Inferring microorganisms of origin targeting antimicrobial resistance markers in metagenomics

Abstract

The present invention provides systems and methods for identifying AMR-labeled hosts from one or more samples, the systems and methods comprising obtaining short read sequence data derived from the one or more samples; identifying one or more AMR markers from the short read sequence data to obtain a short read metric, the short read metric including quantitative metrics such as RPKM, median depth, read count, or other quantitative metrics of any one or more AMR markers of the AMR markers identified in the short read; obtaining one or more reference sequence data; identifying one or more AMR markers from the reference sequence data to obtain reference metrics, the reference metrics including quantitative metrics such as RPKM, median depth, read count, or other quantitative metrics of any one or more AMR markers of the AMR markers identified in the reference sequence; when the average ratio between the short read metric and the reference metric is below a threshold ratio, hosts of the one or more AMR markers in the sample are identified.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 63 / 481,163, filed on January 23, 2023, and U.S. Provisional Application No. 63 / 508,795, filed on June 16, 2023, the contents of each of which are incorporated by reference in their entirety.

[0003] Incorporated by Reference

[0004] Any and all applications for which foreign or domestic priority claims are identified in the Application Data Sheet filed with this application are hereby incorporated by reference in accordance with 37 CFR 1.57. In addition, all publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated herein by reference. To the extent that publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, this specification is intended to supersede and / or take precedence over any such contradictory material. Background Art

[0005] Targeted metagenomics offers many advantages for the detection and characterization of microorganisms and antimicrobial resistance (AMR) markers, including the detection of hundreds of microorganisms and thousands of AMR markers in a single test with greater sensitivity than shotgun metagenomics. A challenge for many metagenomics-based approaches is linking detected AMR markers to the source microorganism. The magnitude of this challenge depends on the specific AMR marker and the microbial composition of the sample: complex matrices such as wastewater may be nearly impossible to deconvolute, while there are important human sample types that may be dominated by only one or a few microorganisms. Summary of the invention

[0006] The methods and systems described herein address the need for more computationally efficient, accurate, and easy-to-use tools for comprehensive diagnostic and metagenomic analysis, and also provide other desirable features.

[0007] One aspect of the present disclosure provides a kind of computer system, and this computer system comprises one or more processors, memory and one or more programs.These one or more programs are stored in memory and are configured to be performed by these one or more processors.These one or more programs are used to identify the presence or absence of one or more conditions in the first sample from the sample source.

[0008] Another aspect of the present disclosure provides a method for identifying a host of antimicrobial resistance (AMR) markers from a sample. In this embodiment, the method includes: obtaining a sample from a source, the sample comprising a plurality of nucleic acids; enriching nucleic acids via target enrichment; sequencing the enriched nucleic acids to generate short reads of the enriched nucleic acids; measuring the short reads for one or more AMR markers to obtain a short read metric, the short read metric including a quantitative metric such as RPKM, median depth, read counts, or other quantitative metrics of any one or more AMR markers identified in the short reads; measuring a reference nucleic acid for the one or more AMR markers to obtain a reference metric, the reference metric including a quantitative metric such as RPKM, median depth, read counts, or other quantitative metrics of any one or more AMR markers identified in the reference nucleic acid; and identifying the host of the one or more AMR markers in the sample when the average ratio between the short read metric and the reference metric is below a threshold ratio.

[0009] Another embodiment is a computer-implemented method for identifying a host of an AMR marker from one or more samples. The embodiment includes: obtaining short read sequence data from one or more samples; identifying one or more AMR markers from the short read sequence data to obtain a short read metric, the short read metric including a quantitative metric such as RPKM, median depth, read count, or other quantitative metrics of any one or more AMR markers identified in the short read; obtaining one or more reference sequence data; identifying one or more AMR markers from the reference sequence data to obtain a reference metric, the reference metric including a quantitative metric such as RPKM, median depth, read count, or other quantitative metrics of any one or more AMR markers identified in the reference sequence; and identifying a host of the one or more AMR markers in the sample when an average ratio between the short read metric and the reference metric is below a threshold ratio.

[0010] Another embodiment is an electronic system for identifying AMR-tagged hosts from a sample, the electronic system comprising a processor and a memory storing instructions, wherein the instructions are configured to perform one of the above methods.

[0011] Further aspects and desired features of the present disclosure will become apparent to those skilled in the art from the following detailed description, in which only exemplary embodiments of the present disclosure are shown and described. As will be appreciated, the present disclosure is capable of other embodiments and different embodiments, and its several details can be modified in various obvious aspects, all of which do not depart from the present disclosure. Therefore, the drawings and description should be considered illustrative in nature, rather than restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] By referring to the following detailed description and accompanying drawings, the features of the examples of the present disclosure will become apparent, wherein like reference numerals correspond to similar but possibly not identical parts. For the sake of brevity, reference numerals or features having previously described functions may or may not be described in conjunction with other drawings in which they appear.

[0013] Figure 1 is a flow chart illustrating a computer-implemented method of identifying a host according to some embodiments.

[0014] Figure 2 is a flow chart illustrating a computer-implemented method of identifying a host according to some embodiments.

[0015] Figure 3 An example workflow for analyzing sample content according to some embodiments is shown.

[0016] Figure 4 A diagram of the Urine Pathogen ID / AMR Panel (UPIP) is shown, including the AMR gene classes targeted in some embodiments (total alleles per gene class).

[0017] Figure 5 is a bar graph showing results from urine samples tested for targeted enrichment with UPIP and sequenced on an NGS platform. The results show the distribution of the occurrence frequency of AMR markers co-detected with at least one associated microorganism.

[0018] Figure 6 is a bar graph showing the distribution of the occurrence frequency of ESBL or carbapenemase AMR markers detected together with one or more detected related microorganisms.

[0019] Figure 7 Shown are graphs detailing the results of a comparison of median depth and RPKM ratios between mecA detected and Staphylococcus species detected in samples in which one or more species were detected.

[0020] Figure 8 Panels AD show quality filtered sample reads mapped to mecA enriched by UPIP. DETAILED DESCRIPTION

[0021] All patents, applications, published applications, and other publications mentioned herein are incorporated herein by reference in their entirety. If a term or phrase is used herein in a manner that is contrary to or inconsistent with the definition set forth in the patents, applications, published applications, and other publications incorporated herein by reference, the use herein takes precedence over the definition incorporated herein by reference.

[0022] Although various embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. Without departing from the present invention, those skilled in the art may conceive of many variations, changes and replacements. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed.

[0023] Throughout this application, various embodiments of the present invention can be presented in range form.It should be understood that the description of the range format is only for convenience and brevity, and should not be interpreted as an inflexible limitation on the scope of this aspect.Therefore, the description of the range should be considered to have all possible sub-ranges disclosed specifically and a single numerical value within the range.For example, the description of a range such as 1 to 6 should be considered to have a specifically described sub-range (such as 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, etc.), and a single numeral within the range, such as 1, 2, 3, 4, 5 and 6.No matter how wide the scope is, this applies.

[0024] Drug-resistant bacteria pose a growing global threat. Molecular diagnostic techniques are promising for both monitoring and clinical applications, providing faster turnaround times compared to traditional culture and antibiotic susceptibility testing (AST). In particular, NGS-based metagenomic analysis allows the detection and characterization of multiple bacteria and antimicrobial resistance (AMR) markers from a single sample. Targeted NGS (also referred to as precise metagenomics) overcomes the sensitivity challenges of shotgun metagenomics, which is particularly critical for low-abundance genes of interest (such as antimicrobial resistance genes). However, molecular methods based on PCR and NGS have all proposed the challenge of linking AMR markers to their host microorganisms. AMR-host association is critical for inferring resistance phenotypes in clinical samples and monitoring the network and trajectory of AMR gene transfer in complex reservoirs (such as soil or wastewater).

[0025] Current methods for linking host-AMR markers are largely limited to whole genome sequencing or PCR-based methods. For example, methicillin-resistant Staphylococcus aureus (MRSA) is characterized by the AMR marker mecA inserted into the orfX locus of Staphylococcus aureus. Other Staphylococcus species may also carry mecA. Therefore, in samples containing a variety of Staphylococcus species, detection of MRSA requires linking the mecA gene to its genomic background. PCR-based methods rely on the use of primers designed for mecA and orfX to detect the right end junction (MREJ) of the Staphylococcus cassette chromosome mec element (SCCmec) between the mecA cassette and the orfX gene. However, this method requires multiple primers to cover the diversity of SCCmec types, and the primers are not robust for new / evolved types. McClure JA et al., A Novel Assay for Detection ofMethicillin-Resistant Staphylococcus aureus Directly From ClinicalSamples.Front.Microbiol.11:1295(2020).

[0026] There are also several NGS-based host-AMR marker linking methods. Chromosome conformation techniques, such as 3C and Hi-C, have been reused for host-AMR links by utilizing the ability to artificially link co-localized DNA chains. Stalder T et al., Linking the resistome and plasmidome to the microbiome.ISME J 13, 2437-2446 (2019); Kalmar L et al., HAM-ART: An optimised culture-free Hi-C metagenomic spipeline for tracking antimicrobial resistance genes in complex microbial communities.PLoS Genet 18 (3): e1009776 (2022). However, these methods involve additional wet laboratory reagents and protocols, making them potentially costly or time-consuming. Long read NGS methods also allow host-AMR marker links. Slizovskiy IB et al., Target-enriched long-read sequencing (TELSeq) contextualizes antimicrobial resistance genes in metagenomes. Microbiome 10, 185 (2022). However, most current long-read technologies have high error rates and are more expensive than short-read NGS.

[0027] The present disclosure provides methods and systems for analyzing samples, and these samples include, for example, samples comprising nucleic acid molecules and / or proteins. The methods and systems of the present disclosure can promote the identification of sequences and the identification and classification of entities subsequently included in one or more samples. For example, the methods and systems provided herein can promote the identification of microorganisms and / or pathogens in samples (such as cell samples obtained from patients). The methods of the present disclosure may include one or more steps, including collecting samples, processing samples to prepare sample contents for sequencing analysis, performing sequencing analysis to generate sequencing reads, processing sequencing reads to identify short sequences associated with samples and their mutual relationships (for example, via k-mer-based analysis as described herein) to generate various information (for example, sequencing metrics, including but not limited to coverage, ANI, total read counts, median depth and RPKM), detecting entities such as pathogens and microorganisms and / or antimicrobial resistance markers in samples based at least in part on sequencing data, interpreting sequencing data and entity recognition data, developing treatment strategies or other strategies based at least in part on sequencing data and entity recognition data, evaluating sequencers and classification algorithm performance, and providing suggestions to medical professionals and / or patients or other subjects.

[0028] Samples can be derived from any useful source and can be processed in any useful manner (e.g., as described herein). For example, samples comprising nucleic acid molecules can be processed to prepare nucleic acid molecules therein for nucleic acid sequencing determination. Alternatively or additionally, samples comprising proteins can be processed to prepare proteins therein for protein or amino acid sequencing determination. Controls can include known sequences, microorganisms and / or pathogens, and / or can correspond to one or more databases (e.g., as described herein). Any useful processing can be used to process samples and extract information about samples to be input into a user interface and for subsequent analysis (e.g., as described herein). Similarly, any useful reagent can be used for the processing of samples. Other detailed information about samples, controls, laboratory procedures and reagents are included below.

[0029] The method and system provided herein can be used to identify microorganisms and viruses in samples. Therefore, the method and system provided herein can be used to evaluate the pollution (for example, environmental pollution, surface pollution, food pollution, air pollution, water pollution or cell culture pollution) of samples, stimulus response (for example, drug responder or non-responder, allergic response or treatment response), infection (for example, bacterial infection, fungal infection or viral infection) and disease state (for example, the presence or absence of disease, the deterioration of disease or the recovery of disease). The sample can be derived from the environment or biological source (for example, as described herein). The presence of microorganisms or viruses in the sample can be analyzed by, for example, analyzing nucleic acid molecules and proteins or polypeptides in the sample (such as nucleic acid molecules and proteins or polypeptides that can be derived from microorganisms or viruses). Analyzing the sample can include detecting the sequence of nucleic acid molecules and proteins or polypeptides, and comparing these sequences with the sequences included in the reference database.

[0030] Sample collection and processing

[0031] Samples can be collected from any source of interest. For example, samples can be collected from biological sources or environmental sources. The biological source of the sample can be derived from a subject, such as a mammal or other animal. The terms "subject", "individual" and "patient" are used interchangeably herein and refer to vertebrates, preferably mammals, and more preferably humans. Samples can be collected from multicellular organisms such as fish, amphibians, reptiles, birds or mammals. Mammals include but are not limited to mice, apes, apes, monkeys, gorillas, humans, livestock (e.g., cattle, pigs, sheep, horses), rodents (e.g., rats, mice), sports animals and pets (e.g., cats, dogs, rabbits). For example, the subject can be a human. Samples can be collected from microbial communities. For example, samples can be collected from vesicular algae such as malaria and dinoflagellates. Also included are tissues, cells and their progeny of biological entities obtained in vivo or cultured in vitro.

[0032] The subject may have or be suspected of having a disease or condition. The subject may be known to have previously had a disease or condition. The subject may have or be suspected of having been exposed to a pathogen, such as a virus or bacteria. The subject may have a risk factor for a given disease. The subject may be healthy or considered healthy. The subject may have a given feature, such as a given weight, height, body mass index or other feature. The subject may have a given ethnic or racial ancestry, place of birth or residence, nationality, disease or remission state, family history or other feature. The subject may be or have spent a period of time in a given location, such as a medical institution or office, hospital, laboratory or clinic. For example, the subject may be or have spent a period of time in a hospital, where they may be suspected of having been exposed to a pathogen. The subject may use or have used (e.g., had implanted or inserted) a medical device such as a catheter, bandage, stent, needle, cannula, breast pump, tube (e.g., tympanostomy tube), hearing aid, prosthesis, defibrillator, artificial hip, artificial knee, pacemaker, implant (e.g., breast implant), screw, rod, suture, disk (e.g., spinal disk), intrauterine contraceptive device, needle, plate, or eyepiece. For example, the subject may have or previously had a catheter inserted. The medical device may provide a mechanism for exposing the subject to a pathogen (e.g., via the formation of a biofilm).

[0033] As used herein, the term "biological sample" can be used interchangeably with the term "sample", and generally refers to a sample obtained from a subject. A biological sample can be obtained directly or indirectly from a subject. The sample can be obtained from a subject via any suitable method, including, but not limited to, spitting, wiping, drawing blood, biopsy, obtaining excrement (e.g., urine, feces, sputum, vomitus, or saliva), excision, scraping, and puncturing. Samples can be obtained from a subject by, for example, entering the circulatory system through a vein or artery, collecting secreted biological samples (e.g., feces, urine, saliva, sputum, etc.), exhaling, or surgically extracting tissue (e.g., biopsy). The sample can be obtained by non-invasive methods, including, but not limited to: scraping the skin or cervix, wiping the cheek, or collecting saliva, urine, feces, menstrual fluid, tears, or semen. Alternatively, the sample can be obtained by an invasive procedure, such as a biopsy, needle aspiration, or phlebotomy. The sample may include body fluids, such as, but not limited to, blood (e.g., whole blood, red blood cells, white blood cells or leukocytes, platelets), plasma, serum, sweat, tears, saliva, sputum, urine, semen, mucus, synovial fluid, breast milk, colostrum, amniotic fluid, bile, bone marrow, interstitial fluid or extracellular fluid, lymphatic fluid, pleural effusion, pleural effusion, aqueous humor, cystic fluid, eyewash, eye aspirate, lung lavage fluid, lung aspirate, buffy coat, or cerebrospinal fluid. For example, a sample may be obtained by a puncture method to obtain a body fluid comprising blood and / or plasma. Such a sample may include cells and cell-free nucleic acid material. Alternatively, the sample may be obtained from any other source, including, but not limited to, blood, sweat, hair follicles, oral tissue, tears, menstrual fluid, feces, or saliva. The biological sample may be a tissue sample or a chemically treated tissue sample, such as a tumor biopsy. The sample can be obtained from any tissue provided herein, including but not limited to skin, heart, lung, kidney, breast, pancreas, liver, intestine, brain, prostate, esophagus, muscle, smooth muscle, bladder, gallbladder, colon or thyroid. The acquisition method provided herein includes a biopsy method, including fine needle aspiration, core needle biopsy, vacuum-assisted biopsy, coarse core biopsy, incisional biopsy, excisional biopsy, punch biopsy, shaving biopsy or skin biopsy. The biological sample can include one or more cells.

[0034] The sample may contain a homogeneous or mixed microbial population, including one or more of viruses, bacteria, protists, ankaryotic protozoa, vesicular algae, archaea, or fungi. Examples of viruses include, but are not limited to, human immunodeficiency virus, Ebola virus, rhinovirus, influenza, rotavirus, hepatitis virus, West Nile virus, ringspot virus, mosaic virus, herpes virus, and lettuce giant vein-associated virus. Non-limiting examples of bacteria include Staphylococcus aureus, Staphylococcus aureus Mu3, Staphylococcus epidermidis, Streptococcus agalactiae, Streptococcus pyogenes, Streptococcus pneumonia, Escherichia coli, Citrobacter koseri, Clostridium perfringens, Enterococcus faecalis, Klebsiella pneumonia, Lactobacillus acidophilus, Listeria monocytogenes, Propionibacterium granulosum, granulosum, Pseudomonas aeruginosa, Serratia marcescens, Bacillus cereus, Yersinia enterocolitica, Staphylococcus simulans, Enterobacter aerogenes, and Enterobacter aerogenes.Examples of fungi include, but are not limited to, Absidia corymbifera, Aspergillus niger, Candida albicans, Geotrichum candidum, Hansenula anomala, Microsporum gypseum, Monilia, Mucor, Penicillium expansum, Rhizopus, Rhodotorula, Saccharomyces cerevisiae, and bayabus), Saccharomyces carlsbergensis, Saccharomyces uvarum and Saccharomyces cerivisiae. The sample may also be a processed sample, such as a preserved, fixed and / or stabilized sample.

[0035] Samples can be collected from environmental sources. For example, samples can be collected from fields (e.g., farmland), lakes, rivers, canals, oceans, watersheds, tanks, reservoirs, pools (e.g., swimming pools), ponds, vents, walls, roofs, soil, plants and / or other environmental sources. Collecting samples from environmental sources can include, for example, collecting water, soil or air in one or more containers such as vials or pipettes. Collecting samples from environmental sources can include contacting water or soil with wicking or bonding materials. Collecting samples from environmental sources can include wiping surfaces.

[0036] Samples can be collected from industrial sources. Industrial sources include, for example, clean rooms (e.g., in manufacturing or research facilities), hospitals, medical laboratories, pharmacies, drug compounding centers, drug production materials and facilities, food processing areas, food production areas, water or waste treatment facilities, and foods. For example, one or more pieces of equipment in a medical facility can be a source for collecting samples. Waiting or consulting areas in a medical facility can also be a source for collecting samples. Collecting samples from industrial sources can include wiping surfaces or contacting surfaces with wicking or adhesive materials.

[0037] Collecting samples may include air or water sampling. For example, samples may be collected from ambient air in a facility (e.g., a medical facility or other facility). Samples may be collected from a subject, such as by collecting exhaled or spitted air from a subject. Air samples may include biological pollutants in the air as aerosols. Such pollutants may include bacteria, fungi, viruses, and pollen. Aerosols may be solid or liquid particles suspended in the air, and may vary in size, such as less than about 100 microns (μm), such as less than about 50 μm, 25 μm, 12 μm, 10 μm, 5 μm, 1 μm, 500 nanometers (nm), 200 nm, 100 nm or less. Particles may be composed of a single unattached organism, or may exist in clusters with other materials (such as with other organisms, dust, organic materials, or inorganic materials). The longer the time that particles suspended in the air are suspended in the air, the more they will be oxidized, and the size may become larger as a result. Bacterial cells and viruses in the form of nutrition may be present in the air in smaller numbers than bacteria or fungal spores. Microorganisms in bioaerosols may or may not be alive. For example, the suspension medium, relative humidity, temperature, oxygen sensitivity, and exposure to electromagnetic radiation can affect the survival of microorganisms in the air. Particles from the air can be deposited on surfaces.

[0038] Air sampling may be affected by factors including temperature, time of year, relative humidity, number and characteristics of facility visitors, indoor flow, relative concentrations of particles or organisms, and performance of air handling system components. When analyzing air samples, multiple samples may be collected from the same or similar locations, such as at the same or different times. The collection of multiple samples may facilitate obtaining accurate and precise analysis of microorganisms and viruses within the samples. Air sampling may include the use of a vacuum pump and airflow measurement equipment, such as an anemometer or flow meter. Air sampling may include impact in a liquid (e.g., air is drawn into a sampler and directed against a liquid surface via a small jet), impact on a solid surface (e.g., air is drawn into a sampler and particles are deposited on a dry surface), sedimentation (e.g., particles settle onto a surface via gravity), filtration (e.g., air is drawn through a filtration mechanism and particles of a desired size are retained), centrifugation (e.g., aerosols are subjected to centrifugal forces and impacted onto a solid surface), electrostatic precipitation (e.g., air is drawn onto an electrostatically charged surface and particles become charged), thermal precipitation (e.g., air is drawn over a thermal gradient and particles repelled from the hot surface settle onto a cooler surface), or a combination thereof.

[0039] The collection of samples may include the sampling of liquids (such as water). Water sampling may be performed to detect waterborne pathogens with clinical significance or to determine the water quality in a facility. For example, water sampling may be used to assess contamination in a dialysis system in a medical facility. The microorganisms in the liquid sample may be alive or not alive. The microorganisms in the treated water may be stressed. Water sampling may include adding one or more chemicals to a water source, for example to change the pH of the water. For example, a reducing agent such as sodium thiosulfate may be added to the water to neutralize residual chlorine or other substances in the sample. A chelating agent may be added to chelate metals in a water sample. A liquid (e.g., water) sample may be combined with a medium configured to affect the growth or health of the microorganisms in the sample, such as a recovery medium that may be a nutrient-rich medium. Water collected from a faucet may be collected after flushing a water line. In an example, water may be collected from a faucet, and the accessories of the faucet from which the water is collected may be removed and analyzed in parallel. Collecting a water sample may include collecting at least 100 milliliters of water in one or more containers. The collection of a water sample may include using a plate, such as an aerobic heterotrophic plate. Prior to collecting the sample, the water may be filtered or otherwise treated (eg, to remove bulky contaminants, including dirt and plant particles).

[0040] Collection of samples may include environmental surface sampling. Samples may be collected from surfaces before or after a sterilization or disinfection process. For example, samples may be collected from surfaces after a sterilization or disinfection process to confirm the effectiveness of the sterilization or disinfection procedure. Sample collection may be performed by contacting the surface with a swab, sponge, wipe, agar surface, or membrane filter, any of which may be wetted prior to contacting the surface. Where applicable, neutralizing chemicals may be used to target disinfectant components. Methods of environmental surface sampling include contacting the surface with a wetted swab, sponge, or wipe and rinsing the collection tool; direct immersion; containment; and direct agar contact of replicate organisms.

[0041] The sample can be collected by a technician (e.g., a laboratory or medical technician), a nurse, a doctor, a health worker, an industrial worker, a health and safety specialist, or any other practitioner. The sample can be collected by an individual from an individual, such as by swabbing the composition of the individual's oral cavity or providing sputum or saliva in a container. The sample collected by the individual can be provided to a medical or laboratory facility for analysis. The sample can be collected from a subject in a medical facility such as a doctor's office, a dialysis center, or a hospital.

[0042] The sample can be contacted with a medium to preserve or enhance the microorganisms and viruses contained therein. The sample can be contacted with a material, for example, to facilitate its collection. For example, the sample can be contacted with peptone or buffered peptone water, phosphate buffered saline, sodium chloride, Ringer's solution (e.g., Karlgen Ringer or thiosulfate Ringer's solution), tryptic soy broth, brain heart perfusion broth or other materials. The sample collected on the material, such as a sample collected from the surface, can be subjected to elution, stirring, ultrasonic bath, centrifugation or other treatment to remove the material from the sampling device and break up any agglomerates (e.g., organism agglomerates) that may be contained therein.

[0043] Samples can be collected or transferred to containers such as vials. Samples can be reconstituted with water or a medium such as a medium rich in nutrients. Samples can be distributed in multiple containers. For example, samples can be divided into multiple containers so that the samples contained in different containers can be subjected to different analyses, used as controls, stored for later use or otherwise processed. Samples can be separated immediately when collected, or separated (e.g., separated from the collection site) after storing and / or transferring the sample. Samples can be transferred under freezing or refrigeration or cold or room temperature conditions.

[0044] The sample may include a variety of materials. As described above, the sample may be treated to remove or inactivate a variety of contaminants, including metals, large aggregates or other materials, and chemical contaminants.

[0045] The sample may include one or more microorganisms or viruses or parasites. One or more microorganisms or viruses in the sample may be associated with the sample source, and may not be considered harmful. For example, hundreds of microorganisms are known to coexist in the oral microbiome, and they are present in samples collected from the subject's oral cavity and may not represent a disease state. Such microorganisms may exist with a symbiotic (e.g., endosymbiotic) relationship with the host organism. One or more microorganisms in the sample may be considered as "healthy" or "normal" microorganisms, or may even be considered to be beneficial to health, such as probiotics. Various microorganisms may contribute to immune health, synthesize useful vitamins or ferment indigestible carbohydrates. Alternatively or additionally, one or more microorganisms or viruses in the sample may be associated with a disease, or may be harmful to a population such as a human population. For example, a microorganism or virus may be a pathogen, which may be a pathogenic agent of an infectious disease. Such microorganisms and viruses may be included in the sample at an acceptable level (e.g., at a level that is unlikely to induce disease or infection in a subject or subject group). Classification methods can be used to classify microorganisms and viruses identified using the methods and systems provided herein (e.g., as described herein).

[0046] The sample may comprise one or more cells or tissues. Alternatively, the sample may be substantially cell-free. A sample that is not a cell-free sample may be processed to provide a cell-free sample. A cell-free sample may be derived from any source (e.g., as described herein), such as tissue, blood, sweat, urine, or saliva. As used herein, a "cell-free sample" generally refers to a sample that is substantially free of cells (e.g., less than 10% cells based on volume).

[0047] The sample may contain one or more proteins or polypeptides. The proteins contained in the sample may initially be provided in a tertiary or quaternary structure. Alternatively, the proteins contained in the sample may be provided in a primary or secondary structure, for example, due to partial or complete denaturation of the protein (e.g., when the sample is contacted with a denaturant). The protein may be contained within a cell or tissue. Alternatively, the protein may not be contained within a cell or tissue.

[0048] The terms "polypeptide", "peptide" and "protein" are used interchangeably herein and refer to polymers of amino acids of any length. The polymer may be linear or branched, it may contain modified amino acids, and it may be interrupted by non-amino acids. The term also includes amino acid polymers that have been modified; for example, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation, such as conjugation with a labeling component. As used herein, the term "amino acid" includes natural and / or non-natural or synthetic amino acids, including glycine and both D or L optical isomers, as well as amino acid analogs and peptide mimetics. Amino acids may be proteinogenic or non-proteinogenic. Examples of proteinogenic amino acids include arginine, histidine, lysine, aspartic acid, glutamic acid, serine, threonine, asparagine, glutamine, cysteine, selenocysteine, glycine, proline, alanine, isoleucine, leucine, methionine, phenylalanine, tryptophan, tyrosine, valine, selenocysteine ​​or pyrrolysine. A proteinogenic amino acid can be a genetically encoded amino acid that can be incorporated into a protein during translation. A non-proteinogenic amino acid can be a naturally occurring amino acid or a non-naturally occurring amino acid. Non-proteinogenic amino acids include amino acids that are not found in proteins and / or are not naturally encoded or found in the genetic code of an organism. Examples of non-proteinogenic amino acids include, but are not limited to, hydroxyproline, selenomethionine, homolysine, 2-aminoisobutyric acid, αγ-aminobutyric acid, ornithine, citrulline, β-alanine (3-aminopropionic acid), δ-aminolevulinic acid, 4-aminobenzoic acid, dehydroalanine, carboxyglutamic acid, pyroglutamic acid, norvaline, norleucine, alloisoleucine, tert-leucine, hexahydropyridine, allothreonine, homocysteine, homoserine, α-amino-n-heptanoic acid, α,β-diaminopropionic acid, α,γ-diaminobutyric acid, β-amino-n-butyric acid, β-aminoisobutyric acid, isovaline, sarcosine, N-ethylglycine, N-propylglycine, N-isopropylglycine, N-methylalanine, N-ethylalanine, N-methylβ-alanine, N-ethylβ-alanine, isoserine, and α-hydroxy-γ-aminobutyric acid.

[0049] The sample may comprise one or more nucleic acid molecules, such as one or more deoxyribonucleic acid (DNA) and / or ribonucleic acid (RNA) molecules (e.g., included in the cell or not included in the cell). The sample may comprise RNA or consist essentially of RNA. The sample may comprise DNA or consist essentially of DNA. The nucleic acid molecule may be contained in the cell. Alternatively or additionally, the nucleic acid molecule may not be included in the cell (e.g., cell-free nucleic acid molecules). Cell-free polynucleotides may be extracellular polynucleotides present in the sample (e.g., a sample from which cells have been removed, a sample that has not been subjected to a lysis step, or a sample that has been treated to separate cellular polynucleotides from extracellular polynucleotides). For example, cell-free polynucleotides include polynucleotides released into the circulation upon cell death, and may be isolated as cell-free polynucleotides from the plasma fraction of a blood sample.

[0050] The term "nucleic acid molecule" is used interchangeably with the terms "polynucleotide", "nucleotide sequence", "nucleic acid", "nucleic acid fragment" and "oligonucleotide" herein. They generally refer to a polymeric form of nucleotides of any length (e.g., deoxyribonucleotides (dNTPs), ribonucleotides (rNTPs), their analogs or mixtures thereof), in which the 3' position of the pentose of one nucleotide is joined to the 5' position of the pentose of the next nucleotide via a phosphodiester group. Polynucleotides may have any three-dimensional structure and may perform any known or unknown function. Nucleic acid molecules may have a length of at least about 10 nucleic acid bases ("bases"), 20 bases, 30 bases, 40 bases, 50 bases, 100 bases, 200 bases, 300 bases, 400 bases, 500 bases, 1 kilobase (kb), 2kb, 3kb, 4kb, 5kb, 10kb, 50kb or more. Oligonucleotides may be composed of a specific sequence of the following four nucleotide bases: adenine (A), cytosine (C), guanine (G) and thymine (T) (when the polynucleotide is RNA, uracil (U) represents thymine (T)). Oligonucleotides may include one or more non-standard nucleotides, nucleotide analogs and / or modified nucleotides. Non-limiting examples of polynucleotides include deoxyribonucleic acid (DNA), genomic DNA, ribonucleic acid (RNA), cell-free DNA (e.g., cfDNA), synthetic DNA / RNA, coding or non-coding regions of genes or gene fragments, sites (locus) defined by linkage analysis, exons, introns, messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA (rRNA), short interfering RNA (siRNA), short hairpin RNA (shRNA), micro RNA (miRNA), ribozymes, cDNA, recombinant polynucleotides, branched polynucleotides, plasmids, vectors, isolated DNA of any sequence, isolated RNA of any sequence, nucleic acid probes and primers. The polynucleotide may comprise one or more modified nucleotides, such as methylated nucleotides and nucleotide analogs. If present, modifications to the nucleotide structure may be imparted before or after polymer assembly. The nucleotide sequence may be interrupted by non-nucleotide components. The polynucleotide may be further modified after polymerization, such as by conjugation with a labeling component.

[0051] The nucleic acid can be a target nucleic acid or a sample nucleic acid. The target nucleic acid can be amplified to produce an amplification product. The target nucleic acid can be, for example, a target DNA or a target RNA. The target nucleic acid can be provided in a biological sample.

[0052] Nucleic acid molecules are composed of multiple nucleotides. During the sequencing procedure, nucleotides can be provided to the nucleic acid template for incorporation, and the detection of incorporation events is used to determine the sequence of the nucleic acid template (for example, as described herein). As used herein, the term "nucleotide" generally refers to a substance comprising a base (for example, a core base), a sugar moiety, and a phosphate moiety. Nucleotide can include a free base connected with a phosphate group. The material comprising a base with three connected phosphate groups can be referred to as nucleoside triphosphates. When nucleotides are added to the growing nucleic acid molecule chain, the formation of the phosphodiester bond between the proximal phosphate of the nucleotide and the growing chain can be accompanied by the hydrolysis of a high-energy phosphate bond and the release of two distal phosphates as pyrophosphate. Nucleotide can be naturally occurring or non-naturally occurring (for example, modified or engineered nucleotides).

[0053] As used herein, the term "nucleotide analog" may include, but is not limited to, nucleotides that may or may not be naturally occurring nucleotides. For example, a nucleotide analog may be derived from and / or include structural similarities to a typical nucleotide, such as adenine-(A), thymine-(T), cytosine-(C), uracil-(U), or guanine-(G). A nucleotide analog may contain one or more differences or modifications relative to a natural nucleotide. Examples of nucleotide analogs include inosine, diaminopurine, 5-fluorouracil, 5-bromouracil, 5-chlorouracil, 5-iodouracil, hypoxanthine, xanthine, deazaxanthine, deazaguanine, isocytosine, isoguanine, 4-acetylcytosine, 5-(carboxyhydroxymethyl)uracil, 5-carboxymethylaminomethyl-2-thiouridine, 5-carboxymethylaminomethyluracil, dihydrouracil, β- D-galactosyl braid, N6-isopentenyl adenine, 1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-adenine, 7-methylguanine, 5-methylaminomethyluracil, 5-methoxyaminomethyl-2-thiouracil, β-D-mannosyl braid, 5-methoxycarboxymethyl Uracil, 5-methoxyuracil, 2-methylthio-D46-isopentenyladenine, uracil-5-oxyacetic acid (v), wybutoxosine, pseudouracil, queosine, 2-mercaptocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, 5-methyluracil, uracil-5-oxyacetic acid methyl ester, uracil-5-oxyacetic acid (v), 5-methyl-2-thiouracil, 3-(3-amino-3-N-2-carboxypropyl)uracil, (acp3)w, 2,6-diaminopurine, ethynyl nucleotide base, 1-propynyl nucleotide base, azido nucleotide base, acid phosphoselenone nucleic acid, and modified forms thereof (e.g., by oxidation, reduction and / or addition of substituents such as alkyl, hydroxyalkyl, hydroxyl or halogen moieties). Nucleic acid molecules (e.g., polynucleotides, double-stranded nucleic acid molecules, single-stranded nucleic acid molecules, primers, adapters, etc.) can be modified at the base portion (e.g., at one or more atoms that can be used to form hydrogen bonds with complementary nucleotides and / or at one or more atoms that cannot form hydrogen bonds with complementary nucleotides), sugar moieties, or phosphate backbones. In some embodiments, nucleotides may include modifications in their phosphate moieties, including modifications to triphosphate moieties. In addition, non-limiting examples of modifications include phosphate chains of greater length (e.g., phosphate chains with 4, 5, 6, 7, 8, 9, 10 or more phosphate moieties), modification with thiol moieties (e.g., α-thiotriphosphates and β-thiotriphosphates), and modification with selenium moieties (e.g., selenophosphate nucleic acids).Nucleotide or nucleotide analogue can comprise the sugar selected from the group consisting of the following items: ribose, deoxyribose and their modified forms (e.g., by oxidation, reduction, and / or addition of substituents such as alkyl, hydroxyalkyl, hydroxyl or halogen moieties). Nucleotide analogues can also comprise modified linker moieties (e.g., replacing phosphate moieties). Nucleotide analogues can also contain amine-modified groups, such as aminoallyl-dUTP (aa-dUTP) and aminohexyl acrylamide-dCTP (aha-dCTP), to allow the covalent attachment of amine-reactive moieties such as N-hydroxysuccinimide esters (NHS). The alternatives to standard DNA base pairs or RNA base pairs in the oligonucleotides of the present disclosure can provide, for example, higher density (in bits per cubic mm), higher safety (resistance to accidental or purposeful synthesis of natural toxins), easier to distinguish and / or lower secondary structures in light-programmed polymerases. Nucleotide analogues can be capable of reacting or combining with detectable moieties for nucleotide detection (e.g., during sequencing processes, as described herein).

[0054] The methods and systems provided herein may include the preparation, use or processing of one or more controls. The control may be collected in the same or different manner as the sample (e.g., as described herein) and may include similar or different contents. For example, the sample and the control may be collected from the same source at the same or different time in the same manner. The sample may be subjected to a first treatment scheme, and the control may be subjected to a second treatment scheme different from the first treatment scheme, or may not be subjected to any substantial treatment. Alternatively or additionally, the control may be prepared by one or more known entities and / or include one or more known entities. For example, the control may include one or more known microorganisms and / or pathogens; in some embodiments, this type of control may be used as an external control. In some embodiments, an internal control may be included to ensure that the assay works, and all reagents show appropriate functions. The control may be processed separately from the sample, or the control may be added to the sample and processed together with the sample. The sample and the control may be subjected to parallel processing, and the comparison between the information obtained about the sample and the control may be used to determine whether the sample includes the same one or more known microorganisms and / or pathogens, and / or to evaluate a laboratory or computational process. For example, the control may include a first microorganism and a second microorganism, and the sample may be suspected of including one or both of the first microorganism and the second microorganism. Can use identical method, reagent and calculation scheme to carry out parallel processing to identify the microorganism that comprises with contrast and sample.To the successful identification of the first microorganism in contrast and the second microorganism and optional quantitative can show that the method and system for handling sample and contrast can effectively handle sample to identify the microorganism that comprises.Similarly, in contrast, the unsuccessful identification of the first microorganism and / or the second microorganism and / or optional quantitative, such as only identifying the single microorganism or the incorrect quantitative of microorganism in the first microorganism and the second microorganism, can show that the method and / or system for handling sample and contrast needs calibration, threshold value adjustment, improved database management or another improvement.The first microorganism and the second microorganism in successful identification and optional quantitative contrast also can be used for identification and / or the given microorganism in the quantitative sample.

[0055] One or more controls can be used to compare with a given sample. For example, a single control can be queried in parallel with a given sample or sample set. Alternatively, multiple controls can be queried in parallel with a given sample or sample set. For example, multiple controls including multiple different known sequences or entities or combinations thereof can be used.

[0056] In some embodiments, 10 or more controls, 100 or more controls, 1000 or more controls, 10,000 or more controls, 100,000 or more controls, or 1×10 6 One or more controls, each control representing a different known sequence.

[0057] In an example, a sample suspected of including a first entity and a second entity (e.g., a first microorganism and a second microorganism) may be queried in parallel with a control known to include the first entity and the second entity or their nucleic acid or amino acid sequences. Alternatively or additionally, a sample suspected of including a first entity and a second entity (e.g., a first microorganism and a second microorganism) may be queried in parallel with a first control known to include the first entity or its nucleic acid or amino acid sequence and a second control known to include the second entity or its nucleic acid or amino acid sequence. Alternatively or additionally, a sample suspected of including a first entity and a second entity (e.g., a first microorganism and a second microorganism) may be queried in parallel with a first control known to include the first entity or its nucleic acid or amino acid sequence, a second control known to include the second entity or its nucleic acid or amino acid sequence, and a third control known to include the first entity and the second entity or their nucleic acid or amino acid sequence. Alternatively or additionally, a sample suspected of including a first entity and a second entity (e.g., a first microorganism and a second microorganism) may be queried in parallel with a first control known to include the first entity and the second entity or their nucleic acid or amino acid sequence and a second control known to not include the first entity or the second entity or their nucleic acid or amino acid sequence.

[0058] The control may comprise a physical sample that has been processed and analyzed (e.g., as described herein). Alternatively or additionally, the control may comprise a control data set comprising a control set of nucleic acid and / or amino acid sequences. For example, the control may comprise a control set of nucleic acid sequences, amino acid sequences, and / or weighted k-mers associated with a control set of nucleic acid or amino acid sequences (e.g., as described herein), which sequences and / or weighted k-mers may correspond to one or more known entities, such as one or more microorganisms. In some embodiments, the control set is a control set of nucleic acid sequences and comprises 10 or more nucleic acid sequences, 100 or more nucleic acid sequences, 1000 or more nucleic acid sequences, 10,000 or more nucleic acid sequences, 100,000 or more nucleic acid sequences, or 1×10 6 In some embodiments, the control set is a control set of amino acid sequences and comprises 10 or more amino acid sequences, 100 or more amino acid sequences, 1000 or more amino acid sequences, 10,000 or more amino acid sequences, 100,000 or more amino acid sequences, or 1×10 6 In some embodiments, the control set is a weighted k-mer control set and comprises 1000 or more weighted k-mers, 10,000 or more weighted k-mers, 100,000 or more weighted k-mers, 1×10 6 or more weighted k-mers, 1×10 7or more weighted k-mers, or 1×10 8 One or more weighted k-mers. Such a data set may have been derived, for example, by a user through an experiment. For example, a user may prepare and process a control sample to provide a control comprising a control data set, the control data set comprising a known set of nucleic acid and / or amino acid sequences, and / or weighted k-mers associated with a known set of nucleic acid and / or amino acid sequences. Alternatively or additionally, a control comprising such a data set may be derived from one or more reference databases (e.g., as described herein).

[0059] The procedure for processing samples may involve the storage and / or transfer of samples. For example, samples may be stored for a period of time after they are collected. Samples may be stored in any useful container for any useful time and under any useful conditions. Samples may be stored, for example, for at least 1 hour, such as at least about 2 hours, 4 hours, 6 hours, 10 hours, 12 hours, 24 hours, 48 ​​hours, 72 hours, 1 week or longer. Samples may be stored in containers in which samples are collected or initially provided. Alternatively, samples may be transferred to one or more different containers for storage. Samples may be stored at room temperature. Alternatively or additionally, samples may be stored in an incubator or in a refrigerator or freezer system. For example, biological samples (e.g., blood samples) may be stored in a refrigerator or freezer until they can be analyzed. For example, samples may be stored at a temperature of up to about 15°C, 10°C, 5°C, 0°C, -5°C or lower.

[0060] The sample can be prepared by combining a first material (e.g., as described herein) and a second material. The first material and the second material can be collected from a subject or source at the same or different times (e.g., the same subject or source). Alternatively or additionally, the sample collected from a subject or source can be subdivided into two or more parts (e.g., for analysis at different times or via different processes).

[0061] The sample may be subjected to one or more processes, including, for example, purification, extraction, filtration, selective precipitation, permeabilization, separation, heating, stirring or centrifugation. One or more such processes may be performed before the sample is subjected to storage and / or analysis as provided herein. Alternatively or additionally, one or more such processes may be performed after the sample has been stored for a period of time, and optionally before the sample is stored for another period of time. The sample may be processed to remove the aggregates of microorganisms and viruses and / or to disaggregate the agglomerates. For example, the sample may be subjected to one or more filtrations, stirring or centrifugation processes to process the agglomerates or agglomerates contained therein. The sample may be reconstructed with a material or medium (such as a growth medium) configured to affect the survival of the microorganisms therein. Alternatively, the sample may be combined with a material configured to kill the microorganisms therein. The sample may be combined with one or more materials to preserve or change aspects of the sample, such as preservatives, buffers or detergents.

[0062] Samples can be transferred between containers before, during or after storage or any treatment described herein. For example, samples can be divided equally to provide multiple samples for one or more different analyses. Samples can be transported from a collection point to a storage point, a processing point and / or an analysis point, any of which can be the same or different. For example, samples can be collected at a first point and transferred to a second point different from the first point for analysis. In another example, samples can be collected in a facility such as a medical facility, optionally stored, and ultimately analyzed in the same facility. Collection and analysis in the same facility can contribute to accurate, precise and rapid detection of materials contained in the sample.

[0063] The sample may be de-identified before, during or after any treatment, and optionally before performing an analysis as provided herein. The de-identification of the sample may include obfuscating the identification information of the sample, such as the subject or source from which the sample was collected, or their detailed information; the time of collection; the place of collection; or other detailed information. This may be performed by assigning an identification code such as a barcode or QR code to the sample. Information linking the identification information of the sample to the identification code may also be stored in a database. The database may be configured to be inaccessible to all or some users to ensure that the identification information of the sample is not readily available to the user. The de-identification of the sample can help ensure that the content that the sample may or may not contain is not considered in advance when analyzing the sample, and also helps to protect the confidentiality of the subject (e.g., patient) in a medical environment.

[0064] Preparing a sample for analysis according to the methods provided herein may include lysing or infiltrating cells (e.g., by contacting the sample with a lysing agent or permeabilizing agent), degrading tissues, and denaturing proteins and nucleic acid molecules (e.g., by contacting the sample with a denaturing agent such as a detergent) to prepare a sample for analysis (e.g., including an internal control material). Sample preparation may also include extracting nucleic acid molecules and / or polypeptides in the sample. For example, sample preparation may include contacting the sample with a reagent configured to degrade the lipid envelope and / or protein coat (e.g., capsid) of the virus to provide access to the genetic material therein. The sample may be divided before such preparation to provide a first aliquot and a second aliquot, which may be processed in parallel but differently from the first aliquot and the second aliquot. For example, the first aliquot may be processed to extract and preserve nucleic acid molecules, while the second aliquot may be processed to extract and preserve polypeptides.

[0065] Nucleic acid sequencing preparation

[0066] The procedure for processing a sample or a portion thereof may involve nucleic acid sequencing. For example, a sample may be processed to extract nucleic acid molecules from cells and viruses and identify nucleic acid sequences associated therewith. Nucleic acid sequencing may be performed in any useful facility using any useful method and by any useful personnel.

[0067] A variety of methods can be used to extract and / or purify nucleic acid molecules from a sample. For example, an organic extraction method can be used to purify nucleic acids. Other non-limiting examples of extraction techniques include: (1) organic extraction followed by ethanol precipitation, for example, using phenol / chloroform organic reagents, with or without an automated nucleic acid extractor, for example, a Model 341 DNA Extractor purchased from Applied Biosystems (Foster City, Calif.); (2) stationary phase adsorption methods; and (3) salt-induced precipitation methods, which are often referred to as "salting-out" methods. Another example of nucleic acid separation and / or purification includes the use of magnetic particles to which nucleic acids can bind specifically or non-specifically, followed by separation of beads and washing using a magnet, and elution of nucleic acids from the beads. An enzymatic digestion step can be performed prior to the separation method to help eliminate unwanted proteins from the sample, such as digestion with proteinase K and / or other similar proteases. If desired, an RNase inhibitor can be added to the lysis buffer. For certain cell or sample types, it may be necessary to add a protein denaturation / digestion step to the protocol. The purification method may involve separation of DNA, RNA, or both. When both DNA and RNA are separated together during or after the extraction process, further steps may be used to purify one or both of them. The extracted nucleic acids may also be produced into subfractions, for example, purified by size, sequence or other physical or chemical methods.

[0068] Nucleic acid molecules can be contacted with one or more adapters or primers to prepare nucleic acid molecules for amplification and / or sequencing processes. As used herein, the terms "adaptor" ("adaptor" and "adapter") are used interchangeably and generally refer to oligonucleotides that can be connected to the ends of nucleic acids. The adapter sequence can include, for example, a priming site, a complement of a priming site, a recognition site for an endonuclease, a common sequence, a promoter, a barcode sequence, a sequencing primer, and a flow cell attachment sequence. The adapter can also incorporate modified nucleotides that modify the properties of the adapter sequence. For example, a thiophosphate group can be incorporated into one of the adapter chains. The adapter can be double-stranded or single-stranded. For example, an adapter coupled to a single-stranded nucleic acid chain can be a single-stranded adapter, and an adapter coupled to a double-stranded nucleic acid molecule can be a double-stranded adapter. The adapter can have any useful length. For example, the adapter may have at least 2, 4, 6, 8, 10, 12, 14, 16, 18, 20 or more nucleotides (e.g., in a given chain). The nucleic acid molecule may include a first adapter at the first end and a second adapter at the second end. For example, a double-stranded nucleic acid molecule may include a first adapter at the first end and a second adapter at the second end, wherein the first adapter and the second adapter include the same nucleic acid sequence (e.g., on the opposite chain). The adapter can be coupled to the nucleic acid molecule in a variety of ways, such as by connection (e.g., flat end connection) or hybridization. The adapter may be configured to promote the amplification of nucleic acid molecules in a nucleic acid amplification reaction. Alternatively or additionally, the adapter may be configured to promote sequencing in a sequencing reaction (e.g., the adapter may include a flow cell or a sequencing adapter).

[0069] The nucleic acid molecules of the sample may undergo an amplification or target enrichment procedure before the sequencing reaction to increase the detectable population of nucleic acid molecules in the sample. Alternatively, the nucleic acid molecules of the sample may not be amplified before sequencing. The terms "amplifying", "amplification" and "nucleic acid amplification" are used interchangeably herein and generally refer to the generation of one or more copies of a nucleic acid or template. For example, the "amplification" of DNA generally refers to the generation of one or more copies of a DNA molecule. An amplicon may be a single-stranded or double-stranded nucleic acid molecule that is generated from a starting template nucleic acid molecule (e.g., a target nucleic acid molecule) by an amplification procedure. Such an amplification procedure may include one or more cycles of extending or connecting sequencing. An amplicon may include a nucleic acid chain, at least a portion of which may be substantially identical or substantially complementary to at least a portion of the starting template. When the starting template is a double-stranded nucleic acid molecule, an amplicon may include a nucleic acid chain that is substantially identical to at least a portion of one chain and substantially complementary to at least a portion of either chain. An amplicon may be single-stranded or double-stranded, regardless of whether the starting template is single-stranded or double-stranded. The amplification of nucleic acids may be linear, exponential or a combination thereof. Amplification can be based on emulsion or can be based on non-emulsion.Non-limiting examples of nucleotide amplification methods include reverse transcription, primer extension, polymerase chain reaction (PCR), ligase chain reaction (LCR), helicase-dependent amplification, bridge amplification, template walking / wildfire amplification, amplification based on nanoballs, asymmetric amplification, rolling circle amplification and multiple substitution amplification (MDA), enrichment based on nucleic acid hybridization capture.When using PCR, any form of PCR can be used, wherein non-limiting examples include real-time PCR, allele-specific PCR, assembly PCR, asymmetric PCR, digital PCR, emulsified PCR, dial-out PCR, helicase-dependent PCR, nested PCR, hot start PCR, inverse PCR, methylation-specific PCR, miniprimer PCR, multiplex PCR, nested PCR, overlap extension PCR, thermal asymmetric staggered PCR and touchdown PCR.In addition, amplification can be carried out in a reaction mixture comprising the various components (for example, primers, templates, nucleotides, polymerases, buffer components, cofactors, etc.) that participate in or promote amplification. In some embodiments, the reaction mixture comprises a buffer that allows for incorporation of context-independent nucleotides, such as, for example, magnesium ions, manganese ions, and isocitrate buffer. The amplification can be clonal amplification. Clonal amplification can provide a concentrated population of nucleic acid molecules comprising the same sequence.

[0070] In an example, a multiplex PCR process can be used to amplify nucleic acid molecules. The amplification process may include multiple biotinylated asymmetric PCR. These methods can make it possible to simultaneously sequence thousands of regions of interest corresponding to nucleic acid molecules from nucleic acid samples. The sensitivity of detecting low amounts of targets in samples is driven by multiplex PCR, and subsequent asymmetric PCR provides increased specificity. Logical partitioning and directional considerations can be used to promote these processes. Such methods can allow high-throughput sequencing of various target sequences without the need to use ligation or enzymatic digestion methods. Examples of such amplification methods are at least described in PCT / US2018 / 060915, the entire contents of which are incorporated herein by reference in their entirety.

[0071] Amplification may involve the use of a polymerase. As used herein, the term "polymerase" or "polymeric enzyme" generally refers to any enzyme capable of catalyzing a polymerization reaction. A polymerase can be used to extend a nucleic acid primer coupled to a template nucleic acid chain by incorporating nucleotides or nucleotide analogs. A polymerase can extend a nucleic acid chain by extending, for example, the 3' end of an existing nucleotide chain, adding new nucleotides that match the template chain one at a time via the generation of a phosphodiester bond. A polymerase may have strand displacement activity or non-strand displacement activity. A polymerase may be a nucleic acid polymerase. A polymerase may have high continuous synthesis ability (e.g., the ability to continuously incorporate nucleotides into a nucleic acid template without releasing the nucleic acid template). A polymerase can incorporate modified nucleotides and dideoxynucleotide triphosphates. A polymerase may have modified nucleotide binding, which can be used for nucleic acid sequencing. Examples of polymerases include, but are not limited to, DNA polymerases, RNA polymerases, thermostable polymerases, wild-type polymerases, modified polymerases, Escherichia coli (E. coli) DNA polymerase I, T7 DNA polymerase, bacteriophage T4 DNA polymerase Φ29 (phi29) DNA polymerase, Taq polymerase, Tth polymerase, Tli polymerase, Pfu polymerase, Pwo polymerase, VENT polymerase, DEEPVENT polymerase, EX-Taq polymerase, LA-Taq polymerase, Sso polymerase, Poc polymerase, Pab polymerase, Mth polymerase, ES4 polymerase, Tru polymerase, Tac polymerase, Tne polymerase, Tma polymerase, Tea polymerase, Tih polymerase, Tfi polymerase, Platinum polymerase Taq polymerase, Tbr polymerase, Tfl polymerase, Pfu-turbo polymerase, Pyrobest polymerase, Pwo polymerase, KOD polymerase, Bst polymerase, Sac polymerase, Klenow fragment, polymerase with 3' to 5' exonuclease activity and their variants, modified products and derivatives. The polymerase can be, for example, a family A or family B polymerase. Examples of family A polymerases include, but are not limited to, Taq, Klenow and Bst polymerases. Examples of family B polymerases include, but are not limited to, Vent (exo-) and Therminator polymerases.

[0072] Nucleotides and nucleotide analogs (eg, as described herein) can be used in nucleic acid amplification reactions. For example, nucleic acid molecules can be amplified using canonical nucleotides, modified nucleotides (eg, nucleotide analogs), or a combination thereof.

[0073] The coupling of adapters to nucleic acid molecules and / or nucleic acid amplification can rely on sequence complementarity and / or can produce nucleic acid chains comprising complementary sequences. As used herein, the term "complementarity" generally refers to the ability of a nucleic acid to form hydrogen bonds with another nucleic acid sequence by traditional Watson-Crick or other non-traditional types. The complementarity percentage represents the percentage of residues in a nucleic acid molecule that can form hydrogen bonds (e.g., Watson-Crick base pairing) with a second nucleic acid sequence (e.g., 5, 6, 7, 8, 9, 10 out of 10 are 50%, 60%, 70%, 80%, 90% and 100% complementary, respectively). "Completely complementary" means that all consecutive residues of a nucleic acid sequence will form hydrogen bonds with the same number of consecutive residues in a second nucleic acid sequence. As used herein, "substantially complementary" refers to a degree of complementarity of at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or 100% over a region of 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50 or more nucleotides, or refers to two nucleic acids that hybridize under stringent conditions. As used herein, the term "complementary sequence" generally refers to a sequence that hybridizes with another sequence. Hybridization between two single-stranded nucleic acid molecules may involve the formation of a double-stranded structure that is stable under certain conditions. If two single-stranded polynucleotides are bound to each other by two or more sequentially adjacent base pairings, they may be considered to be hybridized. In a chain of double-stranded structure, a considerable proportion of nucleotides can be carried out Watson-Crick base pairing with the nucleoside on another chain.Hybridization can also include the pairing of nucleoside analogs (such as deoxyinosine, nucleoside and 2-aminopurine bases etc.) that can be used to reduce the degeneracy of probe, no matter whether this pairing relates to the formation of hydrogen bond.Sequence identity, such as for the purpose of evaluating complementarity percentage, can be measured by any suitable comparison algorithm, including but not limited to Needleman-Wunsch algorithm (see, for example, the EMBOSS Needle alignment device that can be obtained at www.ebi.ac.uk / Tools / psa / emboss_needle / nucleotide.html, optionally with default setting), BLAST algorithm (see, for example, the BLAST alignment tool that can be obtained at blast.ncbi.nlm.nih.gov / Blast.cgi, optionally with default setting), or Smith-Waterman algorithm (see, for example, the EMBOSS Water alignment device that can be obtained at www.ebi.ac.uk / Tools / psa / emboss_water / nucleotide.html, optionally with default setting).Optimal alignment can be assessed using any suitable parameters of a chosen algorithm, including default parameters.

[0074] The amplification process can be performed in solution. Amplification can be performed when the nucleic acid molecules are fixed to a surface, such as the surface of a particle or a surface (e.g., a chip or a flow cell). Alternatively or additionally, amplification can be performed in a compartment such as a well or a droplet (e.g., emulsion PCR). Amplification can be performed in a sequencing instrument. Alternatively, amplification can be performed before the amplified nucleic acid molecules are provided to the sequencing instrument.

[0075] Protein sequencing preparation

[0076] The procedure for processing the sample or a portion thereof may involve protein sequencing. For example, the sample may be processed to extract proteins from cells and viruses and identify polypeptide and / or amino acid sequences associated therewith. Protein sequencing may be performed in any useful facility using any useful method and by any useful personnel.

[0077] The sample containing the protein can be subjected to the Edman degradation process to prepare the protein for sequencing using the Edman sequencer process. The Edman sequencer is capable of sequencing peptide fragments of about 50 amino acids or longer. The preparation method may include contacting the solution containing the protein with a reducing agent such as 2-mercaptoethanol to destroy disulfide bonds. Protective groups (e.g., iodoacetic acid) may be provided to prevent the reformation of bonds. The single chain of the protein can be separated and purified, and the amino acid composition of each chain can be determined. The terminal amino acids of each chain can also be determined. Each chain can be broken into fragments, such as fragments below 50 amino acids in length. These fragments can be separated and purified. The sequence of each fragment can be determined. The process can be repeated with different cleavage patterns, and the sequence of the total protein can be constructed subsequently.

[0078] Protein sequencing can include separating proteins in a sample, such as using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) or chromatography. The separated proteins can be chemically modified to stabilize various residues, such as cysteine ​​residues. The proteins can be digested (e.g., with one or more proteases such as trypsin) to produce a variety of peptides. The peptides can be desalted to remove ionizable contaminants. The peptides can then be subjected to a sequencing process (e.g., as described herein).

[0079] Sequence identification

[0080] The procedure for processing a sample may involve identifying sequences of nucleic acid molecules and / or proteins contained within the sample or its derivatives. The sequences of nucleic acid molecules and proteins may be identified to determine the presence or absence of, for example, microorganisms and viruses in the sample. Identifying sequences of nucleic acid molecules and proteins may include performing one or more sequencing processes.

[0081] As used herein, the terms "nucleic acid sequencing" and "sequencing" generally refer to a process for generating or identifying the sequence of a biomolecule (such as a nucleic acid molecule or a polypeptide). Such a sequence may be a nucleic acid sequence, which may include a sequence of nucleic acid bases (e.g., nucleobases). The sequence may be a polypeptide sequence, which may be a sequence of amino acids. Sequencing may be, for example, single molecule sequencing, sequencing by synthesis, sequencing by hybridization, or sequencing by ligation. Sequencing may be performed using a template nucleic acid molecule immobilized on a support such as a flow cell or one or more beads. A sequencing assay may generate one or more sequencing reads corresponding to one or more template nucleic acid molecules. Sequencing a polypeptide may include, for example, an Edman degradation process, de novo sequencing, mass spectrometry, or a combination thereof.

[0082] As used herein, the term "sequence identity" generally refers to the precise nucleotide-to-nucleotide or amino acid-to-amino acid correspondence of two polynucleotides or polypeptide sequences, respectively. Typically, the technology used to determine sequence identity includes determining the nucleotide sequence of a polynucleotide and / or determining the amino acid sequence encoded therein, and comparing these sequences with a second nucleotide or amino acid sequence. Two or more sequences (e.g., polynucleotides or amino acid sequences) can be compared by measuring their "percent identity" to each other. The percent identity of two sequences (whether nucleic acid or amino acid sequence) is the number of exact matches between the sequences of the two comparisons divided by the length of the shorter sequence and multiplied by 100. Percent identity can also be determined, for example, by using a database or program (such as advanced BLAST computer programs, including version 2.2.9, available from the National Institutes of Health) to compare sequence information. The BLAST program is based on the alignment method of Karlin and Altschul, Proc. Natl. Acad. Sci. USA 87: 2264-2268 (1990), and as discussed in Altschul et al., J. Mol. Biol. 215: 403-410 (1990); Karlin and Altschul, Proc. Natl. Acad. Sci. USA 90: 5873-5877 (1993); and Altschul et al., Nucleic Acids Res. 25: 3389-3402 (1997). In short, the BLAST program defines identity as the number of identical alignment symbols (e.g., nucleotides or amino acids) divided by the total number of symbols of the shorter sequence in the two sequences. The program can be used to determine the percent identity of the entire length of the compared proteins. Default parameters can be provided to optimize searches with short query sequences, such as in the BLASTp program. The program also allows the use of SEG filters to mask fragments of the query sequence, as determined by the SEG program of Wootton and Federhen, Computers and Chemistry 17:149-163 (1993). The desired degree of sequence identity can range from about 80% to 100% and integer values ​​therebetween (e.g., about 80% to about 90%, about 80% to about 95%, about 80% to about 100%, about 85% to about 90%, about 85% to about 95%, about 85% to about 100%, about 90% to about 95%, about 90% to about 100%, or about 95% to about 100%). In general, an exact match means 100% identity over the length of the shortest sequence being compared (or over the lengths of the two sequences, if identical).

[0083] Prior to performing the sequencing process, the sample can be divided into one or more parts. For example, the sample can be divided into a first part for nucleic acid processing and a second part for polypeptide sequencing. The first part and / or the second part can be further subdivided to provide additional sample aliquots for control, storage and / or additional analysis.

[0084] Nucleic acid and protein sequencing can provide complementary information. For example, nucleic acid sequencing can provide in-depth insight into the genes that a cell or organism may express and the proteins that may be produced. Similarly, protein sequencing can provide in-depth insight into the mRNA that may have been included in a given cell or organism. As used herein, "expression" generally refers to the process by which a polynucleotide is transcribed from a DNA template (such as being transcribed into mRNA or other RNA transcripts) and / or the process by which the transcribed mRNA is subsequently translated into peptides, polypeptides, or proteins. Transcripts and encoded polypeptides can be collectively referred to as "gene products." If a polynucleotide is derived from genomic DNA, expression can include the splicing of mRNA in a eukaryotic cell.

[0085] As used herein, the term "differential expression" as applied to a nucleotide sequence or polypeptide sequence in a subject generally refers to overexpression or underexpression of the sequence when compared to the sequence detected in a control. Underexpression also includes the absence of expression of a particular sequence, as evidenced by the absence of detectable expression in a test subject when compared to a control.

[0086] As used herein, "control" generally refers to an alternative subject or sample used in an experiment for comparison purposes.

[0087] The sequencing information of a single sample or multiple samples can be collected. For example, the sequencing information of multiple samples can be collected simultaneously or at different times. The sequencing information collected for multiple samples combined for data processing is optionally after the sequencing information of each different sample is associated with an identification code. Multiple samples can be sequenced simultaneously and processed and distinguished by different identifiers, or multiple samples can be sequenced in the same sequencing process, but loaded at different times.

[0088] Sequencing of nucleic acid molecules

[0089] The nucleic acid molecules of the sample can be interrogated to determine their nucleic acid sequences. For example, nucleic acid sequences of DNA and RNA can be used to identify the source from which they are derived, such as the virus or microorganism from which they are derived. The nucleic acid sequences identified in the sample can be compared with sequences in a database to associate them with the source from which they are derived (e.g., as described herein).

[0090] Nucleic acid sequencing can be performed on a sample or part thereof that has undergone a nucleic acid amplification process. Alternatively, sequencing can be performed on a sample or part thereof that has not undergone a nucleic acid amplification process. Prior to sequencing, the nucleic acid molecules in the sample or part thereof can be fragmented. Alternatively, the nucleic acid molecules may not be fragmented before sequencing. A variety of different schemes can be used to identify the nucleic acid sequence in a sample.

[0091] Different types of nucleic acid molecules may undergo the same or different processing and sequencing. For example, a DNA molecule may undergo a first sequencing process, and an RNA molecule may undergo a second sequencing process, wherein the first sequencing process and the second sequencing process may include at least one process difference. In an example, genomic DNA (such as accessible chromatin) is processed according to a first sequencing method (e.g., using a determination performed by sequencing (ATAC-seq) method for transposase accessible chromatin), while RNA molecules are processed according to a second sequencing method (e.g., targeting RNA molecules including polyA sequences, such as sequencing methods for messenger RNA (mRNA) molecules). Different sequencing procedures may be performed on the same or different samples. For example, a first sequencing method may be performed on the same sample (e.g., at the same or different times) to analyze a first type of nucleic acid molecule, and a second sequencing method may be performed to analyze a second type of nucleic acid molecule, wherein the first sequencing method and the second sequencing method are different, and the first type of nucleic acid molecule and the second type of nucleic acid molecule are different. Alternatively or additionally, a first sequencing method can be performed using a first sample to analyze a first type of nucleic acid molecule, and a second sequencing method can be performed using a second sample to analyze a second type of nucleic acid molecule, wherein the first sequencing method and the second sequencing method are different, the first type of nucleic acid molecule and the second type of nucleic acid molecule are different, and the first sample and the second sample are different. The first sample and the second sample can be aliquots of the same sample (e.g., as described herein).

[0092] Nucleic acid sequencing may be quantitative or near-quantitative. Alternatively, nucleic acid sequencing may be qualitative and may not provide deep insight into the relative amounts of different nucleic acid molecules included within a sample.

[0093] Various sequencing schemes can be adopted.For example, sequencing while synthesis, sequencing while hybridization or sequencing while connection, nanopore sequencing, sequencing by nucleic acid nanoballs, pyrophosphate sequencing, single molecule sequencing (for example, single molecule real-time sequencing), single cell / entity sequencing, large-scale parallel marker sequencing, polymerase clone sequencing, joint probe anchor polymerization, SOLiD sequencing, chain termination (for example, Sanger sequencing), ion semiconductor sequencing, tunneling current sequencing, heliscope single molecule sequencing, sequencing by mass spectrometry, transmission electron microscope sequencing, sequencing based on RNA polymerase or any other method or their combination.Sequencing technology such as Heliscope (Helicos), SMRT technology (Pacific Biosciences) or nanopore sequencing (Oxford Nanopore) can allow direct sequencing of single molecules without prior clonal amplification.Sequencing can be performed with or without target enrichment.Sequencing can be performed in solution.Sequencing can be performed with fixed (for example, directly or indirectly) nucleic acid molecules on substrates.Sequencing can be performed in microfluidic devices.Sequencing can include total sequencing.

[0094] Sequencing can include Helicos True Single Molecule Sequencing (tSMS) (e.g., as described in Harris et al., Science 320:106-109

[2008] ). In a typical tSMS process, a DNA sample is cut into chains of approximately 100 to 200 nucleotides, and a polyA sequence is added to the 3' end of each DNA chain. Each chain is labeled by adding fluorescently labeled adenosine nucleotides. The DNA chains are then hybridized to a flow cell containing millions of oligonucleotide-T capture sites fixed to the surface of the flow cell. The density of templates can be about 100 million templates / cm 2 The flow cell is then loaded into an instrument such as the HeliScope TM In the sequencer, a laser illuminates the surface of the flow cell, revealing the position of each template. A CCD camera can map the position of the template on the surface of the flow cell. The template fluorescent label is then cut and washed away. The sequencing reaction begins by introducing a DNA polymerase and fluorescently labeled nucleotides. Oligo-T nucleic acids are used as primers. The polymerase incorporates the labeled nucleotides into the primers in a template-guided manner. The polymerase and unincorporated nucleotides are removed. The templates that have been guided to incorporate fluorescently labeled nucleotides are identified by imaging the surface of the flow cell. After imaging, a cleavage step removes the fluorescent label, and the process is repeated with other fluorescently labeled nucleotides until the desired read length is reached. Sequence information is collected with each nucleotide addition step.

[0095] Another example process for sequencing polynucleotides is 454 sequencing (Roche) (e.g., as described in Margulies et al. Nature 437:376-380 (2005)). In the first step, DNA is typically sheared into fragments of approximately 300 to 800 base pairs, and the fragments are blunt-ended. Oligonucleotide adapters are then attached to the ends of the fragments. The adapters serve as primers for amplifying and sequencing the fragments. The fragments can be attached to DNA capture beads, such as streptavidin-coated beads, using, for example, adapter B containing a 5'-biotin tag. The fragments attached to the beads are PCR amplified in droplets of an oil-water emulsion. The result is multiple copies of cloned amplified DNA fragments on each bead. In the second step, the beads are captured in a well (e.g., picoliter size). Pyrophosphate sequencing is performed in parallel for each DNA fragment. Adding one or more nucleotides generates a light signal that is recorded in a sequencing instrument by a CCD camera. The signal intensity is proportional to the number of nucleotides incorporated. Pyrosequencing utilizes pyrophosphate (PPi) released when nucleotides are added. PPi is converted to ATP by ATP sulfurylase in the presence of adenosine 5' phosphosulfate. Luciferase uses ATP to convert luciferin to oxyluciferin, and the reaction produces light that is discerned and analyzed.

[0096] Another example of a suitable DNA sequencing technology is SOLiD TM Technology (Applied Biosystems). TM In sequencing while connecting, genomic DNA is sheared into fragments, and adapters are connected to the 5' and 3' ends of the fragments to produce a fragment library. Alternatively, internal adapters can be introduced by connecting adapters to the 5' and 3' ends of the fragments, circularizing the fragments, digesting the circularized fragments to produce internal adapters, and connecting adapters to the 5' and 3' ends of the resulting fragments to produce a paired library. Next, a clonal bead population is prepared in a microreactor containing beads, primers, templates, and PCR components. After PCR, the template is denatured, and the beads are enriched to separate the beads using an extended template. The template on the selected beads is subjected to a 3' modification that allows bonding to a slide. The sequence can be determined by sequential hybridization and connection of partially random oligonucleotides with bases (or base pairs) determined by the center identified by a specific fluorophore. After recording the color, the connected oligonucleotides are cut and removed, and the process is repeated.

[0097] DNA sequencing can be done by Pacific Biosciences' Single Molecule Real-Time (SMRT TM) sequencing technology. In SMRT sequencing, the continuous incorporation of dye-labeled nucleotides is imaged during DNA synthesis. A single DNA polymerase molecule is connected to the bottom surface of a single zero-mode wavelength identifier (ZMW identifier), and the zero-mode wavelength detector obtains sequence information while phosphorylated nucleotides are incorporated into the growing primer chain. ZMW is a restricted structure that can observe DNA polymerase incorporation of a single nucleotide under the background of fluorescent nucleotides that diffuse rapidly outside the ZMW (in microseconds). It takes several milliseconds for nucleotides to be incorporated into the growing chain. During this period, the fluorescent marker is excited and produces a fluorescent signal, and the fluorescent tag is cut. The identification of the corresponding fluorescence of the dye indicates which base has been incorporated. The process can be repeated.

[0098] Sequencing can also include nanopore sequencing (e.g., as described in Soni GV and Meller A. Clin Chem 53: 1996-2001

[2007] ). Nanopore sequencing DNA analysis technology is developed by many companies, including Oxford Nanopore Technologies (Oxford, United Kingdom). Nanopore sequencing is a single molecule sequencing technology, whereby a single molecule of DNA is directly sequenced as it passes through a nanopore. A nanopore can be a hole with a diameter of about 1 nanometer. Immersing the nanopore in a conductive fluid and applying an electric potential (voltage) thereto causes a slight current due to ion conduction through the nanopore. The amount of current flowing is sensitive to the size and shape of the nanopore. When a DNA molecule passes through a nanopore, each nucleotide on the DNA molecule blocks the nanopore to varying degrees, thereby changing the size of the current passing through the nanopore to varying degrees. Therefore, when a DNA molecule passes through a nanopore, this change in current represents a read of a DNA sequence.

[0099] Sequencing may include the use of a chemically sensitive field effect transistor (chemFET) array (e.g., US20090026082). In one example of this technology, a DNA molecule may be placed in a reaction chamber, and the template molecule may be hybridized to a sequencing primer bound to a polymerase. The incorporation of one or more triphosphates into a new nucleic acid chain at the 3' end of the sequencing primer may be discerned as a change in current by a chemFET. The array may have multiple chemFET sensors. In another example, a single nucleic acid may be attached to a bead, and the nucleic acid may be amplified on the bead, and each bead may be transferred to each reaction chamber on a chemFET array, each chamber having a chemFET sensor, and the nucleic acid may be sequenced.

[0100] Sequencing can include Ion Torrent single molecule sequencing, which pairs semiconductor technology with simple sequencing chemistry to convert chemically encoded information (A, C, G, T) directly into digital information (0, 1) on a semiconductor chip. In nature, when nucleotides are incorporated into DNA chains by polymerase, hydrogen ions are released as byproducts. Ion Torrent uses a high-density array of micromechanical holes to perform this biochemical process in a massively parallel manner. Each hole holds a different DNA molecule. Below the hole is an ion-sensitive layer and below the ion sensor. When a nucleotide such as C is added to the DNA template and then incorporated into the DNA chain, a hydrogen ion can be released. The charge from the ion can change the pH of the solution, which can be recognized by the Ion Torrent's ion sensor. The sequencer calls the bases, converting directly from chemical information to digital information. The Ion Personal Genome Sequencer (PGM TM ) The chip is sequentially filled with one nucleotide after another. If the next nucleotide to fill the chip does not match. The voltage change may not be recorded and the base may not be called. If there are two identical bases on the DNA strand, the voltage may be doubled and the chip may record two identical bases being called. Direct identification allows nucleotide incorporation to be recorded in seconds.

[0101] The sequencing process may include detecting a signal such as a fluorescent signal (e.g., an emission signal from a fluorescent marker) with a detector. As used herein, the term "detector" generally refers to a device capable of detecting or measuring a signal (such as a signal indicating the presence or absence of an incorporated nucleotide or nucleotide analog). The detector may include optical and / or electronic components that can detect and / or measure a signal. Non-limiting examples of detection methods involving detectors include optical detection, spectral detection, electrostatic detection, and electrochemical detection. Optical detection methods include, but are not limited to, fluorescence determination and UV-visible light absorption. Spectral detection methods include, but are not limited to, mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, and infrared spectroscopy. Electrostatic detection methods include, but are not limited to, gel-based techniques, such as, for example, gel electrophoresis. Electrochemical detection methods include, but are not limited to, electrochemical detection of amplified products after high performance liquid chromatography separation of the amplified products.

[0102] In some embodiments, sequence reads are obtained by any method known in the art.For example, next generation sequencing (NGS) technology can be used, such as sequencing by synthesis (Illumina), pyrophosphate sequencing (454Life Sciences), ion semiconductor technology (Ion Torrent sequencing), single molecule real-time sequencing (Pacific Biosciences), sequencing by connection (SOLiD sequencing), nanopore sequencing (Oxford Nanopore Technologies) or paired end sequencing.In some embodiments, sequencing by synthesis with a reversible dye terminator is used to perform large-scale parallel sequencing.In some embodiments, sequencing is performed using next generation sequencing technology such as short read technology.In other embodiments, long read sequencing or another sequencing method known in the art is used.

[0103] Next generation sequencing generates millions of short reads (e.g., sequence reads) for each biological sample. Thus, in some embodiments, the multiple sequence reads obtained by next generation sequencing of nucleic acid molecules are DNA sequence reads. In some embodiments, the sequence reads have an average length of at least fifty nucleotides. In other embodiments, the sequence reads have an average length of at least 50, 60, 70, 80, 90, 100, 150, 200, 250, 300 or more nucleotides.

[0104] In some embodiments, sequencing is performed after enrichment of nucleic acids (e.g., cfDNA, gDNA, and / or RNA) containing multiple predetermined target sequences (e.g., human genes and / or non-coding sequences associated with diseases such as cancer). Desirably, sequencing is performed on nucleic acid samples that have been enriched with target nucleic acids, rather than sequencing all nucleic acids separated from biological samples, significantly reducing the average time and cost of sequencing reactions. In some embodiments, nucleic acid samples undergo labeling, which includes using transposons to fragment nucleic acid samples and adding adapter sequences. In some embodiments, transposons are fixed on microbeads. In some embodiments, microbeads are paramagnetic. In some embodiments, the methods described herein include obtaining multiple sequence reads of nucleic acids that have been hybridized with probe sets for hybrid capture enrichment. In some embodiments, probe sets utilize the affinity relationship between biotin and streptavidin, wherein the probe set includes a custom biotinylated probe complementary to a target nucleic acid or polypeptide. The custom biotinylated probe bound to a targeted nucleic acid or polypeptide is then captured by streptavidin. Subsequently, the targeted nucleic acid or polypeptide can be separated and sequenced.

[0105] In some embodiments, the gene package targeted sequencing is performed to an average targeted depth of at least 30X, at least 40X, at least 50X, at least 60X, at least 70X, at least 80X, at least 90X, at least 100X, at least 500X, at least 750X, at least 1000X, at least 2500X, at least 500X, at least 10,000X or greater depth. In some embodiments, the uniformity of the sample above the sequencing depth threshold (e.g., 95% of all targeted base pairs at 300X sequencing depth) is further evaluated. In some embodiments, the sequencing depth threshold is the minimum depth selected by the user or physician. In some embodiments, the gene package targeted sequencing includes probes for between two and 1000 genomic regions, between 500 and 5,000 genomic regions, between 1,000 and 20,000 genomic regions, or between 5,000 and 50,000 genomic regions.

[0106] In some embodiments, sequence reads are obtained by a whole genome sequencing method. In some such embodiments, whole genome sequencing is performed with a sequencing depth lower than that of a smaller target gene package sequencing reaction, because more loci are being sequenced. For example, in some embodiments, whole genome sequencing is performed to an average sequencing depth of at least 0.2X, at least 0.5X, at least 1X, at least 1.5X, at least 2X, at least 2.5X, at least 3X, at least 3.5X, at least 4X, at least 4.5X or more. In some embodiments, whole genome sequencing is performed to an average sequencing depth of no more than 7.5X, no more than 7X, no more than 6.5X, no more than 6X, no more than 5.5X, no more than 5X, no more than 4.5X, no more than 4X, no more than 3.5X, no more than 3X, no more than 2.5X, no more than 2X, no more than 1.5X, no more than 1X or less. In some embodiments, low-pass whole genome sequencing (LPWGS) is performed to an average sequencing depth of about 0.25X to about 5X, or to an average sequencing depth of about 0.5X to about 5X, or to an average sequencing depth of about 1X to about 5X, or to an average sequencing depth of about 2X to about 5X, or to an average sequencing depth of about 3X to about 5X, or to an average sequencing depth of about 1X to about 4X, or to an average sequencing depth of about 1X to about 3X, or to an average sequencing depth of about 1.5X to about 4X, or to an average sequencing depth of about 1.5X to about 3X, or to an average sequencing depth of about 2X to about 3X.

[0107] In some embodiments, 100 or more sequence reads, 1000 or more sequence reads, 10,000 or more sequence reads, 20,000 or more sequence reads, 30,000 or more sequence reads, 40,000 or more sequence reads, 50,000 or more sequence reads, 60,000 or more sequence reads, 70,000 or more sequence reads, 80,000 or more sequence reads, 90,000 or more sequence reads, 100,000 or more sequence reads, 110,000 or more sequence reads, 120,000 or more sequence reads, 130,000 or more sequence reads, 140,000 or more sequence reads, 150,000 or more sequence reads, 160,000 or more sequence reads, 170,000 or more sequence reads, 180,000 or more sequence reads, 190,000 or more sequence reads, 200,000 or more sequence reads, 210,000 or more sequence reads, 220,000 or more sequence reads, 230,000 or more sequence reads, 240,000 or more sequence reads, 250,000 or more sequence reads, 260,000 or more sequence reads, 270,000 or more sequence reads, 280,000 or more sequence reads, 290,000 or more sequence reads, A plurality of sequence reads, 120,000 or more sequence reads, 130,000 or more sequence reads, 140,000 or more sequence reads, 150,000 or more sequence reads, 160,000 or more sequence reads, 170,000 or more sequence reads, 180,000 or more sequence reads, 190,000 or more sequence reads, 200,000 or more sequence reads, 300,000 or more sequence reads, 400,000 or more sequence reads, 500,000 or more sequence reads, 1×10 6 or more sequence reads, 1×10 7 or more sequence reads, or 1×10 8 In some embodiments, each sequence read has a minimum length. In some embodiments, the minimum length is 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more residues. In some embodiments, each sequence read has a maximum length. In some embodiments, the maximum length is a number between 400 residues and 1000 residues. In some embodiments, each sequence length has a maximum length of 500, 600, 700, 800, 900 or 1000 residues.

[0108] Protein Sequencing

[0109] The protein molecules of the sample can be interrogated to determine their protein sequences. The protein sequences can be used to identify the source from which they are derived, such as the virus or microorganism from which they are derived. The protein sequences identified in the sample can be compared with sequences in a database to associate them with the source from which they are derived (e.g., as described herein).

[0110] Prior to sequencing, the protein molecules within the sample or a portion thereof may be fragmented. Alternatively or additionally, the protein molecules may not be fragmented prior to sequencing. A variety of different approaches may be employed to identify protein sequences within a sample.

[0111] Different types of protein molecules may undergo the same or different treatments and sequencing. For example, a protein molecule with a first size or feature may undergo a first sequencing process, and a protein molecule with a second size or feature may undergo a second sequencing process, wherein the first sequencing process and the second sequencing process may include at least one process difference. Different sequencing programs may be performed on the same or different samples. For example, a first sequencing method may be performed on the same sample (e.g., at the same or different times) to analyze a first type of protein molecule, and a second sequencing method may be performed to analyze a second type of protein molecule, wherein the first sequencing method and the second sequencing method are different, and the first type of nucleic acid molecule and the second type of protein molecule are different. Alternatively or additionally, in some embodiments, a first sequencing method is performed using a first sample to analyze a first type of protein molecule, and a second sequencing method may be performed using a second sample to analyze a second type of protein molecule, wherein the first sequencing method and the second sequencing method are different, the first type of nucleic acid molecule and the second type of protein molecule are different, and the first sample and the second sample are different. The first sample and the second sample may be aliquots of the same sample (e.g., as described herein).

[0112] Protein sequencing may be quantitative or near-quantitative. Alternatively, protein sequencing may be qualitative and may not provide deep insight into the relative amounts of different nucleic acid molecules included within a sample.

[0113] Various sequencing schemes can be used. For example, protein sequencing may include the Edman degradation process. Protein sequencing may include sequencing protein fragments and / or entire polypeptides. Fragmentation may use different mechanisms to cleave to produce overlapping fragments. As described herein, fragments and entire polypeptides may be separated and purified before sequencing. Protein sequencing may include mass spectrometry (e.g., matrix-assisted laser desorption / ionization time of flight (MALDI-TOF) mass spectrometry). In some embodiments, direct measurement of peptide mass may provide sufficient information to identify proteins. Additional fragmentation (e.g., in a mass spectrometer) may further provide insight into peptide sequences. Peptides may be alternatively desalted and separated by reversed-phase high performance liquid chromatography (HPLC) coupled to a mass spectrometer, for example, using an electrospray ionization source (ESI). Fragmentation of peptides may be performed via mechanisms such as collision-induced dissociation or post-source decay. The mass-to-charge ratio measured may be compared with the mass value calculated from, for example, in silico proteolysis and fragmentation of a protein sequence database, and matched based on the exact sequence identity or similarity to homologous proteins. Alternatively or additionally, de novo sequencing may be used to analyze protein sequences. The full mass analysis of a protein (e.g., an unfragmented protein) can also be performed by subjecting the unfragmented protein to, for example, ESI-mass spectrometry. This mechanism may be sufficient to confirm the termini of a protein and infer the presence or absence of various post-translational modifications.

[0114] Reagents

[0115] As described herein, one or more different reagents can be used to treat a sample or sample set. For example, a first reagent or reagent set can be used in a first procedure for treating a sample, and a second reagent or reagent set can be used in a second procedure for treating a sample. Reagents can also be included in a sample as buffers, stabilizers, detergents, cryoprotectants, or for any other useful purpose. Reagents can also be used to enrich any target nucleic acid sequence.

[0116] The type, amount, source and other detailed information of reagent can be predetermined by one or more users. Such information can be included in the program selected for processing samples (for example, as described herein). Information about reagent can be input into the system provided herein via an interface (for example, as described herein). Alternatively, information about reagent can be downloaded, uploaded or otherwise accessed from another source. For example, information about reagent can be obtained from a database (for example, as described herein) and / or otherwise provided to the system. Information about reagent can be input, stored, accessed, downloaded, uploaded, viewed, processed and / or otherwise managed by the interface. Information about reagent can include, for example, its time, method, condition and preparation location; volume; density; quality; safety information; storage container type; storage conditions; suspicious contaminants; related personnel associated with reagent; related sample type; related procedures; barcode identifier; and any other potentially useful information. Different reagents and schemes related to their use can be tracked from, for example, purchase or manufacture to their final use and supplemented by the same or different personnel. For example, a first set of reagents used in a first set of programs can be tracked separately from a second set of reagents used in a second set of programs (such as a second set of programs performed by different personnel and / or at different locations or times). Different sets of reagents may include the same reagent. For example, a first set and a second set of reagents may each include a given reagent, which may be tracked within each grouping and / or independently.

[0117] Barcode

[0118] As used herein, the term "barcode" refers to a label or identifier that conveys or is capable of conveying information (e.g., information about a sequence read). The barcode can be part of the analyte or independent of the analyte. The barcode can be attached to the sequence read. In some embodiments, the barcode encoding is selected from the set {1, ..., 1024}, {1, ..., 4096}, {1, ..., 16384}, {1, ..., 65536}, {1, ..., 262144}, {1, ..., 1048576}, {1, ..., 4194304}, {1, ..., 16777216}, {1, ..., 67108864}, or {1, ..., 1×10 12} unique predetermined value.

[0119] Quality Control

[0120] The methods and systems provided herein also provide mechanisms for monitoring the quality of various processes.Quality control methods may include the use of one or more controls (eg, as described herein) that may be processed at least in part in parallel with one or more samples.

[0121] In some embodiments, the performance of the sequencer can be monitored. Sequencer performance monitoring can provide, for example, a control containing one or more known entities or their sequences is input into the sequencer, a sequencing program is executed, and the resulting sequencing reads are evaluated to determine whether the sequencer and the corresponding sequencing process can accurately and accurately identify the known entities or sequences in the control. The evaluation of sequencer performance may include evaluating the ability of one or more known entities or their sequences in the effective quantitative control of the sequencer and / or sequencing program. The evaluation of the sequencer may include regularly (for example, before and / or after the sample is run or during the sample run) inputting a given control or a set of controls into the sequencer. For example, one or more controls may be used to regularly (such as hourly, daily, weekly or monthly) evaluate the sequencer. Alternatively or additionally, one or more controls may be used to evaluate the sequencer before, during or after sample processing, such as evaluating immediately before or after processing the sample, or evaluating within 24 hours of processing the sample. Different controls may be evaluated to evaluate the different sensitivities of the sequencer. For example, a first control comprising a first set of known entities or sequences thereof can be used to evaluate a sequencer before, during, or after analyzing a sample suspected of including an entity of the first set of known entities, and a second control comprising a second set of known entities or sequences thereof can be used to evaluate a sequencer before, during, or after analyzing a sample suspected of including an entity of the second set of known entities. Running controls before, during, or after processing one or more samples can ensure the quality of a sequencing run.

[0122] The sequencing quality can be evaluated based on one or more different metrics. For example, the accuracy and precise identification of specific sequences and their prevalence in samples or controls can be evaluated. Error rates, quality scores (including Phred quality scores) and other metrics can also be used to evaluate sequencing quality.

[0123] In some embodiments, evaluating the quality of a sequencing run can include, for example, demultiplexing and adapter adjustment processes, read quality filtering, read quality trimming, and evaluation of reads after one or more of such processes. In some embodiments, quality evaluation of a sequencing run can involve evaluation of an input library, which in turn can provide feedback on the performance of various sample preparation (e.g., laboratory-performed) procedures.

[0124] In some embodiments, sequencing data including sequencing reads prepared using, for example, next generation sequencing (e.g., as described herein) may undergo an initial quality assessment before undergoing a classification process. For example, sequencing data may be processed to assess the quality of a basic sequencing library prepared in a laboratory, thereby improving the quality of base calls. Analysis of read factors such as sequence diversity, base call Phred quality scores (Q), and the presence of adapter sequences in Fastqs may provide insight into the performance of library preparation. Reads of poor quality, such as reads with more than half of the calls of Q<20, may be filtered out. Adapter sequences may be trimmed from the ends of the sequences, which may be base calls of poor quality with Q<30. After this filtering and pruning, the remaining reads and base calls in the sequencing data (e.g., in a fastq file) may be quantitatively scored by specifying a sample quality score. This score may help inform the reliability of the diagnostic result, particularly in cases where library preparation may be challenging due to the nature of clinical samples (such as high viscosity or low cellularity).

[0125] Classification

[0126] The identification and classification of one or more entities and / or their sequences in a sample may include a variety of processes, including, for example, nucleic acid sequencing and / or protein sequencing. For example, the classification of entities may include sequences with nucleic acid sequencing identification and optionally quantitatively associated with entities. The identification of sequences in a sample may not immediately identify entities in the sample in some cases. For example, a plurality of different entities may include the sequence (e.g., the sequence may be common to a group of entities) or a sequence with high sequence homology, which may be included in short reads or fragmented reads, etc. The abundance of known and unknown microorganisms and pathogens makes it possible to require detailed sequence analysis to accurately identify entities in a sample. This analysis may include identifying short sequence fragments within a wider sequence read, and performing a probability analysis of comparing the sequence with a database of one or more organizations to identify a given sequence associated with a specific entity or entity class.

[0127] The identification of sequences within a given sample or control and the classification of entities within a given sample or control can be performed within a classification module. The classification module may include one or more elements with which a user can interact, including, for example, a display or user interface. The classification module may be operably linked to an interface through which sequencing reads and / or sample and control information may be input, stored, viewed, accessed, downloaded, manipulated, or uploaded. The user may interact with the interface before, during, and / or after the classification process. For example, a user may view, establish, and / or update a threshold for analysis; select or view an analysis scheme; and select or view a reference database; select, manipulate, view, hide a report or other output or otherwise interact with it. The classification module may include a display component, and one or more users may view a report or other output via the display component, including species identification and treatment recommendations. The display may be incorporated into the user interface and may have any useful features.

[0128] The classification module can perform operations locally, in the cloud, via a network, via one or more servers, or any combination thereof. In an example, sample information and sequencing reads can be locally input to a network-based storage system at a first location, and sequence analysis and classification can be performed subsequently via the network. When the sequence analysis and classification process is performed via a network-based user interface at a second location, the user can monitor the sequence analysis and classification process and provide input thereto. Classification can include, for example, read k-aggregation, data merging, preparation and / or access to reference databases, sequence assembly (e.g., via k-mer analysis, exact sequence matching, other sequence recognition processes, and shared sequencing) and read alignment, as well as other processes.

[0129] The classification process can start with filtered and pruned sequencing data (e.g., in the form of fastq files) as input. Initially, the merging process can assign reads to the major categories of organisms, such as bacteria, fungi, parasites and viruses and hosts (e.g., humans). Then, the classification algorithm can compare each merged read set with the reference sequence corresponding to the organism of the specified category. In order to achieve highly computationally efficient sequence comparisons, in some embodiments, the algorithm can decompose the reads into multiple k-mers (e.g., as described herein). Similarly, for a reference database, known sequences can be preprocessed into an index k-mer set for each organism of interest. However, in some embodiments, the known sequences of the reference sequence database are not preprocessed into an index k-mer set for each organism of interest.

[0130] The classification algorithm can score the organisms most likely to be present in a given sample based on the coverage percentage of the reference and the score that takes into account the coverage and uniqueness of the reference sequence covered. In addition, for each putatively detected organism, a consensus sequence can be assembled from the reads to calculate metrics such as percent nucleotide identity. In the case of viruses that tend to have high mutation rates, comparisons to the reference at the nucleotide level can be enhanced by analyzing the translated amino acids at the protein level.

[0131] In some embodiments, the reference database comprises a collection of polynucleotide reference sequences. In some embodiments, the collection of reference polynucleotide sequences comprises more than 100, more than 1000, more than 10,000, more than 100,000, more than 1×10 6 or more than 1×10 7 Reference sequences. In some embodiments, the identity of the species of origin of each reference polynucleotide sequence in the reference polynucleotide sequence set is known. In some embodiments, each reference polynucleotide sequence in the reference polynucleotide sequence set represents a gene sequence from a gene of a species. In some embodiments, each reference polynucleotide sequence in the reference polynucleotide sequence set represents at least 10, 15, 20, 25, 30, 35, 40, 45 or 50 continuous nucleotides of a gene sequence from a gene of a species. In some embodiments, the reference polynucleotide sequence set includes reference polynucleotide sequences from 10 or more, 100 or more, 1000 or more, 10,000 or more or 100,000 different species.

[0132] Read K-aggregation

[0133] The sequencing process can generate multiple sequencing reads. As used herein, "sequencing reads" or "sequence reads" (also referred to as "reads" or "query sequences") generally refer to the inferred sequence of nucleotide bases in a nucleic acid molecule. Sequencing reads can be inferred sequences of nucleic acid bases (e.g., nucleotides) or base pairs obtained via nucleic acid sequencing. Sequencing reads can be generated using, for example, next generation sequencing by a nucleic acid sequencer such as a massively parallel array sequencer (e.g., Illumina or Pacific Biosciences of California). Sequencing reads may correspond to a portion of a genome of a subject or species, or in some cases to all. Sequencing reads may be part of a set of sequencing reads, which may be combined, for example, by alignment (e.g., with a reference genome) to generate a sequence of the genome of a subject. The sequencing reads can be of any suitable length, such as about or more than about 20 nucleotides (nt), 30 nt, 36 nt, 40 nt, 50 nt, 75 nt, 100 nt, 150 nt, 200 nt, 250 nt, 300 nt, 400 nt, 500 nt or longer in length. The sequencing reads can be less than 200 nt, 150 nt, 100 nt, 75 nt or shorter in length. Similarly, the sequencing reads of a polypeptide can be of any suitable length of amino acids, such as about 20 amino acids (aa), 30 aa, 36 aa, 40 aa, 50 aa, 75 aa, 100 aa, 150 aa, 200 aa, 250 aa, 300 aa, 400 aa, 500 aa or longer in length. The length of the sequencing read can be less than 200 aa, 150 aa, 100 aa, 75 aa or shorter. In some embodiments, the first sequencing method can be used to provide a sequencing read of a first length range, and the second sequencing method can be used to provide a sequencing read of a second length range, wherein the first length range is longer than the second length range. The sequencing reads may correspond to overlapping sequences of the genome of the subject or may be non-overlapping. The sequencing reads may include functional sequences, including adapters and barcode sequences. The functional sequences included in the sequencing reads may vary based on the nucleic acid treatment (e.g., nucleic acid amplification) performed before sequencing. The sequencing reads may correspond to DNA and / or RNA molecules. The sequencing reads may be "paired", which means that they are derived from different ends of the nucleic acid fragments. Paired reads may have inserted unknown sequences or overlaps. In some embodiments, the sequencing reads may be overlapping groups or consensus sequences assembled by separate overlapping reads.

[0134] Sequencing reads can be analyzed according to component k-mers. As used herein, "k-mer" generally refers to a subsequence of a given length k that constitutes a sequencing read. For example, the sequence "AGCTCT" can be divided into 3-nt subsequences "AGC", "GCT", "CTC", and "TCT". In this example, each of these subsequences is a k-mer, where k = 3. K-mers can be overlapping or non-overlapping. In the above example, "AGC", "GCT", "CTC", and "TCT" are overlapping k-mers. The k-mer of a sequence may alternatively exist as a non-overlapping k-mer (e.g., only "AGC" and "TCT").

[0135] The k-mer can be about 3 nucleotides (nt), 4 nt, 5 nt, 6 nt, 7 nt, 8 nt, 9 nt, 10 nt, 11 nt, 12 nt, 13 nt, 14 nt, 15 nt, 16 nt, 17 nt, 18 nt, 19 nt, 20 nt, 25 nt, 30 nt, 35 nt, 40 nt, 45 nt, 50 nt, 75 nt, 100 nt or longer in length. The length of a k-mer can be at least about 3 nt, 4 nt, 5 nt, 6 nt, 7 nt, 8 nt, 9 nt, 10 nt, 11 nt, 12 nt, 13 nt, 14 nt, 15 nt, 16 nt, 17 nt, 18 nt, 19 nt, 20 nt, 25 nt, 30 nt, 35 nt, 40 nt, 45 nt, 50 nt, 75 nt, 100 nt or longer. The length of a k-mer can be less than about 30 nt, 25 nt, 20 nt, 15 nt, 10 nt or shorter.The k-mer can be about 3 nt to 10 nt, 3 nt to 13 nt, 3 nt to 15 nt, 3 nt to 20 nt, 3 nt to 25 nt, 3 nt to 30 nt, 3 nt to 35 nt, 3 nt to 40 nt, 3 nt to 45 nt, 3 nt to 50 nt, 3 nt to 55 nt, 3 nt to 60 nt, 3 nt to 65 nt, 3 nt to 70 nt, 3 nt to 75 nt, 3 nt to 80 nt, 3 nt to 85 nt, 3 nt to 90 nt, 3 nt to 95 nt, 3 nt to 100 nt, 3 nt to 110 nt, 3 nt to 111 nt, 3 nt to 112 nt, 3 nt to 113 nt, 3 nt to 114 nt, 3 nt to 115 nt, 3 nt to 120 nt, 3 nt to 125 nt, 3 nt to 126 nt, 3 nt to 127 nt, 3 nt to 128 nt nt to 99 nt, 5 nt to 10 nt, 5 nt to 15 nt, 5 nt to 15 nt, 5 nt to 20 nt, 5 nt to 25 nt, 5 nt to 30 nt, 5 nt to 35 nt, 5 nt to 40 nt, 5 nt to 45 nt, 5 nt to 50 nt, 5 nt to 55 nt, 5 nt to 60 nt, 5 nt to 65 nt, 5 nt to 70 nt, 5 nt to 75 nt, 5 nt to 80 nt, 5 nt to 85 nt, 5 nt to 90 nt, 5 nt to 95 nt, 5 nt to 99 nt. nt, 7 nt to 10 nt, 7 nt to 17 nt, 7 nt to 15 nt, 7 nt to 20 nt, 7 nt to 25 nt, 7 nt to 30 nt, 7 nt to 35 nt, 7 nt to 40 nt, 7 nt to 45 nt, 7 nt to 50 nt, 7 nt to 55 nt, 7 nt to 60 nt, 7 nt to 65 nt, 7 nt to 70 nt, 7 nt to 75 nt, 7 nt to 80 nt, 7 nt to 85 nt, 7 nt to 90 nt, 7 nt to 95 nt, 7 nt to 99 nt, 10 nt to 15 nt, 10 nt to 20 nt, 10 nt to 25 nt, 10 nt to 30 nt, 10 nt to 35 nt, 10 nt to 40 nt, 10 nt to 45 nt, 10 nt to 50 nt, 10 nt to 55 nt, 10 nt to 60 nt, 10 nt to 65 nt, 10 nt to 70 nt, 10 nt to 75 nt, 10 nt to 80 nt, 10 nt to 85 nt, 10 nt to 90 nt, 10 nt to 95 nt, 10 nt to 99 nt, or any range therein.Similarly, a k-mer can be about 3 amino acids (aa), 4 aa, 5 aa, 6 aa, 7 aa, 8 aa, 9 aa, 10 aa, 11 aa, 12 aa, 13 aa, 14 aa, 15 aa, 16 aa, 17 aa, 18 aa, 19 aa, 20 aa, 25 aa, 30 aa, 35 aa, 40 aa, 45 aa, 50 aa, 75 aa, 100 aa, or longer in length. The length of a k-mer can be at least about 3 aa, 4 aa, 5 aa, 6 aa, 7 aa, 8 aa, 9 aa, 10 aa, 11 aa, 12 aa, 13 aa, 14 aa, 15 aa, 16 aa, 17 aa, 18 aa, 19 aa, 20 aa, 25 aa, 30 aa, 35 aa, 40 aa, 45 aa, 50 aa, 75 aa, 100 aa or more. The length of a k-mer can be less than about 30 aa, 25 aa, 20 aa, 15 aa, 10 aa or less.The k-mer can be about 3 aa to 10 aa, 3 aa to 13 aa, 3 aa to 15 aa, 3 aa to 20 aa, 3 aa to 25 aa, 3 aa to 30 aa, 3 aa to 35 aa, 3 aa to 40 aa, 3 aa to 45 aa, 3 aa to 50 aa, 3 aa to 55 aa, 3 aa to 60 aa, 3 aa to 65 aa, 3 aa to 70 aa, 3 aa to 75 aa, 3 aa to 80 aa, 3 aa to 85 aa, 3 aa to 90 aa, 3 aa to 95 aa, 3 aa to 100 aa, 3 aa to 101 aa, 3 aa to 102 aa, 3 aa to 103 aa, 3 aa to 104 aa, 3 aa to 105 aa, 3 aa to 106 aa, 3 aa to 107 aa, 3 aa to 108 aa, 3 aa to 109 aa, 3 aa to 110 aa, 3 aa to 111 aa, 3 aa to 112 aa, 3 aa to 113 aa 5 aa to 99 aa, 5 aa to 10 aa, 5 aa to 15 aa, 5 aa to 15 aa, 5 aa to 20 aa, 5 aa to 25 aa, 5 aa to 30 aa, 5 aa to 35 aa, 5 aa to 40 aa, 5 aa to 45 aa, 5 aa to 50 aa, 5 aa to 55 aa, 5 aa to 60 aa, 5 aa to 65 aa, 5 aa to 70 aa, 5 aa to 75 aa, 5 aa to 80 aa, 5 aa to 85 aa, 5 aa to 90 aa, 5 aa to 95 aa, 5 aa to 99 aa aa, 7 aa to 10 aa, 7 aa to 17 aa, 7 aa to 15 aa, 7 aa to 20 aa, 7 aa to 25 aa, 7 aa to 30 aa, 7 aa to 35 aa, 7 aa to 40 aa, 7 aa to 45 aa, 7 aa to 50 aa, 7 aa to 55 aa, 7 aa to 60 aa, 7 aa to 65 aa, 7 aa to 70 aa, 7 aa to 75 aa, 7 aa to 80 aa, 7 aa to 85 aa, 7 aa to 90 aa, 7 aa to 95 aa, 7 aa to 99 aa, 1 10 aa to 80 aa, 10 aa to 85 aa, 10 aa to 90 aa, 10 aa to 95 aa, 10 aa to 99 aa, or any other range therein. The length of the K-mer analyzed during a given analysis can vary. For example, a first process may analyze k-mers of a first length, and a second process may analyze k-mers of a second length, where the first length and the second length are not the same. The first length may be longer than the second length.Alternatively, the second length may be longer than the first length. Alternatively or additionally, one or more k-mers of different lengths may be analyzed in a given process (e.g., simultaneously). In an example, the first analysis process may compare the k-mer in the sequencing reads with a reference sequence of 21 nt in length, while the second analysis process may compare the k-mer in the sequencing reads with a reference sequence of 7 nt in length. For any given sequence in the comparison step, the k-mer analyzed may be overlapping (such as in a sliding window) and may have the same or different lengths. Although k-mers are generally referred to as nucleic acid sequences herein, sequence comparisons also include comparisons of polypeptide sequences, including comparisons of k-mers comprising amino acids.

[0136] Sequencing information (e.g., sequencing reads) can be provided in any useful format. For example, sequencing reads can be exported as FASTQ files and / or in FASTA format. Sequencing information can be included in a text file represented as ASCII characters.

[0137] In some embodiments, k-mer analysis between sequence reads and a reference sequence is performed and scored as described in U.S. patent application Ser. No. 15 / 724,476, entitled “Methods and Systems and Multiple Taxonomic Classification,” filed on Oct. 4, 2017, which is hereby incorporated by reference.

[0138] data

[0139] Data (e.g., data corresponding to sequencing information, such as sequencing information corresponding to a single sample or sample set) may initially be provided on a local device (e.g., data may be stored locally). Alternatively or additionally, data may be uploaded to a cloud- or network-based storage system (e.g., immediately upon collection or immediately after collection). For example, data may be collected on a local device, and a user may choose to upload the data to a cloud- or network-based storage system (e.g., after performing an initial review of the data). Alternatively, a user may choose to upload the data to a cloud- or network-based storage system as the data is collected. Data may also be stored using a mobile device (such as using a flash drive, a memory drive, or other hardware device). Multiple copies of the data may be stored for any useful period of time (e.g., to provide data backup).

[0140] The data may include identifying information, such as information about the source or subject from which it is derived. Alternatively, the identifying information may be separated from the data (e.g., the data may be de-identified), and the data may be associated with a code (e.g., as described herein). In an example, data for multiple different samples are collected and / or processed simultaneously, and a code is assigned to the data for each different sample, which may or may not include identifying information about the sample.

[0141] The data can be of any useful size and in any useful format.

[0142] The data may undergo one or more processing steps before being stored. In an example, the raw data may be stored locally and may be subjected to at least one processing step to provide pre-processed data. The pre-processed data may have a smaller data size (e.g., the data may be reduced by processing the raw data into blocks, kernels and / or k-mers) and / or different formats. The pre-processed data may be transmitted to a mobile, cloud-based or network-based storage and / or may be stored locally. The initially collected raw data may be deleted (e.g., to save space on a hardware device), such as after a predefined time period. Alternatively, the initially collected raw data may be retained for reference.

[0143] The data collected from nucleic acid sequencing can be stored and / or processed separately from the data collected from protein sequencing. Alternatively, the data collected from nucleic acid sequencing can be stored and / or processed together with the data collected from protein sequencing. In an example, the data collected from nucleic acid sequencing corresponding to the sample can be combined with the data collected from protein sequencing for subsequent processing. These data can be in the same or different formats.

[0144] The data collected from nucleic acid sequencing can be processed separately from the data collected from protein sequencing. Alternatively, the data collected from nucleic acid sequencing can be processed together with the data collected from protein sequencing. The data collected from nucleic acid sequencing of different types of nucleic acid molecules can also be processed differently. For example, data collected from a first type of nucleic acid molecule (e.g., DNA) can be processed differently from data collected from a second type of nucleic acid molecule (e.g., RNA).

[0145] The data may be processed locally and / or externally. For example, a first processor may be used to collect sequencing information, and a second processor may be used to analyze the sequencing information (e.g., after transferring the data from the first processor to a storage location accessible to the second processor). A device on which data is stored locally may be used to process the data. Alternatively or additionally, the data may not be uploaded to a device on which it is processed (e.g., it may be stored in a cloud or network-based storage system and processed locally). Any useful computing device (e.g., as described herein) (including supercomputing devices) may be used to process the data.

[0146] The data may initially be provided in a first file format and changed to a second file format that is different from the first file format. The conversion to the second file format may append information to the data, such as sample identification information and / or information about the data set.

[0147] Data Merge

[0148] Data processing may include dividing sequence information into groups. Groups may include, for example, people, bacteria, fungi, viruses / phages, ambiguous, unknown and other groups. Merging may be based on the comparison of sequences with sequences included in one or more reference databases. The database to which the sequence collected can be compared may be selected by the user (e.g., using a data analysis software interface, such as a web-based software interface). For example, the user may choose to compare the sequence collected with a database (including reference sequences associated with various bacteria (including suspected bacteria included in the sample)). Similarly, if it is suspected that human DNA is included in the sample, the user may choose to compare the sequence collected with a reference sequence database associated with a human genome (e.g., if the source of the sample is a human subject). The analysis program may include a standard database set to which a sequence can be compared. The program may be configured to allow the user to deselect various databases or include additional databases for analysis.

[0149] The merging of the collected sequences into the initial group may include comparing the sequences to one or more databases to obtain an exact sequence match (e.g., 100% sequence identity) and / or may provide some mismatches between the collected sequences and the stored sequences. The threshold for mismatches (e.g., the percentage of sequence identity required for a match between the prompt sequences) may be preloaded into the analysis program and may be optionally changed by the user. Alternatively or additionally, k-mer matching may be used to merge the sequences into the initial grouping. K-mer matching may be performed for k-mers of different lengths, such as for two or more k-mers of different lengths.

[0150] After merging the collected sequences into the initial group, a submerging process can be performed. Submerging can be based on exact k-mer matching (e.g., matching of k-mers of a single size or multiple different sizes) and / or sequence matching. Submerging can also include probabilistic analysis, such as k-mer weight analysis (e.g., as described herein). Submerging for protein sequence analysis can also include multi-frame (e.g., 6-frame) translation processes and / or reduced amino acid alphabet analysis.

[0151] User input may be provided between each processing step described herein. In some embodiments, user input may be required to complete a processing step and to start a subsequent processing step. Alternatively, the data analysis workflow may be automatic. In an example, user input is requested and provided before the data analysis workflow begins, and user input is not provided between processing steps.

[0152] Software routines for generating a sequence record database and comparing sequencing reads with a database can be run on a computer. The comparison can be automatically performed when data is received. The comparison can be performed in response to a user request. The user request can specify which reference database to compare the sample with. The computer may include one or more processors. The processor may be associated with one or more control devices, computing units, and / or other units of a computer system, or may be implanted in firmware as required. If implemented in software, the routine may be stored in any computer-readable memory, such as in RAM, ROM, flash memory, disk, laser disk, or other storage medium. Reports recording databases, sequencing reads, or the results of summarizing database construction or sequence read comparisons may also be stored in any suitable medium, such as in RAM, ROM, flash memory, disk, laser disk, or other storage medium. Similarly, reports recording databases, sequencing reads, or the results of summarizing database construction or sequence read comparisons may be delivered to a computing device via any known delivery method, including, for example, via a communication channel such as a telephone line, the Internet, a wireless connection, or via a removable medium such as a computer-readable disk, a flash drive, etc. Databases, sequencing reads, or reports may be communicated to users locally or remotely using any suitable communication medium. For example, the communication medium can be a network connection, a wireless connection, or an Internet connection. The database or report can be transmitted through such a network or connection (or any other suitable means for transmitting information, including but not limited to mailing a database summary, such as a printout) for user reception and / or review. The recipient can be, but is not limited to, a client, an individual, a healthcare provider, a healthcare manager, or an electronic system (e.g., one or more computers and / or one or more servers). In some embodiments, the database or report generator sends the report to the recipient's device, such as a personal computer, a phone, a tablet computer, or other device. The database or report can be viewed online, saved on the recipient's device, or printed. The comparison of the transmitted sequencing reads with the database can occur after all reads are uploaded. The comparison of the transmitted sequencing reads with the database can start when the sequencing reads are in the process of being uploaded.

[0153] The results of the methods described herein can be assembled in a record database. The record database can include reference sequences identified as present in a sample, and exclude reference sequences for which no corresponding sequencing reads are found, such as by failing to match sequencing reads above a set threshold level. The record database can include reference amino acid sequences identified as present in a sample, and exclude reference amino acid sequences for which no corresponding sequencing reads are found, such as by failing to match sequencing reads above a set threshold level.

[0154] The data processing methods and systems provided herein can be used to identify one or more microorganisms and / or viruses and / or parasites and / or antimicrobial resistance markers and / or host response markers in one or more samples, wherein the host can be a human or an animal or a plant. The source of nucleic acid and protein sequences in one or more samples can be identified with a single species (e.g., a taxonomic group). The terms "taxonomic group" ("taxon", plural "taxa"), "taxonomic group" and "taxonomic unit" are used interchangeably herein and refer to a group of one or more organisms comprising a node in a clustering tree. The level of clustering can be determined by its hierarchical order. A taxonomic group can be a group temporarily assumed to be a valid taxonomic group for phylogenetic analysis purposes. A taxonomic group can be given a name and a score. For example, a taxonomic group can represent a domain, subdomain, kingdom, subkingdom, phylum, subphylum, class, subclass, order, suborder, section, subfamily, genus, subgenus or species. A taxonomic group can represent one or more organisms from true bacteria, protists or fungi at any hierarchical order level. A taxonomic group can be a taxonomic unit experienced in a given analysis (e.g., any existing taxonomic unit under a given study). The taxonomic group may be known or suspected to be included in the sample being analyzed. Alternatively, the taxonomic group may be unknown or suspected not to be included in the sample being analyzed.

[0155] The terms "determine," "measure," "evaluate," "assess," "measure," and "analyze" are used interchangeably herein to refer to any form of measurement, and include determining whether an element is present (e.g., detecting). These terms may include quantitative and / or qualitative determinations. Assessments may be relative or absolute. "Detecting the presence of" may include determining the amount of something present, as well as determining whether it is present.

[0156] As used herein, the term "specificity" or "true negative rate" generally refers to the ability of a test to correctly exclude a disease. For example, in a classification algorithm, the specificity of the algorithm can refer to the proportion of reads known to be not from an organism in a given classification box, which may not be placed in the classification box. In some embodiments, this is calculated by determining the ratio of true negatives (e.g., reads not from a classification box that are not placed in the box) to the total number of reads not derived from an organism in the classification box (e.g., (i) reads not placed in a given classification box and not derived from an organism in the classification box and (ii) reads placed in a classification box that are not derived from an organism in the classification box).

[0157] As used herein, the term "sensitivity" or "true positive rate" generally refers to the ability of a test to correctly identify a disease. For example, in a classification algorithm, the sensitivity of a test can refer to the proportion of reads known to be from an organism in a given classification box, which can be placed in the classification box. In some embodiments, this is calculated by determining the ratio of true positives (e.g., reads from a classification box placed in the box) to the total number of reads from an organism in the classification box (e.g., (i) reads placed in a given classification box and derived from an organism in the classification box and (ii) the sum of reads from an organism in the classification box that are not placed in the classification box).

[0158] The quantitative relationship between sensitivity and specificity can change with selecting different classification cutoff values. This change can be represented using a receiver operating characteristic (ROC) curve. The x-axis of the ROC curve shows the false positive rate of the assay, which can be calculated as (1-specificity). The y-axis of the ROC curve reports the sensitivity of the assay. This allows determining the sensitivity of the assay for a given specificity, and vice versa.

[0159] In one aspect, the present disclosure provides a method for identifying multiple polynucleotides in a sample source. In some embodiments, the method includes providing sequencing reads of multiple polynucleotides from a sample, and for each sequencing read: (a) performing a sequence comparison between the sequencing read and multiple reference polynucleotide sequences with a computer system, wherein the comparison includes calculating a k-mer weight as a measure of the likelihood that the k-mer within the sequencing read is derived from a reference sequence within the multiple reference polynucleotide sequences; (b) if the sum of the k-mer weights of the reference sequence is above a threshold level, the sequencing read is identified as corresponding to a specific reference sequence in a reference sequence database; and (c) assembling a record database containing the reference sequence identified in step (b), wherein the record database excludes reference sequences to which no sequencing read corresponds.

[0160] In another aspect, the present disclosure provides a method for identifying one or more taxa in a sample from a sample source. In some embodiments, the method includes (a) providing sequencing reads of multiple polynucleotides from a sample, and for each sequencing read: (i) performing a sequence comparison between the sequencing read and multiple reference polynucleotide sequences with a computer system, wherein the comparison includes calculating a k-mer weight as a measure of the likelihood that the k-mer within the sequencing read is derived from a reference sequence within the multiple reference polynucleotide sequences; and (ii) calculating the probability that the sequencing read corresponds to a specific reference sequence in a reference sequence database based on the k-mer weight, thereby generating a sequence probability; (b) calculating the scores of the presence or absence of the one or more taxa based on the sequence probability corresponding to the sequence representing the one or more taxa; and (c) identifying the one or more taxa present or absent in the sample based on the corresponding scores; or (d) identifying the one or more taxa present or absent in the sample based on a machine learning method. In some embodiments, the one or more taxa include a first bacterial strain identified as present based on one or more nucleotide differences in the sequence and a second bacterial strain identified as absent. In some embodiments, a first bacterial strain is identified as being present and a second bacterial strain is identified as being absent based on a single nucleotide difference in sequence.

[0161] Reference Database

[0162] The analysis of a sequence (e.g., a sequence corresponding to or derived from a sample, as described herein) may include one or more processes (e.g., a comparison process) in which one or more k-mers of a sequencing read are compared to k-mers of one or more reference sequences (also referred to as "references"). A reference sequence includes any sequence to which a sequencing read is compared. Typically, a reference sequence is associated with some known features, such as the conditions of a sample source, a classification group, a specific species, an expression profile, a specific gene, a specific antimicrobial resistance gene, a specific antiviral resistance gene, a specific anti-toxic resistance gene, a specific anti-parasitic resistance gene, a specific anti-protozoal resistance gene, an associated phenotype such as possible disease progression, drug resistance or pathogenicity, an increased or decreased disease tendency or other feature. Typically, a reference sequence is one of many such reference sequences in a database. Various databases containing various types of reference sequences may be obtained, one or more of which may be used as a reference database alone or in various combinations. A database may include many species and sequence types. A database may be a publicly available database. A database may be a specific locally stored database, such as a database associated with a given sample source. For example, a specific database can provide a comparison between samples collected from a given source over time, such as samples obtained from the same subject or position. Examples of databases include, but are not limited to, NR, UniProt, SwissProt, TrEMBL, and UniRef90 databases. The database may include sequences of specific species from multiple species, such as those sequences for taxonomic classification of species (such as bacteria). The database may be a 16S database, such as a Greengenes database, a UNITE database, or a SILVA database. Marker genes except 16S can be used as reference sequences for identifying microorganisms (e.g., bacteria), such as metabolic genes, genes encoding structural proteins, proteins controlling growth, cell cycle, or reproductive regulation, housekeeping genes, or genes encoding toxicity, toxins, or other pathogenic factors. Specific examples of marker genes include, but are not limited to, 18S rDNA, 23SrDNA, gyrA, gyrB genes, groEL, rpoB genes, fusA genes, recA genes, sod A, coxl genes, and nifD genes. The reference database may include an internal transcribed sequence (ITS) database, such as UNITE, ITSoneDB, or ITS2. The database may contain multiple sequences from a single species, such as the human genome, human transcriptome, model organisms (such as the mouse genome, yeast transcriptome, or C. elegans proteome), or disease vectors (such as bats, ticks, or mosquitoes, and other domestic and wild animals). The reference database may include sequences of human transcripts. The reference sequences in the database may include DNA sequences, RNA sequences, or protein sequences. The reference sequences in the database may include sequences from multiple taxa.In some embodiments, the reference sequence may be from a reference individual or a reference sample source. Examples of reference individual genomes include, for example, maternal genomes, paternal genomes, or genomes of non-cancerous tissue samples. Examples of reference individual or sample sources include human genomes, mouse genomes, or specific serotypes, genovars, strains, variants, or other characteristic types of bacteria, archaea, viruses, phages, fungi, and parasites. The database may include polymorphic reference sequences containing one or more mutations relative to a known polynucleotide sequence. Such polymorphic reference sequences may include different alleles found in a population, such as single nucleotide polymorphisms (SNPs), insertions and deletions, microdeletions, microexpansions, common rearrangements, genetic recombination, or prophage insertion sites, and may include information about their relative abundance compared to non-polymorphic sequences. Polymorphic reference sequences may also be artificially generated from the reference sequence of a database, such as by changing one or more (including all) positions in the reference genome, so that multiple possible mutations that are not in the actual reference database are represented for comparison. The reference sequence database may include one or more reference sequences of a variety of different classification groups, including but not limited to bacteria, archaea, vesicle algae, viruses, fungi, plants, fish, amphibians, reptiles, birds, mammals and people. In some embodiments, the reference sequence database may be composed of sequences from one or more reference individuals or reference sample sources (e.g., 10, 100, 1000, 10000, 100000, 1000000 or more), and each reference sequence in the database may be associated with its corresponding individual or sample source. Based on sequence comparison, unknown samples may be identified as being derived from an individual or sample source represented in the reference database. The reference sequence database may include the reference sequence of one or more genes. The reference sequence database may include the reference sequence of one or more antimicrobial resistance genes, anti-toxic resistance genes, anti-protozoan resistance genes, anti-viral resistance genes, anti-parasitic resistance genes and / or anti-fungal resistance genes, etc.

[0163] Reference database can be made up of the sequence (and optionally the abundance level of sequence) associated with one or more conditions.Multiple conditions can be represented by one or more sequences in the reference database, such as 10,50,100,1000,10000,100000,1000000 or more conditions.For example, reference database can be made up of thousands of sequence groups, and each sequence group is associated with different bacterial contaminants, so that the contamination of sample by any represented bacterium can be detected by sequence comparison according to the method disclosed herein.Condition can be any feature of the source from which sample or sample is derived.For example, reference database can be made up of the gene set associated with the infection of the experimenter derived from by the pollution of microorganism, sample or the host to the response of pathogen.In some embodiments, reference database can be made up of the antimicrobial gene set associated with the infection of the experimenter derived from by the pollution of microorganism, sample or the host to the response of pathogen. Other conditions include, but are not limited to, contamination (e.g., environmental contamination, surface contamination, food contamination, air contamination, water contamination, cell culture contamination), stimulus response (e.g., drug responder or non-responder, allergic response, therapeutic response), infection (e.g., bacterial infection, fungal infection, viral infection), disease state (e.g., presence of disease, exacerbation of disease, recovery from disease), and health state. In some embodiments, the reference database may consist of one or more genes associated with antimicrobial resistance, antiviral resistance, antifungal resistance, antibiotic resistance, or antiparasitic resistance, etc. In some embodiments, the reference database may consist of polynucleotides, amino acid sequences, and / or sequence reads associated with antimicrobial resistance genes, antiviral resistance genes, antifungal resistance genes, or antiparasitic resistance genes, etc. In some embodiments, the reference database may consist of gene names that confer characteristics (e.g., antimicrobial resistance, antiviral resistance, antitoxin resistance, antifungal resistance, antiprotozoal resistance, antiparasitic resistance, etc.), related antibiotics, associated organisms, resistance mechanisms, evidence, metagenomic data, metadata, k-mers, polynucleotides, nucleic acids, protein amino acid sequences, nucleotide sequences, etc.). In some embodiments, the reference database may have metadata. In some embodiments, metadata may be data information that can provide information about other data. In some embodiments, metadata may be descriptive metadata, structural metadata, administrative metadata, reference metadata, statistical metadata, etc.

[0164] In some embodiments, the reference database associated with one or more genes can be a publicly available database or a private database. The database can be, for example, MEGARes, a comprehensive antibiotic resistance database (CARD), a national antibiotic resistance organism database (NDARO), a structured ARG database, an antibiotic resistance gene database (ARDB) or a RESQU database, etc. The reference database can be filled with data. These data can be, for example, sequence reads, polynucleotides, k-mers, nucleic acids, amino acid sequences, genes (e.g., antimicrobial resistance genes, antiviral resistance genes, anti-toxic resistance genes, anti-fungal resistance genes, anti-parasitic resistance genes, antiprotozoan resistance genes, antiprotozoan resistance genes), etc.

[0165] Alternatively or additionally, a reference database may be compiled via management of one or more other databases (including, for example, one or more publicly available or private databases) and / or evaluation of various controls. Management of the reference database may include assigning probability weights to sequences comprising k-mers or portions thereof; selecting sequences associated with a particular entity or entity type; enrichment or depletion of sequences associated with a particular entity or entity type; combination of sequence information from one or more different databases (including locally generated databases); analysis of common genetic mutations; etc.

[0166] When the reference database consists of sequences associated with infectious diseases or contamination, the execution sequence may be derived from and associated with any of a variety of infectious agents. The infectious agent may be a bacterium. Non-limiting examples of bacterial pathogens include mycobacteria (e.g., Mycobacterium tuberculosis, Mycobacterium bovis, Mycobacterium avium, Mycobacterium leprae, and Mycobacterium africanum), Rickettsia, Mycoplasma, Chlamydia, and Legionella. Other examples of bacterial infections include, but are not limited to, infections caused by Gram-positive bacilli (e.g., Listeria, Bacillus such as Bacillus anthracis, Erysipelothrix), Gram-negative bacilli (e.g., Bartonella, Brucella, Campylobacter, Enterobacter, Escherichia, Francisella, Haemophilus, Klebsiella, Morganella, Proteus, Providencia, Pseudomonas, Salmonella, Serratia, Shigella, Vibrio, and Yersinia pestis species), spirochetes (e.g., Borrelia species including Borrelia burgdorferi that causes Lyme disease), anaerobic bacteria (e.g., Actinomycetes and Clostridium species), Gram-positive and Gram-negative cocci, Enterococcus species, Streptococcus species, Pneumococcus species, Staphylococcus species, and Neisseria species.Specific examples of infectious bacteria include, but are not limited to, Helicobacter pylori, Legionella pneumophila, Mycobacterium tuberculosis, Mycobacterium avium, Mycobacterium intracellulare, Mycobacterium kansasii, Mycobacterium gordonii, Staphylococcus aureus, Neisseria gonorrhoeae, Neisseria meningitidis, Listeria monocytogenes, Streptococcus pyogenes (Group A Streptococcus), Streptococcus agalactiae (Group B Streptococcus), Viridans Streptococcus, Streptococcus faecalis, Streptococcus bovis, Streptococcus pneumoniae, Haemophilus influenzae, Bacillus anthracis, Erysipelothrix rhusiopathiae, Clostridium tetani, Enterobacter aerogenes, Klebsiella pneumoniae, Pasteurella multocida, Fusobacterium nucleatum, Streptobacillus moniliformis, Treponema pallidum, Borrelia yawsii, Leptospira, Rickettsia and Actinomyces israelii, Acinetobacter, Bacillus, Bordetella, Borrelia burgdorferi, Brucella, Campylobacter, Chlamydia, Chlamydophila, Clostridium, Corynebacterium, Enterococcus, Haemophilus , Helicobacter, Mycobacterium, Mycoplasma, Stenotrophomonas, Treponema, Vibrio, Yersinia, Acinetobacter baumannii, Bordetella pertussis, Brucella abortus, Brucella canis, Brucella melitensis, Brucella suis, Campylobacter jejuni, Chlamydia pneumoniae, Chlamydia trachomatis, Chlamydia psittaci, Clostridium botulinum, Clostridium difficile, Clostridium perfringens, Corynebacterium diphtheriae, Enterobacter sakazakii, Enterobacter agglomerans, Enterobacter cloacae, Fecal Enterococci, Enterococcus faecalis, Escherichia coli, Francisella tularensis, Helicobacter pylori, Legionella pneumophila, Leptospira renalis, Mycobacterium leprae, Mycobacterium tuberculosis, Mycobacterium ulcerans, Mycoplasma pneumoniae, Pseudomonas aeruginosa, Rickettsia rickettsii, Salmonella typhi, Salmonella typhimurium, Salmonella enterica, Shigella sorrenii, Staphylococcus epidermidis, Staphylococcus saprophyticus, Stenotrophomonas maltophilia, Vibrio cholerae, Yersinia pestis, etc.

[0167] Sequences in the reference database can be associated with viral infectious agents. Non-limiting examples of viral pathogens include herpes viruses (e.g., human cytomegalovirus (HCMV), herpes simplex virus 1 (HSV-1), herpes simplex virus 2 (HSV-2), varicella zoster virus (VZV), Epstein-Barr virus), influenza A virus, and hepatitis C virus (HCV) (see Munger et al., Nature Biotechnology (2008) 26: 1179-1186; Syed et al., Trends in Endocrinology and Metabolism (2009) 21: 33-40; Sakamoto et al., Nature Chemical Biology (2005) 1: 333-337; Yang et al., Hepatology (2008) 48: 1396-1403) or picornaviruses such as coxsackievirus B3 (CVB3) (see Rassmann et al., Anti-viral Research (2007) 76: 150-158). Other viruses include, but are not limited to, hepatitis B virus, HIV, poxvirus, hepadnavirus, retrovirus; and RNA viruses such as flavivirus, togavirus, coronavirus, hepatitis D virus, orthomyxovirus, paramyxovirus, rhabdovirus, bunyavirus, filovirus, adenovirus, human herpes virus, type 8, human papillomavirus, BK virus, JC virus, smallpox, hepatitis B virus, human bocavirus, parvovirus B19, human astrovirus, Norwalk virus, coxsackievirus, hepatitis A virus, poliovirus, rhinovirus, severe acute respiratory syndrome virus, hepatitis C virus, yellow fever virus, dengue virus, West Nile virus, rubella virus, hepatitis E virus, and human immunodeficiency virus (HIV). In some cases, the virus is an enveloped virus. Examples include, but are not limited to, viruses belonging to members of the Hepadnaviridae, Herpesviridae, Iridoviridae, Poxviridae, Flaviviridae, Togaviridae, Retroviridae, Coronaviridae, Filoviridae, Rhabdoviridae, Bunyaviridae, Orthomyxoviridae, Paramyxoviridae, and Arenaviridae families.Other examples include, but are not limited to, hepatocellular carcinoma virus (HBV), woodchuck hepatitis virus, ground squirrel (hepatotropic virus) hepatitis virus, duck hepatitis virus (HBV), herpes simplex virus (HSV), varicella zoster virus, cytomegalovirus (CMV), human cytomegalovirus (HCMV), mouse cytomegalovirus (MCMV), guinea pig cytomegalovirus (GPCMV), Epstein-Barr virus (EBV), human herpesvirus 6 (HHV variants A and B), human herpesvirus 7 (HHV-7), human herpesvirus 8 (HHV-8), Kaposi's sarcoma-associated herpesvirus (KSHV), poxvirus B vaccinia virus, variola virus, smallpox virus (Variola virus, virus), monkeypox virus, cowpox virus, camelpox virus, foot loss virus, mousepox virus, rabbitpox virus, raccoonpox virus, molluscum contagiosum virus, aphthous stomatitis virus, milk tubercle virus, bovine papular stomatitis virus, sheeppox virus, goatpox virus, lumpy skin disease virus, fowlpox virus, canarypox virus, pigeonpox virus, sparrowpox virus, myxoma virus, rabbit fibroma virus, rabbit fibroma virus, squirrel fibroma virus, swinepox virus, tanapox virus, yabapox virus, flavivirus dengue virus, hepatitis C virus (HCV), GB hepatitis virus (GBV-A, GBV-B and GBV-C), West Nile virus, yellow fever virus, St. Louis encephalitis virus, Japanese encephalitis virus, Powassan virus, tick-borne encephalitis virus, Kitsana Forest disease virus, togavirus, Venezuelan equine encephalitis virus VTE virus, Chikungunya virus, Ross River virus, Mayaro virus, Sindbis virus, Rubella virus, Retroviruses Human Immunodeficiency Virus (HIV) type 1 and type 2, Human T-cell Leukemia Virus (HTLV) type 1, type 2 and type 5, Mouse Mammary Tumor Virus (MMTV), Rous Sarcoma Virus (RSV), Lentivirus, Coronavirus, Severe Acute Respiratory Syndrome (SARS) virus, Filovirus Ebola virus, Marburg virus, Metapneumovirus (MPV) such as human metapneumovirus (HMPV), Rabies virus, Vesicular Stomatitis virus, Bunyavirus, Crimean-Congo hemorrhagic fever virus, Rift Valley fever virus, La Crosse virus, Hanta virus, Orthomyxovirus, Influenza virus (A, B, C), Paramyxovirus, Parainfluenza virus (PIV 1, 2 and 3), respiratory syncytial virus (A and B), measles virus, mumps virus, arenavirus, lymphocytic choriomeningitis virus, Junin virus, Machupo virus, Guanarito virus, Lassa virus, Ampari virus, Flexivirus, Ipai virus, Mobala virus, Mopea virus, Latino virus, Parana virus, Pichinde virus, Ponta Toru virus (PTV), Takaribbe virus and Taimia virus.In some embodiments, the virus is a non-enveloped virus, examples of which include, but are not limited to, viruses belonging to members of the Parvoviridae, Circoviridae, Polyomaviridae, Papillomaviridae, Adenoviridae, Iridoviridae, Reoviridae, Birnaviridae, Caliciviridae, and Picornaviridae families. Specific examples include, but are not limited to, canine parvovirus, parvovirus B19, porcine circovirus type 1 and type 2, BFDV (beak and feather disease virus, chicken anemia virus, polyomavirus, simian virus 40 (SV40), JC virus, BK virus, psittacine chick disease virus, human papillomavirus, bovine papillomavirus (BPV) type 1, cottontail rabbit papillomavirus, human adenovirus (HAdV-A, HAdV-B, HAdV-C, HAdV-D, HAdV-E, HAdV-F), avian adenovirus A, bovine adenovirus D, frog adenovirus, reovirus, human circovirus, human coltivirus, mammalian orthoreovirus, blue tongue virus, rotavirus A, rotavirus (groups B to G), Colorado tick fever virus, aquatic reovirus type A, cytoplasmic polyhedrosis virus type 1, Fiji disease virus, rice dwarf disease Viruses include, but are not limited to, polioviruses, rice toothed dwarf virus, insect non-occluded virus type 1, fungal reovirus type 1, bi-RNA virus, bursal disease virus, pancreatic necrosis virus, calicivirus, swine vesicular exanthema virus, rabbit hemorrhagic disease virus, Norwalk virus, sapovirus, picornavirus, human poliovirus (1-3), human coxsackievirus AI-22, 24 (CA1-22 and CA24, CA23 (echovirus 9)), human coxsackievirus (Bl6 (CB16)), human echovirus 1-7, 9, 11-27, 29-33, vilyuish virus, simian enterovirus 1-18 (SEV1-18)), porcine enterovirus 1-11 (PEV1-11), bovine enterovirus 1-2 (BEV1-2), hepatitis A virus, rhinovirus, hepatovirus, cardiovirus, foot-and-mouth disease virus and echovirus. The virus may be a bacteriophage. Examples of bacteriophages include, but are not limited to, T4, T5, lambda phage, T7 phage, G4, P1,. Attachment to Thermoproteus tenax virus 1, M13, MS2, Qβ, PZA, BS32, B103, M2Y (M2), Nf, GA-1, FWLBc1, FWLBc2, FWLLm3, B4. The reference database may contain sequences of pathogenic, protective, or both phages. In some embodiments, the virus is selected from members of the Flaviviridae family (e.g., members of the genera Flavivirus, Pestivirus, and Hepacivirus), including hepatitis C virus, yellow fever virus; tick-borne viruses such as Gadgezi Valley virus, Kedam virus, Kitsana Forest disease virus, Langat virus, Omsk hemorrhagic fever virus, Powassan virus, Royal Farm virus, Karshi virus, tick-borne encephalitis virus, Neudoerfl virus, Sofjin virus, jumping disease virus, and Negishi virus; seabird tick-borne viruses such as Meaban virus, Somalia virus, and Chulini virus; mosquito-borne viruses such as Aroa virus, dengue virus, Kedo virus, Kasipacoli virus, Cotango virus, Japanese encephalitis virus, Murray Valley encephalitis, St. Louis encephalitis virus, Usutu virus, Sini virus, and Sini virus. Luo River virus, Yaounde virus, Kokobela virus, Bagza virus, Ilheus virus, Israeli Turkey meningomyelitis virus, Ntaya virus, Tembusu virus, Zika virus, Banzi virus, Boboy virus, Eje Mountain virus, Jugra virus, Saboya virus, Sepik virus, Uganda S virus, Wessels Brown virus, yellow fever virus; and viruses with unknown arthropod vectors, such as Entebbe bat virus, Yokosuka virus, Abo virus, Bovine Ridge virus, Jutiapa virus, Modoc virus, Salviejo virus, Santa Parrita virus, Bukarassa bat virus, Carey Island virus, Dakar bat virus, Montana mouse-eared bat leukoencephalitis virus, powdered bat virus, Rio Bravo virus, Tamana bat virus and cell fusogen virus. In some embodiments, the virus is selected from a member of the Arenaviridae family, including Ipivirus, Lassa virus (e.g., Josiah strain, LP strain, or GA391 strain), lymphocytic choriomeningitis virus (LCMV), Mobala virus, Mopeya virus, Amapari virus, Flexo virus, Guanarito virus, Junin virus, Latino virus, Machupo virus, Olives virus, Parana virus, Pichinde virus, Piritao virus, Sabia virus, Takarib virus, Taimiami virus, Whitewater River virus, Chapare virus, and Luyo virus. In some embodiments, the virus is selected from a member of the Bunyaviridae family (e.g., members of the genera Hantavirus, Nairovirus, Bunyavirus, and Phlebovirus), including Hantavirus, Sin Nombre virus, Dagbi virus, Bunyawila virus, Rift Valley fever virus, La Crosse virus, Punta Toru virus (PTV), California encephalitis virus, and Crimean-Congo hemorrhagic fever (CCHF) virus.In some embodiments, the virus is selected from members of the Filoviridae family, which includes Ebola virus (e.g., Zaire, Sudan, Ivory Coast, Reston, and Uganda) and Marburg virus (e.g., Angel, Ci67, Mussoko, Popp, Ravn, and Lake Victoria); members of the Togaviridae family (e.g., members of the genus Alphavirus), which includes Venezuelan equine encephalitis virus (VEE), Eastern equine encephalitis virus (EEE), Western equine encephalitis virus (WEE), Sindbis virus, Rubella virus, Semliki Forest virus, Ross River virus, Bama Forest disease virus, members of the Poxviridae family (e.g., members of the genus Orthopoxvirus), which include smallpox virus, monkeypox virus, and vaccinia virus; members of the Herpesviridae family, which include herpes simplex virus (HSV; types 1, 2, and 6), human herpesvirus (e.g., types 7 and 8), cytomegalovirus (CMV), Epstein-Barr virus (EBV), varicella-zoster virus, and Kaposi's sarcoma-associated herpesvirus (KSHV); members of the Orthomyxoviridae family, which include influenza viruses (A, B, and C), such as H5N1 avian influenza virus or H1N1 swine influenza; coronavirus members of the Viridae family, which includes severe acute respiratory syndrome (SARS) virus; members of the Rhabdoviridae family, which includes rabies virus and vesicular stomatitis virus (VSV); members of the Paramyxoviridae family, which includes human respiratory syncytial virus (RSV), Newcastle disease virus, Hendra virus, Nipah virus, measles virus, rinderpest virus, canine distemper virus, Sendai virus, human parainfluenza viruses (e.g., 1, 2, 3, and 4), rhinoviruses, and mumps virus; members of the Picornaviridae family, which includes poliovirus, human enteroviruses (A, B, C, and D), hepatitis A virus, and coxsackieviruses; members of the Hepadnaviridae ... human respiratory syncytial virus (RSV), Newcastle disease virus, Hendra virus, Nipah virus, measles virus, rinderpest virus, canine distemper virus, Sendai virus, human parainfluenza viruses (e.g., 1, 2, 3, and 4), rhinoviruses, and mumps virus; members of the Picornaviridae family, which includes poliovirus, human enteroviruses (A, B, C, and D), hepatitis A virus, and coxsackieviruses; members of the Hepadnaviridae family, which includes members of the Viridae family, which include hepatitis B virus; members of the Papillomaviridae family, which include human papillomavirus; members of the Parvoviridae family, which include adeno-associated virus; members of the Astroviridae family, which include astrovirus; members of the Polyomaviridae family, which include JC virus, BK virus, and SV40 virus; members of the Caliciviridae family, which include Norwalk virus; members of the Reoviridae family, which include rotavirus; and members of the Retroviridae family, which include human immunodeficiency virus (HIV; e.g., types 1 and 2) and human T-lymphotropic virus types I and II (HTLV-1 and HTLV-2, respectively).

[0168] The anti-toxic resistance gene can be associated with a virulent strain as described elsewhere herein. In some embodiments, the anti-toxic resistance gene can be unique for a specific virulent strain, or shared by several virulent strains. The example of virulence gene includes but is not limited to various toxins and pathogenicity factor genes, such as those encoding immunoglobulin binding proteins, serum opacity factors, M proteins, C5a peptidases, Fc binding proteins, collagenases, hyaluronate lyases, streptococcal pyrogenic exotoxins, mitogenic factors, αC proteins, fibrinogen binding proteins, fibronectin binding proteins, coagulase, enterotoxins, exotoxins, leukocidins or V8 proteases. In some embodiments, the gene conferring toxicity resistance can be present on a plasmid in a cell.

[0169] The infectious agent that can be associated with the sequence in the reference database can be a fungus. Examples of infectious fungal agents include, but are not limited to, at least three genera of Aspergillus, Blastomyces, Coccidioides, Cryptococcus, Histoplasma, Paracoccidioides, Sporothrix, and Zygomycetes. Fungal agents may be associated with various diseases and conditions of humans, companion animals, and other species. For example, fungal agents may be associated with rashes, including diaper rashes. Examples of organisms that cause disease in animals include Malassezia furfur, Epidermophyton floccosur, Trichophyton mentagrophytes, Trichophyton rubrum, Trichophyton tonsurans, Trichophyton equinum, Dermatophilus congolensis, Microsporum canis, Microsporu audouinii, Microsporum gypseum, Malassezia ovale, Pseudallescheria, Scopulariopsis, Scedosporium, and Candida albicans. Other examples of fungal infection agents include, but are not limited to, Aspergillus, Blastomyces dermatitidis, Candida, Coccidioides immitis, Cryptococcus neoformans, Histoplasma capsulatum var. capsulatum, Paracoccidioides brasiliensis, Sporothrix schenckii, Zygomycetes, Absidia agaricoides, Rhizomucor pusillus, and Rhizopus arrhizus.

[0170] Another example of an infectious agent to which a sequence in a reference database may be associated is a parasite. Non-limiting examples of parasites include Plasmodium, Leishmania, Babesia, Treponema, Borrelia, Trypanosoma, Toxoplasma gondii, Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale, Plasmodium malariae, Trypanosoma, or Legionella.

[0171] The reference database may combine sequences associated with different infectious agents (e.g., reference sequences associated with infection with multiple bacterial agents, multiple viral agents, and multiple fungal agents). In addition, the reference database may contain sequences identified as being derived from pathogens that have not yet been identified or classified.

[0172] Reference sequences associated with illness also include genetic markers for drug resistance, pathogenicity and disease. A variety of disease-associated markers are known, which can be represented in a reference database. Disease-associated markers can be causal genetic variants. Generally speaking, causal genetic variants are genetic variants with statistical, biological and / or functional evidence associated with a disease or a trait. A single causal genetic variant can be associated with more than one disease or trait. In some embodiments, pathogenic genetic variants can be associated with Mendelian traits, non-Mendelian traits or both. Causal genetic variants can be expressed as variations in polynucleotides, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50 or more sequence differences (such as between polynucleotides comprising causal genetic variants and polynucleotides lacking causal genetic variants at the same relative genomic position). The non-limiting examples of causal genetic variant types include single nucleotide polymorphism (SNP), deletion / insertion polymorphism (DIP), copy number variant (CNV), short tandem repeat (STR), restriction fragment length polymorphism (RFLP), simple sequence repeat (SSR), variable number tandem repeat (VNTR), random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), retrotransposon inter-amplified polymorphism (IRAP), long and short interspersed elements (LINE / SINE), long tandem repeat (LTR), mobile element, retrotransposon microsatellite amplified polymorphism, insertion polymorphism based on retrotransposon, sequence-specific amplified polymorphism and heritable epigenetic modification (e.g., DNA methylation). Causal genetic variants can also be closely related causal genetic variant sets. Some causal genetic variants can be used as sequence variation in RNA polynucleotides to play an impact. At this level, the presence or absence of a kind of RNA polynucleotides also shows some causal genetic variants. And some causal genetic variants cause sequence variation in protein polypeptides. There are multiple causal genetic variants. An example of a causal genetic variant that is a SNP is the Hb S variant of hemoglobin that causes sickle cell anemia. An example of a causal genetic variant that is a DIP is the delta 508 mutation of the CFTR gene that causes cystic fibrosis. An example of a causal genetic variant that is a CNV is trisomy 21 that causes Down syndrome. An example of a causal genetic variant that is a STR is the tandem duplication that causes Huntington's disease. Other non-limiting examples of causal genetic variants are described in WO2014015084A2 and US20100022406. Examples of drug resistance markers include enzymes that confer resistance to various aminoglycoside antibiotics, such as G418 and neomycin (e.g., aminoglycoside 3'-phosphotransferase, 3'APH II, also known as neomycin phosphotransferase II (nptII or "neo")), Zeocin TMor bleomycin (e.g., a protein encoded by the ble gene from Streptoalloteichus hindustanus), hygromycin (e.g., the hygromycin resistance gene, hph, from Streptomyces hygroscopicus or a plasmid isolated from Escherichia coli or Klebsiella pneumoniae encoding a kinase that inactivates hygromycin B by phosphorylation (hygromycin phosphotransferase, HPT), puromycin (e.g., the Streptomyces alboniger puromycin-N-acetyltransferase (pac) gene, or blasticidin (e.g., an acetyltransferase encoded by the bls gene from Streptomyces JCM 4673, or an acetyltransferase encoded by a gene such as bsr from Bacillus cereus or a plasmid from Aspergillus terreus terreus). Other drug resistance markers include, for example, dihydrofolate reductase (DHFR), adenosine deaminase (ADA), thymidine kinase (TK), and hypoxanthine-guanine phosphoribosyltransferase (HPRT). Proteins such as P-glycoprotein and other multidrug resistance proteins act as pumps through which various cytotoxic compounds, such as chemotherapeutic agents such as vinblastine and anthracyclines, are excreted from cells. Markers of pathogenicity include, for example, factors involved in outer membrane protein expression, microbial toxins, factors involved in biofilm formation, factors involved in carbohydrate transport and metabolism, factors involved in cell envelope synthesis, and factors involved in lipid metabolism. Markers of pathogenicity may include, but are not limited to, for example, gp120, Ebola virus envelope proteins or other glycosylated viral envelope proteins or viral proteins.

[0173] The reference database may be composed of host expression profiles associated with a healthy state and / or one or more disease states, wherein certain combinations of expressed genes (or expression levels of specific genes) identify a condition in a subject. The genomes may be overlapping. The reference database composed of sequences associated with a condition may include host expression profiles and groups of sequences associated with other conditions (e.g., reference sequences associated with various infectious agents).

[0174] For example, reference databases may include sequences associated with contamination, such as polynucleotides and / or amino acid sequences from food contaminants, surface contaminants, or environmental contaminants. Examples of common food contaminants are Escherichia coli, Clostridium botulinum, Salmonella, Listeria, and Vibrio cholerae. Examples of surface contaminants are Escherichia coli, Clostridium botulinum, Salmonella, Listeria, Vibrio cholerae, influenza virus, methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus, Pseudomonas, Acinetobacter, Clostridium difficile, and Norovirus. Examples of environmental contaminants are fungi, such as Aspergillus and Wallemia sebi; vesicular algae, such as dinoflagellates; amoebas; viruses; and bacteria. The contaminant may be an infectious agent, examples of which are provided herein.

[0175] In some embodiments, the database of the reference sequence comprises a polynucleotide sequence reversely translated from an amino acid sequence. In this context, translation refers to the process of determining an amino acid sequence from a nucleotide sequence using a codon code. The standard codon code is degenerate, so that multiple trinucleotide codons encode the same amino acid. Therefore, reverse translation generally produces a variety of possible sequences that can encode a specific amino acid sequence. In some embodiments, in order to simplify the process, reverse translation can use a non-degenerate code so that each amino acid is represented by only a single codon. For example, in the standard DNA codon system, phenylalanine is encoded by "TTT" and "TTC". The non-degenerate code can only associate one of the codons with phenylalanine. The sequencing reads can be compared with the non-degenerate, reverse-translated sequence by any method described herein. In addition, the sequencing reads can be translated into all six reading frames and reversely translated using the same non-degenerate code to generate six polynucleotides that do not include alternative codons before comparison. By reverse translating the reference amino acid sequence, comparing it with the translated sequencing reads, and then reversely translating using the same reverse translation code, the nucleic acid sequence can be analyzed in the protein space.

[0176] Access to the reference database may be provided via a network-based connection. Alternatively, the reference database may be stored locally, or may be stored in an accessible cloud, network, or mobile location.

[0177] The reference database may be updated manually and / or by computer. The reference database may require expert knowledge to manually collect, correct and / or annotate the classification database data. The reference database may be updated by crowdsourcing. The reference database may be changed as described elsewhere herein.

[0178] Sequence assembly

[0179] Assembling sequences from sequencing reads associated with a given sample (e.g., sequencing reads identified via sequencing assays as described herein, and / or sequencing reads assigned to a given control) may include using k-mer analysis, probabilistic analysis, taking into account other sequencing reads or portions thereof included in a given sample, taking into account knowledge of the contents and / or source of a given sample, comparison with one or more reference databases, and the like. Identifying sequences associated with a given sample or control may include exact sequence matches. However, certain sequences are known to be conserved among multiple species of a given classification, sometimes with only minor base differences. Therefore, identifying microorganisms and pathogens in a given sample or control at the species level may require more rigorous analysis, as described herein. Identifying sequences associated with a given sample or control may include consensus analysis. Identifying sequences associated with a given sampler or control may include identifying one or more genes, including antimicrobial resistance genes.

[0180] K-mer analysis

[0181] As a supplement or alternative to exact sequence matching, k-mer analysis can be used to identify sequences corresponding to various sources (such as various microorganisms and / or viruses). The reference sequence in a given database of a reference sequence can be associated with a k-mer of a given length (e.g., before being compared with the collected sequence). Before comparison, each reference sequence in the reference sequence database can be associated with a k-mer weight as a measure of the possibility that the k-mer in the reference sequence is derived from the reference sequence. Alternatively, the reference sequence database may include sequences from multiple taxa, and each reference sequence in the reference sequence database is associated with a k-mer weight as a measure of the possibility that the k-mer in the reference sequence is derived from the taxa in multiple taxa. Calculating k-mer weights may include comparing the reference sequence in the database with other reference sequences in the database, such as by the method described herein. Therefore, the k-mer value associated with the sequence or taxa in the database can then be used to determine the k-mer weight of the k-mer in the sequencing read.

[0182] Comparing a sequence (e.g., a nucleic acid sequence, such as a sequencing read, or an amino acid sequence) to a k-mer in a reference sequence may include counting the k-mer matches between the two. The stringency of identifying a match may vary. For example, a match may be an exact match, in which the nucleotide sequence of the k-mer from the sequencing read is identical to the nucleotide sequence of the k-mer from the reference sequence. Alternatively, a match may be an incomplete match, in which 1, 2, 3, 4, 5, 10 or more mismatches between the k-mer of the sequencing read and the k-mer of the reference sequence are allowed. In addition to counting matches, a likelihood (also referred to as a "k-mer weight" or "KW") may also be calculated. The k-mer weight may involve the count of a specific k-mer within a specific reference sequence, the count of a specific k-mer in a set of sequences comprising the reference sequence, and the count of a specific k-mer in all reference sequences in a reference sequence database. In one embodiment, the k-mer weight is calculated according to the following formula, which calculates the k-mer weight as a specific k-mer (K i ) is derived from the reference sequence (ref i ) is as follows:

[0183]

[0184] Where C means return K i The function of the count, C ref (K i ) indicates K in a specific benchmark sequence i The count, C db (K i ) indicates the K in the database i The total kmer count is the total number of kmers in the database. The weight provides a relative, database-specific measure of the likelihood that a k-mer is derived from a particular reference. However, other measures for weighting k-mers are possible. For example, in some embodiments, the k-mer weight is calculated according to the following formula, which calculates the k-mer weight as a function of the specific k-mer (K i ) is derived from the reference sequence (ref i ) is as follows:

[0185] KWref i (K i )=C ref (K i ) / C db (K i ) (Formula.2)

[0186] Where C means return K i The function of the count, and C ref (Ki ) indicates the K in a particular reference sequence i In other embodiments, the k-mer weight is calculated according to the following formula, which calculates the k-mer weight as a count of a particular k-mer (K i ) is derived from the reference sequence (ref i ) is as follows:

[0187]

[0188] Where C means return K i The function of the count, C ref (K i ) indicates K in a specific benchmark sequence i The count, C db (K i ) indicates the K in the database i The total kmer count is the total number of kmers in the database, and x is the base of the logarithm (e.g., 10, π, or any other base). In other embodiments, the k-mer weight is calculated according to the following formula, which calculates the k-mer weight as a specific k-mer (K i ) is derived from the reference sequence (ref i ) is as follows:

[0189]

[0190] Where C means return K i The function of the count, C ref (K i ) indicates K in a specific benchmark sequence i The count, C db (K i ) indicates the K in the database i where the total kmer count is the total number of kmers in the database, and x is the base of the logarithm (e.g., 10, π, or any other base).

[0191] Before comparing the sequencing reads to the reference sequence database, a k-mer weight (or a measure of the likelihood that the k-mer is derived from a given reference sequence) may be calculated for each k-mer in the database and the reference sequence. In some embodiments, when the reference database contains sequences from multiple taxa, each reference sequence may be associated with a measure of the likelihood that the k-mer within the reference sequence is derived from the multiple taxa, or a k-mer weight. As a non-limiting example, the reference database may contain sequences from multiple canine species, and the k-mer weight may be calculated by associating the count of a given k-mer in all canine sequences with its count in the entire database (including other taxa). In some examples, by substituting C in the above equation for ref (K i ) is defined as returning the K i The k-mer weights that measure the likelihood that the k-mer originated from a particular taxon are calculated as a function of the total counts of

[0192] For each reference sequence, the weights derived from the reference database of multiple k-mers in the sequencing read can be added and compared with the threshold. The threshold can be specific to the reference sequence set in the database, and can be selected based on a variety of factors, such as whether the average read length, a specific sequence or a source organism will be identified as being present in the sample. The threshold can be changed by the user. If the sum of the k-mer weights of the reference sequence is higher than the threshold level, the sequencing read can be identified as corresponding to the reference sequence, and optionally corresponds to an organism or taxonomic group associated with the reference sequence. In some embodiments, the read is assigned to the reference sequence with the maximum sum of k-mer weights, which may or may not need to be higher than the threshold. In the case of parallel, where the sequence read has an equal probability of belonging to more than one reference sequence, as measured by the k-mer weight, the sequence read can be assigned to the taxonomically lowest common ancestor (LCA) taking into account the total k-mer weight of each branch of the read along the phylogenetic tree. In general, the corresponding relationship with the reference sequence, organism or taxonomic group indicates that it is present in the sample.

[0193] In some embodiments, the present disclosure includes calculating probability. In some embodiments, the probability of sequencing reads generated from multiple polynucleotides is calculated. In some embodiments, the probability is the probability (or possibility) that the sequencing read corresponds to a specific reference sequence in a reference sequence database based on k-mer weights. The probability of each sequencing read can be calculated, thereby generating multiple sequence probabilities. In some embodiments, the presence or absence of one or more taxa in a sample can be determined based on sequence probability. For example, the probability can identify the first bacterial strain as being present in a sample and the second bacterial strain as not being present in the sample. In some embodiments, the probability is expressed as a percentage (%) or a score. In some embodiments, the presence or absence of one or more genes in a sample can be determined based on sequence probability. For example, the probability can identify the first gene as being present in a sample and the second gene as not being present in a sample. In some embodiments, the probability is expressed as a percentage (%) or a score. In some embodiments, the probability is provided as a score representing the probability. The score can be based on any arbitrary scale, as long as the score indicates a probability (e.g., the probability that a single sequence corresponds to a specific reference sequence, the probability that a specific taxa is present in a sample, or the probability that a single sequence corresponds to a specific reference sequence). This probability or the score representing this probability can be used to determine the presence or absence of one or more taxa in a sample. For example, a probability or score higher than a threshold value can indicate presence, and / or a probability or score lower than a threshold value can indicate absence. This probability or the score representing this probability can be used to determine the presence or absence of one or more genes (for example, one or more antimicrobial resistance genes, antiprotozoan resistance genes, antiviral resistance genes, anti-toxic resistance genes, antifungal resistance genes, antiparasitic genes, etc.) in a sample. This probability or the score representing this probability can be used to determine the presence or absence of one or more genes in a sample. In some embodiments, presence or absence is reported as probability rather than absolute call. This paper provides an example method for calculating such probability. Generally speaking, the example described herein about presence or absence also includes calculating this probability or score of presence or absence.

[0194] Sequence identification

[0195] One or more steps of the methods described herein may be performed in parallel for each of a plurality of sequenced reads (e.g., a plurality of sequenced reads generated from a nucleic acid sequencing process). For example, each of the sequenced reads in the plurality of sequenced reads may be subjected in parallel to a first sequence comparison between the sequenced read and a plurality of reference polynucleotide sequences (e.g., reference polynucleotide sequences from a plurality of different taxa and / or a plurality of different reference databases). Parallel comparison may be different from certain stepwise comparison processes, because sequenced reads with a claimed match in a first reference database may not be subtracted from the query group of sequences for subsequent comparison with a second reference database. In such a stepwise process, a sequence with a claimed match in the first database may be erroneously identified before being compared with a reference database containing a more accurate match (e.g., a correct sequence). In contrast, by comparing a plurality of different reference sequences corresponding to a plurality of different taxa, each sequence may be assigned to the best first taxonomic classification before identifying the sequence or taxa to which the sequence read corresponds with higher specificity. For example, a sequenced read may first be classified as corresponding to a human, bacterial, or fungal sequence before identifying a particular gene, bacterial species, or fungal species to which the sequenced read corresponds. In some cases, this process is referred to as "pooling". Parallel sequence comparisons can include comparisons to sequences from two or more different taxonomic groups, such as 3, 4, 5, 6 or more different taxonomic groups. In some embodiments, the different taxonomic groups can be selected from two or more of the following: bacteria, archaea, viruses, fungi, plants, fish, amphibians, reptiles, birds, mammals, and humans.

[0196] The component in the identification sample may further include the amount of the polynucleotides corresponding to the reference sequence identified in the earlier step. The accuracy of the quantitative method may depend on the sequencing method and / or pretreatment method used to analyze the sample, and the detailed information (e.g., as described herein) of sample collection, storage and preparation. The quantitative method can analyze the absolute amount or relative amount of the component in a given sample. The number of quantitatively identified corresponding sequencing reads can be based on. The number of quantitatively identified corresponding sequencing reads associated with a specific gene (e.g., antimicrobial resistance gene, antiviral resistance gene, anti-toxic resistance gene, anti-protozoal resistance gene, antifungal resistance gene, anti-parasitic resistance gene, etc.). This may include normalizing counts by the total number of reads, the total number of reads associated with the sequence, the length of the reference sequence, or a combination thereof. Examples of such normalization include fragments per kilobase of transcript per million mapped reads (FPKM) and reads per kilobase of transcript per million mapped reads (RPKM), but may also include other methods that take into account the relative amounts of reads in different samples, such as normalizing sequencing reads from samples by the median count ratio observed for each sequence. The difference in amount between samples may indicate a difference between two samples. Quantification can be used to identify differences between subjects, such as comparing taxa present in the microbiota of subjects with different diets, or observing changes in the same subject over time, such as observing taxa present in the microbiota of subjects before and after a specific diet. The quantification can be used to guide the remedial treatment of the subject. In some embodiments, the quantification of antimicrobial genes can guide the use of antimicrobial drugs or combination therapeutics. In some embodiments, quantification can be used to select treatments that weaken or eliminate the expression or protein activity of antimicrobial resistance genes (e.g., by antisense RNA, RNA interference (RNAi) sequences, antibodies, or small molecule inhibitors).

[0197] Method can include determining the presence, absence or abundance of a specific taxon or nucleotide polymorphism in a sample based on the result of an earlier step. The multiple reference polynucleotide sequences can include a sequence group corresponding to a single taxon in the multiple taxon. In some embodiments, based on the sequence analyzed by the method described herein, at least 50, 100, 250, 500, 1000, 5000, 10000, 50000, 100000, 250000, 500000 or 1000000 different taxon can be identified as not existing or existing (and optionally abundance, which can be relative). In some embodiments, the analysis can be performed in parallel. When the identified taxon accounts for less than 1 / 10 of the total population of the taxon in the source sample, the total abundance of the taxon can be determined. 9 , or 1 / 10 6, or 0.05%, the methods, compositions and systems of the present disclosure can realize parallel detection of the presence or absence of taxa in a community of taxa (such as an environment or clinical sample). Detection can be based on sequencing reads corresponding to polynucleotides present in less than 0.01% of the total nucleic acid population. The homology of a specific polynucleotide to other nucleic acids in the population can be at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96% or 97%. In some embodiments, the homology of a specific polynucleotide to other nucleic acids in the population is less than 75%, 50%, 40%, 30%, 20% or 10%. Determining the presence, absence or abundance of a specific taxa may include identifying an individual subject as a sample source. For example, a reference database may include multiple reference sequences, each of which corresponds to a single organism (e.g., a human subject), wherein sequences from multiple different subjects are represented in the reference sequence. The sequence reads of the unknown sample can then be compared to the sequences of the reference database, and based on identifying the sequence reads according to the method, the individual represented in the reference database can be identified as the sample source of the sequence reads. In this case, the reference database may include sequences from at least 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 A sequence of one or more individuals.

[0198] In some embodiments, sequencing reads do not match reference sequences at the level of a specific taxonomic group (e.g., species level) or at any taxonomic level. When no match is found, the corresponding sequence can be added to a reference database based on known features. In some embodiments, when a sequence is identified as belonging to a specific taxonomic group in the multiple taxonomic groups and is not present in the sequence group corresponding to the taxonomic group, it can be added to the sequence group corresponding to the taxonomic group for subsequent sequence comparison. For example, if a bacterial genome is identified as belonging to a specific taxonomic group, such as a genus or a family, but the genome comprises a sequence that is not present in a sequence associated with the taxonomic group, the bacterial genome can be added to a sequence database. Similarly, if a sample is derived from a specific source or condition, sequencing reads can be added to a reference database of a sequence associated with the source or condition for identifying future samples sharing the same source or condition. As another example, as identified according to the method described herein, a sequence that does not have a match at a lower level but has a match at a higher level can be assigned to the higher level, while sequencing reads are also added to the multiple reference sequences corresponding to the taxonomic group. The reference database updated in this way can be used for subsequent sequence comparisons.

[0199] When determining the presence, absence or abundance of a taxon in a plurality of taxa (or a polymorphism in a plurality of polymorphisms), two possible taxa may be juxtaposed to specify a particular sequencing read. In this case, the juxtaposition may be resolved. In one example, the juxtaposition is resolved by determining the sum of the k-mer weights of the reference sequence along each branch of a phylogenetic tree connecting the taxa. The sequencing read may then be assigned to the node connected to the branch with the highest sum of the k-mer weights.

[0200] The method may include determining the presence, absence or abundance of a specific gene (e.g., an antimicrobial resistance gene, an antiviral resistance gene, an antifungal resistance gene, an antiprotozoal resistance gene, or an antiparasitic resistance gene, etc.) or a gene product (e.g., mRNA, protein product) in a sample based on the results of an earlier step. In this case, the plurality of reference polynucleotide sequences typically include a sequence group corresponding to a gene in the plurality of genes. In some embodiments, at least 50, 100, 250, 500, 1000, 5000, 10000, 50000, 100000, 250000, 500000 or 1000000 different genes are identified as absent or present (and optionally abundance, which may be relative) based on the sequences analyzed by the methods described herein. In some embodiments, the analysis is performed in parallel. In some embodiments, when the identified genes account for less than 1 / 10 of the total population of genes in the source sample, the sample is analyzed. 9 , or 1 / 10 6, or 0.05%, the methods, compositions and systems of the present disclosure realize parallel detection of the presence or absence of genes in a community (such as an environment or clinical sample) of a gene. In some embodiments, the detection is based on sequencing reads corresponding to polynucleotides present in less than 0.01% of a total nucleic acid population. The homology of a specific polynucleotide to other nucleic acids in a population may be at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96% or 97%. In some embodiments, the homology of a specific polynucleotide to other nucleic acids in a population is less than 75%, 50%, 40%, 30%, 20% or 10%. Determining the presence, absence or abundance of a specific gene or gene product may include identifying an individual subject as a sample source. For example, a reference database may include multiple reference sequences, each of which corresponds to a single organism (e.g., a human subject), wherein sequences from multiple different subjects are represented in the reference sequence. The sequence reads of the unknown sample can then be compared to the sequences of the reference database, and based on identifying the sequence reads according to the method, the individual represented in the reference database can be identified as the sample source of the sequence reads. In this case, the reference database may include sequences from at least 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 A sequence of one or more individuals.

[0201] When determining the presence, absence or abundance of genes in multiple genes, two possible genes can be juxtaposed to specify a specific sequencing read. In this case, the juxtaposition can be resolved. In one example, the juxtaposition is resolved by determining the sum of the k-mer weights of the reference sequence along each branch of the phylogenetic tree connecting the taxa related to the associated gene. The sequencing read can then be assigned to the node connected to the branch with the highest k-mer weight sum. In one example, the juxtaposition is resolved by determining.

[0202] In the case where the reference database is composed of sequences related to the condition, the method may include the conditions of the source from which the sample or sample is derived. The disease can be identified based on the presence or change of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 100% of the components of the biomarker. Alternatively, the disease can be identified based on the presence or change of less than 20%, 10%, 1%, 0.1%, 0.01%, 0.001%, 0.0001% or 0.00001% of the components of the biomarker. If at least, for example, about 80% of the sequences and / or taxa associated with the disease are identified as existing (or existing at a level associated with the disease), the sample can be identified as being affected by the disease. If at least, for example, about 80% of the sequences and / or genes associated with the disease are identified as existing (or existing at a level associated with the disease), the sample can be identified as being affected by the disease. If there are at least, for example, at least about 90%, 95%, 99% or more (e.g., all) sequences or taxa (or the amount of these) associated with a disorder, the sample can be identified as being affected by the disorder. If there are at least, for example, about 90%, 95%, 99% or more (e.g., all) sequences or genes (or the amount of these) associated with a disorder, the sample can be identified as being affected by the disorder. Wherein the disorder is a disorder from a specific individual, such as an individual subject (e.g., a person from a database of sequences of multiple different people), identifying that the sample is affected by the disorder includes identifying the individual corresponding to the sequence in the database from which the sample is derived. Identifying a subject as a sample source can be based only on a portion of the subject's genomic sequence (e.g., less than about 50%, 25%, 10%, 5% or less).

[0203] The presence, absence or abundance of a specific sequence, polymorphism, gene (e.g., antimicrobial antiviral, antiviral resistance, antitoxic resistance, antifungal resistance, antiparasitic resistance, antiprotozoal resistance, etc.) or gene product or taxon can be used for diagnostic purposes, such as inferring that a sample or subject suffers from a specific condition (e.g., disease), has suffered from a specific condition, or may develop into a specific condition if a sequence read associated with the condition (e.g., from a specific pathogenic organism) is present at a level higher than a control (e.g., an uninfected individual). For another example, sequencing reads can be derived from a host and indicate the presence of a pathogenic organism by measuring the presence, absence or abundance of host genes in a sample. For another example, sequencing reads can be derived from a host and indicate the presence of a pathogenic gene by measuring the presence, absence or abundance of genes in a sample. Presence, absence or abundance can be used to determine the need for intervention, such as medical intervention and / or other treatment regimens and their detailed information. For example, the presence, absence or abundance of a given microorganism or virus in a sample can inform the need for medical intervention (e.g., medical treatment or care), inform the choice of treatment regimen and the intensity and / or aggressiveness of the intervention, and provide insight into the effectiveness of a given treatment regimen and / or other interventions, wherein a reduction in the number of sequencing reads from a pathogenic factor during or after the completion of the treatment regimen, or a change in the presence, absence or abundance of a specific host response gene indicates that the treatment regimen may be effective, while no change or insufficient change indicates that the treatment regimen may be ineffective. The sample may be measured before the start of treatment or measured once or more after the start of treatment. In some examples, the treatment of an infected subject may be changed based on the monitoring results. The identification of pathogens or other elements in a sample (e.g., a subject in a sample) may also inform other interventions, including practical interventions. Examples of such interventions may include: how others, including visitors and medical staff, interact with the subject, including the use of personal protective equipment (PPE) and potential isolation recommendations; equipment and locations suitable for nursing subjects; and the frequency and extent of cleaning of equipment and locations used to care for subjects.

[0204] In some embodiments, one or more samples (e.g., blood, plasma, other body fluids, tissues, swab samples, etc.) with known conditions can be used to establish biomarkers for the conditions. Biomarkers can be established by associating a record database with conditions. Biomarkers can be established by associating the presence, absence, or abundance of the various genes with conditions. The disease can be any disease described herein. For example, multiple samples from a specific environmental source can be used to identify sequences and / or taxa and / or genes associated with the environmental source, thereby establishing biomarkers composed of those sequences and / or taxa so associated. In some embodiments, multiple samples from a specific environmental source can be used to identify sequences and / or genes associated with the environmental source, thereby establishing biomarkers composed of those sequences and / or taxa so associated. In general, the term "biomarker" is used to refer to the association of the presence, absence, or abundance of multiple sequences and / or taxa and / or genes with specific diseases (such as classification, diagnosis, prognosis, and / or prediction results of diseases in subjects); sample source; contaminated by one or more pollutants; or other diseases. Biomarkers can be used as a reference database associated with a condition for identifying the condition in another sample. Establishing a biomarker can include determining the presence, absence, and / or amount of at least about 10, 50, 100, 1000, 10000, 100000, 1000000 or more sequences and / or taxa in a sample using a single assay. For example, establishing a biomarker can include determining the presence, absence, and / or amount of at least 10, 50, 100, 1000, 10000, 100000, 1000000 or more sequences and / or genes in a sample using a single assay. Establishing a biomarker can include comparing sequencing reads of one or more samples representing a disease with sequencing reads of one or more samples that do not represent a disease. For example, a biomarker can be composed of gene expression that participates in host response in individuals infected with a virus (e.g., an immune response), and the sequence can be compared with sequences from subjects that are not infected or infected with some other agents (e.g., bacteria). In this case, the presence, absence, or abundance of a particular sequencing read may be associated with a viral infection rather than a bacterial infection. As another example, a biomarker may consist of gene sequences involved in a variety of antiviral responses, and the presence, absence, or abundance of sequencing reads associated therewith may indicate a particular class or type of viral infection. A biomarker associated with a reference database consists of sequences (and optionally levels) of host transcripts and / or sequences (and optionally levels) of transcripts or genomes of one or more infectious agents.In one specific example, the reference database can be common mutations or gene fusions found in cancer cells, and the presence, absence, or abundance of sequencing reads associated with the biomarker can indicate whether the patient has or does not have a detectable cancer, the type of cancer for the detectable cancer, a preferred treatment, whether an existing treatment is effective, and / or a prognosis.

[0205] Comparing sequences according to the method provided herein can provide a variety of benefits. For example, the computing resources used in the execution of the method can be significantly reduced relative to a reference method (such as a method based on traditional sequence alignment). For example, the speed of identifying multiple sequences in a sample is significantly improved. In some embodiments, sequencing reads are identified as corresponding to a specific reference sequence in a reference sequence database and can be completed in less than 5 seconds, less than 4 seconds, less than 3 seconds or less than 1 second in real time, 10,000 or more sequences, 20,000 or more sequences, 30,000 or more sequences, 40,000 or more sequences, 50,000 or more sequences, or 100,000 or more sequences. In some embodiments, at least about 500,000, 1,000,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000, 10,000,000 or more sequences are identified in real time per minute. The sequence set and processor for the fiducial marker sequence recognition process can be any sequence set and processor as described herein. In some embodiments, the sequencing reads for the benchmark markers include sequences from two or more of bacteria, viruses, fungi, and humans. The execution of the methods described herein can be defined relative to reference tools such as SURPI (see, e.g., Naccache, SN et al., A cloud-compatible bioinformatics pipeline for ultrarapidpathogen identification from next-generation sequencing of clinicalsamples. Genome research 24, 1180-1192 (2014)) or Kraken (see, e.g., Wood and Salzberg, "Kraken: ultrafast metagenomic sequence classification using exact alignments," Genome biology 15, R46 (2014), which is hereby incorporated by reference). In some embodiments, using the same data set and computer hardware, the method of the present disclosure is at least 5 times, 10 times, 50 times, 100 times, 250 times, or faster than SURPI in reaching results that are at least as accurate as SURPI. In some embodiments, the method of the present disclosure provides improved accuracy relative to the reference analysis tool. For example, using the same data set and computer hardware, accuracy may be improved by at least 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25% or more.In some embodiments, the sequence and / or taxonomic group present in the known sample is identified with at least about 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or higher accuracy. In some embodiments, the method provided herein is operable to distinguish two or more different polynucleotides based only on several sequence differences. For example, the method provided herein can be used for distinguishing two or more taxonomic group strains (for example, bacterial strains) based on the low degree sequence variation between the taxonomic group compared. In some embodiments, the method provided herein can be used for distinguishing two or more genes based on the low degree sequence variation between the gene compared. In some embodiments, one or more taxonomic groups include one or more nucleotide differences (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 25, 50 or more differences) identified as the first bacterial strain present and the second bacterial strain identified as non-existent based on the sequence. In some embodiments, taxonomic groups are distinguished based on less than 25, 10, 5, 4, 3, 2 or less sequence differences. In some embodiments, based on a single nucleotide difference (e.g., SNP) in the sequence, a first bacterial strain is identified as present, and a second bacterial strain is identified as not present. In some embodiments, one or more genes may include a first bacterial strain identified as present and a second bacterial strain identified as not present based on one or more nucleotide differences (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 25, 50 or more differences) in the sequence. In some embodiments, genes can be distinguished based on less than 25, 10, 5, 4, 3, 2 or fewer sequence differences.

[0206] Total sequencing

[0207] Consensus sequencing methods can be used to analyze sequences associated with a sample.As used herein, "consensus sequence" generally refers to a nucleotide sequence or an amino acid sequence, which is the calculated order of the most common residues found at each position in an alignment of the sequences.

[0208] Sequence alignment can be as described elsewhere herein. In some embodiments, residue can be nucleotide and / or amino acid. In some embodiments, the order of most common residues can be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100, 1000, 10000 or more. In some embodiments, the order of most common residues can be at most about 10000, 1000, 100, 50, 10, 9, 8, 7, 6, 5, 4, 3, 2 or less. In some embodiments, the order of most common residues can be about 1 to 10000, 1 to 1000, 1 to 100, 1 to 50, 1 to 10, 1 to 5 residues.

[0209] In some embodiments, the consensus sequence can be a sequence with similar structure in different organisms. In some embodiments, the consensus sequence can be a sequence with similar function in different organisms. In some embodiments, the consensus sequence can be a sequence with similar structure and function in different organisms. In some embodiments, different organisms can be the same organism. In some embodiments, different organisms can come from different sample sources. In some embodiments, different organisms can come from the same sample source.

[0210] In some embodiments, the protein binding site may be represented by a consensus sequence. In some embodiments, the protein binding site consensus sequence may be a short sequence of nucleotides. In some embodiments, the protein binding site consensus sequence may be a short sequence of nucleotides that may be found several times in a genome.

[0211] In some embodiments, average nucleotide identity can be a measure of nucleotide level similarity. In some embodiments, average nucleotide identity can be a measure of nucleotide level similarity between the regions of at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100, 1000 or more genomes. In some embodiments, average nucleotide identity can be a measure of nucleotide level similarity between the regions of at most about 1000, 100, 50, 10, 9, 8, 7, 6, 5, 4, 3, 2 or less genomes. In some embodiments, average nucleotide identity can be a measure of nucleotide level similarity between the regions of about 2 to 1000, 2 to 100, 2 to 50, 2 to 10, 2 to 5 genomes.

[0212] In some embodiments, average nucleotide identity can be a measure of nucleotide level similarity between sample sources. In some embodiments, average nucleotide identity can be a measure of nucleotide level similarity between at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100, 1000 sample sources. In some embodiments, average nucleotide identity can be a measure of nucleotide level similarity between at most about 1000, 100, 50, 10, 9, 8, 7, 6, 5, 4, 3, 2 or less sample sources. In some embodiments, average nucleotide identity can be a measure of nucleotide level similarity between about 2 to 1000, 2 to 100, 2 to 50, 2 to 10, 2 to 5 sample sources.

[0213] In some embodiments, the average nucleotide identity can be a measure of the nucleotide level similarity between a sample source and a reference sequence. In some embodiments, the average nucleotide identity can be a measure of the nucleotide level similarity between at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100, 1000 sample sources and at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100, 1000 reference sequences.

[0214] In some embodiments, the average nucleotide identity can be a measure of nucleotide level similarity between up to about 1000, 100, 50, 10, 9, 8, 7, 6, 5, 4, 3, 2 sample sources and up to about 1000, 100, 50, 10, 9, 8, 7, 6, 5, 4, 3, 2 reference sequences.

[0215] In some embodiments, sequence alignment can be a way of arranging sequences to identify consensus sequences. In some embodiments, sequence alignment can be a way of arranging sequences to identify regions of similarity that may be the result of a relationship between sequences. In some embodiments, sequences may be from, for example, DNA, RNA, or protein, etc. In some embodiments, regions of similarity may be the result of a function, structure, and / or evolutionary relationship between sequences. In some embodiments, consensus sequences may represent the result of multiple sequence alignments.

[0216] In some embodiments, the aligned sequences of nucleotides and / or amino acid residues can be represented as rows in a matrix. In some embodiments, gaps can be inserted between residues. In some embodiments, gaps can be inserted between residues so that identical and / or similar features can be aligned in consecutive columns.

[0217] In some embodiments, if the two sequences in the alignment share a common ancestor, mismatches can be explained as point mutations. In some embodiments, if the two sequences in the alignment share a common ancestor, mismatches can be explained as point mutations introduced in one or both lineages when they diverged from each other.

[0218] In some embodiments, if the two sequences in the alignment share a common ancestor, the gaps can be interpreted as indels (e.g., insertion and / or deletion mutations). In some embodiments, if the two sequences in the alignment share a common ancestor, the gaps can be interpreted as indels (e.g., insertion and / or deletion mutations) introduced in one or both lineages as they diverged from each other.

[0219] In some embodiments, sequence alignment can be a sequence alignment of a protein. In some embodiments, the degree of similarity between the amino acids of a protein occupying a specific position in a sequence can be interpreted as a measure of the degree of conservation of a specific region or sequence motif between pedigrees. In some embodiments, the absence of a substitution between two sequence alignments in a specific region of a sequence can indicate that the region has structural and / or functional importance. In some embodiments, only very conservative substitutions (i.e., substitutions of amino acids whose side chains have similar biochemical properties) in a specific region of a sequence can indicate that the region has structural and / or functional importance. In some embodiments, the conservation of base pairs (e.g., base pairs of DNA nucleotide bases, base pairs of RNA nucleotide bases) can indicate similar functional and / or structural effects.

[0220] In some embodiments, the method can perform overlapping detection of sequences. In some embodiments, the method can use an algorithm. The algorithm can be, for example, a greedy algorithm to a suffix tree. Using a greedy algorithm to a suffix tree can allow for specific matches and errors in a wide range. Using a greedy algorithm to a suffix tree can provide flexibility and / or sensitivity in overlapping reads of widely different lengths and / or error patterns (e.g., hybridization assembly of long reads from a sequencing platform with short reads from different platforms).

[0221] In some embodiments, the method can facilitate identifying overlapping regions in sequence data having high insertion and / or deletion rates relative to substitution rates, for example using a modified k-mer error model and / or a modified suffix tree query algorithm.

[0222] In some embodiments, the method may use a parallelized version of the AMOS layout algorithm Tigger. In some embodiments, the method may use a parallelized version of the AMOS layout algorithm Tigger and a consensus algorithm. In some embodiments, the consensus algorithm may use a probabilistic graphical model to represent the error characteristics of long reads.

[0223] In some embodiments, the method can further improve the sequence alignment construct. In some embodiments, simulated annealing and / or unconventional objective functions can be used for alignment refinement. In some embodiments, alignment refinement can include the use of global chaining in conjunction with sparse dynamic programming.

[0224] In some embodiments, the method may be a computer-implemented method. The computer-implemented method may identify a sequence overlap region between a plurality of sequencing reads. In some embodiments, the method may include providing the plurality of sequencing reads within a data structure. In some embodiments, the method may produce a k-mer set with deletions and / or insertions. In some embodiments, the method may search the data structure for regions of sequencing reads that match the first k-mer of the k-mer set. In some embodiments, these regions may be identified as sequence overlap regions between sequencing reads. In some embodiments, the method may search the data structure with additional k-mers in the k-mer set to identify other regions of sequence overlap between sequencing reads. In some embodiments, the k-mer set may include a k-mer containing a deletion and / or a k-mer containing an insertion, a k-mer with multiple deletions, a k-mer with multiple insertions, a k-mer with a substitution, or a combination thereof.

[0225] In some embodiments, the k-mer collection may have a combined indel rate of about 1% to about 40%. In some embodiments, the k-mer collection may have a combined indel rate of about 1% to about 5%, about 1% to about 10%, about 1% to about 15%, about 1% to about 20%, about 1% to about 25%, about 1% to about 30%, about 1% to about 35%, about 1% to about 40%, about 5% to about 10%, about 5% to about 15%, about 5% to about 20%, about 5% to about 25%, about 5% to about 30%, about 5% to about 35%, about 5% to about 40%, about 10% to about 15%, about 10% to about 20%, about 10% to about 25%, about 10% to about 30%, about 10% to about 35%, about 10% to about 40%, about 10% to about 15%, about 10% to about 20%, about 10% to about 25%, about 10% to about 30%. In some embodiments, the k-mer set may have a combined indel rate of about 1%, about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 15% to about 40%, about 20% to about 25%, about 20% to about 30%, about 20% to about 35%, about 20% to about 40%, about 25% to about 30%, about 25% to about 35%, about 25% to about 40%, about 30% to about 35%, about 30% to about 40%, or about 35% to about 40%. In some embodiments, the k-mer set may have a combined indel rate of about 1%, about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, or about 40%. In some embodiments, the k-mer set may have a combined indel rate of at least about 1%, about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, or about 35%. In some embodiments, the k-mer set may have a combined indel rate of at most about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, or about 40%.

[0226] In some embodiments, the k-mer set can be stored and / or searched in a data structure such as a hash table, a suffix tree, a suffix array, or a sorted list. In some embodiments, a greedy algorithm can be used to search the data structure. In some embodiments, a greedy algorithm modified to allow the k-mer to have mutations (such as insertions, deletions, and substitutions) can be used to search the data structure. In some embodiments, an O(N) algorithm can be used to search the data structure. In some embodiments, an O(N) algorithm including a Bloom filter can be used to search the data structure. In some embodiments, a Bloom filter can optionally store the k-mer set.

[0227] In some embodiments, providing sequencing reads can include performing at least about one spike-in-by-sequencing assay. In some embodiments, providing sequencing reads can include performing about 1 to about 1000 spike-in-by-sequencing assays. In some embodiments, providing sequencing reads can comprise performing about 1 to about 5, about 1 to about 10, about 1 to about 25, about 1 to about 50, about 1 to about 100, about 1 to about 1000, about 5 to about 10, about 5 to about 25, about 5 to about 50, about 5 to about 100, about 5 to about 1000, about 10 to about 25, about 10 to about 50, about 10 to about 100, about 10 to about 1000, about 25 to about 50, about 25 to about 100, about 25 to about 1000, about 50 to about 100, about 50 to about 1000, or about 100 to about 1000 sequencing-by-incorporation assays. In some embodiments, providing sequencing reads may include performing about 1, about 5, about 10, about 25, about 50, about 100, or about 1000 sequencing-while-spiking assays. In some embodiments, providing sequencing reads may include performing at least about 1, about 5, about 10, about 25, about 50, or about 100 sequencing-while-spiking assays. In some embodiments, providing sequencing reads may include performing at most about 5, about 10, about 25, about 50, about 100, or about 1,000 sequencing-while-spiking assays.

[0228] In some embodiments, the sequencing-by-incorporation assay can be performed in a confined reaction volume. In some embodiments, the confined reaction volume can be a zero-mode waveguide.

[0229] In some embodiments, redundant sequencing methods may include resequencing and / or sequencing multiple copies of the template molecule. In some embodiments, redundant sequencing methods may be used to generate sequencing reads. In some embodiments, sequencing reads may be filtered, for example, before being included in a data structure, and such filtering may be performed based on various criteria including, but not limited to, read quality and / or call quality. In some embodiments, one or more of the multiple sequencing reads, data structures, k-mer sets, sequence overlap regions, and / or additional sequence overlap regions may be stored on a computer-readable medium and / or displayed on a screen, as described elsewhere herein.

[0230] In some embodiments, the method can identify regions of sequence overlap between sequencing contigs. In some embodiments, the method can obtain a plurality of first sequencing contigs from a first plurality of sequencing reads. In some embodiments, the method can obtain a plurality of first sequencing contigs from a first plurality of sequencing reads from a first sequencing method.

[0231] In some embodiments, the method may obtain a second plurality of second sequencing contigs from the second plurality of sequencing reads. In some embodiments, the method may obtain a second plurality of second sequencing contigs from the second plurality of sequencing reads from the second sequencing method. In some embodiments, the first sequencing method and the second sequencing method may be different from each other. In some embodiments, the first sequencing method and the second sequencing method may be the same. In some embodiments, the method may incorporate the first sequencing contigs and / or the second sequencing contigs into the data structure.

[0232] In some embodiments, the method may generate a k-mer set. In some embodiments, the method may search for regions of sequencing overlaps that match the first k-mer of the k-mer set in a data structure. In some embodiments, these regions may be identified as regions of sequence overlap between the first sequencing overlap and the second sequencing overlap. In some embodiments, the method may repeat the search with other k-mers in the k-mer set. In some embodiments, the method may repeat the search with other k-mers in the k-mer set to identify other regions of sequence overlap between the first sequencing overlap and the second sequencing overlap. In some embodiments, the k-mer set may be optionally stored and / or searched in a data structure such as a hash table, a suffix tree, a suffix array, or a sorted list. Various algorithms such as greedy algorithms and / or O(N) algorithms may be used to search the data structure. Various algorithms may include a Bloom filter. In some embodiments, a Bloom filter may optionally store a k-mer set. In some embodiments, at least one of the first sequencing method or the second sequencing method may be a sequencing-while-incorporation method. In some embodiments, at least one of the sequencing contigs, data structures, k-mer sets, sequence overlap regions, and additional sequence overlap regions can be stored on a computer readable medium and / or displayed on a screen, as described elsewhere herein.

[0233] In some embodiments, the first plurality of sequencing reads may be long. In some embodiments, the first plurality of sequencing reads may be continuous. In some embodiments, the sequencing reads and the second plurality of sequencing reads may be short and / or paired end sequencing reads.

[0234] In some embodiments, the method may identify sequence overlap regions between sequencing overlap groups. In some embodiments, the method may further include obtaining multiple third sequencing overlap groups from a third plurality of sequencing reads from a third sequencing method. In some embodiments, the third sequencing method may be different from the first sequencing method and the second sequencing method. In some embodiments, the method may incorporate the third sequencing overlap group into the data structure. In some embodiments, the region identified during the search may be a sequence overlap region between the first sequencing overlap group, the second sequencing overlap group, and the third sequencing overlap group. In some embodiments, the first sequencing method and the second sequencing method may be selected from pyrophosphate sequencing, tSMS sequencing, Sanger sequencing, Solexa sequencing, SMRT sequencing, SOLID sequencing, Ksam-Gilbert measurement, nanopore sequencing, and semiconductor sequencing.

[0235] In some embodiments, the method may compare the sequence reads to a reference sequence. In some embodiments, the method may include mapping a short sequence of the sequence reads to a reference sequence. In some embodiments, the method may include mapping a short sequence of the sequence reads to a reference sequence using, for example, a suffix array, a global chain, identifying regions to which multiple subsequences of the sequence reads within the reference sequence are mapped, scoring and remapping the regions using sparse dynamic programming, and / or alignment matching, for example, using at least one of a base call quality value and a banded affine or paired HMM alignment. In some embodiments, scoring and mapping may be performed iteratively.

[0236] In some embodiments, sequence reads may be provided. Sequence reads may be provided by performing a sequencing reaction on a target nucleic acid. A reference sequence of a target nucleic acid may be provided, and a set of subsequences in the sequence reads may be identified. In some embodiments, if each of the subsequences matches a portion of the reference sequence, a set of subsequences in the sequence reads may be identified. The set of subsequences may be improved by scoring and realigning the subsequences with the reference sequence, optionally iterating. The set of subsequences may be improved by using sparse dynamic programming to score and realign the subsequences with the reference sequence, optionally iterating. Banded dynamic programming alignments such as affine or paired HMMs may be used to score and realign the final set of subsequences to provide a final alignment of the sequence reads with the reference sequence.

[0237] In some embodiments, the identification of matching subsequences may include finding all exact matches that may be longer than the minimum match length k and match the reference sequence from the sequence read. In some embodiments, the identification of subsequences that match the portion of the reference sequence in the sequence read may be performed using a suffix array and / or a BWT-FM index. In some embodiments, the identification of subsequences that match the portion of the reference sequence in the sequence read may include clustering exact matches using a global chain. Clustering may include sorting exact matches by position within the reference sequence and within the sequence read. Clustering may include sorting exact matches by position within the reference sequence and within the sequence read, and finding a first subset of non-overlapping exact matches, which may be greater than any other subset of non-overlapping exact matches. In some embodiments, the first subset may be identified as a cluster, and the cluster may be one of a set of subsequences. In some embodiments, the set of subsequences may be scored and sorted before the refinement step. In some embodiments, after scoring and / or realignment, each iteration of refinement re-determines a subset of non-overlapping exact matches. In some embodiments, the method may further identify the largest subset of these subsets. In some embodiments, the banded alignment may include aligning all bases in the sequence reads to a reference sequence using an alignment from sparse dynamic programming as a guide. In some embodiments, a mapping quality value may preferably be calculated. In some embodiments, the various steps of the method may be implemented on a computer, for example, using computer readable code, and various results or outputs from the various steps may be stored on a computer readable medium and / or displayed on a computer monitor, as described elsewhere herein.

[0238] In some embodiments, the system may be configured to generate a consensus sequence. In some embodiments, the system may include a computer memory. In some embodiments, the computer memory may include a sequence read of a target nucleic acid. In some embodiments, the computer memory may include a reference sequence of a target nucleic acid. In some embodiments, the computer memory may include a computer readable code for finding a subsequence set of a portion of a matching reference sequence in a sequence read. In some embodiments, the computer memory may include a computer readable code for refining the subsequence set. In some embodiments, the refinement includes scoring and / or re-aligning the subsequence using sparse dynamic programming. In some embodiments, the computer memory may include a computer readable code for scoring and re-aligning the final subsequence set using a banded alignment. In some embodiments, the banded alignment can align the sequence read with the reference sequence. In some embodiments, the computer memory may be configured to store the output of at least one of the steps of the method. In some embodiments, the system may include a monitor for displaying at least one of the outputs of at least one of the steps of the sequence read, the reference sequence, and / or the method as described elsewhere herein.

[0239] In some embodiments, the system may be configured to generate a consensus sequence. In some embodiments, the system may include a computer memory. The computer system may include a sequence read of a target nucleic acid. In some embodiments, the computer memory may include a reference sequence of a target nucleic acid. In some embodiments, the system may include a computer readable code for finding a subsequence set of a portion of a matching reference sequence in a sequence read. In some embodiments, the computer readable code may refine the subsequence set. In some embodiments, the refinement includes scoring and re-aligning the subsequence using sparse dynamic programming. In some embodiments, the computer readable code for scoring and re-aligning the final subsequence set may use a banded alignment. In some embodiments, the banded alignment may align the sequence read with the reference sequence. In some embodiments, the computer memory may be configured to store the output of at least one of the steps of the method. In some embodiments, the system may include a monitor for displaying at least one of the outputs of at least one of the steps of the method described elsewhere herein, a sequence read, a reference sequence, and at least one of the steps of the method described elsewhere herein.

[0240] In some embodiments, the system can be configured to generate a consensus sequence. In some embodiments, the system includes a computer memory. In some embodiments, the computer memory can include a set of sequence reads; computer readable code for applying an overlap detection algorithm to the set of sequence reads and generating detected overlap sets between pairs of sequence reads; computer readable code for assembling the set of sequence reads into an ordered layout based on the detected overlap sets; and a memory for storing the ordered layout.

[0241] In some embodiments, the method may identify the periodicity of the repeat sequence reads. The method may include calculating a self-alignment scoring matrix. In some embodiments, the method may include calculating a self-alignment scoring matrix with special boundary conditions for repeat sequence reads. In some embodiments, the method may sum the scoring matrix to generate a graph. In some embodiments, the graph may provide a cumulative match score within a base pair offset range. In some embodiments, the method may identify a peak set with the highest cumulative match score in the graph. In some embodiments, the method may determine a first base pair offset of a first peak in the set. In some embodiments, the first peak may have a base pair offset lower than any other peak. In some embodiments, the method may identify the periodicity of the repeat sequence reads as the amount of the first base pair offset. In some embodiments, the method may determine at least a second base pair offset of a second peak in the set. In some embodiments, the second peak may have a base pair offset lower than any other peak except the first peak. In some embodiments, the method may use a second base pair offset to verify the first base pair offset. In some embodiments, the periodicity of the repeat sequence reads determined by the method herein may be used during overlap detection within the repeat sequence reads.

[0242] In some embodiments, the method can analyze sequence information. In some embodiments, the method can analyze the assembly of overlapping sequence data into contigs. In some embodiments, the method can determine a consensus sequence. In some embodiments, the method can analyze the sequence of a biomolecule sequence, such as a nucleic acid, an amino acid, a polypeptide, or a protein.

[0243] In some embodiments, the method can provide de novo assembly and consensus sequence determination by analyzing biomolecule (eg, nucleic acid, polypeptide, amino acid, etc.) sequence data.

[0244] In some embodiments, the method may include a first step for sequence analysis. The first step may include determining one or more sequence reads, or the continuous order of molecular units or monomers in a sequence. For example, a nucleic acid sequencing read may include the order of nucleotides or bases in a polynucleotide (e.g., a template molecule and / or a polynucleotide chain complementary thereto). In some embodiments, the determination of sequence reads that can be analyzed by the methods provided herein include, for example, Sanger sequencing, shotgun sequencing, pyrophosphate sequencing (454 / Roche), SOLiD sequencing (Life Technologies), ISMS sequencing (Helicos), Sequencing, and in some preferred cases, single molecule real-time (SMRT TM ) sequencing (Pacific Biosciences of California).

[0245] In some embodiments, for each type of sequencing technology, experimental data collected during one or more sequencing reactions can be analyzed to determine one or more sequence reads for a given template nucleic acid that undergoes a sequencing reaction. For example, pyrophosphate sequencing can rely on the generation of light by an enzymatic reaction after the incorporation of nucleotides into a nascent strand that can be complementary to the template nucleic acid. In some embodiments, fluorescently labeled oligonucleotides can be detected during SOLID sequencing. In some embodiments, fluorescently labeled nucleotides can be used for tSMS, In SMRT sequencing reactions. In some embodiments, in SMRT sequencing, differentially labeled nucleotides, template nucleic acids, and polymerase sets may be present in a reaction mixture. When the polymerase processes the template nucleic acid, a nascent chain complementary to the template nucleic acid may be synthesized. The label on each nucleotide may be linked to a portion of the nucleotide that may not be incorporated into the nascent chain. The labeled nucleotides in the reaction mixture may be combined with the active site of the polymerase. In some embodiments, during the binding and subsequent incorporation of the constituent nucleoside monophosphate, the label may be removed and may diffuse away from the complex. In some embodiments, the label may be linked to the terminal phosphate group of the nucleotide. In some embodiments, the label may be cleaved from the nucleotide by the enzymatic activity of the polymerase, which cleaves the polyphosphate chain between α and β phosphates. In some embodiments, since the detection of the fluorescent signal may be limited to a small portion of the reaction mixture including the polymerase in a zero-mode waveguide (ZMW), a series of fluorescent pulses are detectable and attributable to the incorporation of the nucleotide into the nascent chain, wherein the specific emission detected indicates a specific type of nucleotide (e.g., A, G, T, or C). In some embodiments, by analyzing various features of the pulse trace, which may include a series of detected fluorescent pulses, the sequence of the incorporated nucleotides can be determined, and by complementarity, the sequence of at least a portion of the template nucleic acid can be derived therefrom. The identification of the type and order of incorporated nucleotides can be performed using a computer-implemented method.

[0246] In some embodiments, different sequencing technologies may have different inherent error characteristics in the sequence reads they produce. In some embodiments, the redundancy in the sequence data can be used to identify and / or correct errors in a single sequence read. Various methods can be used to generate sequence data with such redundancy. For example, the reaction can be repeated, such as by iterative sequencing of the same template, or by sequencing a plurality of copies of a given template separately. In doing so, a plurality of reads can be generated for one or more regions of the template nucleic acid. In some embodiments, each read is completely or partially overlapped with at least one other read in the data set generated by redundant sequencing. In some embodiments, sequencing can be started in different regions of the template using different primers to sequence the different regions of the template. In some embodiments, the resulting sequence reads can overlap to allow the sequence similarity between the parts of the different reads overlapping in those regions to construct a consensus sequence representing the true sequence of the different regions of the template nucleic acid.

[0247] In some embodiments, sequence reads for a given template sequence can be assembled as described elsewhere herein. In some embodiments, sequence reads for a given template sequence can be assembled based on sequence overlap between reads. Figure 1 In some embodiments, the alignment of reads relative to each other can provide the position of each read relative to other reads. In some embodiments, the alignment of reads relative to each other can provide the position of each read relative to the template nucleic acid. In some embodiments, longer and / or more accurate reads are conducive to overlapping group assembly. In some embodiments, known reference sequences (e.g., from a public database or repository, or as described elsewhere herein) can also be used during the construction of overlapping groups. In some embodiments, the region covered by two or more single sequence reads having at least overlapping fragments corresponding to the region can be subjected to more accurate sequence determination. In some embodiments, the overlapping portions of the sequence reads corresponding to the region can be compared or otherwise analyzed relative to each other. In some embodiments, during the assembly process, bases that are incorrectly called can be identified and optionally corrected in a single read. In some embodiments, this information can be used to determine a more accurate consensus sequence for the region. In some embodiments, once the alignment between the separated reads is determined, the best or most likely call for each position in the overlapping portion can be determined, assigned to the position in the consensus sequence, and used to determine the most likely call for the position in the original template molecule.

[0248] In some embodiments, the consensus sequence of the template molecule is determined to be promoted by the accurate comparison of overlapping sequencing reads. In some embodiments, the accurate comparison of overlapping sequencing reads can allow determination of which positions in a single read correspond to a single position in the template sequence. In some embodiments, certain sequence read features may complicate the comparison. For example, some sequencing techniques can produce very short sequence reads, which require very high coverage multiples to ensure sufficient coverage of the template sequence. In some embodiments, even at high coverage multiples, these reads may not allow resolution of highly repeated regions, such as regions longer than the typical length of the read. In some embodiments, other sequencing techniques can produce long sequencing reads, which allow better resolution of repeated regions and promote assembly, but this may be done at the expense of accuracy. In some embodiments, the error type characterizing the sequence read can be substitution (e.g., bases mis-incorporated or mis-called) and insertion and deletion (e.g., bases counted or omitted).

[0249] In some embodiments, for example, in order to identify the overlapping regions between sequence reads, the method provides a comparison of single sequence reads to each other. In some embodiments, the overlapping regions between the recognition sequence reads can be used to determine the exact sequence of the template molecule. In some embodiments, the overlapping regions between the recognition sequence reads can be used to determine the accurate sequence of the template molecule subjected to the sequencing reaction. In some embodiments, different types of sequence reads can be combined into a single contig or scaffold. In some embodiments, different types of sequence reads can be combined into a single contig, or combined into a scaffold, which may include a position where a base call has not yet been determined (e.g., corresponding to a gap in the original sequence read), which may be designated as "N" in the scaffold. For example, a less accurate long sequence read can be combined with a short but more accurate sequence read using a hybrid assembly method, as further described elsewhere herein. Long reads can be advantageous for placing small reads in contigs or scaffolds, and base calls in short reads can be given more weight in the final consensus sequence determination due to their higher inherent accuracy. In some embodiments, the desired features inherent to each type of sequence read can be used to maximize the accuracy of the resulting assembly.

[0250] In some embodiments, these methods can use BLASR (basic local alignment with continuous refinement). In some embodiments, the method can use BLASR, which can use a combination of data structures in short read segment mapping with sparse dynamic programming alignment method. In some embodiments, the BWT-FM index or suffix array of the genome can be queried to produce clusterable short exact matches. In some embodiments, the method can give the approximate start and end coordinates that the reads in the genome should be aligned. In some embodiments, a more detailed alignment can be produced by using sparse dynamic programming between the short exact match sets in the reads of the region mapped to it. In some embodiments, dynamic programming can be used in the region guided by the sparse dynamic programming alignment to produce a final detailed alignment.

[0251] In some embodiments, the method can compare and assemble nucleic acid sequencing reads. In some embodiments, nucleic acid sequencing reads may contain overlapping or redundant sequence information. In some embodiments, the method can be used in combination with other comparison and assembly methods as described elsewhere herein. For example, overlap detection may include one or more alignment algorithms that use a reference sequence to compare each read. In some embodiments, for regions containing target sequences, the reference sequence may be known, and the reference sequence may be used to produce comparisons using a variant of the central star algorithm. In some embodiments, sequence alignment may include one or more alignment algorithms that can compare each read relative to each other read without using a reference sequence (e.g., a de novo assembly routine), for example, PHRAP, CAP, ClustalW, T-Coffee, AMOS agree or other dynamic programming MSA.

[0252] In some embodiments, the method may compare and assemble sequence reads based at least in part on a known reference sequence. In some embodiments, comparing and assembling sequence reads may be based at least in part on a known reference sequence. In some embodiments, comparing and assembling sequence reads based at least in part on a known reference sequence may be resequencing or mapping as described elsewhere herein. In some embodiments, sequence reads may be mapped to a reference sequence. In some cases, sequence reads may be mapped to a reference sequence, and loci that may have base calls different from the reference sequence may be further analyzed to determine whether a given locus is incorrectly called in a sequence read, and / or whether it may represent a true variation (e.g., mutation, SNP variant, etc.). In some embodiments, a variation may distinguish the nucleotide sequence of a reference sequence from the nucleotide sequence of a template nucleic acid that is sequenced to produce a sequence read. In some embodiments, a variant may include a plurality of adjacent positions in a reference and / or sequencing read, for example, as in the case of an insertion, deletion, inversion, or translocation. In some embodiments, a sequence may be assembled based on a comparison of a reference sequence and a sequence read that is similar but not necessarily identical to at least a portion of a reference sequence.

[0253] In some embodiments, the method can compare and assemble sequence reads that do not use known reference sequences. In some embodiments, comparing and assembling sequence reads can be referred to as being used for de novo sequencing. In some embodiments, sequence reads can be analyzed to identify overlapping regions. In some embodiments, sequence reads can be compared to each other to produce overlapping groups. In some embodiments, overlapping groups can be subjected to consensus sequence determination, for example, to form new, previously unknown sequences, such as when the genome of an organism can be sequenced for the first time. In some embodiments, de novo assembly may be several orders of magnitude slower. In some embodiments, de novo assembly may have more memory intensiveness than resequencing assembly. In some embodiments, de novo assembly may require analysis or comparison of each read with each other read, for example, in a paired manner. In some embodiments, the sequence reads themselves can be used as a reference in an alignment algorithm.

[0254] In some embodiments, the method can perform hybrid assembly of nucleic acid sequencing reads. In some embodiments, the method can assemble long (e.g., TM SMRT TM Sequencing generated sequences (“PacBio reads”) and short (e.g., The method for hybrid assembly can obtain reads from different sequencing methods and align them with each other. In some embodiments, more and longer sequence reads can promote the recognition of sequence overlap. In some embodiments, more and longer sequence reads may have a higher error rate than reads from short read technology. In some embodiments, short sequence reads can be aligned faster. In some embodiments, when the template for generating short sequence reads contains a repeat (identical or nearly identical) or a large rearrangement (such as an inversion or translocation) longer than the length of the short read, the short sequence reads may be more difficult to align. In some embodiments, longer reads from the first platform can be used to form a baseline, and other types of reads (e.g., reads from a short read platform) can be added to the baseline. In some embodiments, the method can allow sequencing data from different platforms to be combined to provide overall higher quality data, such as higher redundancy or compensation due to one or more weaknesses of one platform and the advantages of another platform. In some embodiments, based on a higher quality sequence generated by another other platform, hybrid assembly can be used to select a region of high-quality reads from one platform.

[0255] In some embodiments, the method can be used for de novo assembly using hybrid assembly. In some embodiments, the overlap in hybrid assembly can be enhanced or filtered in various ways. For example, the candidate overlapping region observed in the long read can be confirmed with the region in the short read that overlaps the candidate overlapping region in the long read. In some embodiments, if the candidate overlapping region between the long read or the long read and the short read is flanked or spanned by the paired read or the gated read, they can be confirmed. In some embodiments, the confirmation of the candidate overlap can be completed by comparing with the reference sequence. In some embodiments, the region not aligned with the reference sequence can be targeted for more active misassembly detection. In some embodiments, the analysis of the experimental sequence read data can cover the reference sequence (for example, due to genetic variability, errors in the reference sequence determination, etc., it may contain sequence data that does not correspond to the template sequence).

[0256] In some embodiments, the method may include de novo assembly. In some embodiments, de novo assembly may include a first step. In some embodiments, the first step may be overlap detection. In some embodiments, overlap detection may be performed in pairs. In some embodiments, two sequence reads may be compared and / or analyzed relative to each other at a time. In some embodiments, the process may continue until all sequence reads have been compared with all other sequence reads. In some embodiments, de novo assembly may include a second step. In some embodiments, the second stage may be a layout, in which the overlap detected in the first stage may be used to sort all sequence reads with such overlap relative to each other. In some embodiments, de novo assembly may include a third step. In some cases, the third step may be a consensus sequence determination, in which the positions within the overlapping regions that may be different in different reads may be further analyzed to determine the best call for the position, for example, based on the quality score of a single base call and the frequency of each type of base call within the sequence read set including the position. In some embodiments, de novo assembly may produce assembled reads or contigs. In some embodiments, de novo assembly may provide the best sequence for the template nucleic acid from which the sequence read is obtained.

[0257] In some embodiments, the method for hybrid assembly may include an overlap determination step. In some embodiments, the method for hybrid assembly may include a layout step. In some embodiments, the method for hybrid assembly may include a consensus sequence determination step. In some embodiments, the input sequence may have a high confidence read or overlap group from a variety of different sequencing technologies such as short reads and long reads. In some embodiments, the different sequencing technologies used in hybrid assembly may produce sequence reads and / or overlap groups with different error profiles, such as sequencing and / or assembly errors characterized by different types and / or frequencies. In some embodiments, the method may assemble overlap groups (e.g., FASTA format) from different technologies to produce hybrid overlap groups or scaffolds, which can be presented as oriented overlap groups in a linear graph (e.g., in FASTA or graphml format). In some embodiments, depending on the type of reads used in the hybrid assembly process, the resulting linear graph may contain unclear regions or gaps, for example, where one or more positions are not covered by the assembled overlap group. For example, in some cases, the original sequence read may not include a position within the gap, and in other cases, the quality of the call within the gap region may be determined to be too low to include these calls in the hybrid assembly process.

[0258] In some embodiments, the method for hybrid assembly can be used to use the read from the second sequencing technology to perform error correction in the read of a sequencing technology. For example, the read from the low error, short read sequencing technology can be used to correct the error in the read from the error-prone, long read sequencing technology. In some embodiments, this error correction assembly method can be performed as follows: for N iterations, the sequence reads from the sequencing technology with lower original accuracy and the sequence read set from the sequencing technology with higher original accuracy can be used to perform comparison. In some embodiments, the sequence reads may have a longer read length. In some embodiments, BLASR can be used as an alignment method. In some embodiments, the alignment output can be converted into a SAM file format, and SAMTOOLS can be used to generate a stacked formatted version of MSA. In some embodiments, stacking files can be used for error correction. In some embodiments, the stacking file may include, for example, the position to be corrected, the number of reads covering the position from a more accurate sequencing technology, the base previously present at the position, the type of error correction event (e.g., deletion, insertion, substitution), the base corrected, the PHRED score of the base of the common base and the base corrected. In some embodiments, for each read base position recorded in the stack, the generated common call can be accepted or rejected according to (a) the number of more accurate reads for determining the common call, (b) the percentage of common consistency in the more accurate reads, and (c) the PHRED value of the majority of the call bases. In some embodiments, a summary of the accepted common calls can be generated. In some embodiments, the summary of the accepted common calls can be used to create updated sequence reads of less accurate sequencing technologies. In some embodiments, the updated sequence reads can be stored and optionally subjected to further iterations ("correction iterations") of alignment and error correction methods to generate further updated sequences. In some embodiments, once all iterations are completed, an overall summary of all error corrections incorporated into the sequence reads from less accurate sequencing technologies can be generated. In some embodiments, the stacking step can be optimized by selecting the region within the read to be corrected instead of correcting the entire read. In some embodiments, the selection of such regions can be guided by the results of previous correction iterations.

[0259] In some embodiments, the method for de novo assembly may include multiple steps. In some embodiments, the first step may be to determine the overlap between reads. In some embodiments, the second step may be to arrange the overlapping reads in a linear order by aligning the overlapping regions to each other for a set of reads that may overlap with at least one other read. In some embodiments, the third step may be to build a final consensus sequence from the oriented reads.

[0260] In some embodiments, overlapping components and sequence similarity regions between sequence reads can be identified. The assembly process may assume that such overlapping regions are derived from the same position in the template nucleic acid. In some embodiments, once the overlapping region has been identified, the sequence reads may be arranged so that the overlapping regions are aligned with each other. In some embodiments, most or all template nucleic acids may be represented in a sequence read set aligned in this way. In some embodiments, in a common step, a common base call for each position in the template nucleic acid may be determined based on a sequence read set containing each position. For example, in a case where all base calls are the same in a sequence read set, a base call may become a common base call. In some embodiments, in the case where there are different base calls in different sequence reads, the best base call may be determined based on various criteria, including but not limited to the quality of the base call in each single sequence read, the frequency of base calls of each type on a sequence read set. In some embodiments, the process may be iterative, for example, to further refine the consensus sequence. In some embodiments, methods for de novo assembly of sequence reads can have high insertion-deletion rates, e.g., exceeding 5%, or 10%, or 15%, or in some cases up to 20% error rates. In some embodiments, a greedy suffix tree can detect overlaps using sequence reads with an accuracy of about 80%. In some embodiments, an algorithm using a Bloom filter can detect overlaps using sequence reads with an accuracy of only about 85%.

[0261] In some embodiments, the input of the assembly construction can be a set of sequence reads generated from a single template nucleic acid sequence (e.g., via redundant sequencing of one or more template molecules and / or sequencing of the same template molecule). In some embodiments, the output may include a pairwise overlapping set, a layout or overlapping group containing sequence reads (these sequence reads contain regions represented by paired overlaps) and / or a single consensus sequence that best represents a nucleotide sequence present in the original template nucleotide sequence or its complement, etc. In some embodiments, the assembly process may generate overlapping sets. In some embodiments, the overlapping set can be used to align the sequence read set to form an overlapping group. In some embodiments, the overlapping set can be analyzed to determine a single consensus sequence. In some embodiments, the generation of a consensus sequence may be important for a variety of further analyses of sequences determined for a template, for example, in identifying sequence variants, performing functional analysis based on homology with known genes or regulatory sequences, or comparing it with other sequences to determine the evolutionary relationship between different species, subspecies or strains, etc.

[0262] In some embodiments, the method for de novo assembly can be derived from AMOS assembler, which is an open source whole genome assembler available from AMOS consortium. In some embodiments, the method can use a mixture of python and C / C++, and the combination of SWIG and AMOS library. In some embodiments, SWIG can be a tool to simplify the integration of C / C++ and common scripting languages. In some cases, a filtering step may be included between the common step and the termination assembly decision. In some embodiments, Amo CTG may be added to the filtering step. In some embodiments, in the filtering step, overlapping groups with low coverage or a small amount of reads may be filtered out. In some embodiments, overlapping groups may be filtered out because these overlapping groups may be due to low-frequency error sequences, such as chimeras. In some embodiments, the final scaffolding step may not be performed. In some embodiments, the final scaffolding step can be replaced by the hybridization assembly method described herein.

[0263] In some embodiments, the method for de novo overlap detection may include pairwise analysis of sequence reads in the raw data set to determine the overlapping regions between paired reads. In some embodiments, this step may be computationally expensive. In some embodiments, for large genomes, the comparison of millions of single reads may be involved (for possible trillions of paired comparisons). In some embodiments, the sequence assembly algorithm may apply a fast filter to determine the possible overlapping read pairs. For example, the various methods for filtering and pruning data may be used, such as vector pruning, quality filtering, length filtering, no call read filtering, low complexity filtering, shadow read filtering, read pruning or end pruning, etc.

[0264] In some embodiments, determination of sequence assembly can also include read quality analysis (e.g., using TraceTuner TM, Phred, etc.), signal density, peak data (e.g., height, width, shape, proximity to adjacent peaks, etc.), information indicating read orientation (e.g., 5'→3" designations), clear range identifiers indicating the available range of calls in the sequence, etc. In some embodiments, such read quality can be used to exclude certain low-quality reads from the alignment process. In some embodiments, not every call in every read is used in the overlap detection process. In some embodiments, a high raw error rate may indicate the benefit of selecting only reads with high quality (e.g., high certainty). For example, the quality of the calls in each read can be measured, and only calls identified as high quality can be used in the alignment process. In some embodiments, if at least a portion of the calls to a position in a repeated sequence are below a quality standard, the position may not be included in the overlap detection operation. In some embodiments, the quality of a given call is not included in the overlap detection operation. Quality can depend on many factors. In some embodiments, the quality of a given call may be related to the sequencing technology used. For example, factors that may be considered when determining the call quality include signal-to-noise ratio, power-to-noise ratio, signal strength, tracking features, flanking sequences ("sequence context") and known performance parameters of sequencing technology, such as consistency changes based on read length. In some embodiments, the quality measure of the observed call may be based at least in part on the comparison of the metric of such other factors with the metric observed during the sequencing of known sequences. Methods and software for generating sequence calls and associated quality information are widely available. For example, PHRED is an example of a basic caller that can output quality scores for each call. After paired overlapping sets have been generated, calls of lower quality can be added back to the comparison, or optionally can be completely absent from the assembly process, or can be added back to the comparison at a later stage.

[0265] In some embodiments, after having identified paired overlapping sets by overlap detection methods, scores can be assigned to each overlap. In some embodiments, scores allow for distinguishing correct and incorrect overlaps. In some embodiments, a score threshold can be set so that a very small amount of overlap exceeding the threshold may be incorrect. In some embodiments, a score threshold can be set so that a very small amount of overlap exceeding the threshold may be incorrect, and all overlaps below the threshold are ignored. In some embodiments, the score can be the Smith-Waterman comparison result of two sequences. In some embodiments, other methods of overlapping scoring methods as described elsewhere herein can be used.

[0266] In some embodiments, detecting overlap can be, for example, the region of exact matches between search sequence reads after filtering as described elsewhere herein. In some embodiments, simple lookup tables, hash functions, or more complex structures (such as, overlapping algorithms, such as, suffix trees) can be used to detect exact matches. In some embodiments, suffix trees can provide desired features, such as fast creation and query lookup time (respectively O (n) and O (l), where n is the size of the database). In some embodiments, the method can modify the suffix tree query algorithm to create a greedy suffix tree overlap algorithm that can allow insertion and deletion. In some embodiments, greedy suffixes can maintain the desired creation and query time of suffix trees.

[0267] In some embodiments, the input of the method may include two FASTA format sequence sets, i.e., a query and a target. In some embodiments, the FASTA format is a widely used text-based format for representing nucleotides or amino acids using single-letter codes to represent nucleotides or peptide sequences. In some embodiments, a compressed suffix tree may be created from the target sequence. In some embodiments, a greedy algorithm may then be used to compare each query sequence with the suffix tree. In some embodiments, the greedy algorithm may attempt to find the shortest common supersequence of a given sequence read set by calculating the pairwise alignment of all sequence reads; select two reads with the maximum overlap; merge the two selected reads; and repeat these steps until only one merged read remains. In some embodiments, the method may return a match that meets two user-specific parameters, m is the minimum number of matching nucleotides, and e is the maximum number of errors. In some embodiments, an error is an insertion or deletion between the query sequence and the target sequence. In some embodiments, for high error rate data, e may be large (e.g., e=35, m=80) relative to m.

[0268] In some embodiments, the greedy algorithm can alternate between two modes. In some embodiments, in the first mode, it is possible to attempt to accurately match as many query sequences as possible with the target suffix tree. In some embodiments, after further accurate matching is impossible, the greedy algorithm can enter the second mode. In some embodiments, the second mode may introduce errors (e.g., substitutions, insertions, or deletions) in the query sequence. In some embodiments, after each introduced error, the greedy algorithm can return to the first mode, greedily attempting to accurately match (modified now) query sequences as much as possible. In some embodiments, the greedy algorithm can continue to alternate between the two modes until it terminates. In some embodiments, the greedy algorithm can terminate when it has matched a certain threshold or more characters from the query, or it has been forced to introduce at least a certain number of errors.

[0269] In some embodiments, the greedy algorithm may not be an exhaustive overlap detection algorithm. In some embodiments, the greedy algorithm may not find all matches that satisfy the constraints m and e. In some embodiments, the number of matches returned for a specific query sequence can be increased by starting the greedy algorithm at different positions (e.g., every 10 bases) along the query. In some embodiments, the algorithm can be used in the context of iterative assembly, wherein overlapping can be detected in multiple stages, thereby allowing the algorithm to capture the overlap that it missed in previous iterations and avoid generating an assembly that is excessively segmented.

[0270] In some embodiments, the greedy algorithm can be used together with the data structure except the suffix tree. In some embodiments, other data structures such as hash or lookup table can be used. In some embodiments, compared with the suffix tree, the suffix array consumes less memory, but may have a longer query time. In some embodiments, when introducing errors in the sequence, the method based on hash and lookup table may suffer from the reference space locality of reduction. In some embodiments, by appropriate cache scheme, the suffix array can provide a better reference locality attribute than the suffix tree.

[0271] In some embodiments, a greedy suffix tree overlap algorithm may be used during de novo assembly. In some embodiments, a greedy suffix overlap algorithm may be used to map observed sequence reads to known or candidate target sequences (e.g., generated based on the sequence reads themselves). In some embodiments, a suffix tree may be constructed from a target database (e.g., FASTA or pls.h5). In some embodiments, a greedy suffix tree algorithm may be used to compare a query database (a database containing sequence read data) to the tree. In some embodiments, the tree alternates between two modes: 1) exact match of the query to the tree; and 2) mutation of the query. In some embodiments, the algorithm greedily accepts the longest match, which may include up to a specified number of errors. In some embodiments, the results may be checked using a banded Smith-Waterman algorithm. In some embodiments, the results may be output in an AMOS OVL message.

[0272] In some embodiments, sequence alignment is performed using a method of continuous refinement to map single molecule sequencing reads. In some embodiments, the algorithm that can be used to perform this continuous alignment process is referred to as the basic local alignment (BLASR) algorithm via continuous refinement. In some embodiments, the algorithm can be understood as having two basic steps: 1) finding a high-scoring match for the reads in the genome of a reference sequence (which may be derived from sequence reads in a de novo assembly), and 2) refining the match until a sequence homologous to the read is found in the reference sequence. In some embodiments, the first step may involve matching a short subsequence or sufficient sequence of the observed sequence reads to a reference sequence using a suffix array (based on a short read mapping method).

[0273] In some implementations, the short read aligner may use a Burrows-Wheeler transform (BWT) string to perform the search.

[0274] In some embodiments, the second step of BLASR can use global chaining to find a high scoring set of anchors. In some embodiments, sparse dynamic programming can be used to score the resulting putative matches. In some embodiments, a hidden Markov model with quality values ​​of the called bases can be used to align the matches.

[0275] In some embodiments, the BLASR method may have any number of steps. In some embodiments, the BLASR algorithm may detect candidate intervals by clustering short exact matches. In some embodiments, the BLASR algorithm may use sparse dynamic programming to approximate the alignment of reads to candidate intervals. In some embodiments, the BLASR algorithm may use sparse dynamic programming alignments as a guide to elaborate banded alignments. In some embodiments, read base positions may be assigned to reference positions during elaborate banded alignments.

[0276] In some embodiments, the method for determining the overlap between sequence data may involve using k-mer between reads to identify small regions of exact matches. In some embodiments, sequences sharing a large number of k-mers may come from the same region of the sequence to be identified, for example, a genomic sequence. In some embodiments, the value of k may be the length of the matching region. In some embodiments, the value of k may be the length of the matching region, and may be approximately 20 to 30 base pairs. In some embodiments, data structures such as suffix trees or hash tables may be used to quickly find these regions. In some embodiments, for two overlapping reads sharing an exact k-mer, the two reads may have a low error rate and / or be long enough to compensate for a high chance of error. In some embodiments, for sequencing reads with relatively frequent errors, the method may be modified to allow errors in k-mer.

[0277] In some embodiments, the gapped k-mer method can provide insertion-deletion tolerance for potential overlaps between detection reads. For example, when searching for a match with a k-mer in a particular read, the algorithm enumerates all kd-mers that can be created from the k-mer by introducing d deletions. In some embodiments, for example, if the original k-mer is ATGC (k=4) and the expected number of deletions is 1 (d=1), the method can generate four 3-mers, each missing a base or gap at one of the four positions in the original 4-mer: TGC, AGC, ATC, and ATG. In some embodiments, the method can allow insertions or substitutions.

[0278] In some embodiments, the method may have several parameters that can be varied or altered. In some embodiments, for example, the length of the k-mer; the number of insertions, deletions, or substitutions (if any); the data structure (hash table, suffix tree, suffix array, or sorted list) in which the k-mer is found; and whether k-mers with gaps in these data structures are explicitly stored or only implicitly searched for can be changed or adjusted. In some embodiments, the optimal value for each of these parameters may depend on the characteristics of the genome being sequenced and the computational resources available for assembly.

[0279] In some embodiments, Bloom filters can be used in an O(N) algorithm to determine sequence pairs with matching overlaps in order to reduce run time and speed up analysis. In some embodiments, the algorithm can provide an increase in analysis speed greater than 100 times without any significant loss in sensitivity. In some embodiments, Bloom filters can be used to store a collection of all sequence read identifiers from a given analysis of sequences containing specific features. In some embodiments, an identifier Bloom filter can be constructed for each potential feature and can be used to determine candidate read pairs that share a large number of features. In some embodiments, the execution feature can be the presence or absence of a specific k-mer (with gaps or without gaps) in the sequence.

[0280] In some embodiments, the method input can be two sequence read files, namely the query and the target, which can be the same file or two or more different files. In some embodiments, a Bloom filter can be created for each possible k-mer. In some embodiments, each Bloom filter may contain m bits, where m can be approximately two to ten times the number of sequences expected to have each feature. In some embodiments, the target sequence database can be scanned in linear time, thereby processing the target sequences in sequence. In some embodiments, each sequence identifier can be encoded by h hash functions (e.g., h=2) and converted to a value between 0 and m. In some embodiments, for each k-mer in the sequence, h bits corresponding to the hash value of the sequence identifier can be set in the Bloom filter of the k-mer. In some embodiments, a compact representation of the presence or absence of each k-mer in each read in the target database can be constructed.

[0281] In some embodiments, each query sequence may be used to interrogate the Bloom filter again in linear time. In some embodiments, each query sequence may be converted into a set of k-mers, and the Bloom filters for each of these k-mers may then be summed. In some embodiments, the bits that are set multiple times in the Bloom filter sum may correspond to hash values ​​of sequence identifiers that share a large number of k-mers with the query sequence. In some embodiments, the inverse hash of the h hash values ​​that map each sequence identifier may be used to retrieve the target identifier for that particular query.

[0282] In some embodiments, the method including the bloom filter may have a running time of O (N). In some embodiments, some operations in the basic operations such as building bloom filters, querying them and summing the resulting bloom filters can be easily parallelized. In some embodiments, depending on the size of the sequence to be compared, the identifier bloom filter may require a large amount of memory during analysis. In some embodiments, the Smith-Waterman alignment algorithm can be used to check the alignment subsequently. In some embodiments, larger assemblies (such as human genomes) may require more memory. In some embodiments, a target database of size G can be represented using a bloom filter of 2G to 10G. In some embodiments, if distributed on multiple nodes, for example, chunking can be used to facilitate analysis of a larger collection.

[0283] In some embodiments, the method may include at least two free parameters that can be modified while retaining the purpose of determining the overlapping area between sequence reads. In some embodiments, the first parameter may be the number of bits (in) stored in each Bloom filter. In some embodiments, increasing this value may increase the sensitivity of the algorithm. In some embodiments, this may increase memory consumption. In some embodiments, the second parameter may be the number of hash functions (h) used to encode sequence read identifiers. This value may be as low as 1 or as high as m-1. Increasing h may increase or decrease sensitivity, depending on the value of m and the average number of bits set in a particular Bloom filter. In some embodiments, there may be a wider family of algorithms involving the use of features other than the presence or absence of k-mer to construct identifier Bloom filters. In some embodiments, some may be closely related to the k-mer concept, but may be deconstructed after the sequence has been transformed in some way. For example, in some embodiments, the transformation includes collapsing all homopolymers before k-mer recognition. In some embodiments, the transformation includes converting all GCs to one and converting all ATs to zero. In some embodiments, a class of features that are completely unrelated to the presence of k-mers may summarize the entire sequence in some way, such as using the presence or absence of high GC content.

[0284] In some embodiments, steps may be taken to maximize efficiency during overlap detection operations, for example to reduce the occurrence of both duplicate comparisons and missed comparisons.

[0285] In some embodiments, some sequence reads may include redundant sequence information. For example, nucleic acid molecules may be repeatedly sequenced in a single sequencing reaction to produce multiple sequence reads of the same template molecule, for example, by a method based on rolling circle replication. In some embodiments, concatemer molecules comprising multiple copies of the template sequence may be subjected to sequencing while synthesizing to produce long sequence reads of multiple complements comprising copies. In some embodiments, when circular or concatemer molecules are used as templates for iterative or redundant sequencing, the final sequence reads should have a periodic structure. For example, when a circular template is repeatedly processed by a polymerase, such as in rolling circle replication, long sequencing reads of multiple complementary sequences of the template may be generated, which may be referred to as sibling reads. In some embodiments, periodic patterns may be difficult to identify in some cases, for example, when using a template of an unknown sequence (e.g., size and / or nucleotide composition) and / or when the resulting sequence data includes an error call or other type of error (e.g., insertion or deletion).

[0286] In some embodiments, the template may include a known sequence that can be used to align the multiple sibling reads within the entire redundant sequencing read segment to each other and / or to a known reference sequence. In some embodiments, the known sequence can be an adapter that can be linked to the template before sequencing, or it can be a partial sequence of the template, for example, where the partial sequence is used to pull down a specific region of the genome from a complex genomic sample. In some embodiments, by identifying the alignment positions between multiple occurrences of the known sequence within the sequencing read segment, the periodicity of the read segment can be inferred.

[0287] In some embodiments, the template does not contain a known sequence that can be reliably aligned to infer periodicity. In some embodiments, this can be accomplished by aligning the sequenced read to itself and finding self-similar patterns using a standard alignment algorithm.

[0288] In some embodiments, the entire self-alignment score matrix can be used to calculate the amount of autocorrelation similar to a continuous signal. The autocorrelation function can be used to infer the periodicity of discrete sequences with high insertion and / or deletion error rates. In some embodiments, the information of the entire self-alignment score matrix can be used to estimate the periodicity of the sequence. In some embodiments, a special boundary condition can be used to calculate the self-alignment score matrix, which can be adjusted according to the known characteristics of the sequencing data and / or the template from which the sequencing data is generated. In some embodiments, the self-alignment score matrix may include the summation of the scoring matrices for all different lags. In some embodiments, the self-alignment score matrix may include identifying the peak value and its periodicity for inferring the periodicity of the sequence data. In some embodiments, the self-alignment score matrix may include using the periodicity of the sequence data to guide the self-alignment of the sibling reads in the sequence data.

[0289] In some embodiments, to reveal non-zero offset self-alignments, a special boundary condition may be imposed that forces all diagonal elements of the scoring matrix to be zero. In some embodiments, this may prevent zero offset self-alignments from contributing to the scoring matrix. In some embodiments, in the absence of this boundary condition, the contribution of zero offset self-alignments may mask or obscure non-zero offset self-alignments.

[0290] In some embodiments, a spatial genome assembler may be provided. In some embodiments, the sequence will be treated as a string, and string matching techniques may be used to identify overlaps between reads to combine short reads into longer reads. In some embodiments, the method may map DNA reads into an N-spatial coordinate system, such that any given length of DNA becomes an N-dimensional thread through space.

[0291] In some embodiments, the method can use the association between sibling reads generated from the same template molecule to improve overlap detection for de novo assembly. In some embodiments, the assembly method can use a consensus sequence discovery process to combine sibling reads into a single consensus read. In some embodiments, sibling reads can be analyzed without a consensus sequence determination, but at the same time still consider their relationship as multiple reads of the same template sequence. In some embodiments, the method can be extended to any method for mapping reads to reference sequences or assigning information to specific sibling reads, which can be usefully shared between their siblings.

[0292] In some embodiments, summing can be used to share overlap score information between sibling reads. In some embodiments, an alignment algorithm (such as one of those described elsewhere herein) can be used to initially call or identify overlap between reads. In some embodiments, the scores of read pairs belonging to the same sibling group (e.g., generated from the same template molecule) can be combined by adding the scores. In some embodiments, the overlap scores of combined sibling reads can provide a significant improvement in the true positive rate, thereby proving that more overlaps are correctly detected even in the presence of varying error rates and false positive rates. In other cases, other methods of combining scores, such as maximum, minimum, and product, can be used.

[0293] In some embodiments, the method can use multiple sequence alignment (MSA) to establish homology relationships between three or more sequences (e.g., nucleotide or amino acid sequences). In some embodiments, multiple sequence alignment can be used to construct phylogenetic trees, understand structure-sequence relationships, highlight conserved sequence motifs, and is particularly relevant to the sequencing methods provided herein, providing a basis for determining the consensus sequence of a given set of sequencing reads from the same template.

[0294] In some embodiments, the method provides an MSA refinement process using simulated annealing and different objective functions. In some embodiments, a simulated annealing framework can be used to search and evaluate the solution space.

[0295] In some embodiments, the initial alignment can be a close approximation of the optimal solution. In some embodiments, each new candidate alignment can be generated by locally perturbing the current alignment. In some embodiments, the alignment can be interrupted by randomly selecting a column in the MSA and performing a gap shift operation with a certain probability for each sequence with a gap in the column. In some embodiments, the gap shift can occur on the right or left side of the current column. In some embodiments, each new candidate can be evaluated using the GeoRatio objective function, which scores the alignment block.

[0296] In some embodiments, the scoring mechanism may calculate the geometric mean of the signal-to-noise ratio within a column, where a column is a set of calls for a given position in an assembled read. For example, in nucleotide sequence data, a column may be a set of base calls for nucleotide positions overlapped by multiple assembled sequencing reads, where each read provides one of the performed base calls.

[0297] In some embodiments, if the score of the new candidate alignment is better than the current solution, the new candidate alignment may be accepted, and if the score is worse, the new candidate alignment is accepted with a certain probability. In some embodiments, in order to prevent the algorithm from getting stuck in a local optimum, bad trades may sometimes be made. In some embodiments, an exponential decay function can be used to set the temperature used at each iteration of the process, and as the temperature decreases, the chance that you may accept a bad solution decreases. In some embodiments, after deciding to accept or reject a candidate, the process either stops (if the termination criteria are met) or proceeds to the next iteration. In some embodiments, the termination criteria are met when there is no improvement in n iterations or after a predefined number of iterations.

[0298] In some embodiments, to evaluate the results of MSA refinement, the consensus call accuracy at low coverage (2-6×) can be compared. In some embodiments, the alignment problem can be made more difficult and realistic by mutating the reference at every 500th position to a random but different base. In some embodiments, the mutated reference (representing the resequencing reference) can be used for read alignment and initial MSA construction. In some embodiments, the original reference (representing the sample) can be used for consensus sequence comparison. In some embodiments, this MSA refinement improves low coverage consensus calls.

[0299] Identification of antimicrobial resistance genes

[0300] The present disclosure provides systems and methods for determining the presence, absence or abundance of specific genes in a sample (e.g., based on the results of an earlier step, as described herein). In this case, the multiple reference polynucleotide sequences typically include sequence groups corresponding to individual genes in the multiple genes. In some embodiments, based on the sequence analyzed by the method described herein, at least 50, 100, 250, 500, 1000, 5000, 10000, 50000, 100000, 250000, 500000 or 1000000 different genes are identified as not present or present (and optionally abundance, which can be relative). In some embodiments, the analysis is performed in parallel. In some embodiments, when the identified gene accounts for less than 0.05% of the total population of genes in the source sample, the methods, compositions and systems of the present disclosure can achieve parallel detection of the presence or absence of genes in the colony of genes (such as environmental or clinical samples). In some embodiments, detection is based on sequencing reads corresponding to polynucleotides present in less than 0.01% of the total nucleic acid population. The homology of a specific polynucleotide to other nucleic acids in the population may be at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96% or 97%. In some embodiments, the homology of a specific polynucleotide to other nucleic acids in the population is less than 75%, 50%, 40%, 30%, 20% or 10%. Determining the presence, absence or abundance of a specific taxon may include identifying an individual subject as a sample source. For example, a reference database may include multiple reference sequences, each corresponding to a single organism (e.g., a human subject), wherein sequences from multiple different subjects are represented in the reference sequence. The sequencing reads of an unknown sample may then be compared to the sequences of the reference database, and based on identifying sequencing reads according to the method, the individuals represented in the reference database may be identified as sample sources of sequencing reads. In this case, the reference database may include data from at least 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 A sequence of one or more individuals.

[0301] In some embodiments, identifying the presence, absence, or abundance of a gene or genes can be used to diagnose a disorder based on the degree of similarity between the gene or genes detected in a sample and a biomarker for the disorder.

[0302] The presence, absence or abundance of a gene can be used for diagnostic purposes, such as if a sequence read from a specific disease gene is present at a higher level than a control (e.g., an uninfected individual), it is inferred that the sample or subject suffers from a specific condition (e.g., a disease). In an example, sequencing reads can be derived from a host and indicate the presence of a pathogenic gene by measuring the presence, absence or abundance of host genes in a sample. Presence, absence or abundance can be used to infer the effectiveness of treatment, wherein the reduction in the number of sequencing reads from a pathogenic agent after treatment, or the presence, absence or abundance of a specific host response gene indicates that treatment is effective, and no change or insufficient change indicates that treatment is ineffective. The sample can be measured before treatment begins or measured once or multiple times after treatment begins. In some examples, the treatment of an infected subject is changed based on monitoring results.

[0303] The present disclosure provides a method for identifying one or more related antimicrobial resistance genes associated with a sample source. The sample source may be as described elsewhere herein. In some embodiments, the met...

Claims

1. A method for identifying a host with an antimicrobial resistance (AMR) marker from a sample, the method comprising: obtaining a sample from a source, the sample comprising a plurality of nucleic acids from a plurality of hosts; enriching the nucleic acid via target enrichment; sequencing the enriched nucleic acid to generate reads of the enriched nucleic acid; assaying the reads for one or more AMR markers to obtain read metrics; obtaining a reference metric associated with one or more of the AMR markers identified in the reference nucleic acid; as well as When the average ratio between the read metric and the reference metric is below a threshold ratio, a host of the one or more AMR markers in the sample is identified.

2. The method according to claim 1, wherein the average ratio comprises: the average RPKM ratio between the RPKM of the read metric and the reference metric, and The average median depth ratio between the median depths of the read metric and the reference metric. The method according to claim 1 , wherein the threshold ratio is 2. . The method according to claim 1 , wherein the threshold value is 1.

5.

5. The method according to any one of claims 1 to 4, further comprising decomposing the read segment into a plurality of k-mers.

6. The method of claim 5, wherein the reference nucleic acid comprises a plurality of index k-mers for each organism of interest.

7. The method according to any one of claims 1 to 6, wherein the enrichment is performed in parallel with the sequencing.

8. The method of any one of claims 1 to 7, wherein the enrichment comprises capture probes constructed to contact one or more nucleic acids of interest among a plurality of nucleic acids from the sample.

9. The method of any one of claims 1 to 8, wherein the source comprises an environmental source.

10. The method of any one of claims 1 to 8, wherein the source comprises an industrial source.

11. The method of claim 10, wherein the industrial source comprises wastewater.

12. The method of any one of claims 1 to 8, wherein the source comprises, or is derived from, bacteria.

13. The method of any one of claims 1 to 8, wherein the source comprises a virus, or is derived from a virus.

14. The method of any one of claims 1 to 8, wherein the source comprises, or is derived from, a fungus.

15. The method according to claims 1 to 8, wherein the source is obtained from a mammal.

16. The method of claim 15, wherein the mammal comprises a human.

17. The method of any one of claims 1 to 16, wherein the host comprises a microorganism.

18. The method of any one of claims 1 to 17, wherein identifying the host of the one or more AMR markers in the sample is also based on comparing the methylation patterns on the reads of the one or more AMR markers with the methylation patterns on a reference genome of a potential host.

19. The method of claim 18, wherein comparing the methylation pattern on the reads of the one or more AMR markers to the methylation pattern on a reference genome of a potential host comprises determining whether identical methylated sequence motifs are present in both methylation patterns.

20. The method of claim 18, further comprising identifying N6-methyladenine (6mA), N4-methylcytosine (4mC) and / or 5-methylcytosine (5mC) modifications in the reads of the one or more AMR markers.

21. The method of any one of claims 1 to 20, wherein the read metrics include RPKM, median depth and / or read count of any one or more of the AMR markers identified in the reads.

22. The method of any one of claims 1 to 21, wherein the reference metric comprises RPKM, median depth and / or read count of any one or more of the AMR markers identified in the reference nucleic acid.

23. The method of any one of claims 1 to 22, wherein obtaining the reference metric comprises assaying the reference nucleic acid for the one or more AMR markers.

24. A computer-implemented method for identifying AMR-marked hosts from one or more samples, the method comprising: obtaining read sequence data derived from one or more samples, each of the one or more samples comprising a plurality of nucleic acids from a plurality of hosts; identifying one or more AMR markers from the read sequence data to obtain read metrics; obtaining reference metrics associated with any one or more of the AMR markers identified in the reference sequence; as well as When the average ratio between the read metric and the reference metric is below a threshold ratio, a host of the one or more AMR markers in the sample is identified.

25. The method according to claim 24, wherein the average ratio comprises: the average RPKM ratio between the RPKM of the read metric and the reference metric, and The average median depth ratio between the median depths of the read metric and the reference metric.

26. The method of claim 24 or 25, wherein the threshold ratio is 2.

27. The method according to any one of claims 24 to 26, wherein the threshold value is 1.

5.

28. The method according to any one of claims 24 to 27, wherein the enrichment is performed in parallel with the sequencing.

29. The method of any one of claims 24 to 28, wherein the enrichment comprises capture probes constructed to contact one or more nucleic acids of interest among a plurality of nucleic acids from the sample.

30. The method of any one of claims 24 to 29, wherein the sample is derived from an environmental source.

31. The method of any one of claims 24 to 29, wherein the sample originates from an industrial source.

32. The method of claim 31 , wherein the industrial source comprises wastewater.

33. The method according to any one of claims 24 to 29, wherein the sample comprises bacteria, or is derived from said bacteria.

34. The method of any one of claims 24 to 29, wherein the sample comprises a virus, or is derived from a virus.

35. The method of any one of claims 24 to 29, wherein the sample comprises, or is derived from, a fungus.

36. The method of claims 24 to 29, wherein the sample is obtained from a mammal.

37. The method of claim 36, wherein the mammal comprises a human.

38. The method of any one of claims 24 to 37, wherein the read sequence data comprises a plurality of polypeptide sequence reads.

39. The method of claim 38, wherein the reference sequence data comprises a plurality of polypeptide sequence reads.

40. The method of any one of claims 24 to 37, wherein the read sequence data comprises a plurality of nucleic acid reads.

41. The method of claim 40, wherein the reference sequence data comprises a plurality of nucleic acid reads.

42. The method of claim 40 or 41, further comprising decomposing the read sequence data into a plurality of k-mers.

43. The method of claim 42, wherein the reference sequence data comprises a plurality of indexed k-mers for each organism of interest.

44. The method of any one of claims 24 to 43, wherein identifying the host of the one or more AMR markers in the sample is also based on comparing the methylation patterns on the reads of the one or more AMR markers with the methylation patterns on a reference genome of a potential host.

45. The method of claim 44, wherein comparing the methylation pattern on the reads of the one or more AMR markers to the methylation pattern on a reference genome of a potential host comprises determining whether identical methylated sequence motifs are present in both methylation patterns.

46. ​​The method of claim 44, further comprising identifying N6-methyladenine (6mA), N4-methylcytosine (4mC) and / or 5-methylcytosine (5mC) modifications in the reads of the one or more AMR markers.

47. The method of any one of claims 24 to 46, wherein the read metrics include RPKM, median depth and / or read count of any one or more of the AMR markers identified in the reads.

48. A method according to any one of claims 24 to 47, wherein the reference metrics include RPKM, median depth and / or read count of any one or more of the AMR markers identified in the reference sequence.

49. The method of any one of claims 24 to 48, wherein obtaining the reference metric comprises obtaining one or more reference sequence data and identifying one or more of the AMR markers from the reference sequence data.

50. An electronic system for identifying AMR-marked hosts from a sample, the electronic system comprising: a memory storing instructions; One or more processors programmable to execute instructions including the method according to any one of claims 24 to 49.

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