Systems and methods for clinical decision support

Abstract

The present disclosure provides systems and methods for clinical decision support of the diagnosis of infection. In an aspect, a system or method of clinical decision support (CDS) may comprise CDS of metagenomic next-generation sequencing results of clinical samples, (e.g., cerebral spinal fluid) to evaluate sample quality and microbial composition to diagnose a microbial infection (e.g., of the central nervous system).

Description

WSGR Docket No. 63228-705.601SYSTEMS AND METHODS FOR CLINICAL DECISION SUPPORTCROSS-REFERENCE

[0001] This application claims the benefit of US Provisional Application Serial Number 63 / 706,437 filed on October 11, 2024, and US Provisional Application Serial Number 63 / 800,222 filed on May 5, 2025, each of which is incorporated by reference herein in its entirety.BACKGROUND

[0002] The advancement of diagnostic tests in clinical microbiology has been aided by the advent of metagenomic sequencing, a technology that enables an assessment of microbial taxa present in a sample. Timely, accurate, and reproducible detection of pathogenic microbes is crucial for effective treatment and management of infections. However, detection and diagnosis of microbial infections is often unreliable and unreproducible. Moreover, rare, novel, or unexpected pathogens go undetected, leading to delayed diagnosis, misdiagnosis, or potential complications.SUMMARY

[0003] There are over 30,000 suspected meningitis and encephalitis cases annually. With a mortality rate of nearly 30%, infection of the central nervous system (CNS) is a medical emergency, requiring quick diagnosis and immediate treatment. Infections of the central nervous system, such as meningitis, encephalitis, and brain abscess, are rapidly progressing and can lead to blindness, paralysis, and death. With over 400 different pathogens implicated in meningitis and encephalitis, CNS infection can be caused by a wide range of pathogenic microbes including bacteria, DNA viruses, RNA viruses, fungi, and / or parasites, and the standard diagnostic paradigm is costly and inefficient. Therefore, accurate and rapid detection of microbial presence in subject samples is critical, ensuring that subjects receive tailored and targeted therapies specific to their diagnosed condition.

[0004] Diagnosis of CNS infection may require using laboratory tests, e.g., metagenomic next-generation sequencing (mNGS), of a subject sample, e.g., cerebral spinal fluid (CSF), to detect the presence of one or more pathogens. However, these tests may not be reliable and reproducible, and pathogens can go undetected, leading to delayed treatment and undesirable treatment outcomes.

[0005] Recognized herein is the need for rapid, reliable, and reproducible systems and methods for interpreting the multi-faceted results of laboratory tests, e.g., mNGS, of subject samples to facilitate pathogen detection and infection diagnosis.WSGR Docket No. 63228-705.601

[0006] The present disclosure provides systems and methods that may advantageously provide evaluation of sample quality and microbial composition, enhancing the speed, reliability and reproducibility of diagnostic interpretations. Systems and methods provided herein can allow for the detection of a multitude of infections agents simultaneously, with a single test, without a priori clinical suspicion, and focus on advancing turnaround time (TAT), modularity, scalability, clinical interpretation, and auditability, e.g., for regulatory compliance. Systems and methods provided herein that can further provide for detection of sub-types or strains of infectious agents have epidemiological implications for outbreak surveillance, lineage tracing, and public health applications. Systems and methods provided herein provide, in part, processes of modernizing and scaling the detection of pathogens to expand, assess, and improve clinical performance, and reduce turnaround time. Additionally, systems and methods can provide pathogen detection with a flexible architecture that enables easy expansion into other sample types and clinical indications. System and methods provided herein can pave the way for broader application and advancements in unbiased mNGS diagnostics.

[0007] Provided herein in some embodiments is a method comprising: (a) sequencing nucleic acids obtained or derived from a biological sample of a subject, thereby generating sequence data; (b) computer processing the sequence data to generate an output indicative of a presence or quantitative measure of microbial nucleic acids in the biological sample; (c) presenting to a user, via a user interface of an electronic display, a visualization of the output; (d) receiving from the user, via the user interface of the electronic display, annotations related to the output, wherein the annotations are clinically interpretable; and (e) generating an electronic report for the subject, based at least in part on the output and the annotations, wherein the electronic report is indicative of a presence of a pathogenic infection, an absence of a pathogenic infection, or a risk of a pathogenic infection. In some embodiments, the method further comprises receiving, from a clinician, clinical data of the subject. In some embodiments, the annotations are based at least in part on the clinical data of the subject.

[0008] In an aspect, provided herein is a method comprising generating an electronic report. In some embodiments, generating the electronic report comprises compiling the annotations related to the output. In some embodiments, provided herein is a method comprising receiving from a user annotations related to the output. In some embodiments, the electronic report is reviewed by a second user. In some embodiments, the electronic report is approved by a second user. In some embodiments, the user and the second user have different roles. In some embodiments, the user or the second user does not have access to Protected Health Information (PHI) of the subject. In some embodiments, the annotation comprises one or more of a direct field entry, a comment, an interpretation, a sample note, and an internal note. In someWSGR Docket No. 63228-705.601 embodiments, the annotation comprises a clinically relevant annotation related to the output. In some embodiments, the annotation is documented by a clinician. In some embodiments, the annotation is clinically auditable.

[0009] In certain embodiments, the electronic report comprises one or more of a sequencing metric, a taxonomy classification, an organism flagging, and a sample metadata. In some specific embodiments, the sequencing metric comprises a quality control metric. In certain embodiments, the sequencing metric comprises a microbe abundance metric or an internal control metric. In some embodiment, the electronic report is generated within 1, 2, 3, 4, 5, 6, or 7 business days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 1, 2, 3, 4, 5, 6, or 7 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 1, 2, 3, 4, 5, 6, 9, 10, 12, 15, 20, 22, or 24 hours from the sequencing of the biological sample. In some embodiments, the electronic reportis generated within about 1 to 31, 1 to 14, 1 to 7, 1 to 5, 1 to 3, or 1 to 2 business days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within about 1 to 31, 1 to 14, 1 to 7, 1 to 5, 1 to 3, or 1 to 2 days from the sequencing of the biological sample.

[0010] In an aspect, a method provided herein reduces an incidence of false positive results indicating the presence of a pathogenic infection. In some embodiments, the method reduces an incidence of false negative results indicating the absence of a pathogenic infection. In some embodiments, at least 1%, 5%, 10%, 15% 20%, 25%, 30%, 35%, 40%, 45%, or 50% more pathogens are detected as compared to conventional testing.

[0011] In an aspect, a method provided herein comprises training the user. In some embodiments, the training comprises the user correctly detecting a presence of a pathogenic infection, an absence of a pathogenic infection, or a risk of a pathogenic infection. In some embodiments, a method provided herein comprises assessing a proficiency or a competency of the user.

[0012] In an aspect, a method provided herein comprises presenting to a user a visualization of an output. In some embodiments, the visualization comprises one or more of a batch processing visualization, a sequencing visualization, a quality control visualization, a specimen detail visualization, a taxonomic selection visualization, a heatmap visualization, coverage visualization, and a report previewing visualization.

[0013] In an aspect, a method provided herein comprises sequencing nucleic acids obtained or derived from a biological sample of a subject. In certain embodiments, sequencing comprises one or more of nucleic acid extraction, nucleic acid isolation, nucleic acid fragmentation, reverse transcription (e.g., of RNA), transcript fragmentation, adapter ligation, amplification, targetedWSGR Docket No. 63228-705.601 enrichment, nucleic acid library preparation, bisulfite conversion, and methylation conversion. In some embodiments, sequencing comprises one or more of targeted sequencing, single molecule real-time sequencing, exon or exome sequencing, intron sequencing, electron microscopy-based sequencing, panel sequencing, transistor-mediated sequencing, direct sequencing, random shotgun sequencing, Sanger dideoxy termination sequencing, whole - genome sequencing, sequencing by hybridization, pyrosequencing, duplex sequencing, cycle sequencing, single-base extension sequencing, solid phase sequencing, high-throughput sequencing, massively parallel signature sequencing, emulsion PCR, co -amplification at lower denaturation temperature -PCR (COLD-PCR), multiplex PCR, sequencing by reversible dye terminator, paired-end sequencing, near-term sequencing, exonuclease sequencing, sequencing by ligation, short-read sequencing, single molecule sequencing, sequencing-by-synthesis, realtime sequencing, reverse-terminator sequencing, long-read sequencing, nanopore sequencing, 454 sequencing, Solexa Genome Analyzer sequencing, SOLiD™ sequencing, and MS-PET sequencing. In some embodiments, sequencing comprises a polymerase chain reaction (PCR) or isothermal amplification. In specific embodiments, the amplification comprises amplification reagents, the amplification reagents comprising reagents for one or more of polymerase chain reaction (PCR), transcription mediated amplification (TMA), helicase dependent amplification (HD A), circular helicase dependent amplification (cHDA), strand displacement amplification (SDA), loop mediated amplification (LAMP), exponential amplification reaction (EXPAR), rolling circle amplification (RCA), ligase chain reaction (LCR), simple method amplifying RNA targets (SMART), single primer isothermal amplification (SPIA), multiple displacement amplification (MDA), nucleic acid sequence based amplification (NASBA), hinge -initiated primer-dependent amplification of nucleic acids (HIP), nicking enzyme amplification reaction (NEAR), or improved multiple displacement amplification (HMD A).

[0014] In some embodiments, the computer processing further comprises comparing the quantitative measure of microbial nucleic acids to a threshold. In some embodiments, the computer processing further comprises aligning the sequence data to a reference genome. In some specific embodiments, the reference genome comprises a human reference genome or a microbial reference genome. In certain embodiments, the computer processing further comprises filtering the sequence data. In certain specific embodiments, the filtering comprises distinguishing human-derived reads and microbe-derived reads. In certain specific embodiments, the filtering comprises removing human -derived reads.

[0015] In some embodiments, the nucleic acid comprises one or more of double-stranded (ds) nucleic acids, single stranded (ss) nucleic acids, deoxyribose nucleic acids (DNA), ribose nucleic acids (RNA), complementary DNA (cDNA), dsDNA, ssDNA, circulating nucleic acids,WSGR Docket No. 63228-705.601 circulating cell-free nucleic acids, circulating DNA, circulating RNA, cell -free nucleic acids, cell-free DNA, cell-free RNA, circulating cell-free DNA, cell-free dsDNA, cell-free ssDNA, circulating cell-free RNA, genomic DNA, plasmid DNA, mitochondrial DNA, cell-free pathogen nucleic acids, circulating pathogen nucleic acids, circular DNA, circular RNA, circular single-stranded DNA, circular double-stranded DNA. In some specific embodiments, the RNA comprises one or more of messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and non-coding RNA.

[0016] In some embodiments, the biological sample comprises one or more of whole blood, blood serum, blood plasma, bile, breath, urine, feces, sebum, tissue, breast milk, hair, saliva, sputum, sweat, tears, lymphatic fluid, serous fluid, synovial fluid, pericardial fluid, peritoneal fluid, pleural fluid, cystic fluid, cerebrospinal fluid, seminal fluid, vaginal secretion, amniotic fluid, nasal fluid, otic fluid, interstitial fluid, gastric fluid, intestinal fluid, transudates, exudates, liquids containing single or multiple cells, liquids containing organelles, fluidized tissues, fluidized organisms, liquids containing multi-celled organisms, biological swabs, and biological washes.

[0017] In some embodiments, the electronic display comprises one or more of a personal computer (e.g., portable computer), a slate or tablet personal computer (PC), a mobile electronic device, a smartphone, a personal digital assistant, and a wearable device.

[0018] In some embodiments, the pathogenic infection comprises one or more of myotoxic disease, respiratory disease, pulmonary infections, pneumonia, mononucleosis, cutaneous infections, keratitis, peritonitis, osteomyelitis, sinusitis, endophthalmitis, mycetoma, soft tissue infections, cellulitis, impetigo, bacteremia, toxic shock syndrome, ventriculitis, urinary tract infections, surgical site infections, wound infections, abscesses, meningitis, encephalitis, sepsis, and peritonitis. In some specific embodiments, the pathogenic infection comprises meningitis or encephalitis. In some embodiments, the pathogenic infection comprises infection with a pathogen, the pathogen comprising one or more of a filamentous fungus, a gram negative bacterium, a gram positive bacterium, a yeast, a parasite, a DNA virus, and an RNA virus. In some embodiments, the pathogen comprises a strain or a subtype of a species of one or more of a filamentous fungus, a gram negative bacterium, a gram positive bacterium, a yeast, a parasite, a DNA virus, and an RNA virus.

[0019] In certain embodiments, the filamentous fungus comprises one or more of a fungus of the genus Acremonium, Alternaria, Aspergillus, Cladosporium, Curvularia, Fusarium, Histoplasma, Lichtheimia, Lomentospora, Mucor, Paecilomyces, Penicillium, Rhizomucor , Rhizopus, Scedosporium, Schizophyllum, and Trichoderma. In some embodiments, the filamentous fungus comprises one or more of Acremonium spp., Alternaria spp., AlternariaWSGR Docket No. 63228-705.601 alternata, Alternaria infectoria, Aspergillus spp., Aspergillus carneus, Aspergillus clavatus, Aspergillus flavus. Aspergillus fumigatus, Aspergillus nidulans. Aspergillus niger. Aspergillus ochraceus, Aspergillus lerreus. Aspergillus uslus. Aspergillus versicolor, Aspergillus parasiticus, Cladosporium spp., Curvularia spp., Fusarium spp., Fusarium avenaceum, Fusarium culmorum, Fusarium equiseti, Fusarium fujikuroi, Fusarium graminearum, Fusarium nivale, Fusarium proliferatum, Fusarium oxysporum, Fusarium roseum, Fusarium solani, Fusarium verticillioides, Histoplasma spp., Histoplasma capsulatum, Lichtheimia spp., Lichtheimia corymbifera, Lichtheimia ramose, Lomentospora spp., Lomentospora prolificans, Mucor s vp.. Mu cor circinelloides, Paecilomyces spp., Paecilomyces variotii, Penicillium spp., Penicillium aurantiogriseum, Penicillium brunneum, Penicillium citreoviride, Penicillium citrinin, Penicillium claviforme , Penicillium crustosum, Penicillium expansum, Penicillium griseofulvum, Penicillium hirsutum, Penicillium islandicum, Penicillium kloeckeri, Penicillium roqueforti, Penicillium rubrum, Penicillium rugulosum, Penicillium verrucossum, Penicillium viridicatum, Rhizomucor spp., Rhizomucorpusillus, Rhizopus spp., Rhizopus arrhizus, Rhizopus microspores, Scedosporium spp., Scedosporium apiospermum, Schizophyllum spp., Schizophyllum commune , and Trichoderma spp. In some embodiments, the pathogenic infection comprises one or more of dermatomycoses, epidermophyton, allergic bronchopulmonary mycosis, allergic fungal rhino sinusitis, mycotic keratitis (e.g., keratomycosis), mucormycosis, otomycosis, onychomycosis, aspergillosis (e.g., invasive aspergillosis or pulmonary aspergillosis), eumycetoma, and fungemia.

[0020] In certain embodiments, the gram negative bacterium comprises one or more of a bacterium of the genus Acinetobacter, Chlamydia, Citrobacter , Enterobacter , Escherichia, Klebsiella, Moraxella, Proteus, Pseudomonas, Shigella, Salmonella, and Yersinia. In some embodiments, the gram negative bacterium comprises one or more of Acinetobacter spp., Acinetobacter baumannii, Chlamydia spp., Chlamydia trachomatis, Citrobacter spp., Enterobacter spp., Escherichia coli, Klebsiella spp., Moraxella spp., Moraxella sp. KI 664, Moraxella bovis, Moraxella canis, Moraxella catarrhalis, Moraxella lacunata, Moraxella nonliquefaciens, Moraxella osloensis, Moraxella phenylpyruvica, Proteus spp., Pseudomonas spp., Pseudomonas aeruginosa, Salmonella spp., Salmonella enterica, Salmonella bongori, Shigella spp., Shigella dy senter iae , Shigella flexneri, Shigella boydii, Shigella sonnei, Yersinia spp., and Yersinia pestis.

[0021] In certain embodiments, the gram positive bacterium comprises one or more of a bacterium of the genus Staphylococcus, Streptococcus, Clostridium, Enterococcus, and Mycobacterium. In certain embodiments, the gram positive bacterium comprises one or more of Staphylococcus spp., Staphylococcus aureus, Streptococcus spp., Streptococcus pyogenes,WSGR Docket No. 63228-705.601Clostridium spp., Clostridium difficile, Enterococcus spp., Enterococcus faecalis , Enterococcus gallinarum, My cobacterium spp., and Mycobacterium tuberculosis. In some embodiments, the pathogenic infection comprises one or more of strep throat, tuberculosis, and Methicillin - resistant Staphylococcus aureus (MRSA) infection.

[0022] In certain embodiments, the yeast comprises one or more of a yeast of the genus Candida or Cryptococcus . In some embodiments, the yeast comprises one or more of Candida auris, Candida albicans, Candida glabrata, Candida parap silo sis, Candida tropicalis, Candida krusei, Cryptococcus albidus, Cryptococcus curvatus, Cryptococcus gattii, Cryptococcus laurentii, Cryptococcus neoformans, and Cryptococcus uniguttulatus. In some embodiments, the pathogenic infection comprises one or more of oral thrush, candidiasis (e.g., invasive candidiasis, cutaneous candidiasis, nail candidiasis, esophageal candidiasis, or vaginal candidiasis), and candidemia.

[0023] In certain embodiments, the parasite comprises one or more of a protozoa, a helminth, or an ectoparasite. In some embodiments, the pathogenic infection comprises one or more of Lyme disease, malaria, giardiasis, toxoplasmosis, intestinal worm infections, lice, chagas disease, schistosomiasis, babesiosis, pinworm infection, hookworm, leishmaniasis, trichomoniasis, amebiasis, cryptosporidiosis, schistosomiasis, tapeworms, toxocariasis, ascariasis, cysticercosis, echinococcosis, filariasis, neurocysticercosis, onchocerciasis, roundworms, African trypanosomiasis, paragonimiasis, strongyloidiasis, and trichuriasis.

[0024] In certain embodiments, the DNA virus comprises one or more of herpesvirus, cytomegalovirus (CMV), human papillomavirus (HPV), adenovirus, hepatitis B virus (HBV), poxvirus, and polyomavirus. In some embodiments, the herpesvirus comprises one or more of herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), Kaposi sarcoma- associated herpesvirus (gamma herpesvirus), varicella-zoster virus (VZV), and Epstein-Barr virus. In some embodiments, the pathogenic infection comprises chicken pox or shingles.

[0025] In certain embodiments, the RNA virus comprises one or more of influenza virus, respiratory syncytial virus (RSV), coronavirus (e.g., SARS-CoV-2), enterovirus, norovirus, rotavirus, human immune deficiency virus (HIV), hepatitis C virus (HCV), Rift Valley fever virus, Tick-borne encephalitis virus, Zika virus, Dengue virus, West Nile virus, Ebola virus, yellow fever virus, Saint Louis encephalitis virus (SLEV), Eastern Equine encephalitis virus (EEEV), La Crosse encephalitis virus (LCEV), and Japanese encephalitis virus.

[0026] In some embodiments, the pathogenic infection comprises a symptom. In some embodiments, the symptom comprises one or more of fever, severe headache, stiff neck, sensitivity to light, nausea, vomiting, confusion, altered mental status, seizure, personality changes, coma, cancer, asthma, brain granuloma, arthritis, anemia, diarrhea, dysentery,WSGR Docket No. 63228-705.601 vomiting, loss of appetite, fever chills, fatigue, weight loss, malnutrition, and abdominal pain, and skin lesions. In some embodiments, the symptom comprises one or more of fever, severe headache, stiff neck, sensitivity to light, nausea, vomiting, confusion, altered mental status, seizure, and personality changes. In some embodiments, the pathogenic infection is an antibiotic resistant infection. In some embodiments, the pathogenic infection comprises a pathogen associated with cancer.

[0027] In some embodiments, the pathogenic infection is treated. In some embodiments, the pathogenic infection is treated with an antimicrobial. In some specific embodiments, the antimicrobial comprises one or more of an antibiotic, antifungal, antiparasitic, and an antiviral.

[0028] In certain embodiments, the electronic report comprises a performance metric that is indicative of a presence of a pathogenic infection. In some embodiments, the performance metric comprises one or more of an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and Fl score. In some specific embodiments, the performance metric has a value of atleast 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97%. In some embodiments, the performance metric is an Area Under the Receiver Operating Characteristic Curve (AUROC) or correlation coefficient. In some specific embodiments, the performance metric has a value of at least 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, or 0.97. In some embodiments, the electronic report comprises a performance metric that is indicative of a presence of a pathogenic infection comprising a sub-type or strain of a species of pathogen. In some embodiments, the risk comprises one or more of a likelihood, probability, odds ratio, risk ratio, attributable risk, standardized morality ratio, and a Bayesian Risk Assessment.INCORPORATION BY REFERENCE

[0029] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material.BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure are obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings (also “Figure” and “FIG.” herein), of which:WSGR Docket No. 63228-705.601

[0031] FIG. 1A illustrates the clinical landscape of central nervous system (CNS) infections.

[0032] FIG. IB illustrates the taxonomic diversity of CNS infections.

[0033] FIG. 2 illustrates an example wet lab step of a system or method of clinical decision support provided herein.

[0034] FIG. 3 illustrates an example dry lab step of a system or method of clinical decision support provided herein.

[0035] FIG. 4 illustrates an example workflow of a system or method of clinical decision support provided herein that delivers results in about two business days.

[0036] FIG. 5 provides an example pipeline for delivery of taxonomic assignments of a system or method of clinical decision support provided herein.

[0037] FIG. 6 illustrates an example bioinformatics pipeline comprising modules of a system or method of clinical decision support provided herein.

[0038] FIG. 7 illustrates an example workflow, e.g., execution flow and order of tasks (directed acyclic graph; DAG), of a system or method of clinical decision support provided herein.

[0039] FIG. 8 illustrates an example of continuous improvement / continuous deployment (CI / CD) of a system or method of clinical decision support provided herein.

[0040] FIG. 9 illustrates an example of a clinical decision support application user interface.

[0041] FIG. 10 illustrates an example of a clinical decision support workflow.

[0042] FIG. 11 A illustrates an example of a clinical decision support user interface for batch processing.

[0043] FIG. 11B illustrates an example of a clinical decision support user interface for sequencing and quality control.

[0044] FIG. 11C illustrates an example of a clinical decision support user interface providing specimen details.

[0045] FIG. HD illustrates an example of a clinical decision support user interface for taxonomic selection.

[0046] FIG. HE illustrates an example of a clinical decision support user interface providing heatmap and coverage.

[0047] FIG. HF illustrates an example of a clinical decision support user interface for report previewing.

[0048] FIG. 12 illustrates a method of computational viral subtyping of a system or method of clinical decision support provided herein.WSGR Docket No. 63228-705.601

[0049] FIG. 13 illustrates a method of assembly of sequence reads, multiple sequence alignment (MSA), and phylogenetic placement of a method of clinical decision making provided herein.

[0050] FIG. 14 provides an example of phylogenic placement of a sample previously reported as positive for St. Louis Encephalitis Virus (SLEV) using a subtyping workflow of a system or method of clinical decision support provided herein.

[0051] FIG. 15A provides an example of strain-typing using a subtyping approach of a system or method of clinical decision support provided herein.

[0052] FIG. 15B provides an example of a validation of a strain-typing using a subtyping approach of a system or method of clinical decision support provided herein.

[0053] FIG. 16A provides an example of the percentage of subjects remaining admitted to a hospital per day after a cerebrospinal fluid (CSF) sample collection when metagenomic nextgeneration sequencing (mNGS) results were delivered to the subject or clinical provider; subject samples were analyzed either using a system or method of clinical decision support provided herein or using conventional methods.

[0054] FIG. 16B provides an example of the Turnaround Time (TAT) for samples analyzed with (i) a system or method of clinical decision support provided herein when a subject sample was directly shipped from the hospital to the processing facility (left); (ii) a system or method of clinical decision support provided herein when a subject sample was not directly shipped from the hospital to the processing facility (center); and (iii) the University of California, San Francisco (UCSF) bioinformatics analysis pipeline (left).

[0055] FIG. 17 provides an example of the consistency of organism detection between a system or method of clinical decision support provided herein and SURPI, the UCSF bioinformatics analysis pipeline.

[0056] FIG. 18A provides an example of the comparative performance of conventional diagnostics to UCSF mNGS sequencing.

[0057] FIG. 18B provides an example of a positive detection of a pathogen {Cryptococcus neoformans) in a sample of a subject using a system or method of clinical decision support provided herein.

[0058] FIG. 18C provides an example of the distribution of biomass of a pathogen (Cryptococcus neoformans) in samples from subjects analyzed using a system or method of clinical decision support provided herein.

[0059] FIG. 19A provides an exemplary timeline of detection of a pathogenic infection in a subject using a system or method of clinical decision support provided herein .WSGR Docket No. 63228-705.601

[0060] FIG. 19B provides an example of elevated pathogen (Moraxella) biomass in a sample of the subject of FIG. 19A compared to historical mNGS samples.

[0061] FIG. 19C provides an example genome coverage plot showing patient-derived (FIG. 19A) mNGS read alignment to a Moraxella sp. reference genome.

[0062] FIG. 19D provides an example maximum likelihood phylogenetic tree constructed from patient-derived (FIG. 19A) ribosomal RNA operon (RRN) consensus sequence.DETAILED DESCRIPTION

[0063] While various embodiments of the invention have been shown and described herein, it is obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.

[0064] Various terms used throughout the present description may be read and understood as follows, unless the context indicates otherwise: “or” as used throughout is inclusive, as though written “and / or”; singular articles and pronouns as used throughout include their plural forms, and vice versa; similarly, gendered pronouns include their counterpart pronouns so that pronouns should not be understood as limiting anything described herein to use, implementation, performance, etc. by a single gender; “exemplary” should be understood as “illustrative” or “exemplifying” and not necessarily as “preferred” over other embodiments. Further definitions for terms may be set out herein; these may apply to prior and subsequent instances of those terms, as are understood from a reading of the present description .Whenever the term “at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least,” “greater than” or “greater than or equal to” applies to each of the numerical values in that series of numerical values . For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3.

[0065] Whenever the term “no more than,” “less than,” or “less than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “no more than,” “less than,” or “less than or equal to” applies to each of the numerical values in that series of numerical values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1 .

[0066] The term “subject,” as used herein, generally refers to a human such as a patient. The subject may be a person (e.g., a patient) with a disease, disorder, or condition, or a person that has been treated for a disease, disorder, or condition, or a person that is being monitored for aWSGR Docket No. 63228-705.601 disease, disorder, or condition, or a person that is suspected of having the disease, disorder, or condition, or a person that does not have or is not suspected of having the disease, disorder, or condition. The disease, disorder, or condition maybe an infectious disease, an immune disorder or disease, an injury, or a rare disease. The infectious disease may be caused by bacteria (e.g., gram positive or gram negative bacteria), viruses (e.g., DNA or RNA viruses), fungi (e.g., filamentous fungi) and / or parasites. For example, the disease or disorder may be bacterial, viral, fungal, or parasitic meningitis; encephalitis, cytomegalovirus, or brain abscess.

[0067] The clinical utility of metagenomic sequencing in the diagnosis of microbial infections can rely on the rapid, accurate, and reproducible interpretation of the complex data that metagenomic sequencing generates (FIG. 1A). This highlights the need for sophisticated clinical decision support software (CDS) that allows trained medical professionals to analyze, interpret, and report results in a manner that is systematic, auditable, and seamlessly integrated into clinical workflows.

[0068] Recognized herein is the need for methods and systems for clinical decision support, e.g., of laboratory-developed tests (LDTs), designed to aid in the diagnosis of infections, such as central nervous system (CNS) infections, through unbiased metagenomic next-generation sequencing (mNGS). With over 26,000 species of bacteria, over 10,000 viral species, over 16,000 fungal species, and over 15,000 parasitic species implicated in CNS infection (FIG. IB), such tests can offer a comprehensive overview of the microbial landscape in a clinical sample, such as cerebral spinal fluid (CSF), facilitating the detection of rare, novel, or unexpected pathogens critical for diagnosing CNS infections. Additionally, a CDS provided herein can streamline infectious disease workup on samples, e.g., cerebrospinal fluid samples. For example, a system or method provided herein can detect viruses, bacteria, fungi, and parasites at one time (simultaneously) from one sample of CSF. Moreover, a CDS provided herein can detect pathogenic infection that are difficult to detect by conventional methods (e.g., culturing or gram staining) and can detect pathogenic infection of samples from subjects who had already received treatment (e.g., antimicrobials, antibiotics, steroids, antifungals, antiparasitic, or antivirals) when the sample was collected. For example, a CDS provided herein can detect pathogenic organisms that are fastidious or bio-film forming. In some embodiments, a system or method provided herein can detect at least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50% more pathogens than conventional testing. In some embodiments, a system or method provided herein can detect about 20% or greater more pathogens than conventional testing. In some embodiments, a system or method provided herein can detect about 22% more pathogens than conventional testing. A system or method provided herein can reduce an incidence of false positive results indicating the presence of a pathogenic infection. A system or method providedWSGR Docket No. 63228-705.601 herein can reduce the incidence of false negative results indicating the absence of a pathogenic infection.

[0069] Systems and methods provided herein address this critical need with a solution for rapid data analysis and results reporting tailored to metagenomic sequencing of subject samples, e.g., CSF samples. The systems and methods provided herein provide rapid evaluation of sample quality, microbial composition, and microbial sub-typing identification while incorporating features to increase accuracy of pathogen detection and diagnosis, such features including auditability, user authentication, and comprehensive data tracking. These features enhance the reliability and reproducibility of diagnostic interpretations, thereby supporting informed clinical decision-making and improving subject outcomes on a clinically -relevant timeline.Methods and Systems of Clinical Decision Support

[0070] Provided herein in some embodiments are systems and methods for clinical decision support (CDS). In some embodiments, a CDS comprises a software package that aids in the interpretation and reporting of clinical results. For example, a CDS system provided herein can aid in the interpretation and reporting of metagenomic next-generation sequencing (mNGS) results. A CDS system provided herein can comprise a laboratory-developed test (LDT) that aids in the interpretation and reporting of clinical results, e.g. , mNGS results. A CDS provided herein can comprise a CDS system that provides a single interface for the interpretation of clinical results. A CDS provided herein can comprise a CDS system that provides an interface for the interpretation of clinical results without the need for switching between multiple applications or contexts.

[0071] In some embodiments, a system or method provided herein comprises a CDS designed to aid in the diagnosis of CNS infections. For example, a system or method provided herein can comprise a CDS for data analysis and results reporting tailored to metagenomic sequencing of CSF samples.

[0072] In some embodiments, a CDS provided herein comprises a wet lab step (FIG. 2). A wet lab step can comprise an automated protocol focused on precision, sterility, speed, and / or throughput. A wet lab step can comprise one or more of : providing a sample, e.g. , a cerebrospinal fluid (CSF) sample; lysis of all organisms in the sample; purification of DNA and RNA; enrichment for RNA viruses; enrichment for DNA viruses, bacteria, fungi, and / or parasites; conversion to DNA libraries and RNA libraries, amplification and pooling; and sequencing.

[0073] In some embodiments, a CDS provided herein comprises a dry lab step (FIG. 3). A dry lab step provided herein can have improved speed, accuracy, scalability, and / or auditability.WSGR Docket No. 63228-705.601A dry lab step provided herein can comprise one or more of: preprocessing; background extraction (e.g., human host background extraction); microbial identification; identification of viral, bacterial, fungal, and parasitic pathogens in the sample; clinical interpretation; and automated results report.

[0074] Provided herein in some embodiments is a CDS comprising a modular and scalable mNGS platform. A CDS provided herein can comprise a curated database of potential CNS pathogens. A curated database can comprise a subset of a larger database, e.g., an NCBI NT database, on taxa implicated (or potentially implicated) in CNS infection. Redundant entries in the data can be collapsed by sequence clustering, low -quality submissions can be removed, and high-quality reference sequences (RefSeqs) can be prioritized when available.

[0075] In some embodiments, a system or methods provided herein can comprise a bioinformatics pipeline. A bioinformatics pipeline can comprise self -contained, interchangeable components (modules) with standardized inputs and outputs (SeqSamples) (FIG. 6). The SeqSample object can be a custom data class that can serve as a standardize input and output of all step / modules in the pipeline. A single file, e.g., a FastQ file, can initialize a SeqSample. In some embodiments, the pipeline is organized as a series of tasks, e.g., tasks managed by Flyte. Each task can execute in its own containerized (docker) environment and can accept (as input) and outputs a SeqSample. In some embodiments, a bioinformatics pipeline provided herein facilitates rapid prototyping and independent testing of each module.

[0076] In some embodiments, tasks are organized and grouped into workflows (FIG. 7). For example, serial or parallel execution of tasks and workflows can be managed, e.g., by Flyte, during calculation as a directed acyclic graph (DAG). In some embodiments, the execution flow and order of tasks (DAG) is managed, e.g., by Flyte on AWS EKS. In some embodiments, all workflow and task execution are cached and memorized for easy iteration, retrieval, and reproducibility. In some embodiments, execution resources in a workflow scale automatically. For example, execution resources can scale from a single sample to hundreds of samples (in parallel) with no input or configuration changes required. In some embodiments, tasks and workflows are swappable. For example, new workflows or addition modules can be implemented easily, either programmatically or through a drag-and-drop user-interface.Workflows can be continuously improved and deployed (CI / CD), e.g., in production, staging, and development steps (FIG. 8).

[0077] In some embodiments, a bioinformatics pipeline provided herein comprises one or more of the modules: preprocessing, host removal, control removal, taxonomy, and postprocessing. In some embodiments, the preprocessing module comprises one or more of: quality filter, complexity filter, and quality control checks. In some embodiments, the taxonomyWSGR Docket No. 63228-705.601 module comprises one or more of: a curated database, bacterial alignment, viral alignment, fungal alignment, parasitic align, and taxonomic alignment. In some embodiments, the postprocessing module can comprise: genomic coverage, enrichment vs. internal control, cross- refence of clinically significant pathogens, batch quality control, and a push to a database.

[0078] In some embodiments, the output of a bioinformatic pipeline is output to clinical decision support (CDS) software, e.g. , an interactive user interface (UI). In some embodiments, UI provided herein comprises a drag-and-drop user-interface.

[0079] A system or method provided herein can provide functionalities, e.g., functionalities for the interpretation of clinical results. In some embodiments, a system or method provided herein comprises one or more of the functionalities of: (1) secure access for approved users with tiered levels of functionality / access to review clinical sample test results, e.g., results that are not or do not comprise Protected Health Information (non-PHI); (2) systematic review and approval of key quality control (QC) metrics for a given sample and / or batches of samples, e.g., review of clinical sample data gated on QC approval; (3) presentation and visualization of mNGS test results; (4) ability to trigger secondary bioinformatics workflows and present results in an auditable fashion; (5) clinical report previewing and issuance; (6) integration of a customer relationship management (CRM) system for reporting results to customers; (7) comprehensive logging of all actions performed by a user for auditability, traceability, and reproducibility. In some embodiments, a system or method provided herein comprises a functionality of secure access for approved users with tiered levels of functionality / access to review clinical sample test results. In some embodiments, test results can comprise results that are or do not comprise non- PHI. In some embodiments, a system or method provided herein comprises a functionality of systematic review and approval of key QC metrics for a given sample and / or batches of samples. In some embodiments, review of clinical sample data can be gated on QC approval. In some embodiments, a system or method provided herein comprises a functionality of presentation and visualization of mNGS test results. In some embodiments, a system or method provided herein comprises a functionality of ability to trigger secondary bioinformatics workflows and present results in an auditable fashion. In some embodiments, a system or method provided herein comprises a functionality of clinical report previewing and issuance. In some embodiments, a system or method provided herein comprises a functionality of integration of CRM system for reporting results to customers. In some embodiments, a system or method provided herein comprises a functionality of comprehensive logging of all actions performed by a user for auditability, traceability, and reproducibility.

[0080] A system or method of clinical decision support provided herein can provide clinical decision interpretive software. Clinical decision interpretive software can comprise a decisionWSGR Docket No. 63228-705.601 application. In some embodiments, a decision application comprises one or more of results visualization; selection and reporting; investigation; and annotation and PDF generation (FIG. 9). In some embodiments, a decision application comprises results visualization. In specific embodiments, results visualization comprises output of an analysis pipeline, the output comprising one or more of sequencing metrics, taxonomy classifications, organism flagging and sample metadata. In some embodiments, a decision application comprises selection and reporting. In specific embodiments, selection and reporting comprises efficient selection of relevant findings through an auditable and user-friendly interface to deliver a high quality report to clinicians. In some embodiments, a decision application comprises investigation. In specific embodiments, investigation comprises enablement of review and confirmation of read specificity using one or more of Fasta download, Blast alignment and genomic coverage maps. In some embodiments, a decision application comprises annotation and PDF generation. In specific embodiments, annotation and PDF generation comprises preparation of a result from direct field entry, comments, interpretation, sample notes, and internal notes. In specific embodiments, annotation and PDF generation comprises review and approval workflow for remote sign out integrated with salesforce.

[0081] Clinical decision interpretive software can comprise a decision workflow. In some embodiments, a decision workflow can comprise one or more of batch processing, sequencing and quality control, specimen details, taxonomic selection, heatmap and coverage and report previewing (FIG. 10). In some embodiments, a decision workflow comprises batch processing (FIG. 11 A). In some embodiments, a decision workflow comprises sequencing and quality control (FIG. 11B). In some embodiments, a decision workflow comprises specimen details (FIG. 11C). In some embodiments, a decision workflow comprises taxonomic selection (FIG. 11D). In some embodiments, a decision workflow comprises heatmap and coverage (FIG. HE). In some embodiments, a decision workflow comprises report previewing (FIG. HF).

[0082] In some embodiments, a clinical decision support system provided herein provides for the mitigation of operator error. Operator error can be a risk category associated with report generation. Operator errors, specifically in data interpretation, can generate an inaccurate result where false negative or positive results may be reported. In some embodiments, mitigation of operation error comprises documentation. Documentation can comprise documentation of one or more of risk assessment results, risk assessment plans, or risk assessment reports. Documentation can comprise the generation of reports or logs. In some embodiments, mitigation of operation error comprises strategies, e.g., strategies for mitigation of operator errors resulting in inaccurate results. Strategies for mitigation of operator error comprise software design, e.g., highlighting QC failures or requiring comment for failure on review; training and competency ofWSGR Docket No. 63228-705.601 technical staff; provider, e.g., CMSB provider, discussions; and proficiency testing to assess performance of post-analytical processes.

[0083] In some embodiments, a system or method of clinical decision provided herein provides for the classification and detection of pathogens. For example, a system or method provided herein can provide epidemiological insights. FIG. 2, FIG. 12, and FIG. 14 illustrate an example workflow and bioinformation pipeline, sequencing and analysis methodology, and pathogen classification and analysis results of a system or method for clinical decision support provided herein, respectively.

[0084] In an aspect, provided herein is a method comprising processing sequence data to generate an output indicative of a presence or quantitative measure of microbial nucleic acids in a biological sample, receiving clinically interpretable annotations related to the output from a user, and generating an electronic report indicative of a presence of a pathogenic infection, an absence of a pathogenic infection, or a risk of a pathogenic infection. In certain embodiments, generating the electronic report comprises compiling the annotations related to the output. In some embodiments, the method comprises sequencing nucleic acids obtained or derived from a biological sample of a subject, thereby generating sequence data. In some embodiments, the method comprises computer processing the sequence data to generate an output indicative of a presence or quantitative measure of microbial nucleic acids in the biological sample . In some embodiments, the method comprises presenting to a user, via a user interface of an electronic display, a visualization of the output. In some embodiments, the method comprises receiving from the user, via the user interface of the electronic display, annotations related to the output, wherein the annotations are clinically interpretable. In some embodiments, the method comprises training the user. The training can be required for clinicians (e.g., a user or a second user of the CDS system who is a clinician). For example, the training can comprise scenariobased training that is provided to users within dedicated training versions of the system of CDS. In some embodiments, the training comprises batches of data, and the user being trained must correctly detect the presence or absence pathogens in the data. The CDS system can be designed to minimize the required user training. In some embodiments, the method comprises assessing a proficiency or a competency of the user. In some embodiments, the method comprises generating an electronic report for the subject, based at least in part on the output and the annotations, wherein the electronic report is indicative of a presence of a pathogenic infection, an absence of a pathogenic infection, or a risk of a pathogenic infection.

[0085] In some embodiments, the method comprises: (a) sequencing nucleic acids obtained or derived from a biological sample of a subject, thereby generating sequence data; (b) computer processing the sequence data to generate an output indicative of a presence or quantitativeWSGR Docket No. 63228-705.601 measure of microbial nucleic acids in the biological sample; (c) presenting to a user, via a user interface of an electronic display, a visualization of the output; (d) receiving from the user, via the user interface of the electronic display, annotations related to the output, wherein the annotations are clinically interpretable; and (e) generating an electronic report for the subject, based at least in part on the output and the annotations, wherein the electronic report is indicative of a presence of a pathogenic infection, an absence of a pathogenic infection, or a risk of a pathogenic infection.

[0086] The method can further comprise receiving, from a clinician, clinical data of the subject. In some embodiments, the annotations are based at least in part on the clinical data of the subject. The annotations can comprise one or more of a direct field entry, a comment, an interpretation, a sample note, and an internal note. In some embodiments, the annotations comprise a direct field entry. In some embodiments, the annotations comprise a comment. In some embodiments, the annotations comprise an interpretation. In some embodiments, the annotations comprise a sample note. In some embodiments, the annotations comprise an internal note. The annotation can comprise a clinically relevant annotation related to the output. In some embodiments, the annotation is documented by a clinician.

[0087] The electronic report can be reviewed by a second reviewer. In some embodiments, the electronic report is approved by a second user. The electronic report can comprise one or more of a sequencing metric, a taxonomy classification, an organism flagging, and a sample metadata. In some embodiments, the electronic report comprises a sequencing metric. The sequencing metric can comprise a quality control metric. In some embodiments, the sequencing metric comprises a microbe abundance metric or an internal control metric. In some embodiments, the electronic report comprises a taxonomy classification. In some embodiments, the electronic report comprises an organism flagging. In some embodiments, the electronic report comprises sample metadata.

[0088] The electronic report can be rapidly generated. In some embodiments, the electronic report is generated within 1, 2, 3, 4, 5, 6, 7, 10, 14, 21, 28, or 31 business days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 1 business day from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 2 business days from the sequencing of the biological sample. In some embodiments, the electronic reportis generated within 3 business days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 4 business days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 5 business days from the sequencing of the biological sample. In some embodiments, the electronic reportis generated within 6 business days from theWSGR Docket No. 63228-705.601 sequencing of the biological sample. In some embodiments, the electronic report is generated within 7 business days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 10 business days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 14 business days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 21 business days from the sequencing of the biological sample. In some embodiments, the electronic reportis generated within 28 business days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 31 business days from the sequencing of the biological sample.

[0089] In some embodiments, the electronic report is generated within 1, 2, 3, 4, 5, 6, 7, 10, 14, 21, 28, or 31 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 1 day from the sequencing of the biological sample. In some embodiments, the electronic reportis generated within 2 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 3 days from the sequencing of the biological sample. In some embodiments, the electronic reportis generated within 4 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 5 days from the sequencing of the biological sample. In some embodiments, the electronic reportis generated within 6 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 7 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 10 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 14 days from the sequencing of the biological sample. In some embodiments, the electronic reportis generated within 21 daysfromthe sequencing of the biological sample. In some embodiments, the electronic reportis generated within 28 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 31 days from the sequencing of the biological sample.

[0090] In some embodiments, the electronic report is generated within 1, 2, 3, 4, 5, 6, 9, 10, 12, 15, 20, 22, or 24 hours from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 1 hour from the sequencing of the biological sample. In some embodiments, the electronic reportis generated within 2 hours from the sequencing of the biological sample. In some embodiments, the electronic reportis generated within 3 hours from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 4 hours from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 5 hours from the sequencing of the biological sample. In some embodiments, the electronic reportis generated within 6 hours from the sequencing of theWSGR Docket No. 63228-705.601 biological sample. In some embodiments, the electronic reportis generated within 9 hours from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 10 hours from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 12 hours from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 15 hours from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 20 hours from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 22 hours from the sequencing of the biological sample. In some embodiments, the electronic report is generated within 24 hours from the sequencing of the biological sample.

[0091] In some embodiments, the electronic report is generated within about 1 to 31, 1 to 14, 1 to 7, 1 to 5, 1 to 3, or 1 to 2 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 31 day s from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 14 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 7 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 5 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 3 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 2 days from the sequencing of the biological sample.

[0092] In some embodiments, the electronic report is generated within about 1 to 31, 1 to 14, 1 to 7, 1 to 5, 1 to 3, or 1 to 2 business days from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 31 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 14 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 7 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 5 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 3 days from the sequencing of the biological sample. In some embodiments, the electronic report is generated with about 1 to about 2 days from the sequencing of the biological sample.

[0093] In some embodiments, a system or method of clinical decision support provided herein comprises a clinical support service for clinical providers. A clinical support service can comprise a consultative discussion between a clinical provider and a clinician of a system or method of clinical decision support provided herein. In some embodiments, a clinician of aWSGR Docket No. 63228-705.601 system or method of clinical decision support provided herein is a board -certified physician with expertise in infection disease, clinical microbiology, molecular diagnostics, or mNGS. Consultative discussions can be focused on the clinical significance of an individual subject’s mNGS results, for example for atypical organism detections or opportunistic pathogens, and can support clinical provider interpretation and decision -making. Consultative discussions can comprise discussion of diagnosis and treatment of a subject. In some embodiments, consultative discussions comprise discussion of clinical interpretation, clinical utility, available treatments (e.g., for a pathogen detected in a sample of a subject), or clinical indications.

[0094] In some embodiments, a system or method of clinical decision support provided herein comprises a laboratory-developed test (LDT). The LDT can be certified by an accreditation or regulatory organization. For example, the LDT can be a Clinical Laboratory Improvement Amendments (CLIA)-certified or a College of American Pathologists (CAP)- certified LDT. In some embodiments, a system or method provided herein comprises a CLIA- certified LDT. In some embodiments, a system or method provided herein comprises a CAP- certified LDT. In some embodiments, a system or method provided herein comprises a CLIA- certified and CAP-certified LDT.

[0095] Provided herein in some embodiments are systems and methods for clinical decision support comprising sequencing nucleic acids obtained or derived from a biological sample of a subject. The nucleic acid (e.g., a nucleic acid that is sequenced) can comprise one or more of double-stranded (ds) nucleic acids, single stranded (ss) nucleic acids, deoxyribose nucleic acids (DNA), ribose nucleic acids (RNA), complementary DNA (cDNA), dsDNA, ssDNA, circulating nucleic acids, circulating cell-free nucleic acids, circulating DNA, circulating RNA, cell-free nucleic acids, cell-free DNA, cell-free RNA, circulating cell-free DNA, cell-free dsDNA, cell- free ssDNA, circulating cell -free RNA, genomic DNA, plasmid DNA, mitochondrial DNA, cell- free pathogen nucleic acids, circulating pathogen nucleic acids, circular DNA, circular RNA, circular single-stranded DNA, circular double-stranded DNA. RNA can comprise one or more of messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and non -coding RNA.

[0096] A biological sample (e.g., a biological sample of a subject) of a system or method provided herein can comprise one or more of whole blood, blood serum, blood plasma, bile, breath, urine, feces, sebum, tissue, breast milk, hair, saliva, sputum, sweat, tears, lymphatic fluid, serous fluid, synovial fluid, pericardial fluid, peritoneal fluid, pleural fluid, cystic fluid, cerebrospinal fluid, seminal fluid, vaginal secretion, amniotic fluid, nasal fluid, otic fluid, interstitial fluid, gastric fluid, intestinal fluid, transudates, exudates, liquids containing single orWSGR Docket No. 63228-705.601 multiple cells, liquids containing organelles, fluidized tissues, fluidized organisms, liquids containing multi-celled organisms, biological swabs, and biological washes.

[0097] In an aspect, provided herein are systems and methods for clinical decision support comprising sequencing nucleic acids obtained or derived from a biological sample of a subject. In some embodiments, the sequencing comprises one or more of nucleic acid extraction, nucleic acid isolation, nucleic acid fragmentation, reverse transcription (e.g., of RNA), transcript fragmentation, adapter ligation, amplification, targeted enrichment, nucleic acid library preparation, bisulfite conversion, and methylation conversion. The sequencing can comprise one or more of targeted sequencing, single molecule real-time sequencing, exon or exome sequencing, intron sequencing, electron microscopy -based sequencing, panel sequencing, transistor-mediated sequencing, direct sequencing, random shotgun sequencing, Sanger dideoxy termination sequencing, whole-genome sequencing, sequencing by hybridization, pyrosequencing, duplex sequencing, cycle sequencing, single-base extension sequencing, solid phase sequencing, high-throughput sequencing, massively parallel signature sequencing, emulsion PCR, co-amplification at lower denaturation temperature-PCR (COLD-PCR), multiplex PCR, sequencing by reversible dye terminator, paired -end sequencing, near-term sequencing, exonuclease sequencing, sequencing by ligation, short-read sequencing, single molecule sequencing, sequencing-by-synthesis, real-time sequencing, reverse-terminator sequencing, long-read sequencing, nanopore sequencing, 454 sequencing, Solexa Genome Analyzer sequencing, SOLiD™ sequencing, and MS-PET sequencing. The sequencing can comprise a polymerase chain reaction (PCR) or an isothermal amplification.

[0098] In some embodiments, when the sequencing comprises amplification, the amplification comprises amplification reagents. The amplification reagents can comprise reagents for one or more of polymerase chain reaction (PCR), transcription mediated amplification (TMA), helicase dependent amplification (HD A), circular helicase dependent amplification (cHDA), strand displacement amplification (SDA), loop mediated amplification (LAMP), exponential amplification reaction (EXPAR), rolling circle amplification (RCA), ligase chain reaction (LCR), simple method amplifying RNA targets (SMART), single primer isothermal amplification (SPIA), multiple displacement amplification (MDA), nucleic acid sequence based amplification (NASBA), hinge -initiated primer-dependent amplification of nucleic acids (HIP), nicking enzyme amplification reaction (NEAR), or improved multiple displacement amplification (IMDA).

[0099] In an aspect, provided herein are system and methods for clinical decision support comprising computer processing sequence data. The sequence data can be computer processed to generate an output indicative of a presence or quantitative measure of microbial nucleic acidsWSGR Docket No. 63228-705.601 in a biological sample. In some embodiments, the sequence data can be computer processed to generate an output indicative of a presence of microbial nucleic acids in a biological sample. In some embodiments, the sequence data can be computer processed to generate an output indicative of a quantitative measure of microbial nucleic acids in a biological sample. In some embodiments, the computer processing comprises comparing the quantitative measure of microbial nucleic acids to a threshold.

[0100] In some embodiments, the computer processing comprises aligning the sequence data to a reference genome. The reference genome can comprise a human reference genome or a microbial reference genome. In specific embodiments, the reference genome comprises a human reference genome. In specific embodiment, the reference genome comprises a microbial reference genome.

[0101] In some embodiments, the computer processing comprises filtering the sequence data. The filtering can comprise distinguishing human-derived reads and microbe-derived reads. The filtering can comprise removing human-derived reads.

[0102] Provided herein in some embodiments is a system or method of clinical decision support comprising computer processing sequence data to generate an output indicative of a quantitative measure of microbial nucleic acids in a biological sample. In some embodiments, the quantitative measure comprises a number of sequence reads aligning to a microbial reference genome.

[0103] Provided herein in some embodiments is a system or method of clinical decision support comprising generating an electronic report that is indicative of a presence of a pathogenic infection an absence of a pathogenic infection, or a risk of a pathogenic infection. In some embodiments, the electronic report comprises a performance metric that is in indicative of a presence of a pathogenic infection. In some embodiments, the electronic report comprises a performance metric that is indicative of a presence of a pathogenic infection comprising a subtype or strain of a species of pathogen.

[0104] The performance metric can comprise one or more of an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and Fl score. In some embodiments, the performance metric has a value of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.9% or 99.99%. In some embodiments, the performance metric has a value of at least 50%. In some embodiments, the performance metric has a value of at least 55%. In some embodiments, the performance metric has a value of at least 60%. In some embodiments, the performance metric has a value of at least 65%. In some embodiments, the performance metric has a value of at least 70%. In some embodiments, the performance metric has a value of at least 75%. In some embodiments, the performance metricWSGR Docket No. 63228-705.601 has a value of at least 80%. In some embodiments, the performance metric has a value of at least 85%. In some embodiments, the performance metric has a value of at least 90%. In some embodiments, the performance metric has a value of at least 95%. In some embodiments, the performance metric has a value of at least 96%. In some embodiments, the performance metric has a value of at least 97%. In some embodiments, the performance metric has a value of at least 98%. In some embodiments, the performance metric has a value of at least 99%. In some embodiments, the performance metric has a value of at least 99.9%. In some embodiments, the performance metric has a value of at least 99.99%.

[0105] The performance metric can be an Area Under the Receiver Operating Characteristic Curve (AUROC) or a correlation coefficient. The performance metric can have a value of at least 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.96, 0.97, 0.98, or 0.99. In some embodiments, the performance metric has a value of at least 0.5. In some embodiments, the performance metric has a value of at least 0.55. In some embodiments, the performance metric has a value of at least 0.6. In some embodiments, the performance metric has a value of at least 0.65. In some embodiments, the performance metric has a value of at least 0.7. In some embodiments, the performance metric has a value of at least 0.75. In some embodiments, the performance metric has a value of at least 0.8. In some embodiments, the performance metric has a value of at least 0.85. In some embodiments, the performance metric has a value of at least 0.9. In some embodiments, the performance metric has a value of at least 0.95. In some embodiments, the performance metric has a value of at least 0.96. In some embodiments, the performance metric has a value of at least 0.97. In some embodiments, the performance metric has a value of at least 0.98. In some embodiments, the performance metric has a value of at least 0.99.

[0106] Provided herein in some embodiments is an electronic report that is indicative of a risk of pathogenic infection. In some embodiments, the risk comprises one or more of a likelihood, probability, odds ratio, risk ratio, attributable risk, standardized morality ratio, and a Bayesian Risk Assessment.User Experience

[0107] Provided herein in some embodiments are systems and methods for clinical decision support (CDS) comprising a user (e.g., clinician) experience. CDS can provide for clinical interpretation and reporting. In some embodiments, CDS comprises an interactive user interface. In an interactive UI, data, e.g., the output of a bioinformatics pipeline provided herein, can be presented and contextualized. In some embodiments, output, e.g., the output of a bioinformatics pipeline, is presented with internal control and / or statistical metrics. A UI can provide directWSGR Docket No. 63228-705.601 interaction with primary data and methods to launch secondary analyses (e.g., BLAST coverage maps, etc.).

[0108] In some embodiments, access to clinical decision support is controlled through strict user permission. Users roles can be defined within a CDS provided herein. In some embodiments, access to clinical decision support is controlled through user permissions to user roles. The permissions for a role can, for example, protect access (e.g., to information in the CDS or to tasks in the CDS), and can allow for specific specializations (e.g., clinician or informatician) to be utilized in specific areas of the CDS. Users can be assigned default permission upon user creation. For example, a default permission can be “read only”, in which the user can only read or view data. In some embodiments, a medical assistant user is assigned “medical assistanf ’ default permissions, which allows the medical assistant user (e.g., a user of a system or method of clinical decision support provided herein) to create data; select taxa; and complete general work. In some embodiments, a medical director user in assigned“medical director” default permissions, which allow the medical director (e.g., a second user of a system or method of clinical decision support provided herein) to approve or fail electronic reports. In some embodiments, a user is assigned “admin” permissions, which provide all permissions not used in production. In some embodiments, a user is assigned “informatics” permissions, which allow the user to create data but do not allow the user to perform any approval work.

[0109] In some embodiments, users or secondusers of CDS can utilize CDS (e.g., annotate the output or approve the electronic report) without seeing any Protected Health Information (PHI) of a subject. In some embodiments, users or second users of CDS can utilize CDS (e.g., annotate the output or approve the electronic report) seeing only the Protected Health Information (PHI) of a subject that is relevant to their role.

[0110] In some embodiments, every user of CDS is logged and / or is auditable. In some embodiments, the annotation is clinically auditable. For example, the annotation can be auditable such that a CDS provided herein can capture all the Application Programming Interfaces (APIs) to and from the user interface. This can support auditability by allowing post- hoc analysis of the resources (e.g., a search engine or scientific data base query or other bioinformatics tools), if any, a user utilized while making a clinical decision.

[0111] In some embodiments, a user experience comprises one or more of a login, landing page, batch selection, QC review, QC decision, data access, visualization, flagging, further analysis, and report generation. In some embodiments, a user experience comprises a login. The Login can comprise an approved user logging in using account credentials. In some embodiments, a user experience comprises a landing page. The landing page can comprise theWSGR Docket No. 63228-705.601 system displaying recent sample batches processed by the LDS. In some embodiments, a user experience comprises a batch selection. The batch selection can comprise the user selecting a sample batch for review. In some embodiments, a user experience comprises a QC review. The QC review can comprise the user being shown a summary of QC metrics, highlighting any out- of-specification values. The metrics can comprise internal controls and sequencing quality. In some embodiments, a user experience comprises QC decision. The QC decision can comprise the user passing or failing the batch or individual samples, with notes for documentation.

[0112] In some embodiments, a user experience comprises data access. Data access can comprise the user accessing mNGS results for each clinical sample.

[0113] In some embodiments, a user experience comprises visualization. Visualization can comprise viewing of the entire batch with microbe abundance metrics visualized in a heatmap, filterable by organism class and / or metric thresholds.

[0114] In some embodiments, the visualization comprises one or more of a batch processing visualization, a sequencing visualization, a quality control visualization, a specimen detail visualization, a taxonomic selection visualization, a heatmap visualization, coverage visualization, and a report previewing visualization. In some embodiments, the visualization comprises a batch processing visualization. In some embodiments, the visualization comprises a quality control visualization. In some embodiments, the visualization comprises a specimen detail visualization. In some embodiments, the visualization comprises a taxonomic selection visualization. In some embodiments, the visualization comprises a heatmap visualization. In some embodiments, the visualization comprises a coverage visualization. In some embodiments, the visualization comprises a report previewing visualization.

[0115] In some embodiments, a user (e.g., a Medical Assistant) compiles an electronic report. For example, the user can select taxonomic entries to be included in the electronic report. The user can also indicate that the electronic reportis ready for review and approval by a second user. The second user (e.g., a Medical Director) can review the electronic report, and based at least in part on the review, either approve or fail the electronic report. If the electronic report is approved by the second user, it can then be submitted to a downstream system for to be sent to a clinician.

[0116] A visualization can be presented to a user via the user interface of an electronic display. The electronic display can comprise one or more of a personal computer (e.g., portable computer), a slate or tablet personal computer (PC), a mobile electronic device, a smartphone, a personal digital assistant, and a wearable device.

[0117] In some embodiments, a user experience comprises flagging. Flagging can comprise flagging of microbial taxa in each sample for clinical reporting.WSGR Docket No. 63228-705.601

[0118] In some embodiments, a user experience comprises further analysis. Further analysis can comprise initiation of additional bioinformatics analyses and visualizations, including genomic coverage maps, NCBI BLAST queries, and primary read file downloads.

[0119] In some embodiments, a user experience comprises report generation. Report generation can comprise editing, previewing, amending, and sign off for clinical reports for customer delivery via a CRM system.Pathogenic Infection

[0120] In an aspect, provided herein are systems and methods of clinical decision support that generate an electronic report for a subject that is indicative of a presence of a pathogenic infection, an absence of a pathogenic infection, or a risk of a pathogenic infection. In some embodiments, the electronic reportis indicative of a presence of a pathogenic infection. In some embodiments, the electronic reportis indicative of an absence of a pathogenic infection. In some embodiments, the electronic report is indicative of a risk of a pathogenic infection.

[0121] In some embodiments, the pathogenic infection comprises myotoxic disease, respiratory disease, pulmonary infections, pneumonia, mononucleosis, cutaneous infections, keratitis, peritonitis, osteomyelitis, sinusitis, endophthalmitis, mycetoma, soft tissue infections, cellulitis, impetigo, bacteremia, toxic shock syndrome, ventriculitis, urinary tract infections, surgical site infections, wound infections, abscesses, meningitis, encephalitis, sepsis, and peritonitis. In some embodiments, the pathogenic infection comprises meningitis or encephalitis. In some embodiments, the pathogenic infection comprises meningitis. In some embodiments, the pathogenic infection comprises encephalitis. The pathogenic infection can be an antibiotic resistant infection. In some embodiments, the pathogenic infection comprises a pathogen associated with cancer.

[0122] In some embodiments, the pathogenic infection comprises infection with a pathogen, the pathogen comprising one or more of a filamentous fungus, a gram negative bacteria, a gram positive bacteria, a yeast, a parasite, a DNA virus, and an RNA virus. In some embodiments, the pathogen comprises a filamentous fungus. In some embodiments, the pathogen comprises a gram negative bacterium. In some embodiments, the pathogen comprises a gram positive bacterium. In some embodiments, the pathogen comprises a yeast. In some embodiments, the pathogen comprises a parasite. In some embodiments, the pathogen comprises a DNA virus. In some embodiments, the pathogen comprises an RNA virus.

[0123] A pathogen can comprise a strain or a subtype of a species of one or more of a filamentous fungus, a gram negative bacteria, a gram positive bacteria, a yeast, a parasite, a DNA virus, and an RNA virus. In some embodiments, the pathogen comprises a strain or aWSGR Docket No. 63228-705.601 subtype of a species of filamentous fungus. In some embodiments, the pathogen comprises a strain or a subtype of a species of gram negative bacteria. In some embodiments, the pathogen comprises a strain or a subtype of a species of gram positive bacteria. In some embodiments, the pathogen comprises a strain or a subtype of a species of yeast. In some embodiments, the pathogen comprises a strain or a subtype of a species of parasite. In some embodiments, the pathogen comprises strain or a subtype of a species of DNA virus. In some embodiments, the pathogen comprises strain or a subtype of a species of RNA virus.

[0124] A pathogen provided herein can comprise a fungus. In some embodiments, the fungus comprises a filamentous fungus. The filamentous fungus can comprise one or more of a fungus of the genus Acremoniiim. Alternaria, Aspergillus, Cladosporium, Curvularia, Fusarium, Histoplasma, Lichtheimia, Lomentospora, Mucor, Paecilomyces, Penicillium, Rhizomucor , Rhizopus, Scedosporium, Schizophyllum, and Trichoderma. In some embodiments, the filamentous fungus comprises one or more of Acremonium spp., Alternaria spp., Alternaria alternata, Alternaria infectoria, Aspergillus spp., Aspergillus carneus, Aspergillus clavatus, Aspergillus flavus, Aspergillus fumigatus, Aspergillus nidulans, Aspergillus niger, Aspergillus ochraceus, Aspergillus terreus, Aspergillus ustus, Aspergillus versicolor, Aspergillus parasiticus, Cladosporium spp., Curvularia spp., Fusarium spp., Fusarium avenaceum, Fusarium culmorum, Fusarium equiseti, Fusarium fujikuroi, Fusarium graminearum, Fusarium nivale, Fusarium proliferatum, Fusarium oxysporum, Fusarium roseum, Fusarium solani, Fusarium verticillioides, Histoplasma spp., Histoplasma capsulatum, Lichtheimia spp., Lichtheimia corymbifera, Lichtheimia ramose, Lomentospora spp., Lomentospora prolificans, Mucor spp., Mucor circinelloides, Paecilomyces spp., Paecilomyces variotii, Penicillium spp., Penicillium aurantiogriseum, Penicillium brunneum, Penicillium citreoviride, Penicillium citrinin, Penicillium claviforme , Penicillium crustosum, Penicillium expansum, Penicillium griseofulvum, Penicillium hirsutum, Penicillium islandicum, Penicillium kloeckeri, Penicillium roqueforti, Penicillium rubrum, Penicillium rugulosum, Penicillium verrucossum, Penicillium viridicatum, Rhizomucor spp., Rhizomucor pusillus, Rhizopus spp., Rhizopus arrhizus, Rhizopus microspores, Scedosporium spp., Scedosporium apiospermum, Schizophyllum spp., Schizophyllum commune, and Trichoderma spp.

[0125] A pathogenic infection provided herein can comprise infection with a filamentous fungus. The pathogenic infection with a filamentous fungus can comprises one or more of dermatomycoses, epidermophyton, allergic bronchopulmonary mycosis, allergic fungal rhinosinusitis, mycotic keratitis (e.g., keratomy cosis), mucormycosis, otomycosis, onychomycosis, aspergillosis e.g., invasive aspergillosis or pulmonary aspergillosis), eumycetoma, and fungemia.WSGR Docket No. 63228-705.601

[0126] A pathogen provided herein can comprise a gram negative bacterium. A gram negative bacteria can comprise one or more of a bacterium of the genus Acinetobacter , Chlamydia, Citrobacter, Enter obac ter , Escherichia, Klebsiella, Moraxella, Proteus, Pseudomonas, Shigella, Salmonella, and Yersinia. In some embodiments, the gram negative bacterium comprises one or more of Acinetobacter spp., Acinetobacter baumannii, Chlamydia spp., Chlamydia trachomatis, Citrobacter spp., Enterobacter spp., Escherichia coli, Klebsiella spp., Moraxella spp., Moraxella sp. K1664, Moraxella bovis, Moraxella canis, Moraxella catarrhalis, Moraxella lacunata, Moraxella nonliquefaciens, Moraxella osloensis, Moraxella phenylpyruvica, Proteus spp., Pseudomonas spp., Pseudomonas aeruginosa, Salmonella spp., Salmonella enterica, Salmonella bongori, Shigella spp., Shigella dysenteriae , Shigella flexneri, Shigella boydii, Shigella sonnei, Yersinia spp., and Yersinia pestis.

[0127] A pathogen provided herein can comprise a gram positive bacterium. The gram positive bacterium can comprise one or more of a bacterium of the genus Staphylococcus, Streptococcus, Clostridium, Enterococcus, and Mycobacterium. In some embodiments, the gram positive bacterium comprises one or more of Staphylococcus spp., Staphylococcus aureus, Streptococcus spp., Streptococcus pyogenes, Clostridium spp., Clostridium difficile, Enterococcus spp., Enterococcus faecalis, Enterococcus gallinarum, Mycobacterium spp., and Mycobacterium tuberculosis.

[0128] A pathogenic infection provided herein can comprise infection with a gram positive bacterium. The pathogenic infection with a gram positive bacterium can comprise one or more of strep throat, tuberculosis, and Methicillin -resistant Staphylococcus aureus (MRSA) infection.

[0129] A pathogen provided herein can comprise a yeast. The yeast can comprise one or more of a bacteria of the genus Candida or the genus Cryptococcus . In some embodiments, the yeast comprises one or more of Candida auris, Candida albicans, Candida glabrata, Candida parapsilosis, Candida tropicalis, Candida krusei, Cryptococcus albidus, Cryptococcus curvatus, Cryptococcus gattii, Cryptococcus laurentii, Cryptococcus neoformans, and Cryptococcus uniguttulatus.

[0130] A pathogenic infection provided herein can comprise infection with a yeast. The pathogenic infection with a yeast can comprises one or more of oral thrush, candidiasis (e.g., invasive candidiasis, cutaneous candidiasis, nail candidiasis, esophageal candidiasis, or vaginal candidiasis), and candidemia.

[0131] A pathogen provided herein can comprise a parasite. The parasite can comprise one or more of a protozoa, a helminth, or an ectoparasite.

[0132] A pathogenic infection provided herein can comprise infection with a parasite. The pathogenic infection with a parasite can comprise one or more of Lyme disease, malaria,WSGR Docket No. 63228-705.601 giardiasis, toxoplasmosis, intestinal worm infections, lice, chagas disease, schistosomiasis, babesiosis, pinworm infection, hookworm, leishmaniasis, trichomoniasis, amebiasis, cryptosporidiosis, schistosomiasis, tapeworms, toxocariasis, ascariasis, cysticercosis, echinococcosis, filariasis, neurocysticercosis, onchocerciasis, roundworms, African trypanosomiasis, paragonimiasis, strongyloidiasis, and trichuriasis.

[0133] A pathogen provided herein can comprise a DNA virus. The DNA virus can comprise one or more of herpesvirus, cytomegalovirus (CMV), human papillomavirus (HPV), adenovirus, hepatitis B virus (HBV), poxvirus, and polyomavirus. In some embodiments, the herpesvirus comprises one or more of herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), Kaposi sarcoma-associated herpesvirus (gamma herpesvirus), varicellazoster virus (VZV), and Epstein-Barr virus.

[0134] A pathogenic infection provided herein can comprise infection with a DNA virus. The pathogenic infection with a DNA virus can comprise one or more of chicken pox or shingles.

[0135] A pathogen provided herein can comprise an RNA virus. In some embodiments, the RNA virus comprises one or more of influenza virus, respiratory syncytial virus (RSV), coronavirus (e.g., SARS-CoV-2), enterovirus, norovirus, rotavirus, human immune deficiency virus (HIV), hepatitis C virus (HCV), Rift Valley fever virus, Tick -borne encephalitis virus, Zika virus, Dengue virus, West Nile virus, Ebola virus, yellow fever virus, Saint Louis encephalitis virus (SLEV), Eastern Equine encephalitis virus (EEEV), La Crosse encephalitis virus (LCEV), and Japanese encephalitis virus. In some embodiments, the pathogen comprises a strain or sub-type of Saint Louis encephalitis virus (SLEV). In some embodiments, the pathogen comprises a strain or sub-type of enterovirus. In some embodiments, the pathogen is a singlestranded RNA. In some embodiments, the pathogen is a double -stranded RNA virus.

[0136] In some embodiments, a pathogenic infection provided herein comprises a symptom. In some embodiments, the symptom comprises one or more of fever, severe headache, stiff neck, sensitivity to light, nausea, vomiting, confusion, altered mental status, seizure, personality changes, coma, cancer, asthma, brain granuloma, arthritis, anemia, diarrhea, dysentery, vomiting, loss of appetite, fever chills, fatigue, weight loss, malnutrition, and abdominal pain, and skin lesions. In some embodiments, the symptom comprises one or more of fever, severe headache, stiff neck, sensitivity to light, nausea, vomiting, confusion, altered mental status, seizure, and personality changes.

[0137] In some embodiments, the pathogenic infection is treated. The pathogenic infection is treated with an antimicrobial. Examples of antimicrobials include but are not limited to one or more of an antibiotic, antifungal, antip arasitic, and an antiviral. In some embodiments, theWSGR Docket No. 63228-705.601 pathogenic infection is treated with an antibiotic. In some embodiments, the pathogenic infection is treated with an antifungal. In some embodiments, the pathogenic infection is treated with an antip arasitic. In some embodiments, the pathogenic infection i s treated with an antiviral.EXAMPLESExample 1: Metagenomic Next-Generation Sequencing (mNGS) for the Rapid, Unbiased Detection of DNA and RNA Pathogens in Cerebrospinal Fluid (CSF)

[0138] A system or method of clinical decision support provided herein was developed and validated to serve as a mNGS platform for hypothesis-free, unbiased detection of DNA and RNA pathogens in CSF from patient with suspected infection meningitis or encephalitis. The system or method provided agnostic comprehensive detection of viral, bacterial, fungal, and parasitic species in a single test.

[0139] A biological sample of a subject, e.g., a human patient suspected or diagnosed with having encephalitis or meningitis, was collected at a medical institution using a kit (e.g., a kit provided to the medical institution to utilize in a CDS provided herein). Information was collected by a clinician at the medical institution to complete a test requisition form, the information clinical data of the subject. The sample and requisition form were then sent to a facility where the sample was analyzed following a system or method provided herein. An electronic report, e.g., an automated results report, was generated within about two business days of the receipt of the sample at the facility. These methods and processes were a fully automated and integrated workflow that delivered clinically -relevant results rapidly (within about two business days). See, e.g., FIG. 4.

[0140] At the facility, the sample was analyzed using Wet Lab Processing. The sample, e.g., a CSF sample was stabilized in buffer and the cells in the sample were lysed. Genetic material from the sample comprising RNA (e.g., RNA virus genomes) and DNA (e.g., of DNA viruses, bacteria, fungi, or parasites) was isolated and purified. The DNA and RNA from the sample were converted into next-generation sequencing (NGS) libraries. The NGS libraries were then amplified and pooled. The amplified and pooled NGS libraries were sequenced. These dry lab step were designed to optimize precision, sterility, speed, and throughput. See, e.g., FIG. 2.

[0141] After sequencing of the NGS libraries, a quality control of the preprocessing (stabilization, lysis, isolation, conversion to NGS libraries, amplification and pooling) and sequencing was performed. Human host background genetic material was then subtracted from the sequencing results. Alignment of the filtered sequencing results to microbial sequences see, e.g., FIG. IB) and taxonomic assignment were then performed. This alignment and taxonomicWSGR Docket No. 63228-705.601 assignment provided an output indicative of a presence or a quantitative measure of microbial nucleic acid in the sample. See, e.g., FIG. 6. Via a user interface of an electronic display (e.g., of a computer monitor), a user was the presented with clinically -interpretable visualizations of the output. Through the user interface, the user provided clinically -interpretable annotations related to the output indicative of a presence or a quantitative measure of microbial nucleic acid in the sample. An electronic report for the subject was then generated, based at least in part on the output and the annotations. The electronic report was indicative of a pathological infection, an absence of a pathogenic infection, or a risk of a pathogenic infection. See, e.g., FIG. 3. Thus, system or method of clinical decision support provided herein delivered species-level taxonomic assignments, and can provide higher resolution viral subtyping where clinically relevant. See, e.g., FIG. 5Example 2: Computational Viral Subtyping

[0142] Viral reads that aligned to a species of interest identified by a system or method of clinical decision support provided herein, see, e.g., Example 1, were used to generate a kmer- based hash vector (a signature) using a sourmash package (Pierce et al., 2019; see, e.g., FIG. 12). Pierce NT, Irber L, Reiter T, et al. Large-scale sequence comparisons with sourmash, F1000Research2019, is incorporated herein by reference in its entirety. The kmer signature was compared to a curated database of signatures from genomic sequences assigned to that species I the NCBI NT database. A similarity measure based on kmer containment, average nucleotide identity (ANI), was used to select the representative genome used for templated assembly of the reads to generate a consensus sequence (a contig). This sequence was then taxonomically placed within phylogenic context using the multiple sequence alignment (MSA) based approach IQ- TREE (Nguyen et al., 2015; see, e.g., FIG. 13). Nguyen, Lam-Tung, et al. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum -likelihood phylogenies, Molecular biology and evolution 32.1 (2015): 268-274, is incorporated herein by reference in its entirety.Example 3: Rapid, Simultaneous Viral Pathogen Detection, Subtyping, and Strain Differentiation Through Unbiased Metagenomic Sequencing: St. Louis Encephalitis

[0143] Whether mNGS sequencing data generated during routine clinical testing could be further leveraged to achieve higher-resolution taxonomic assignments for viral strain and subtyping, expanding the clinical and epidemiological applications of a system or method of clinical decision support provided herein, was assessed.

[0144] Table 1 provides nine selected results from clinical CSF samples that had more than 1,000 bases of coverage. The rows in bold indicate the species with phylogeny results presented in this example.WSGR Docket No. 63228-705.601

[0145] Table 1: Sub-Species Calls in Clinical CSF SamplesWSGR Docket No. 63228-705.601

[0146] A patient sample previously reported as positive for St. Louis Encephalitis Virus (SLEV) yielded 19,736 reads aligning to the SLEV genome. On running this sample through a subtyping workflow (see, for example, Example 2), the phylogenetic analysis showed that the sample was most similar to a strain sequenced from a mosquito pool collected in 2016 from Kern county. These results were concordant with the published case from the University of California San Francisco (UCSF) that reported the patient as being an oil field worker from the same geographic region (Chiu et al., 2017). Chiu, Charles Y, et al. “Diagnosis of Fatal Human Case of St. Louis Encephalitis Virus Infection by Metagenomic Sequencing, California, 2016,”WSGR Docket No. 63228-705.601Emerging infectious diseases, Oct 2017, is incorporated herein by reference in its entirety. FIG. 14 provides a phylogenetic wheel showing the geographic proximity of SLEV subtypes closely related to the sample (shown in bold).Example 4: Rapid, Simultaneous Viral Pathogen Detection, Subtyping, and Strain Differentiation Through Unbiased Metagenomic Sequencing: St. Louis Encephalitis Virus:Enterovirus

[0147] Two Enterovirus (EV) detections from a system or method of clinical decision support provided herein were further strain-typed using the subtyping approach described in Example 2. The Enterovirus A sample most closely matched to an A71 strain, while the Enterovirus B sample showed the highest similarity to a Coxasackievirus B4 (CV B4) strain. FIG. 15A provides a phylogenetic wheel showing these results with the A71 strain and the Coxasackievirus B4 strain provided in bold. To validate these results, the samples were analyzed using the EV-genotypingtool developed by the RIVM National Institute for Public Health and the Environment (Kroneman et al., 2011). Kroneman A, Vennema H, Deforche K, et al. An automated genotyping tool for enteroviruses and noroviruses, J Clin Virol. , 2011, is incorporated herein by reference in their entirety. FIG. 15B provides an example of the validation results from RIVM of the Coxasackievirus B4 strain (CV B4).Example 5: Reduced Turnaround Time Expands the Window of Actionability for Clinical Management Decisions

[0148] Reduced turnaround time (TAT) and earlier ordering of metagenomic nextgeneration sequencing (mNGS) testing in the diagnostic workflow expands the window of clinical actionability for clinical management decisions. Conventional methods of cerebrospinal fluid (CSF) mNGS had an average TAT of 8.2 days, with results available when a majority of subjects have died or been discharged. In comparison, systems and methods of clinical decision support provided herein, similar to those provided in Example 2, in the first three months of clinical testing, had an average TAT of 2.1 days from sample receipt. FIG. 16A provides the percentage of subjects remaining admitted to a hospital per day after a cerebrospinal fluid (CSF) sample collection at the time when metagenomic next-generation sequencing (mNGS) results were delivered to the subject or clinical provider. Subject samples were analyzed using either a system or method of clinical decision support provided herein or using the UCSF bioinformatics analysis pipeline, SURPI (“sequence based ultrarapid pathogen identification”), method (Naccache et al., 2014). Naccache et al., A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples, Genome research (2014): 1180-1192, is incorporated herein by reference in its entirety. FIG. 16BWSGR Docket No. 63228-705.601 provides laboratory TAT, showingthe 25th to 75th percentiles (box) and 5th to 95th percentiles (whisker). Provided in FIG. 16B are TATs for samples analyzed with (i) a system or method of clinical decision support provided herein when a subject sample was directly shipped from the hospital to the processing facility (left); (ii) a system or method of clinical decision support provided herein when a subject sample was not directly shipped from the hospital to the processing facility (center); and (iii) the SURPI method of UCSF (left). A shortened TAT can lead to patients and clinicians receiving results earlier than in conventional methods (e.g., SURPI) and can enhance the clinical actionability of mNGS testing, including the optimization, cessation, or reduction of therapies.

[0149] Over 4,800 clinical sample were retrospectively analyzed using a system or method of clinical decision support provided herein. This analysis showed that mNGS was ordered late in the hospital course, an average of 5.9 days after patient admission. This analysis was based on data from Benoit et al., 2024. Benoit, Patrick, et al., Seven-year performance of a clinical metagenomic next-generation sequencing test for diagnosis of central nervous system infections, Nature medicine 30.12 (2024): 3522-3533 is incorporated herein by reference in its entirety. These results suggest that earlier ordering of mNGS testing (i.e., at the time of admission and initial lumbar puncture) for patients with suspected meningitis and encephalitis would increase clinical actionability by expanding the pool of patients who would benefit from the results.

[0150] A system or method of clinical decision support provided herein can comprise a clinical support service for clinical providers. Clinical support services can comprise a consultative discussion between a clinical provider and board -certified physicians with expertise in infection disease, clinical microbiology, molecular diagnostics, or mNGS. Clinical support service discussions occurred for 9% of cases during the first three months of clinical testing of a system or method of clinical decision support provided herein.Example 6: Organism Detection and Sensitivity of Detection

[0151] Clinical samples were analyzed using a system or method of clinical decision support provided herein similar to that described in Example 2. FIG. 17 provides a comparison of bioinformatics output read counts for 28 pathogens for samples analyzed with a system or method of clinical decision support provided herein or with the SURPI, the UCSF bioinformatic analysis pipeline (Naccache et al., 2014). This comparison showed a correlation of organism detection output metrics. Black markers indicate DNA reads and blue markers indicate RNA read counts. All values were found to be within 15% of each other except for one sample that that had a higher read count using a system or method of clinical decision support provided herein and three samples that had higher read counts using SURPI.WSGR Docket No. 63228-705.601

[0152] The performance of conventional diagnostics as describedin Wilson et al., 2019, was compared to the performance of UCSF mNGS. Wilson, Michael R., et al., Clinical metagenomic sequencing for diagnosis of meningitis and encephalitis, New England Journal of Medicine 380.24 (2019): 2327-2340, is incorporated herein by reference in its entirety. A subject CSF sample was tested at UCSF by mNGS with a negative result, FIG. 18A. The clinical diagnosis for the subject was Cryptococcal meningitis, with positive PCR and Cryptococcal antigen (titer 1 :8) results. The previous UCSF mNGS results were considered a false negative.

[0153] A remnant of the subject sample was tested with a system or method of clinical decision support provided herein and reveal signal for Cryptococcus neoformans (FIG. 18B). This result was interpreted as a positive detection for this organism. As the results were concordant with the clinical diagnosis, they were considered a true positive. An analysis of Cryptococcus neoformans biomass in the subject sample showed distinct signal compared to negative samples (FIG. 18C).Example 7: Identification of a Novel Moraxella Species in a Culture-Negative CNS Shunt Infection

[0154] Central nervous system (CNS) shunt infections are often culture -negative, potentially due to fastidious pathogens, low microbial burden, or prior antimicrobial treatment. A case of pediatric CNS shunt infection was analyzed, in which the CSF gram stain was positive but multiple cultures and other tests were repeatedly negative (FIG. 19A); mNGS using a system or method of clinical decisions support provided herein identified a potentially novel Moraxella species.

[0155] Using a system or method of clinical decision support provided herein, which was a CLIA-certified and CAP-accredited laboratory-developed test (LDT), a sample of CSF for a pediatric subject with a CNS shunt infection was analyzed. CNS shunt-associated infections often involve fastidious or biofilm -forming organisms that can evade culture, particularly with prior antimicrobial exposure. Despite antibiotic pre -treatment, a system or method of clinical decision support provided herein identified a pathogen consistent with CSF Gram stain result in a case repeatedly negative by culture and multiple other tests, providing a definitive diagnosis.

[0156] The nucleic acids obtained or derived from the sample (DNA or RNA) were deep sequenced (>10M reads / library). Moraxella bacteria were detected at subthreshold levels, despite the presence of high host background, reducing sensitivity for detecting pathogens (FIG. 19A). The detected Moraxella species closely aligned with Moraxella bovis and atypical species. FIG. 19B provides a histogram of loglO-transformed total microbial biomass (pg) for Moraxella species detected in CSF samples submitted for clinical mNGS testing. The patientWSGR Docket No. 63228-705.601 sample (arrow) showed significantly higher organism biomass than the background level seen in historical samples, supporting the presence of true infection rather than environmental contamination.

[0157] Sequences aligned to multiple Moraxella species, making species-level identification challenging. Genomic coverage over the ribosomal RNA operon (RRN) was sufficient to build a consensus sequence and perform phylogenetic analysis. The closest phylogenetic matches by RRN were Moraxella sp. K1664 along with other Group I Moraxella species. FIG. 19C provides a genome coverage plot of read alignment to the Moraxella sp. KI 664 genome. The upper panel shows genome-wide consensus identity and read coverage at 1.7% of the genome. The lower panel shows zoomed-in coverage across the RRN which was used for phylogenetic analysis. Table 2 provides reads per million (rpM), rpM ration (normalized abundance relative to non-template control signal), number of unique reads (by exact sequence match), and microbial biomass (fg) for individual Moraxella species observed. Moraxella (species- declassified reads) indicates reads assigned to the genus level without confident species -level classification. Moraxella genus (total) indicates the sum across all Moraxella species and declassified reads. Elevated rpM and biomass were observed for AT. bovis with lower contributions from several other Moraxella species, supporting the presence of a novel or divergent organism within the Moraxella genus. Phylogenetic placement between these multiple species indicates a potentially novel Moraxella organism.

[0158] Table 2: Sequencing Metrics for Moraxella DNA Library Reads Detected by mNGSWSGR Docket No. 63228-705.601

[0159] FIG. 19D provides a maximum likelihood phylogenetic (scale: Tamura-Nei = 0.01) tree constructed from patient-derived RRN consensus sequence. This phylogenetic tree demonstrates that the patient sample (boxed) clusters within Group I Moraxella. The patient sequence appears closely related to Moraxella sp. KI 664, with approximately equivalent distance between several closely -related Moraxella species, supporting the presence of a potentially novel Moraxella species.

[0160] Moraxella is an uncommon CNS pathogen and is rarely reported in shunt infections; its identification using systems or methods of clinical decision support provided herein underscores the diagnostic limits of culture-based microbiology and the value of the instant systems and methods in high-risk, culture-negative infections. Systems and methods of clinical decision support provided herein enabled the diagnosis of a culture -negative CNS shunt infection caused by a potentially novel Moraxella species, underscoring the clinical utility of mNGS in complex infection and the ability to detect emerging or atypical pathogens missed by conventional diagnostics. This case supports broader integration of mNGS into diagnostic workup for unexplained or culture-negative CNS infections, particularly in scenarios of CSF- associate shunts, devices, or other hardware.

Claims

WSGR Docket No. 63228-705.601CLAIMS1. A method comprising: a) sequencing nucleic acids obtained or derived from a biological sample of a subject, thereby generating sequence data; b) computer processing the sequence data to generate an output indicative of a presence or quantitative measure of microbial nucleic acids in the biological sample; c) presenting to a user, via a user interface of an electronic display, a visualization of the output; d) receiving from the user, via the user interface of the electronic display, annotations related to the output, wherein the annotations are clinically interpretable; and e) generating an electronic report for the subject, based at least in part on the output and the annotations, wherein the electronic report is indicative of a presence of a pathogenic infection, an absence of a pathogenic infection, or a risk of a pathogenic infection.

2. The method of claim 1 , further comprising receiving, from a clinician, clinical data of the subject.

3. The method of claim 2, wherein the annotations are based at least in part on the clinical data of the subject.

4. The method of any one of claims 1- 3, wherein generating the electronic report comprises compiling the annotations related to the output.

5. The method of any one of claims 1 - 4, wherein the electronic report is reviewed by a second user.

6. The method of any one of claims 1 - 5, wherein the electronic report is approved by a second user.

7. The method of any one of claims 1 - 6, wherein the user and the second user have different roles.WSGR Docket No. 63228-705.6018. The method of any one of claims 1 - 7, wherein the annotation comprises one or more of a direct field entry, a comment, an interpretation, a sample note, and an internal note.

9. The method of any one of claims 1 - 8, wherein the electronic report comprises one or more of a sequencing metric, a taxonomy classification, an organism flagging, and a sample metadata.

10. The method of claim 9, wherein the sequencing metric comprises a quality control metric.11 . The method of claim 9 or 10, wherein the sequencing metric comprises a microbe abundance metric or an internal control metric.

12. The method of any one of claims 1 - 11, wherein the method reduces an incidence of false positive results indicating the presence of a pathogenic infection.

13. The method of any one of claims 1 - 12, wherein the method reduces an incidence of false negative results indicating the absence of a pathogenic infection.

14. The method of any one of claims 1 - 13 wherein atleast 1%, 5%, 10%, 15% 20%, 25%, 30%, 35%, 40%, 45%, or 50% more pathogens are detected as compared to conventional testing.

15. The method of any one of claims 1 - 14, further comprising training the user.

16. The method of claim 15, wherein the training comprises the user correctly detecting a presence of a pathogenic infection, an absence of a pathogenic infection, or a risk of a pathogenic infection.

17. The method of any one of claims 1 - 16, further comprising assessing a proficiency or a competency of the user.WSGR Docket No. 63228-705.60118. The method of any one of claims 1 - 17, wherein the visualization comprises one or more of a batch processing visualization, a sequencing visualization, a quality control visualization, a specimen detail visualization, a taxonomic selection visualization, a heatmap visualization, coverage visualization, and a report previewing visualization.

19. The method of any one of claims 1 - 18, wherein the annotation comprises a clinically relevant annotation related to the output.

20. The method of any one of claims 1 - 19, wherein the annotation is documented by a clinician.

21. The method of any one of claims 1 - 20, wherein the annotation is clinically auditable.

22. The method of any one of claims 1 - 21, wherein the user or the second user does not have access to Protected Health Information (PHI) of the subject.

23. The method of any one of claims 1 - 22, wherein the electronic report is generated within 1, 2, 3, 4, 5, 6, or 7 business days from the sequencing of the biological sample.

24. The method of any one of claims 1 - 22, wherein the electronic report is generated within 1, 2, 3, 4, 5, 6, or 7 days from the sequencing of the biological sample.

25. The method of any one of claims 1 - 22, wherein the electronic report is generated within 1, 2, 3, 4, 5, 6, 9, 10, 12, 15, 20, 22, or 24 hours from the sequencing of the biological sample.

26. The method of any one of claims 1 - 22, wherein the electronic report is generated within about 1 to 31, 1 to 14, 1 to 7, 1 to 5, 1 to 3, or 1 to 2 business days from the sequencing of the biological sample.

27. The method of any one of claims 1 - 22, wherein the electronic report is generated within about 1 to 31, 1 to 14, 1 to 7, 1 to 5, 1 to 3, or 1 to 2 days from the sequencing of the biological sample.WSGR Docket No. 63228-705.60128. The method of any one of claims 1 - 27, wherein sequencing comprises one or more of nucleic acid extraction, nucleic acid isolation, nucleic acid fragmentation, reverse transcription (e.g., of RNA), transcript fragmentation, adapter ligation, amplification, targeted enrichment, nucleic acid library preparation, bisulfite conversion, and methylation conversion.

29. The method of any one of claims 1 - 28, wherein sequencing comprises one or more of targeted sequencing, single molecule real-time sequencing, exon or exome sequencing, intron sequencing, electron microscopy -based sequencing, panel sequencing, transistor- mediated sequencing, direct sequencing, random shotgun sequencing, Sanger dideoxy termination sequencing, whole-genome sequencing, sequencing by hybridization, pyrosequencing, duplex sequencing, cycle sequencing, single-base extension sequencing solid phase sequencing, high-throughput sequencing, massively parallel signature sequencing, emulsion PCR, co -amplification at lower denaturation temperature-PCR (COLD-PCR), multiplex PCR, sequencing by reversible dye terminator, paired -end sequencing, near-term sequencing, exonuclease sequencing, sequencing by ligation, short-read sequencing, single molecule sequencing, sequencing-by-synthesis, real-time sequencing, reverse-terminator sequencing, long-read sequencing, nanopore sequencing, 454 sequencing, Solexa Genome Analyzer sequencing, SOLiD™ sequencing, and MS- PET sequencing.

30. The method of any one of claims 1 - 29, wherein sequencing comprises a polymerase chain reaction (PCR) or isothermal amplification.31 . The method of any one of claims 28 - 30, wherein the amplification comprises amplification reagents, the amplification reagents comprising reagents for one or more of polymerase chain reaction (PCR), transcription mediated amplification (TMA), helicase dependent amplification (HD A), circular helicase dependent amplification (cHDA), strand displacement amplification (SDA), loop mediated amplification (LAMP), exponential amplification reaction (EXPAR), rolling circle amplification (RCA), ligase chain reaction (LCR), simple method amplifying RNA targets (SMART), single primer isothermal amplification (SPIA), multiple displacement amplification (MDA), nucleic acid sequence based amplification (NASBA), hinge -initiated primer-dependent amplification of nucleic acids (HIP), nicking enzyme amplification reaction (NEAR), or improved multiple displacement amplification (HMD A).WSGR Docket No. 63228-705.60132. The method of any one of claims 1 -31, wherein the computer processing further comprises comparing the quantitative measure of microbial nucleic acids to a threshold.

33. The method of any one of claims 1 - 32, wherein the computer processing further comprises aligning the sequence data to a reference genome.

34. The method of claim 33, wherein the reference genome comprises a human reference genome or a microbial reference genome.

35. The method of any one of claims 1 - 34, wherein the computer processing further comprises filtering the sequence data.

36. The method of claim 35, wherein the filtering comprises distinguishing human-derived reads and microbe-derived reads.

37. The method of claim 36, wherein the filtering comprises removing human-derived reads.

38. The method of any one of claims 1 - 37, wherein the nucleic acid comprises one or more of double-stranded (ds) nucleic acids, single stranded (ss) nucleic acids, deoxyribose nucleic acids (DNA), ribose nucleic acids (RNA), complementary DNA (cDNA), dsDNA, ssDNA, circulating nucleic acids, circulating cell-free nucleic acids, circulating DNA, circulating RNA, cell -free nucleic acids, cell-free DNA, cell-free RNA, circulating cell-free DNA, cell-free dsDNA, cell-free ssDNA, circulating cell-free RNA, genomic DNA, plasmid DNA, mitochondrial DNA, cell-free pathogen nucleic acids, circulating pathogen nucleic acids, circular DNA, circular RNA, circular single -stranded DNA, circular double-stranded DNA.

39. The method of claim 38, wherein the RNA comprises one or more of messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and non-coding RNA.

40. The method of any one of claims 1 - 39, wherein the biological sample comprises one or more of whole blood, blood serum, blood plasma, bile, breath, urine, feces, sebum, tissue, breast milk, hair, saliva, sputum, sweat, tears, lymphatic fluid, serous fluid, synovial fluid, pericardial fluid, peritoneal fluid, pleural fluid, cystic fluid, cerebrospinal fluid, seminal fluid, vaginal secretion, amniotic fluid, nasal fluid, otic fluid, interstitialWSGR Docket No. 63228-705.601 fluid, gastric fluid, intestinal fluid, transudates, exudates, liquids containing single or multiple cells, liquids containing organelles, fluidized tissues, fluidized organisms, liquids containing multi-celled organisms, biological swabs, and biological washes.41 . The method of any one of claims 1 - 40, wherein the electronic display comprises one or more of a personal computer (e.g., portable computer), a slate or tablet personal computer (PC), a mobile electronic device, a smartphone, a personal digital assistant, and a wearable device.

42. The method of any one of claims 1 - 41, wherein the pathogenic infection comprises one or more of myotoxic disease, respiratory disease, pulmonary infections, pneumonia, mononucleosis, cutaneous infections, keratitis, peritonitis, osteomyelitis, sinusitis, endophthalmitis, mycetoma, soft tissue infections, cellulitis, impetigo, bacteremia, toxic shock syndrome, ventriculitis, urinary tract infections, surgical site infections, wound infections, abscesses, meningitis, encephalitis, sepsis, and peritonitis.

43. The method of claim 42, wherein the pathogenic infection comprises meningitis or encephalitis.

44. The method of any one of claims 1 - 43, wherein the pathogenic infection comprises infection with a pathogen, the pathogen comprising one or more of a filamentous fungus, a gram negative bacterium, a gram positive bacterium, a yeast, a parasite, a DNA virus, and an RNA virus.

45. The method of any one of claims 1 - 44, wherein the pathogen comprises a strain or a subtype of a species of one or more of a filamentous fungus, a gram negative bacterium, a gram positive bacterium, a yeast, a parasite, a DNA virus, and an RNA virus.

46. The method of claim 44 or 45, wherein the filamentous fungus comprises one or more of a fungus of the ypnu Acre monium, Alternaria, Aspergillus, Cladosporium, Curvularia, Fusarium, Histoplasma,Lichtheimia, I.omenlospora. Mucor. l’aecilomyces, Penicillium, Rhizomucor , Rhizopus, Scedosporium, Schizophyllum, and Trichoderma.

47. The method of any one of claims 44 - 46, wherein the filamentous fungus comprises one or more of Acremonium spp., Alternaria spp., Alternariaalternata, Alternaria infectoria,WSGR Docket No. 63228-705.601Aspergillus spp., Aspergillus carneus, Aspergillus clavatus, Aspergillus flavus, Aspergillus fumigatus, Aspergillus nidulans. Aspergillus niger. Aspergillus ochraceus, Aspergillus lerreus. Aspergillus uslus. Aspergillus versicolor, Aspergillus parasiticus, Cladosporium spp., Curvularia spp., Fusarium spp., Fusarium avenaceum, Fusarium culmorum, Fusarium equiseti, Fusarium fujikuroi, Fusarium graminearum, Fusarium nivale, Fusarium proliferatum, Fusarium oxysporum, Fusarium roseum, Fusarium solani, Fusarium verticillioides,Histoplasma spp., Histoplasma capsulatum,Lichtheimia spp., Lichtheimia corymbifera, Lichtheimia ramose, Lomentospora spp., Lomentospora prolificans, Mucor spp . , Mucor circinelloides, Paecilomyces spp. , Paecilomyces variotii, Penicillium spp., Penicillium aurantiogriseum, Penicillium brunneum, Penicillium citreoviride , Penicillium citrinin, Penicillium claviforme, Penicillium crustosum, Penicillium expansum, Penicillium griseofulvum, Penicillium hirsutum, Penicillium islandicum, Penicillium kloeckeri, Penicillium roqueforti, Penicillium rubrum, Penicillium rugulosum, Penicillium verrucossum, Penicillium viridicatum, Rhizomucor spp., Rhizomucor pusillus, Rhizopus spp., Rhizopus arrhizus, Rhizopus microspores, Scedosporium spp., Scedosporium apiospermum, Schizophyllum spp., Schizophyllum commune, and Trichoderma spp.

48. The method of any one of claims 1 - 47, wherein the pathogenic infection comprises one or more of dermatomycoses, epidermophyton, allergic bronchopulmonary mycosis, allergic fungal rhinosinusitis, mycotic keratitis (e.g., keratomycosis), mucormycosis, otomycosis, onychomycosis, aspergillosis (e.g., invasive aspergillosis or pulmonary aspergillosis), eumycetoma, and fungemia.

49. The method of claim 44 or 45, wherein the gram negative bacterium comprises one or more of a bacterium of the genus Acinetobacter , Chlamydia, Citrobacter, Enterobacter , Escherichia, Klebsiella, Moraxella, Proteus, Pseudomonas, Shigella, Salmonella, and Yersinia.

50. The method of claim 44 or 45, wherein the gram negative bacterium comprises one or more of Acinetobacter spp., Acinetobacter baumannii, Chlamydia spp., Chlamydia trachomatis, Citrobacter spp., Enterobacter spp., Escherichia coli, Klebsiella spp., Moraxella spp., Moraxella sp. K1664, Moraxella bovis, Moraxella canis, Moraxella catarrhalis, Moraxella lacunata, Moraxella nonliquefaciens, Moraxella osloensis, Moraxella phenylpyruvica, Proteus spp., Pseudomonas spp., PseudomonasWSGR Docket No. 63228-705.601 aeruginosa, Salmonella spp., Salmonella enterica, Salmonella bongori, Shigella spp., Shigella dysenteriae , Shigella flexneri, Shigella boydii, Shigella sonnei, Yersinia spp., and Yersinia pestis.

51. The method of claim 44 or 45, wherein the gram positive bacterium comprises one or more of a bacterium of the genus Staphylococcus, Streptococcus, Clostridium, Enterococcus, and Mycobacterium.

52. The method of claim 44 or 45, wherein the gram positive bacterium comprises one or more of Staphylococcus spp., Staphylococcus aureus, Streptococcus spp., Streptococcus pyogenes, Clostridium spp., Clostridium difficile, Enterococcus spp., Enterococcus faecalis, Enterococcus gallinarum, Mycobacterium spp., and Mycobacterium tuberculosis.

53. The method of any one of claims 1 - 45 or 51 - 52, wherein the pathogenic infection comprises one or more of strep throat, tuberculosis, and Methicillin -resistant Staphylococcus aureus (MRS A) infection.

54. The method of claim 44 or 45, wherein the yeast comprises one or more of a yeast of the genus Candida or Cryptococcus.

55. The method of claim 44 or 45, wherein the yeast comprises one or more of Candida auris, Candida albicans, Candida glabrata, Candida parapsilosis, Candida tropicalis, Candida krusei, Cryptococcus albidus, Cryptococcus curvatus, Cryptococcus gattii, Cryptococcus laurentii, Cryptococcus neoformans, and Cryptococcus uniguttulatus .

56. The method of any one of claims 1 - 45 or 54 - 55, wherein the pathogenic infection comprises one or more of oral thrush, candidiasis (e.g., invasive candidiasis, cutaneous candidiasis, nail candidiasis, esophageal candidiasis, or vaginal candidiasis), and candidemia.

57. The method of claim 44 or 45, wherein the parasite comprises one or more of a protozoa, a helminth, or an ectoparasite.

58. The method of claim 1 - 45 or 57, wherein the pathogenic infection comprises one or more of Lyme disease, malaria, giardiasis, toxoplasmosis, intestinal worm infections,WSGR Docket No. 63228-705.601 lice, chagas disease, schistosomiasis, babesiosis, pinworm infection, hookworm, leishmaniasis, trichomoniasis, amebiasis, cryptosporidiosis, schistosomiasis, tapeworms, toxocariasis, ascariasis, cysticercosis, echinococcosis, filariasis, neurocysticercosis, onchocerciasis, roundworms, African trypanosomiasis, paragonimiasis, strongyloidiasis, and trichuriasis.

59. The method of claim 44 or 45, wherein the DNA virus comprises one or more of herpesvirus, cytomegalovirus (CMV), human papillomavirus (HPV), adenovirus, hepatitis B virus (HBV), poxvirus, and polyomavirus.

60. The method of claim 59, wherein the herpesvirus comprises one or more of herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), Kaposi sarcoma- associated herpesvirus (gamma herpesvirus), varicella-zoster virus (VZV), and Epstein- Barr virus.

61. The method of any one of claims 1 - 45 or 59 - 60, wherein the pathogenic infection comprises chicken pox or shingles.

62. The method of claim 44 or 45, wherein the RNA virus comprises one or more of influenza virus, respiratory syncytial virus (RSV), coronavirus (e.g., SARS-CoV-2), enterovirus, norovirus, rotavirus, human immune deficiency virus (HIV), hepatitis C virus (HCV), Rift Valley fever virus, Tick-borne encephalitis virus, Zika virus, Dengue virus, West Nile virus, Ebola virus, yellow fever virus, Saint Louis encephalitis virus (SLEV), Eastern Equine encephalitis virus (EEEV), La Crosse encephalitis virus (LCEV), and Japanese encephalitis virus.

63. The method of any one of claims 1 - 62, wherein the pathogenic infection comprises a symptom.

64. The method of claim 63, wherein the symptom comprises one or more of fever, severe headache, stiff neck, sensitivity to light, nausea, vomiting, confusion, altered mental status, seizure, personality changes, coma, cancer, asthma, brain granuloma, arthritis, anemia, diarrhea, dysentery, vomiting, loss of appetite, fever chills, fatigue, weight loss, malnutrition, and abdominal pain, and skin lesions.WSGR Docket No. 63228-705.60165. The method of claim 64, wherein the symptom comprises one or more of fever, severe headache, stiff neck, sensitivity to light, nausea, vomiting, confusion, altered mental status, seizure, and personality changes.

66. The method of any one of claims 1 - 65, wherein the pathogenic infection is an antibiotic resistant infection.

67. The method of any one of claims 1 - 66, wherein the pathogenic infection comprises a pathogen associated with cancer.

68. The method of any one of claims 1 - 67, wherein the pathogenic infection is treated.

69. The method of any one of claims 1 - 68, wherein the pathogenic infectionis treated with an antimicrobial.

70. The method of any one of claims 1 - 69, wherein the antimicrobial comprises one or more of an antibiotic, antifungal, antiparasitic, and an antiviral.

71. The method of any one of claims 1 - 70, wherein the electronic report comprises a performance metric that is indicative of a presence of a pathogenic infection.

72. The method of claim 71, wherein the performance metric comprises one or more of an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and Fl score.

73. The method of claim 71 or 72, wherein the performance metric has a value of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97%.

74. The method of claim 71, wherein the performance metric is an Area Under the Receiver Operating Characteristic Curve (AUROC) or correlation coefficient.

75. The method of claim 71 or 74, wherein the performance metric has a value of at least 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, or 0.97.WSGR Docket No. 63228-705.60176. The method of any one of claims 1 - 75, wherein the electronic report comprises a performance metric that is indicative of a presence of a pathogenic infection comprising a sub-type or strain of a species of pathogen.

77. The method of any one of claims 1 - 76, wherein the risk comprises one or more of a likelihood, probability, odds ratio, risk ratio, attributable risk, standardized morality ratio, and a Bayesian Risk Assessment.

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