherein the phylogenetic construction network comprises of parsimony.

173. The method of any one of claims 147-172, wherein the phylogenetic construction network comprises of maximum likelihood.

174. The method of any one of claims 140-173, wherein the phylogenetic construction network comprises of coalescent prior.

175. The method of claim 174, wherein the coalescent prior comprises of augmented ancestral prior.

176. The method of any one of claims 140-175, wherein the phylogenetic construction network comprises recombination, reassortment, or both.

177. The method of any one of claims 140-176, the phylogenetic construction network comprises of an uncorrelated relaxed molecular clock.

178. The method of any one of claims 140-177, wherein the method further comprises unifying genome alignments.

179. The method of any one of claims 151-178, wherein the EMAT encodes one or more missing nucleic acids in the plurality of nucleic acid sequences with a missation.

180. The method of any one of claims 151-179, wherein the phylogenetic construction network comprises of N-pruning.

181. The method of claim 180, wherein N-pruning comprises of removing branches whose tips comprise one or more missing nucleic acids.

182. The method of any one of claims 151-181, wherein the phylogenetic construction network comprises of subtree pruning and / or regrating (SPR).

183. The method of any one of claims 151-182, wherein the phylogenetic construction network comprises of mutation directed-SPR.

184. The method of any one of claims 151-182, wherein the phylogenetic construction network comprises of subtree slide, Wilson-Balding move, inner node displacement, branch reform, junction reform, or any combination thereof.

185. The method of any one of claims 151-184, wherein the plurality of nucleic acid sequences comprise partial genomic sequences, whole genomic sequences, or a combination thereof.

186. The method of any one of claims 174-185, wherein the phylogenetic construction network comprises of relative selective advantages, the relative selective advantage comprises of coupling the coalescent prior and the plurality of nucleic acid sequences at each point in a model tree.

187. The method of any one of claims 151-186, wherein the phylogenetic construction network comprises of an evolution model, the evolution model comprises of mutation rates trained on whole sequences of the plurality of nucleic acid sequences.

188. The method of any one of claims 140-187, wherein the phylogenetic construction network performs the processing step through a web browser on a user’s computing device.

189. The method of claim 188, wherein the processing step is visualized through a graphical user interface, the graphical user interface is presented on a user’s computing device, and the graphical user interface is accessed by a user computing device on a website or on an application of a service provider.

190. The method of claim 189, wherein one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof are visualized.

191. The method of claim 190, wherein the processing step can be terminated by a user visualizing one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof.

192. The method of any one of claims 140-188, wherein the method further comprises of mean lineage prevalence charts, distribution of times at which a node arose, a listing of mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof wherein mean lineage prevalence charts, distribution of times at which a node arose, a listing of the mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation overtime and / or with associated uncertainty, or a combination thereof can be performed during or after the processing step.

193. The method of any one of claims 189-192, wherein the visualization comprises of labeling the one or more phylogenetic trees.

194. The method of claim 193, wherein labeling the one or more phylogenetic trees comprises of labeling branches of the one or more phylogenetic trees.

195. The method of claim 194, wherein labeling branches comprises of labeling branches with an adjustable credibility threshold, wherein the adjustable credibility threshold comprises of confidence and uncertainty of a branch.

196. The method of any one of claims 189-195, wherein the phylogenetic construction network comprises of computing and identifying a most recent common ancestor (MRCA) of at least two nodes on the one or more phylogenetic trees selected by a user through the graphical user interface.

197. The method of claim 188-196, wherein the method further comprises generating a summarized representation of the one or more phylogenetic trees, the summarized representation may further be visualized through a graphical user interface.

198. The method of claim 197, wherein the summarized representation of the one or more phylogenetic trees comprises of one or more relationships between a root node and a user selected one or more lineages, one or more MCRA, or a combination thereof.

199. The method of claim 197 or 198, wherein the summarized representation comprises of one or more mutations in the one or more phylogenetic trees, wherein a summary of one or more mutations comprises of a number or percentage of trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which comprise the one or more mutations, or any combination thereof.

200. The method of any one of claims 189-199, wherein visualization allows a user to search for and select one or more mutations, the one or more mutations may comprise mutations labeled by a number or percentage of one or more phylogenetic trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which comprise the one or more mutations, or any combination thereof.

201. The method of any one of claims 151-200, further comprises generating a pruned one or more phylogenetic trees.

202. The method of claim 201, wherein the pruned one or more phylogenetic trees comprise every inner node comprising a mutation even immediately upstream in at least one of the one or more phylogenetic trees.

203. The method of claim 202, wherein the pruned one or more phylogenetic trees are generated by: i. tagging one or more inner nodes comprising a mutation immediately upstream of the one or more inner nodes and identifying a MRCA for the one or more inner nodes and ii. removing inner nodes not tagged in step (i) and connecting children nodes of the removed inner nodes to a most recent tagged ancestor node.

204. The method of any one of claims 140-203, wherein the plurality of nucleic acid sequences comprises of a pathogen nucleic acid sequence.

205. The method of claim 204, wherein the pathogen comprises of a virus, bacteria, fungi, or protozoa.

206. The method of claim 204, wherein the pathogen is a virus.

207. A system of constructing a phylogenetic tree from a plurality of nucleic acid sequences, comprising: a storage device; anda processor communicatively coupled to the storage device, wherein the processor executes application code instructions that are stored in the storage device to cause the system to: a) receive, by one or more computing device, the plurality of nucleic acid sequences, wherein the nucleic acid sequences are associated with a time stamp; b) process the plurality of nucleic acid sequences and associated time stamps with a phylogenetic construction network; and c) generate, by the phylogenetic construction network, one or more phylogenetic trees.

208. The system of claim 207 further comprising: d) transmit, by the one or more computing devices, the one or more phylogenetic trees to a user device associated with a user.

209. The system of any one of claims 207-208, wherein the phylogenetic construction network is trained to reduce tree size and reduce mutations.

210. The system of any one of claims 207-209, wherein the plurality of nucleic acid sequences are embedded.

211. The system of claim 210, wherein embedding comprises encoding the plurality of nucleic acid sequences into one or more vectors.

212. The system of claim 211, wherein the one or more vectors are weight vectors.

213. The system of any one of claims 207-212, wherein the phylogenetic construction network comprises of one or more model tree data structures.

214. The system of claim 212 or 213, wherein weights, of the weight vectors, encode a probability of a nucleic acid sequence at a node of the model tree.

215. The system of any one of claims 207-214, wherein the weights are trained to encode a probability.

216. The system of claim 213-215, wherein the one or more model trees comprise mutation- annotated trees.

217. The system of claim 216, wherein the mutation-annotated trees comprise explicit mutation- annotated tree (EMAT).

218. The system of claim 217, wherein the EMAT is a binary or an n-array tree data structure.

219. The system of claim 217 or 218, wherein the EMAT comprise one or more point-mutations per branch of the model tree.

220. The system of claim 219, wherein the time stamp is associated with the one or more point- mutations on the branches of the model tree.

221. The system of claim 220, wherein one or more branch of the model tree comprises of two or more point-mutations and the phylogenetic construction network merges the two or more point- mutations.

222. The system of any one of claims 216-221, wherein nodes of the model trees comprise a nucleic acid sequence or portion thereof corresponding to the plurality of nucleic acid sequences.

223. The system of any one of claims 216-222, wherein the time stamp corresponds to the nucleic acid sequence or portion thereof on nodes of the model tree.

224. The system of any one of claims 207-223, wherein the phylogenetic construction network comprises of Bayesian inference.

225. The system of any one of claims 207-224, wherein the phylogenetic construction network comprises of a Jukes-Cantor model.

226. The system of claim 225, wherein the Jukes-Cantor model does not comprise rate heterogeneity.

227. The system of any one of claims 207-226, wherein the phylogenetic construction network comprises of Maximum-Clade-Credibility tree.

228. The system of any one of claims 207-227, wherein the phylogenetic construction network comprises of Felsenstein pruning.

229. The system of any one of claims 207-228, wherein the phylogenetic construction network comprises of Markov Chain Monte Carlo.

230. The system of claim 229, wherein the Markov Chain Monte Carlo comprises of a Metropolis-Hastings criterion.

231. The system of any one of claims 207-230, wherein the phylogenetic construction network comprises of an annealing factor.

232. The system of any one of claims 216-230, wherein the phylogenetic construction network comprises of one or more cluster moves.

233. The system of claim 232, wherein the one or more cluster moves comprise one or more changes to one or more neighboring branches of the model tree.

234. The system of claim 232 or 233, wherein the phylogenetic construction network comprises of two or more cluster moves and the two or more cluster moves are performed in parallel.

235. The system of claim 234, wherein the two or more cluster moves performed in parallel comprises of separating neighboring branches from the model tree and fixing a boundary node that is a parent node to the neighboring branches.

236. The system of any one of claims 232-235, wherein the one or more clusters are selected randomly.

237. The system of claim 236, wherein separated neighboring branches are reassembled into a global model tree.

238. The system of any one of claims 207-237, wherein the phylogenetic construction network comprises of parsimony.

239. The system of any one of claims 207-238, wherein the phylogenetic construction network comprises of maximum likelihood.

240. The system of any one of claims 207-239, wherein the phylogenetic construction network comprises of coalescent prior.

241. The system of claim 240, wherein the coalescent prior comprises of augmented ancestral prior.

242. The system of any one of claims 207-241, wherein the phylogenetic construction network comprises recombination, reassortment, or both.

243. The system of any one of claims 207-242, the phylogenetic construction network comprises of an uncorrelated relaxed molecular clock.

244. The system of any one of claims 207-243, wherein the system further comprises unifying genome alignments.

245. The system of any one of claims 217-244, wherein the EMAT encodes one or more missing nucleic acids in the plurality of nucleic acid sequences with a missation.

246. The system of any one of claims 217-245, wherein the phylogenetic construction network comprises of N-pruning.

247. The system of claim 246, wherein N-pruning comprises of removing branches whose tips comprise one or more missing nucleic acids.

248. The system of any one of claims 217-247, wherein the phylogenetic construction network comprises of subtree pruning and / or regrating (SPR).

249. The system of any one of claims 217-248, wherein the phylogenetic construction network comprises of mutation directed-SPR.

250. The system of any one of claims 217-248, wherein the phylogenetic construction network comprises of subtree slide, Wilson-Balding move, inner node displacement, branch reform, junction reform, or any combination thereof.

251. The system of any one of claims 217-250, wherein the plurality of nucleic acid sequences comprise partial genomic sequences, whole genomic sequences, or a combination thereof.

252. The system of any one of claims 240-251, wherein the phylogenetic construction network comprises of relative selective advantages, the relative selective advantage comprises of coupling the coalescent prior and the plurality of nucleic acid sequences at each point in a model tree.

253. The system of any one of claims 217-252, wherein the phylogenetic construction network comprises of an evolution model, the evolution model comprises of mutation rates trained on whole sequences of the plurality of nucleic acid sequences.

254. The system of any one of claims 207-253, wherein the phylogenetic construction network performs the processing step through a web browser on a user’s computing device.

255. The system of claim 254, wherein the processing step is visualized through a graphical user interface, the graphical user interface is presented on a user’s computing device, and the graphical user interface is accessed by a user computing device on a website or on an application of a service provider.

256. The system of claim 255, wherein one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof are visualized.

257. The system of claim 256, wherein the processing step can be terminated by a user visualizing one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof.

258. The system of any one of claims 207-257, wherein the system further comprises of mean lineage prevalence charts, distribution of times at which a node arose, a listing of mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof wherein mean lineage prevalence charts, distribution of times at which a node arose, a listing of the mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof can be performed during or after the processing step.

259. The system of any one of claims 254-258, wherein the visualization comprises of labeling the one or more phylogenetic trees.

260. The system of claim 259, wherein labeling the one or more phylogenetic trees comprises of labeling branches of the one or more phylogenetic trees.

261. The system of claim 260, wherein labeling branches comprises of labeling branches with an adjustable credibility threshold, wherein the adjustable credibility threshold comprises of confidence and uncertainty of a branch.

262. The system of any one of claims 254-261, wherein the phylogenetic construction network comprises of computing and identifying a most recent common ancestor (MRCA) of at least two nodes on the one or more phylogenetic trees selected by a user through a graphical user interface.

263. The system of claim 254-262, wherein the system further comprises generating a summarized representation of the one or more phylogenetic trees, the summarized representation may further be visualized through a graphical user interface.

264. The system of claim 263, wherein the summarized representation of the one or more phylogenetic trees comprises of one or more relationships between a root node and a user selected one or more lineages, one or more MCRA, or a combination thereof.

265. The system of claim 263 or 264, wherein the summarized representation comprises of one or more mutations in the one or more phylogenetic trees, wherein a summary of one or more mutations comprises of a number or percentage of trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which comprise the one or more mutations, or any combination thereof.

266. The system of any one of claims 254-265, wherein visualization allows a user to search for and select one or more mutations, the one or more mutations may comprise mutations labeled by a number or percentage of one or more phylogenetic trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which comprise the one or more mutations, or any combination thereof.

267. The system of any one of claims 217-266, further comprises generating a pruned one or more phylogenetic trees.

268. The system of claim 267, wherein the pruned one or more phylogenetic trees comprise every inner node comprising a mutation even immediately upstream in at least one of the one or more phylogenetic trees.

269. The system of claim 268, wherein the pruned one or more phylogenetic trees are generated by: iii. tagging one or more inner nodes comprising a mutation immediately upstream of the one or more inner nodes and identifying a MRCA for the one or more inner nodes; and iv. removing inner nodes not tagged in step (i) and connecting children nodes of the removed inner nodes to a most recent tagged ancestor node.

270. The system of any one of claims 207-269, wherein the plurality of nucleic acid sequences comprises of a pathogen nucleic acid sequence.

271. The system of claim 270, wherein the pathogen comprises of a virus, bacteria, fungi, or protozoa.

272. The system of claim 271, wherein the pathogen is a virus.

273. A computer program product, comprising: a non-transitory computer-readable storage device having computer-executable program instructions embodied thereon that when executed by a computer cause the computer to construct a phylogenetic tree from a plurality of nucleic acid sequences, the computer-executable program instructions comprising: a) computer-executable program instructions to receive, by one or more computing devices, the plurality of nucleic acid sequences, wherein the nucleic acid sequences are associated with a time stamp; b) computer-executable program instructions to process the plurality of nucleic acid sequences and associated time stamps with a phylogenetic construction network; and c) computer-executable program instructions to generate, by the phylogenetic construction network, one or more phylogenetic trees.

274. The computer program product of claim 273, further comprising: f. transmit, by the one or more computing devices, the one or more phylogenetic trees to a user device associated with a user.

275. The computer program product of any one of claims 273-274, wherein the phylogenetic construction network is trained to reduce tree size and reduce mutations.

276. The computer program product of any one of claims 273-275, wherein the plurality of nucleic acid sequences are embedded.

277. The computer program product of claim 276, wherein embedding comprises encoding the plurality of nucleic acid sequences into one or more vectors.

278. The computer program product of claim 277, wherein the one or more vectors are weight vectors.

279. The computer program product of any one of claims 273-278, wherein the phylogenetic construction network comprises of one or more model tree data structures.

280. The computer program product of claim 278 or 279, wherein weights, of the weight vectors, encode a probability of a nucleic acid sequence at a node of the model tree.

281. The computer program product of any one of claims 278-280, wherein the weights are trained to encode a probability.

282. The computer program product of claim 279-281, wherein the one or more model trees comprise mutation-annotated trees.

283. The computer program product of claim 282, wherein the mutation-annotated trees comprise explicit mutation-annotated tree (EMAT).

284. The computer program product of claim 283, wherein the EMAT is a binary or an n-array tree data structure.

285. The computer program product of claim 283 or 284, wherein the EMAT comprise one or more point-mutations per branch of the model tree.

286. The computer program product of claim 285, wherein the time stamp is associated with the one or more point-mutations on the branches of the model tree.

287. The computer program product of claim 286, wherein one or more branch of the model tree comprises of two or more point-mutations and the phylogenetic construction network merges the two or more point-mutations.

288. The computer program product of any one of claims 279-287, wherein nodes of the model trees comprise a nucleic acid sequence or portion thereof corresponding to the plurality of nucleic acid sequences.

289. The computer program product of claim 279-288, wherein the time stamp corresponds to the nucleic acid sequence or portion thereof on nodes of the model tree.

290. The computer program product of any one of claims 273-289, wherein the phylogenetic construction network comprises of Bayesian inference.

291. The computer program product of any one of claims 273-290, wherein the phylogenetic construction network comprises of a Jukes-Cantor model.

292. The computer program product of claim 291, wherein the Jukes-Cantor model does not comprise rate heterogeneity.

293. The computer program product of any one of claims 273-292, wherein the phylogenetic construction network comprises of Maximum-Clade-Credibility tree.

294. The computer program product of any one of claims 273-293, wherein the phylogenetic construction network comprises of Felsenstein pruning.

295. The computer program product of any one of claims 279-294, wherein the phylogenetic construction network comprises of Markov Chain Monte Carlo.

296. The computer program product of claim 295, wherein the Markov Chain Monte Carlo comprises of a Metropolis-Hastings criterion.

297. The computer program product of any one of claims 279-296, wherein the phylogenetic construction network comprises of an annealing factor.

298. The computer program product of any one of claims 279-296, wherein the phylogenetic construction network comprises of one or more cluster moves.

299. The computer program product of claim 298, wherein the one or more cluster moves comprise one or more changes to one or more neighboring branches of the model tree.

300. The computer program product of claim 298 or 299, wherein the phylogenetic construction network comprises of two or more cluster moves and the two or more cluster moves are performed in parallel.

301. The computer program product of claim 300, wherein the two or more cluster moves performed in parallel comprises of separating neighboring branches from the model tree and fixing a boundary node that is a parent node to the neighboring branches.

302. The computer program product of any one of claims 298-301, wherein the one or more clusters are selected randomly.

303. The computer program product of claim 301, wherein the separated neighboring branches are reassembled into a global model tree.

304. The computer program product of any one of claims 273-303, wherein the phylogenetic construction network comprises of parsimony.

305. The computer program product of any one of claims 279-304, wherein the phylogenetic construction network comprises of maximum likelihood.

306. The computer program product of any one of claims 273-305, wherein the phylogenetic construction network comprises of coalescent prior.

307. The computer program product of claim 306, wherein the coalescent prior comprises of augmented ancestral prior.

308. The computer program product of any one of claims 273-307, wherein the phylogenetic construction network comprises recombination, reassortment, or both.

309. The computer program product of any one of claims 273-308, the phylogenetic construction network comprises of an uncorrelated relaxed molecular clock.

310. The computer program product of any one of claims 273-309, wherein the computer program product further comprises unifying genome alignments.

311. The computer program product of any one of claims 283-310, wherein the EMAT encodes one or more missing nucleic acids in the plurality of nucleic acid sequences with a missation.

312. The computer program product of any one of claims 283-311, wherein the phylogenetic construction network comprises of N-pruning.

313. The computer program product of claim 312, wherein N-pruning comprises of removing branches whose tips comprise one or more missing nucleic acids.

314. The computer program product of any one of claims 283-313, wherein the phylogenetic construction network comprises of subtree pruning and / or regrating (SPR).

315. The computer program product of any one of claims 283-314, wherein the phylogenetic construction network comprises of mutation directed-SPR.

316. The computer program product of any one of claims 283-314, wherein the phylogenetic construction network comprises of subtree slide, Wilson-Balding move, inner node displacement, branch reform, junction reform, or any combination thereof.

317. The computer program product of any one of claims 283-316, wherein the plurality of nucleic acid sequences comprise partial genomic sequences, whole genomic sequences, or a combination thereof.

318. The computer program product of any one of claims 306-317, wherein the phylogenetic construction network comprises of relative selective advantages, the relative selective advantage comprises of coupling the coalescent prior and the plurality of nucleic acid sequences at each point in a model tree.

319. The computer program product of any one of claims 283-318, wherein the phylogenetic construction network comprises of an evolution model, the evolution model comprises of mutation rates trained on whole sequences of the plurality of nucleic acid sequences.

320. The computer program product of any one of claims 273-319, wherein the phylogenetic construction network performs the processing step through a web browser on a user’s computing device.

321. The computer program product of claim 320, wherein the processing step is visualized through a graphical user interface, the graphical user interface is presented on a user’s computing device, and the graphical user interface is accessed by a user computing device on a website or on an application of a service provider.

322. The computer program product of claim 321, wherein one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof are visualized.

323. The computer program product of claim 322, wherein the processing step can be terminated by a user visualizing one or more sampled trees, mutation rate, convergence of the one or more phylogenetic trees, evolutionary time, number of mutations, or any combination thereof.

324. The computer program product of any one of claims 273-323, wherein the computer program product further comprises of mean lineage prevalence charts, distribution of times atwhich a node arose, a listing of mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof wherein mean lineage prevalence charts, distribution of times at which a node arose, a listing of the mutations that intervened between pair of nodes, the distribution of the time at which mutations arose between pair of nodes, a plot of the prevalence of a mutation over time and / or with associated uncertainty, or a combination thereof can be performed during or after the processing step.

325. The computer program product of any one of claims 320-324, wherein the visualization comprises of labeling the one or more phylogenetic trees.

326. The computer program product of claim 325, wherein labeling the one or more phylogenetic trees comprises of labeling branches of the one or more phylogenetic trees.

327. The computer program product of claim 326, wherein labeling branches comprises of labeling branches with an adjustable credibility threshold, wherein the adjustable credibility threshold comprises of confidence and uncertainty of a branch.

328. The computer program product of any one of claims 320-327, wherein the phylogenetic construction network comprises of computing and identifying a most recent common ancestor (MRCA) of at least two nodes on the one or more phylogenetic trees selected by a user through a graphical user interface.

329. The computer program product of claim 320-328, wherein the computer program product further comprises generating a summarized representation of the one or more phylogenetic trees, the summarized representation may further be visualized through a graphical user interface.

330. The computer program product of claim 329, wherein the summarized representation of the one or more phylogenetic trees comprises of one or more relationships between a root node and a user selected one or more lineages, one or more MCRA, or a combination thereof.

331. The computer program product of claim 329 or 330, wherein the summarized representation comprises of one or more mutations in the one or more phylogenetic trees, wherein a summary of one or more mutations comprises of a number or percentage of trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which comprise the one or more mutations, or any combination thereof.

332. The computer program product of any one of claims 320-331, wherein visualization allows a user to search for and select one or more mutations, the one or more mutations may comprise mutations labeled by a number or percentage of one or more phylogenetic trees containing the one or more mutations, a number of times of introduction of the one or more mutations, a most common one or more inner nodes which comprise the one or more mutations, or any combination thereof.

333. The computer program product of any one of claims 283-332, further comprises generating a pruned one or more phylogenetic trees.

334. The computer program product of claim 333, wherein the pruned one or more phylogenetic trees comprise every inner node comprising a mutation even immediately upstream in at least one of the one or more phylogenetic trees.

335. The computer program product of claim 334, wherein the pruned one or more phylogenetic trees are generated by: v. tagging one or more inner nodes comprising a mutation immediately upstream of the one or more inner nodes and identifying a MRCA for the one or more inner nodes; and vi. removing inner nodes not tagged in step (i) and connecting children nodes of the removed inner nodes to a most recent tagged ancestor node.

336. The computer program product of any one of claims 273-335, wherein the plurality of nucleic acid sequences comprises of a pathogen nucleic acid sequence.

337. The computer program product of claim 336, wherein the pathogen comprises of a virus, bacteria, fungi, or protozoa.

338. The computer program product of claim 337, wherein the pathogen is a virus.

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