Metagenomics for microbial identification

Abstract

A clinical decision support system and method for identifying microorganisms in a sample by determining long read nucleic acid fragment sequence data (LRS data) derived from multiple species in the sample, performing a taxonomic classification of the LRS data, determining abundance levels of multiple reference genomes based on the taxonomic identifiers of the LRS data, aligning the LRS data with a subset of the reference genomes, and performing a coverage analysis to determine the identity of one or more microorganisms present in the sample based on the coverage analysis.

Description

[Technical field]

[0001] The present disclosure relates generally to systems and methods for microbial identification based on metagenomic data. [Background technology]

[0002] This background description is provided to generally present the content of the present disclosure. Nothing in this background section is admitted explicitly or implicitly as prior art to the present disclosure.

[0003] Current clinical diagnosis of infectious diseases relies on identification of the causative microorganism in the laboratory. Accurate and rapid identification of microorganisms is essential to optimize and streamline antimicrobial therapy. Gold standard laboratory identification of microorganisms often requires culture of the microorganism. Culture-based methods are growth-dependent and have inherent biases for non-fastidious, fast-growing species. Due to the growth-dependent nature of culture-based methods, the time it takes to determine the presence of certain microorganisms can vary from days to weeks depending on the growth rate of the microorganism. Microbial pathogens that are non-viable or non-cultivable in the medium used are missed by culture-based methods.

[0004] Molecular diagnostics such as targeted polymerase chain reaction (PCR) assays are increasingly being used in clinical settings to reduce the time it takes to obtain clinically actionable results. However, simple targeted PCR assays require a priori knowledge of the potential pathogen, and all non-targeted pathogens and intended pathogens with mutations in the targeting (priming) site will be missed. Furthermore, it is very difficult to design PCR primers that have both high specificity and sensitivity for a particular pathogen strain. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Charalampous, T., Kay, GL, Richardson, H., Aydin, A., Baldan, R., Jeanes, C., ... and O'Grady, J. (2019) Nanopore metagenomics enables rapid clinical diagnosis of bacterial lower respiratory infection, Nature Biotechnology, 37(7), 783~792 Summary of the Invention [Problem to be solved by the invention]

[0006] It is desired to address or ameliorate one or more of the shortcomings or limitations associated with the prior art, or at least provide a useful alternative. [Means for solving the problem]

[0007] In some embodiments, a clinical decision support system is provided, comprising: receiving long read nucleic acid sequence data (LRS data) obtained from the sample, where the LRS data includes a plurality of records; performing a taxonomic classification of the LRS data to assign one or more taxonomic identifiers to each record in the LRS data; determining abundance levels of a plurality of reference genomes in the sample based on the taxonomic identifier; aligning the LRS data to a subset of a reference genome, where the subset of the reference genome has been demonstrated to have abundance in the sample that meets or exceeds a predefined abundance level; performing a coverage analysis based on an alignment of the LRS data with the subsets of the reference genome to obtain coverage estimates for each of the subsets of the reference genome; and Identifying one or more microbial species present in the sample based on the coverage estimates. The present invention relates to a clinical decision support system including one or more processing units configured to:

[0008] In some embodiments, the LRS data is obtained from an uncultured clinical sample.

[0009] In some embodiments, each record of the LRS data contains at least 1,000 base pairs of data.

[0010] In some embodiments, the determination of the LRS data is performed in parallel with the taxonomic classification.

[0011] In some embodiments, the LRS data determination, taxonomic classification and coverage analysis steps are performed in parallel.

[0012] In some embodiments, the coverage analysis is performed using a statistical distribution to estimate the breadth of coverage of a subset of the reference genome by the LRS data, optionally where the statistical distribution is a Poisson distribution or a negative binomial distribution.

[0013] In some embodiments, the at least one processing unit is further configured to align records in the LRS data with records in the antimicrobial resistance genome database to determine the presence of antimicrobial resistant species in the sample.

[0014] In some embodiments, performing the taxonomic classification comprises determining a K-mer profile for each record in the LRS data.

[0015] In some embodiments, the K value ranges from 3 to 31 nucleotides.

[0016] In some embodiments, assigning one or more taxonomic identifiers to each record in the LRS data is based on the K-mer profile of the respective record.

[0017] In some embodiments, the taxonomic identifier represents an operational taxonomic unit (OTU), which refers to one or a combination of one or more of a domain, kingdom, phylum, class, order, family, genus, species, strain, or individual genome.

[0018] In some embodiments, the subset of genomes is selected based on the identified OTUs.

[0019] In some embodiments, aligning the LRS data to a subset of the reference genome is based on the total number of matched nucleotides and the read coverage score.

[0020] In some embodiments, the coverage analysis comprises determining the percentage of breadth of coverage of each genome in the subset of reference genomes by the LRS data.

[0021] Some embodiments provide a computer-implemented method for microbial identification, comprising: receiving long read nucleic acid fragment sequence data (LRS data) obtained from the sample; performing a taxonomic classification of the LRS data to assign one or more taxonomic identifiers to each record in the LRS data; determining abundance levels of a plurality of reference genomes based on the taxonomic identifiers of the LRS data; aligning the LRS data to a subset of a reference genome, the subset of the reference genome demonstrating abundance in the sample at or above a predefined abundance level; performing a coverage analysis based on an alignment of the LRS data with the candidate genome; Identifying one or more microbial species present in the sample based on the coverage estimates. The present invention relates to a computer-implemented method,

[0022] Some embodiments provide a method for detecting infection by one or more microorganisms in a subject, the method comprising: determining long read nucleic acid fragment sequence data (LRS data) from a sample obtained from the subject; performing a taxonomic classification of the LRS data to assign one or more taxonomic identifiers to each record in the LRS data; determining abundance levels of a plurality of reference genomes based on the taxonomic identifiers of the LRS data; In response to one or more candidate genomes of the plurality of reference genomes reaching or exceeding a predefined abundance level, aligning the LRS data with the candidate genome; performing a coverage analysis based on an alignment of the LRS data with the candidate genome; determining the identity of one or more microorganisms present in the sample based on the coverage analysis so as to detect infection with one or more microorganisms in the subject. The present invention relates to a method comprising the steps of:

[0023] Exemplary embodiments of the present invention are illustrated by way of example in the accompanying drawings, in which like reference numbers indicate the same or similar elements, and in which: [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 is a schematic diagram illustrating a portion of a method according to the present disclosure. [Diagram 2] FIG. 2 is another schematic diagram illustrating a portion of a method according to the present disclosure. [Diagram 3] FIG. 1 is a block diagram of a system according to the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] The present disclosure relates to a system for identifying pathogens in samples obtained from humans or other animals. The embodiments identify pathogens using accurate, rapid and unbiased genetic and metagenomic sequence-based techniques. The embodiments provide culture-free identification of unknown pathogens to improve the speed and accuracy of detection of pathogens in samples and reduce the time to generate information to drive effective treatment. Some embodiments relate to a clinical decision support system (300 in FIG. 3) that generates information regarding the identity of pathogens present in a sample based on sequencing data derived from sample data. The decision support system supports clinical decision making including decisions regarding treatment based on the identity of the pathogen. The clinical decision support system of some embodiments can also generate reports including details of the identities of identified pathogens, coverage analysis statistics, etc. The embodiments can be deployed in clinical settings such as hospitals to provide an all-in-one microbial intelligence service. Some embodiments also detect antimicrobial resistant (AMR) strains of pathogens in samples.

[0026] Embodiments streamline laboratory processing protocols and advanced computational algorithms for metagenomic pathogen detection and identification in clinical samples. Embodiments can be applied directly to non-cultured clinical samples, such as sputum, bronchoalveolar lavage (BAL), swabs, and blood culture samples, to detect and identify microbial species present in the sample.

[0027] Figure 1 shows a schematic diagram of a portion of the technology that allows for the identification of microbial species present in a clinical sample (110) by metagenomic sequencing. Real-time unbiased sequencing according to embodiments allows for the detection of all or most of the clinically relevant pathogens present in the sample within a practical time frame. An aliquot of the clinical sample, which may contain viral, bacterial or fungal pathogens, is subjected to lysis and total nucleic acid extraction (step 120). The total nucleic acid extract is then used for library preparation for downstream nanopore long-read DNA sequencing.

[0028] The real-time analysis algorithm of the embodiment is initiated when sequencing begins. The DNA sequence is processed by the algorithm in real time, and when the microbial species is detected with high confidence, the platform reports the result to the user. The sequence-based technology of the embodiment has been developed for the detection and identification of pathogens directly from clinical samples. The technology of the embodiment can be integrated with the laboratory protocols and computational algorithms of the embodiment that process the DNA sequence data in real time. Figure 2 shows some components of the embodiment that perform pathogen detection and identification.

[0029] FIG. 3 illustrates a clinical decision support system 300 and its associated components, including a sequencing platform 340 and a reference genome database 350. A biological sample 305 is obtained from a human. The sample is processed by the sequencing platform 340, which generates long read sequencing data (LRS data 345). The LRS data is processed by the decision support system 300 to identify one or more microbial species present in the sample. The decision support system includes at least one processing unit 310 and a memory 320 that includes instructions for implementing various data processing algorithms / modules of the embodiments. The modules include a taxonomic classifier 322, an alignment module 324, and a coverage analyzer 326. The clinical decision support system 300 also includes a display 360 for presenting results generated by the decision support system.

[0030] The present disclosure contemplates any suitable number of systems 300. The present disclosure contemplates computer system 300 taking any suitable physical form. By way of example, and not limitation, computer system 300 may be an embedded computer system, a system on a chip (SOC), a single board computer system (SBC) (e.g., a computer on module (COM) or a system on module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile phone, a personal digital assistant (PDA), a server, a tablet computer system, or a combination of two or more thereof. Where appropriate, computer system 300 may include one or more computer systems 300 and may be single or distributed, may span multiple locations, may span multiple machines, may span multiple data centers, or may reside in a cloud, which may include one or more cloud components in one or more networks. Where appropriate, one or more computer systems 300 may perform one or more steps of one or more methods described or illustrated herein without substantial spatial or temporal limitations. One or more computer systems 300 may perform one or more steps of one or more methods described or illustrated herein at different times or in different locations, where appropriate.

[0031] Clinical sample processing, nucleic acid extraction and library preparation Routine clinical samples such as bronchoalveolar lavage (BAL), screening swabs and blood culture samples are collected from patients according to routine clinical practice. Samples can be collected aseptically in sterile containers and transported to diagnostic laboratories within the hospital for processing within one hour of collection. In some embodiments, total nucleic acid extraction and library preparation can be performed according to nanopore long-read sequencing protocols (e.g., SQK-LSK109 / LSK110). Embodiments can incorporate alternative sequencing technologies suitable for pathogen identification purposes. If a MinION flow cell is used, up to 24 or 96 samples (depending on the choice of barcoding kit) can be sequenced in the same run. The analytical techniques of the embodiments are applicable to any long-read DNA sequencing platform capable of generating "reads" of at least 1,000 bases in length. Some embodiments can incorporate a nanopore sequencing platform to obtain long-read DNA sequencing data. The sequencing data may be referred to as long read nucleic acid sequence data (LRS data). LRS data includes multiple records, each record associated with a particular sequencing read obtained from a sample.

[0032] Pathogen Identification and Detection Once sequencing begins, each read received by the system 300 is classified into a species by using a rapid taxonomic classifier 322 based on the curated genome database 350 (step 210 in FIG. 2). The pathogen identification process is performed continuously as LRS data is received by the system 300. The system 300 keeps track of the abundance of the identified species in the sample as the LRS data is gradually received. Depending on the sequencing throughput, once the abundance of the species reaches a certain threshold (i.e., the total number of bases is equal to or greater than the genome size of the given species), the algorithm selects a representative genome associated with the species. The DNA sequences (or reads) are aligned to the representative genome using a long read alignment tool (alignment module 324, step 220 in FIG. 2), as shown in the schematic diagram in FIG. 2.

[0033] Sequencing of long-read DNA sequencing fragment data is performed over multiple intervals. For each interval, the embodiment obtains DNA sequence data that may be stored in a FASTQ format file, including multiple "reads", i.e., DNA fragments with different lengths (1000-100,000 nucleotides). A K-mer profile is extracted for each read. A K-mer refers to all subsequences of a read with length K, where K ranges from 3 to 31 nucleotides.

[0034] The taxonomic classifier assigns one or more taxonomic identifiers to each read based on the K-mer profile and a reference genome database 350 accessible to the taxonomic classifier. The taxonomic identifiers represent operational taxonomic units (OTUs). An OTU may refer to a domain, kingdom, phylum, class, order, family, genus, species, strain, or individual genome. The reference genome database may include DNA sequences of microbial genomes that are intended to be detected in the sample. The breadth of the identification capabilities of the system can be advantageously expanded by expanding the reference genome database to cover a larger number of species. As more LRS data is received from the sequencing platform, the system 300 incrementally updates the counts for each OTU (e.g., species-level OTU counts). When the abundance levels of a subset of OTUs reach or exceed a predefined threshold, such OTUs are marked as a subset of the reference genome for focusing subsequent analysis by the convergence analyzer. The system 300 monitors the OTU counts and initiates coverage analysis for a subset of the reference genome when the corresponding species count / OTU count exceeds a threshold, which may be defined based on the total number of sequenced nucleotides and the genome size of each species.

[0035] Coverage analysis (step 230 in FIG. 2) is performed by comparing the observed and expected breadth of coverage of the LRS data with respect to the associated reference genome to detect the presence of a species. Based on the assumption that the entire genome has been sequenced, a Poisson distribution may be used to estimate the breadth of coverage taking into account the total number of bases sequenced. Other alternative distributions that model the sequencing coverage may alternatively be incorporated. Alternative distributions include the negative binomial distribution. This step advantageously reduces the false positive rate caused by nanopore sequencing errors or noise in the genome database. This reduction in false positive results allows the algorithm of the embodiment to outperform existing algorithms (see performance comparison table below).

[0036] An embodiment may select a subset of reference genomes for each species based on OTU counts at the strain or individual genome level. An embodiment may align the classified reads to the associated reference genome and identify the genomic regions covered by the reads. A read may be said to align with a genome if the identity score (total number of matched nucleotides / alignment length x 100) is at least 80%, 85%, or 90% and / or the read coverage score (alignment length / read length x 100) is at least 80%, 85%, or 90%. Given the alignment records, an embodiment may calculate the coverage breadth percentage for each species. An embodiment may report the presence of a species if the coverage percentage is at least 40-90% of the expected coverage for each species. The expected coverage percentage may follow a Poisson distribution that takes into account the total number of sequenced nucleotides and the genome size of each species. In parallel with the pathogen detection and identification module, an additional antimicrobial resistance (AMR) module can align each read to an AMR gene record in a reference genome database. The AMR gene record contains the DNA sequence of a gene previously reported as an indicator of antimicrobial resistance. An LRS read can be said to align with an AMR gene if the identity score is at least 80%, 85% or 90% and the gene coverage score (alignment length / gene length x 100) is at least 80%, 85% or 90%. The system reports a list of AMR genes detected in the input sample.

[0037] Performance comparison To compare the detection and identification performance of the embodiments, nanopore long-read sequencing data of 41 direct clinical samples was obtained from 38 sputum, 2 endotracheal tube aspirates (ETAs), and 1 bronchoalveolar lavage (BAL). The percentage of human genome in the samples ranged from 0.14% to 83.71%. The comparison reported the microbial species detected by culture-based and qPCR-based methods, as well as those identified by their metagenomic pipelines.

[0038] The results of the metagenomics pipeline proposed in Charalampous, T., Kay, GL, Richardson, H., Aydin, A., Baldan, R., Jeanes, C., ...and O'Grady, J. (2019) Nanopore metagenomics enables rapid clinical diagnosis of bacterial lower respiratory infection, Nature Biotechnology, 37(7), 783-792 were compared to results obtained using an embodiment using culture and qPCR results as ground truth, as shown in Table 1 below. Across 41 samples, the presence of 44 pathogens was confirmed by either culture or qPCR methods, with 8 pathogens confirmed as negative. While Charalampous et al. were able to identify all 44 pathogens (100% sensitivity), an embodiment according to the present disclosure identified 39 pathogens (89% sensitivity). However, whereas Charalampous et al. identified six species that were confirmed negative by qPCR (25% specificity), embodiments according to the present disclosure did not report any such misidentified species (100% specificity). Furthermore, nine pathogens were not identified by culture methods but were identified by metagenomic methods and confirmed by qPCR. These results demonstrate the advantage of embodiments according to the present disclosure that pathogens not detected by prior art methods are easily detected by metagenomic sequencing.

[0039]

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[0040] In summary, the techniques according to the embodiments allow for the detection of species missed by routine clinical cultures that are confirmed by qPCR. Some embodiments also allow for the detection of additional microbial species without the need for specific PCR primers. Some embodiments also advantageously improve specificity (100%) and overall accuracy (90%) compared to Charalampous et al. in experiments conducted to compare the performance of such embodiments.

[0041] Integration into clinical practice The technology of the embodiment utilizes a nanopore long-read sequencing platform that allows real-time analysis of DNA sequences as they become available. Once sequencing begins, DNA reads are processed in real-time and an electronic or digital report documenting the findings is continuously updated as sequencing progresses. The report can be displayed on the display 360 of the system 300. If a new species (or new AMR genes) is detected and confirmed by coverage analysis, the report is updated accordingly. According to the results in Table 1, pathogens can be identified within 1-2 hours after sequencing begins. Adding the time for sample transportation and processing (typically less than 2 hours), DNA extraction (typically less than 2 hours), and library preparation (approximately 4 hours), the total turnaround time for identifying species in a sample can be less than a day. An electronic report documenting the detected microbial species and AMR genes is generated within a practical time frame to guide clinical decision making.

[0042] The technology of the embodiments can be deployed in clinical laboratories. Streamlined laboratory protocols and algorithms can be used to directly detect microbial species in clinical samples. The embodiments can be used in parallel with or as a replacement for some of the traditional pathogen detection methods. The embodiments can also be used for challenging clinical cases that are not detected by all routine pathogen detection tests, but where clinical suspicion of infection remains. Table 2 lists exemplary hardware and software specifications used by some embodiments.

[0043] In some embodiments, an infection in a subject may be detected based on the identity of one or more microorganisms present in a sample. The clinical decision support system may assist in the selection of a therapeutic agent to administer to a subject if an infection with one or more microorganisms is detected in a subject.

[0044] [Table 2]

[0045] Embodiments provide algorithms for real-time analysis of long-read sequencing data generated from long-read DNA sequencing platforms. By utilizing the unique properties of long-read data, the algorithms identify microbial species within metagenomic samples, reducing false positive rates and improving overall accuracy over existing metagenomic pipelines. Embodiments provide the ability to directly detect and identify pathogens and other microbial species in clinical samples without the need for culture or specific PCR. Embodiments require fewer reads, which can be obtained within 1-2 hours, reducing time to detection and supporting timely clinical decision making. The technology setup of embodiments is advantageously portable and can be deployed in any location with a reliable power supply.

[0046] Generally, methods for identifying pathogens in metagenomic samples are designed for NGS technology (i.e., Illumina sequencing platform). The assumption that existing methods can be applied to sequencing data from any platform leads to reduced accuracy, as we observed in the performance comparison section. Our technology is designed to utilize long-read information and support real-time analysis for long-read sequencing platforms.

[0047] The embodiments provide a flexible and scalable technology. Some embodiments can process from a single sample to a batch of 96 samples that can be analyzed per run. The embodiments allow for both random access and batch testing based on laboratory requirements. The embodiments can also be adapted to detect microorganisms in other sample types, such as fecal or skin samples, as well as in food and environmental samples. Long-read nucleic acid fragment sequence (LRS) data includes sequencing data of at least 1,000 base pairs or more of DNA or RNA molecules. Long-read nucleic acid fragment sequence data can be obtained using nanopore sequencing, or PacBio sequencing, or any other long-read sequencing technology.

[0048] The predefined abundance levels include levels of abundance that are considered statistically significant in terms of identifying a microorganism in a sample. The predefined abundance levels may include a level where the total number of sequenced bases in a sample is equal to or greater than the genome size of a given species.

[0049] The reference genomes include genomes corresponding to various species that may be present in the sample. The reference genomes may be stored in a genome database populated by routine clinical analysis of samples by total nucleic acid extraction and long-lead ligation. The subset of reference genomes is selected based on taxonomic identifiers assigned to the LRS data obtained from the sample. The selection of the subset of reference genomes advantageously avoids the need to align a large amount of LRS data with a large number of reference genomes, making the method of the embodiment computationally feasible. The subset of reference genomes may also be referred to as representative genomes or candidate reference genomes.

[0050] Aligning the LRS data with the candidate genome includes matching nucleotides of the LRS data with the genome.

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[0051] The coverage analysis involves calculating the percentage of coverage width for each candidate genome based on the alignment results. The results of the coverage analysis can be presented in the form of a coverage distribution graph, as shown in FIG. 2.

[0052] Some embodiments relate to a method for treating an infection by one or more microorganisms in a subject, the method comprising: determining long read nucleic acid fragment sequence data (LRS data) from a sample obtained from the subject; performing a taxonomic classification of the LRS data to assign one or more taxonomic identifiers to each record on the LRS data; determining an abundance level of a plurality of reference genomes based on the taxonomic identifiers of the LRS data; aligning the LRS data with a candidate genome in response to one or more candidate genomes of the plurality of reference genomes reaching or exceeding a predefined abundance level; performing a coverage analysis based on the alignment of the LRS data with the candidate genome; determining an identity of one or more microorganisms present in the sample based on the coverage analysis to detect an infection by the one or more microorganisms in the subject; and administering a therapeutic agent to the subject if an infection by the one or more microorganisms is detected in the subject.

[0053] It will be understood that many further modifications and permutations of various aspects of the described embodiments are possible, and the described aspects are therefore intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims.

[0054] Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" and "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps, but not the exclusion of any other integer or step or group of integers or steps.

[0055] Any reference in this specification to any prior publication (or information derived therefrom), or to any publicly known matter, is not, and should not be construed as, a form of acknowledgment or admission or any suggestion that the prior publication (or information derived therefrom), or publicly known matter forms part of the common general knowledge in the field of endeavor to which this specification pertains.

Claims

1. 1. A clinical decision support system, comprising: receiving long read nucleic acid sequence data (LRS data) obtained from the sample, wherein the LRS data includes a plurality of records; performing a taxonomic classification of the LRS data to assign one or more taxonomic identifiers to each record in the LRS data; determining abundance levels of a plurality of reference genomes in the sample based on the taxonomic identifier; aligning the LRS data to a subset of the reference genomes, where the subset of reference genomes have been demonstrated to have abundance in the sample that meets or exceeds a predefined abundance level; performing a coverage analysis based on the alignment of the LRS data with the subset of reference genomes to obtain a coverage estimate for each of the subset of reference genomes; and Identifying one or more microbial species present in the sample based on the coverage estimates. A clinical decision support system comprising one or more processing units configured to:

2. The system of claim 1 , wherein the LRS data is obtained from an uncultured clinical sample.

3. The system of claim 1 , wherein each record of the LRS data contains at least 1,000 base pairs of data. The system of claim 1 , wherein the determination of the LRS data is performed in parallel with a taxonomic classification.

4. The system of claim 1 , wherein the steps of determining, taxonomic classification and coverage analysis of the LRS data are performed in parallel.

5. The coverage analysis is performed using a statistical distribution to estimate the breadth of coverage of the subset of the reference genome by the LRS data; Optionally, the statistical distribution is a Poisson distribution or a negative binomial distribution.

6. The system of claim 1 , wherein at least one processing unit is further configured to align records of the LRS data with records in an antimicrobial resistance genome database to determine the presence of antimicrobial resistant species in the sample.

7. The system of claim 1 , wherein performing taxonomic classification comprises determining a K-mer profile for each record in the LRS data.

8. The system of claim 7, wherein the K value is in the range of 3 to 31 nucleotides.

9. 8. The system of claim 7, wherein assigning one or more taxonomic identifiers to each record in the LRS data is based on the K-mer profile of each record.

10. 10. The system of claim 9, wherein the taxonomic identifier represents an operational taxonomic unit (OTU), which refers to one or a combination of one or more of a domain, kingdom, phylum, class, order, family, genus, species, strain, or individual genome.

11. The system of claim 10, wherein the subset of genomes is selected based on identified OTUs.

12. 2. The system of claim 1, wherein aligning the LRS data to the subset of a reference genome is based on a total number of matched nucleotides and a read coverage score.

13. 2. The system of claim 1, wherein coverage analysis comprises determining a percentage of breadth of coverage of each genome in the subset of reference genomes by the LRS data.

14. 1. A computer-implemented method for microbial identification, comprising: receiving long read nucleic acid fragment sequence data (LRS data) obtained from the sample; performing a taxonomic classification of the LRS data to assign one or more taxonomic identifiers to each record in the LRS data; determining abundance levels of a plurality of reference genomes based on the taxonomic identifiers of the LRS data; aligning the LRS data to the subset of reference genomes, the subset of reference genomes demonstrating abundance in the sample at or above a predefined abundance level; performing a coverage analysis based on the alignment of the LRS data with a candidate genome; identifying one or more microbial species present in said sample based on the coverage estimates.

4. A computer-implemented method comprising:

15. 1. A method for detecting infection by one or more microorganisms in a subject, comprising: determining long read nucleic acid fragment sequence data (LRS data) from a sample obtained from the subject; performing a taxonomic classification of the LRS data to assign one or more taxonomic identifiers to each record in the LRS data; determining abundance levels of a plurality of reference genomes based on the taxonomic identifiers of the LRS data; aligning the LRS data with one or more candidate genomes of the plurality of reference genomes in response to the candidate genome reaching or exceeding a predefined abundance level; performing a coverage analysis based on the alignment of the LRS data with the candidate genome; determining the identity of one or more microorganisms present in said sample based on said coverage analysis so as to detect infection with said one or more microorganisms in said subject. A method comprising:

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