Methods and applications of subnucleosomal cell-free nucleic acids fragmentomics

Abstract

Provided herein are improved methods and systems and systems for analyzing cfNA, including improved methods and systems for fragmentomics and their various applications. The methods and systems described herein can be used for assessing gene expression or replication, disease diagnosis, and evaluating immune responses.

Description

Attorney Docket No. 47697-763601METHODS AND APPLICATIONS OF SUBNUCLEOSOMAL CELL-FREE NUCLEIC ACIDS FRAGMENTOMICSCROSS-REFERENCE

[0001] This application claims the benefit of U. S. Provisional Application No. 63 / 740,972 filed December 31, 2024, the contents of which are incorporated by reference in their entirety.BACKGROUND

[0002] Massively parallel sequencing (MPS), as the name implies, is a high-throughput technology that can generate an enormous amount of information about the genetic makeup of an organism. MPS is particularly useful for genomic studies that analyze sequences across a genome such as whole genome sequencing. MPS can be used to study cell-associated DNA, as well as cell-free DNA shed into a variety of samples including blood.SUMMARY

[0003] Studies of cell-free DNA sequence read data have shown correlations between fragment lengths of nucleosomal ranges (-160 bp) being depleted in loci correlated with gene expression. Cell-free nucleic acid (cfNA) sequencing methods often focus on nucleosomal fragments (-160 bp), overlooking the unique diagnostic potential of subnucleosomal fragments (<120 bp). Provided herein are improved methods for analyzing subnucleosomal cfNA fragments and their various applications related to assessing gene expression, diagnosing diseases, and evaluating immune responses. This Summary introduces a selection of concepts that are described further below in the Detailed Description. This Summary is not intended to limit the scope of the claimed subject matter.

[0004] Described herein in a some embodiments is a method for preparing a nucleic acid library from a subject useful for a fragmentomics analysis, the method comprising: (a) providing a substantially cell-free sample comprising cell-free DNA (cfDNA) from a subject, wherein the cfDNA comprises double-stranded cell-free DNA (dscfDNA) and single-stranded cell-free DNA (sscfDNA); (b) producing a fraction of the cfDNA in (a) by: (i) size discrimination of the cfDNA; and (ii) selectively removing cfDNA fragments less than approximately 25 nucleotides in length and greater than approximately 120 nucleotides in length to enrich for cfDNA fragments between 25-120 nucleotides in length; and (c) analyzing a fragment length profile of the cfDNA that is between 25-120 nucleotides in length to determine gene expression. In some embodiments, the cfDNA fraction after (b) comprises: (i) at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least 90% of cfDNA fragments with a lengthAttorney Docket No. 47697-763601range between 25 nucleotides in length and 120 nucleotides in length; (ii) a greater proportion of cfDNA fragments that are less than 120 nucleotides in length than cfDNA fragments that are greater than 120 nucleotides in length; (iii) a combination of i) and ii). In some embodiments, the cfDNA fraction after (b) comprises at least 50% of fragments with a length range between 25 nucleotides in length and 120 nucleotides in length

[0005] Described herein in some embodiments is a method of analyzing gene activation or replication comprising: (a) obtaining a sample comprising fragments of cell-free DNA (cfDNA) derived from a mammalian subject; (b) physically enriching the sample for cfDNA fragments below about 120 nucleotides in length, thereby producing a fraction of cfDNA comprising cfDNA fragments associated with one or more mammalian regulatory elements; (c) sequencing the fraction of cfDNA; (d) identifying a subset of cfDNA fragments of the fraction of cfDNA associated with the one or more mammalian regulatory elements; (e) preparing a fragmentomic profile of the subset of cfDNA fragments, wherein the fragmentomic profile comprises a parameter selected from the group consisting of: i) fragment length, ii) frequency or abundance of fragments of specific lengths, ii) fragment genomic position, and iv) any combination thereof; and (f) f) comparing the fragmentomic profile with a reference fragmentomic profile and detecting the gene activation or replication based on an increase in cfDNA fragments of 40 to60 nucleotides in length compared to the reference fragmentomic profile.

[0006] Described herein in some embodiments is a method of analyzing cell-free DNA (cfDNA) to detect gene expression, the method comprising: quantifying a first number of cell- free DNA (sscfDNA) sequence reads with a length range between 25-120 nucleotides in length that span a first genetic locus and comparing the quantity to a second number of sscfDNA sequence reads with a length range between 25-120 nucleotides in length that span a second genetic locus; and (a) detecting gene expression associated with the first locus based in part on the first number of sscfDNA sequence reads being greater than the second number of sscfDNA sequence reads; or (b) detecting an absence of gene expression associated with the first locus based in part on the first number of sscfDNA sequence reads being equal to or less than the second number of sscfDNA sequence reads.

[0007] Described herein in some embodiments is a method for analyzing cell-free DNA (cfDNA) from a biological sample, the method comprising: (a) obtaining sequencing data from cfDNA fragments from the biological sample, wherein the cfDNA fragments comprise nucleic acid fragments having a length between 24 nucleotides and 120 nucleotides; (b) mapping the cfDNA fragments to a reference genome to determine genomic positions of the cfDNA fragments; (c) computing a coverage metric at a plurality of candidate cis-regulatory elements by quantifying a number of cfDNA fragments mapping to each candidate cis-regulatory elementAttorney Docket No. 47697-763601relative to flanking genomic regions; (d) correlating the coverage metric at each candidate cis-regulatory element with a chromatin accessibility measurement for a plurality of cell types to generate a cell-type specific fragmentomic signature; and (e)determining a relative contribution of each cell type to the cfDNA in the biological sample based in part on the cell-type specific fragmentomic signature. In some embodiments, the candidate cis-regulatory elements comprise at least one of promoter-like signature elements, enhancer-like signature elements, or CTCF-binding elements. In some embodiments, the enhancer-like signature elements comprise proximal enhancer elements located within 200 nucleotides of a transcription start site and distal enhancer elements located more than 200 nucleotides from a transcription start site. In some embodiments, the chromatin accessibility measurement comprises DNase hypersensitivity data from an ENCODE database. In some embodiments, the coverage metric is computed as a ratio of cfDNA fragment coverage within a region of interest centered on each candidate cis-regulatory element to cfDNA fragment coverage in flanking regions outside the region of interest. In some embodiments, the region of interest comprises a window of approximately 500 nucleotides on each side of a center of the candidate cis-regulatory element, and wherein the flanking regions comprise windows of approximately 500 nucleotides located between 1000 and 1500 nucleotides from the center of the candidate cis-regulatory element. In some embodiments, the methods further comprise aggregating the coverage metric across multiple candidate cis-regulatory elements associated with a common gene or functional annotation to generate an aggregated fragmentomic feature. In some embodiments, the functional annotation comprises at least one of a gene ontology term, a biological pathway, and a transcription factor binding site. In some embodiments, the methods further comprise normalizing the sequencing data to correct for sample-specific biases in fragment length and GC content using spike-in control molecules In some embodiments, the nucleic acid fragments having a length between 24 nucleotides and 120 nucleotides comprise transcription factor-bound DNA fragments. In some embodiments, the analyzing the fragment length profile of the cfDNA that is between 25-120 nucleotides in length to determine gene expression comprises a method of detecting gene expression as described in this disclosure. In some embodiments, the analyzing comprises analyzing cfDNA fragments sequence reads that are less than 90 nucleotides in length. In some embodiments, the analyzing comprises analyzing cfDNA fragments sequence reads that are less than 80 nucleotides in length. In some embodiments, the methods further comprise classifying a disease state of a subject from which the biological sample was obtained based on the relative contribution of each cell type to the cfDNA. In some embodiments, the cfDNA fragments 40-60 nucleotides in length are doublestranded cell-free DNA (dscfDNA). In some embodiments, the methods further comprise analyzing microbial cell-free DNA (mcfDNA) sequencing reads, wherein the mcfDNAAttorney Docket No. 47697-763601sequencing reads are derived from mcfDNA from the mammalian subject. In some embodiments, the methods further comprise preparing a microbial fragmentomic profile of the mcfDNA sequencing reads. In some embodiments, the methods further comprise preparing a library by attached adapters comprising an overhang to single-stranded cfDNA fragments within the sample. In some embodiments, the cfDNA fragments 40-60 nucleotides in length comprise single-stranded cfDNA or a mixture of single-stranded and double-stranded cfDNA In some embodiments, the methods further comprise preparing a library that preferentially captures single-stranded cfDNA or both single-stranded and double-stranded cfDNA. In some embodiments, the mammalian regulatory element is an enhancer, a cis-regulatory element, or distal regulatory element. In some embodiments, the mammalian regulatory element is a distal regulatory element that is a silencer or an insulator. In some embodiments, the distal regulatory element is at least 500 nucleotides in length or 1000 nucleotides in length away from a transcription start site (TSS) regulated by the distal regulatory element or from a gene regulated by the distal regulatory element. In some embodiments, the mammalian regulatory element is a promoter. In some embodiments, the fragments 40-60 nucleotides in length are evident as a distinct peak when fragment length is graphically compared with fragment quantity. In some embodiments, the distinct peak is about 20 to about 120 bases in width. In some embodiments, the methods further comprise physically enriching the cfDNA for cfDNA fragments that are at most about 120 nucleotides in length. In some embodiments, the mammalian regulatory element binds a transcription factor selected from the group consisting of: TFIID, TFIIA, TFIIB, TFIIF, TFIIH, FOXP2, SOX2, PAX6, HOX, GATA3, OTX2, TP53, MYC, NF-kB, API, STAT3, HIFla, ER, AR, GR, PPARy, FOXP3, NRF2, NF AT, IRF3 / IRF7, T-bet, RFX5, OCT4, NANOG, KLF4, c-MYC, CLOCK / BMAL1, SREBP1, FOXO1, CTCF, E2F, SP1, RUNX1, MEF2, CREB, EGR1, RelA / p65, SMAD, and YAP / TAZ. In some embodiments, the cfDNA comprises human cell-free DNA (cfDNA). In some embodiments, the cfDNA comprises human non-fetal cfDNA. In some embodiments, the mammalian regulatory element controls expression of a gene preferentially expressed in non-hematopoietic cells over hematopoietic cells. In some embodiments, the mammalian regulatory element controls expression of a gene preferentially expressed in hematopoietic cells over non-hematopoietic cells. In some embodiments, the methods further comprise preparing a profile of fragment lengths associated with multiple mammalian regulatory elements. In some embodiments, the multiple mammalian regulatory elements are associated with regulatory factors or transcription factors correlated with immune system activation. In some embodiments, the regulatory factors or transcription factors comprise one or more transcription factors selected from the group consisting of: ZNF776, STN1: CTC1-STN1-TEN1, SQSTM1, RUVBL2, FOXCI, PAF1, PAX-5, andNR4A2. In some embodiments,Attorney Docket No. 47697-763601the regulatory elements are not bound to a Transcription Start Site (TSS). In some embodiments, the sample is a bodily fluid. In some embodiments, the sample is a plasma sample. In some embodiments, the sample comprises a whole blood, a plasma, a serum, a lymph, a synovial fluid, a cerebrospinal fluid (CSF), a saliva, a gastric juice, a bile, a pancreatic juice, an intestinal fluid, a respiratory tract mucosal secretion, a semen, a cervical mucus, a vaginal secretion, a urine, a sebum, a breast milk, an amniotic fluid, a pericardial fluid, a pleural fluid, a peritoneal fluid, a bronchoalveolar lavage (BAL), a gastric lavage, a peritoneal lavage, a nasal lavage, a bladder lavage, a rectal lavage, a wound lavage, a joint lavage (arthrocentesis), an eye lavage, a sinus lavage, or any combination thereof. In some embodiments, no extraction of nucleic acids has been performed on the sample prior to adding sequencing adaptors to the cfDNA. In some embodiments, the sample is a sample of purified cfDNA. In some embodiments, the reference profile of fragment lengths relates to fragments associated with a mammalian regulatory element associated with a gene with inactive or low expression. In some embodiments, the sequencing is conducted at a sequencing depth of 10x-30x. In some embodiments, the cfDNA is not digested with micrococcal nuclease. In some embodiments, the size discrimination or physical enriching of the cfDNA is accomplished by using gel electrophoresis, automated gel electrophoresis, chromatography, filtration, centrifugation, magnetic beads, a spin column, or any combination thereof. In some embodiments, the analyzing further comprises generating a cell-type deconvolution output indicating proportions of cfDNA originating from different cell populations. In some embodiments, the methods further comprise detecting a site of an infection based on the proportions of cfDNA originating from different cell populations.

[0008] Described herein is a method of detecting an infection in a subject comprising: (a) preparing a sequencing library by attaching adapters to cfDNA fragments within a sample from the subject; (b) performing Next Generation Sequencing (NGS) on the sequencing library and producing sequencing reads for cfDNA fragments associated with one or more mammalian regulatory elements that regulate an immune response; (c) preparing a fragmentomic profile for the cfDNA fragments associated with one or more mammalian regulatory elements that regulate an immune response wherein the fragmentomic profile comprises fragment length, quantity of fragments of a specified length, fragment genomic position, or a combination thereof; (d) comparing the fragmentomic profile with a reference fragmentomic profile and detecting an immune response in the subject based on an increase in fragments 40-60 nucleotides in length compared to the reference fragmentomic profile; (e) after detecting the immune response in the subject, performing a genomics analysis of a sample of cfDNA from the subject to identify microbial cell-free nucleic acids; and (f) based on the microbial cell-free nucleic acids, identifying a candidate microbe associated with the immunological response in the subject. InAttorney Docket No. 47697-763601some embodiments, the methods further comprise analyzing microbial cell-free DNA (mcfDNA) sequencing reads, wherein the mcfDNA sequencing reads are derived from mcfDNA from the mammalian subject. In some embodiments, the methods further comprise preparing a microbial fragmentomic profile of the mcfDNA sequencing reads. In some embodiments, the regulatory factors or transcription factors comprise one or more transcription factors selected from the group consisting of: ZNF776, STN1: CTC1-STN1-TEN1, SQSTM1, RUVBL2, FOXCI, PAF1, PAX-5, and NR4A2. In some embodiments, the methods further comprise detecting a disease or disorder in the subject based on a relative level of gene activation or gene expression compared to a reference value. In some embodiments, the methods further comprise administering a treatment to the subject to treat the disease or disorder. In some embodiments, the disease or disorder comprises a cancer. In some embodiments, the cancer comprises a lung cancer, a colorectal cancer, a breast cancer, a prostate cancer, a liver cancer, or pancreatic cancer. In some embodiments, the disease or disorder comprises an autoimmune disease or inflammatory disease. In some embodiments, the autoimmune disease or inflammatory disease comprises an inflammatory bowel disease, systemic lipid erythematosus, rheumatoid arthritis, or psoriatic arthritis. In some embodiments, the disease or disorder comprises a liver disease, a kidney disease, a cardiovascular disease, a neurodegenerative or neurological disease, a pulmonary disease, a fibrotic disease, or a combination thereof. In some embodiments, the disease or disorder comprises an infectious disease. In some embodiments, the comprises a pregnancy-related condition. In some embodiments, the fragments 40-60 nucleotides in length are doublestranded. In some embodiments, the methods further comprise preparing a DNA library by attaching double-stranded adapters to double-stranded cfDNA within the sample. In some embodiments, the fragments 40-60 nucleotides in length comprise single-stranded cfDNA or a mixture of single-stranded and double-stranded cfDNA. In some embodiments, the mammalian regulatory element is an enhancer, a cis-regulatory element, or distal regulatory element. In some embodiments, the mammalian regulatory element is a distal regulatory element that is a silencer or an insulator. In some embodiments, the fragments 40-60 nucleotides in length are evident as a distinct peak when fragment length is graphically compared with fragment quantity. In some embodiments, the distinct peak is about 20 to about 120 nucleotides in length in width. In some embodiments, the cfDNA is human cell-free DNA (cfDNA). In some embodiments, the cfDNA is human non-fetal cfDNA. In some embodiments, the mammalian regulatory element controls expression of a gene preferentially expressed in hematopoietic cells over non-hematopoietic cells. In some embodiments, the mammalian regulatory element controls expression of a gene preferentially expressed in peripheral blood mononuclear cells (PBMCs). In some embodiments, the sample is a sample of purified cfDNA. In some embodiments, the reference profile ofAttorney Docket No. 47697-763601fragment lengths relates to fragments associated with a mammalian regulatory element associated with a gene with inactive or low expression. In some embodiments, the NGS is conducted at a sequencing depth of 10x-30x. In some embodiments, the cfDNA is not digested with micrococcal nuclease.

[0009] Described herein in some embodiments is a method for identifying and analyzing cell-free deoxyribonucleic acid (cfDNA) fragments from a human subject, the method comprising: (a) subjecting the cfDNA fragments to library preparation and high-throughput sequencing to generate sequence information representative of cfDNA fragments from the human subject, wherein the cfDNA fragments have not been subjected to micrococcal nuclease digestion; (b) performing multi-parametric analysis of the aligned sequence information, thereby generating a multi-parametric model representative of the cfDNA fragments, wherein the multi-parametric model comprises one or more parameters selected from the group consisting of: (i) length of cfDNA fragments that align with one or more mammalian regulatory elements; (ii) quantity of cfDNA fragments that align with genomic positions within the one or more mammalian regulatory elements; and (iii) quantity of cfDNA fragments that align with mcfDNA sequences; and (c) performing, with a computer, statistical analysis with a trained classifier to classify the multi-parametric model as being indicative of gene activation or replication based on projected binding of the cfDNA fragments to non-nucleosomal regulatory elements.

[0010] Described herein in some embodiments is a method for analyzing gene activation or replication in a subject comprising: (a) sequencing cell-free DNA (cfDNA) obtained from the subject; (b) identifying and quantifying fragment lengths of the cfDNA at genomic regions associated with a mammalian regulatory element, thereby obtaining a profile of fragment lengths associated with the mammalian regulatory element; and (c) detecting the gene activation or replication when the profile of fragment lengths comprises an increase of fragments 40-60 nucleotides in length compared to a profile of fragment lengths of a mammalian regulatory element associated with a gene with inactive or low expression.

[0011] Described herein in some embodiments is a method of assessing gene activation or replication comprising: (a) physically enriching a sample for cfDNA fragments below about 200 nucleotides in length to produce an enriched sample; (b) sequencing the enriched sample to produce a set of fragment sequences; (c) preparing a fragmentomic profile of a subset of the fragment sequences associated with one or more mammalian regulatory elements; (d) comparing the fragmentomic profile with a reference fragmentomic profile; and (e) assessing the gene activation or replication based on an increase in cfDNA fragments 40-60 nucleotides in length compared to the reference fragmentomic profile. In some embodiments, the methods further comprise analyzing microbial cell-free DNA (mcfDNA) sequencing reads, wherein the mcfDNAAttorney Docket No. 47697-763601sequencing reads are derived from mcfDNA from the mammalian subject. In some embodiments, the methods further comprise preparing a microbial fragmentomic profile of the mcfDNA sequencing reads. In some embodiments, the fragments 40-60 nucleotides in length are evident as a distinct peak when fragment length is graphically compared with fragment quantity. In some embodiments, the distinct peak is about 20 to about 120 nucleotides in length in width. In some embodiments, the subject is a mammal. In some embodiments, the subject is not pregnant or suspected of being pregnant. In some embodiments, the subject does not have cancer or is not suspected of having cancer. In some embodiments, the subject is a human. In some embodiments, the method further comprises analyzing cfDNA that has been marked as sscfDNA. In some embodiments, the marking comprises contacting the ssDNA in the sample with an enzyme that preferentially converts cytosine residues to uracil residues in ssDNA. In some embodiments, the contacting with the enzyme that preferentially converts cytosine residues to uracil residues in ssDNA is performed after the contacting the ssDNA in the sample with the methylcytosine dioxygenase TET2. In some embodiments, the enzyme that preferentially converts cytosine residues to uracil residues in ssDNA is an APOBEC enzyme. In some embodiments, the marking comprises ligating an adapter specific to single-stranded DNA (ssDNA) to the sscfDNA or incorporating an adapter specific to ssDNA into the sscfDNA via primer extension. In some embodiments, the method further comprises digesting double stranded DNA (dsDNA). In some embodiments, the digesting dsDNA comprises adding a dsDNA specific nuclease to the sample. In some embodiments, the method further comprises adding process control molecules to the sample. In some embodiments, the analyzing sequence reads comprises analyzing at least 1000, at least 10,000, at least 100,000, or at least 1,000,000 sequence reads. In some embodiments, the analyzing sequence reads comprises aligning at least 1000, at least 10,000, at least 100,000, or at least 1,000,000 sequence reads.

[0012] Described herein in some embodiments is a system configured to perform any of the method described herein. In some embodiments, the system comprises a computer readable memory operatively coupled to a processor, wherein the computer readable memory comprises instructions to perform the method of any one of the preceding claims

[0013] Described herein in some embodiments is a system for determining cell-type contributions from cell-free DNA, comprising: (a) a processor; and (b) a memory storing instructions that, when executed by the processor, cause the system to: (i) receive sequencing data representing cfDNA fragments from a biological sample, wherein the cfDNA fragments are enriched for nucleic acid fragments having a length between 24 nucleotides and 120 nucleotides; (ii) align the cfDNA fragments to genomic coordinates corresponding to candidate cis-regulatory elements; (iii) calculate a coverage ratio for each candidate cis-regulatory element, wherein theAttorney Docket No. 47697-763601coverage ratio comprises a ratio of cfDNA fragment coverage within a central window of the candidate cis-regulatory element to cfDNA fragment coverage in flanking regions; (iv) compare the coverage ratio to reference chromatin accessibility data for a plurality of cell types; and (v) output an estimate of cell-type proportions contributing to the cfDNA based on the comparison.

[0014] In some embodiments, the system comprises: (a) a flowcell comprising a glass surface with surface-bound oligonucleotides, lanes or patterned nanowells, inlet and outlet ports for reagents, and sealed microfluidic channels; (b) a reagent cartridge; (c) a peristaltic or syringe pump; (d) a waste reservoir; and (e) an optical imaging system comprising a laser, an excitation optic, an emission filter, a high-sensitivity camera, and optical housing. In some embodiments, the system comprises: (a) a nanopore array comprising a Complementary Metal Oxide Semiconductor (CMOS) sensor platform; (b) a fluidics manifold and reagents channel; (c) a temperature control unit; (d) a reagent reservoir; and (e) a waste reservoir. In some embodiments, the nucleic acid fragments comprise at least one of single-stranded DNA fragments and doublestranded DNA fragments. In some embodiments, the nucleic acid fragments have a length of less than 90 nucleotides. In some embodiments, the nucleic acid fragments have a length of less than 80 nucleotides. In some embodiments, the instructions further cause the system to normalize the sequencing data to correct for fragment length bias and GC content bias using spike-in control molecules having known concentrations and fragment lengths. In some embodiments, normalizing the sequencing data comprises fitting a response function to recovery rates of the spike-in control molecules and applying a weight to each cfDNA fragment based on the response function. In some embodiments, the reference chromatin accessibility data comprises DNase hypersensitivity z-scores from an ENCODE database for each of the plurality of cell types. In some embodiments, the candidate cis-regulatory elements comprise at least one of promoter-like signature elements, proximal enhancer elements located within 200 nucleotides of a transcription start site, distal enhancer elements located more than 200 nucleotides from a transcription start site, and CTCF-binding elements.

[0015] Described herein in some embodiments is a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising: (a) receiving fragment length and genomic position data for a plurality of cfDNA fragments obtained from a subject; (b) selecting cfDNA fragments having a length between 24 nucleotides and 120 nucleotides; (c) computing fragmentomic features at genomic loci corresponding to candidate cis-regulatory elements, wherein the fragmentomic features comprise coverage enrichment relative to flanking regions; (d) correlating the fragmentomic features with cell-type specific chromatin accessibility profiles; and (e) generating a cell-type deconvolution output indicating proportions of cfDNA originating from different cell populations. In someAttorney Docket No. 47697-763601embodiments, the cfDNA fragments comprise at least one of single-stranded nucleic acid fragments and double-stranded nucleic acid fragments. In some embodiments, selecting cfDNA fragments comprises selecting fragments having a length of less than 90 nucleotides. In some embodiments, the selecting cfDNA fragments comprises selecting fragments having a length of less than 80 nucleotides. In some embodiments, the candidate cis-regulatory elements comprise at least one of promoter-like signature elements, proximal enhancer elements located within 200 nucleotides of a transcription start site, distal enhancer elements located more than 200 nucleotides from a transcription start site, and CTCF-binding elements. In some embodiments, the operations further comprise aggregating the fragmentomic features across multiple candidate cis-regulatory elements associated with a common functional annotation comprising at least one of a gene ontology term, a biological pathway, and a cell-type specific marker gene set. In some embodiments, the operations further comprise classifying a disease state of the subject based on the cell-type deconvolution output, wherein the disease state comprises at least one of an inflammatory bowel disease, a colorectal cancer, and an organ transplant rejection.INCORPORATION BY REFERENCE

[0016] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference in their entireties to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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 will be 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 of which:

[0018] FIG. 1 shows the fragment length profiles of the cfDNA used in the methods described herein.

[0019] FIGs.2A-2C shows the Windowed Protection Score (WPS) from cfDNA captured from Process 2. FIG.2A shows the WPS metric around the exon TSS of highly (left) and lowly (right) expressed genes in PBMC. At the positions around the promoter transcription start site (TSS), WPS is lower for genes with high expression (left) but not for the genes with low (right) expression in peripheral blood mononuclear cell (PBMC). FIG. 2B shows the WPS metricAttorney Docket No. 47697-763601around the exon TSS regions of highly (left) and lowly (right) expressed genes. FIG. 2C shows the WPS metric around the promoter TSS regions for cfDNA captured from Process 1.

[0020] FIG.3A shows the fragment size distribution of cfDNA from Process 1. FIG.3B shows the fragment size distribution of cfDNA from Process 2. Fragment size distribution is shown over all promoters, partitioned by the expression level of the associated gene.

[0021] FIG.4A shows the quantitative correlation between RNAseq and fragmentomic metrics observed at the promoters. FIG. 4B shows the quantitative correlation between RNAseq and fragmentomic metrics observed at the promoters. FIG.4C shows differentiation of different cell populations.

[0022] FIG.5A shows the sequencing depth peaks from Process 1 clustering near the ENCODE loci. FIG. 5B shows cumulative de-novo and random peaks as a function of distance to nearest ENCODE site.

[0023] FIGs. 6A-6D show that the Process 1 coverage is enriched at ENCODE loci (FIG. 6A:Promoter loci; FIG. 6B: Proximal enhancers; FIG. 6C: distal enhancers; FIG. 6D: CTCF-only).

[0024] FIGs. 7A-7D depict that the Process 2 coverage is depleted at the promoters and enriched at other ENCODE cCREs (FIG. 7A: Promoter loci; FIG. 7B: Proximal enhancers;FIG. 7C: distal enhancers; FIG. 7D: CTCF-only).

[0025] FIGs. 8A-8D show the Process 1 coverage pattern at various regulatory elements, partitioned by open / closed chromatin in T-cells. FIG. 8A shows the Process 1 coverage pattern at promoters, partitioned by open / closed chromatin in T-cells. FIG. 8B shows the Process 1 coverage pattern at proximal enhancers, partitioned by open / closed chromatin in T-cells. FIG.8C shows the Process 1 coverage pattern at distal enhancers, partitioned by open / closed chromatin in T-cells. FIG. 8D shows the Process 1 coverage pattern at CTCF-only loci, partitioned by open / closed chromatin in T-cells. The Z-scores were partitioned into 5 percentiles of open / closed, with yellow denoting the most “open” cCREs and blue denoted the most “closed”. FIG. 8E depicts the scatter of promoter z-score versus coverage enrichment.

[0026] FIGs. 9A-9D show the Process 2 coverage pattern at various regulatory elements, partitioned by open / closed chromatin in T-cells. FIG. 9A shows the Process 2 coverage pattern at promoter loci, partitioned by open / closed chromatin in T-cells. The Z-scores were partitioned into 5 percentiles of open / closed, with yellow denoting the most “open” cCREs and blue denoted the most “closed”. FIG. 9B shows the Process 2 coverage pattern at proximal enhancer loci, partitioned by open / closed chromatin in T-cells. FIG. 9C shows the Process 2 coverage pattern at distal enhancer loci, partitioned by open / closed chromatin in T-cells. FIG. 9D shows the Process 2 coverage pattern at CTCF-only loci, partitioned by open / closed chromatin in T-cells.FIG. 9E depicts the scatter of promoter z-score versus coverage enrichment.Attorney Docket No. 47697-763601

[0027] FIGs. 10A-10B show that the Process 1 cfDNA coverage is enriched at ENCODE loci within a single sample. FIG. 10A shows that the Process 1 cfDNA coverage is enriched at ENCODE loci within a single sample. FIG. 10B shows patterns as a function of open / closed states within each ENCODE cCRE at the individual sample level.

[0028] FIG. 11 shows the z-score at various ENCODE loci. The distal enhancers exhibited the most variation.

[0029] FIGs. 12A-12B show the enhancer coverage enrichment for kidney cells in transplant. The enhancer coverage shows enrichment for kidney cells in transplant, as depicted in FIG. 12A (left). Conversely, cfDNA from the unspecified fever sample showed no quantitative enrichment in “open” kidney enhancers (yellow) over “closed” (blue), as depicted in FIG. 12A (right). FIG.12B shows the result from the aggregated samples.

[0030] FIG. 13A depicts the important transcription factors identified by human ultrashort cfDNA fragmentomics in infection and inflammation. FIG. 13B depicts the human cfDNA fragmentome profiles (both genomic and mitochondrial) in transplant over time.

[0031] FIG. 14 is an embodiment of the system described herein.

[0032] FIG. 15 shows two example methodological processes for processing either (1) doublestranded cfDNA or (2) single-stranded and double-stranded cfDNA from a human plasma sample.

[0033] FIG. 16 shows example analyses which can be performed using cfDNA reads collected from human plasma samples. QC and Filtered (length wise) reads can be aligned to specific genomic regions of interest and a sliding window can be used to determine density of counts across genomic regulatory region of interest (transcription start site, CRE, enhancer, repressor, etc). Gene or protein expression resources can be used to identify specific regions of interest for highly expressed genes within a specific tissue or cell population. Coverage ratios can then be computed for a specific region or specific regions of interest and then fed into a machine learning classifier.

[0034] FIG. 17 shows an example of relative fragment density as a function of position relative to locus (bp).

[0035] FIG. 18 shows an example analysis leveraging a T cell dataset and a rest dataset, which could be composed on additional non-T-cell types aggregated or compared to in a direct 1:1 manner across multiple cell types.

[0036] FIG. 19 shows example computed features.

[0037] FIGs. 20A-20C show example schematic diagrams demonstrating a potential cause of the difference in fragment lengths between open and closed chromatin. FIG. 20A shows an example stretch of DNA bound in four nucleosomes in a closed chromatin state at theAttorney Docket No. 47697-763601transcription start site (TSS), and expected number and size of resulting fragments from DNase digestion. FIG. 20B shows the DNA opening into a more open chromatin state at the TSS with only two sections bound to nucleosomes, and expected number and size of resulting fragments from DNases digestion. FIG.20C shows the binding of RNA Polymerase II Complex (“Pol II”) to the open chromatin state at the TSS with only one section of the DNA bound to a single nucleosome, and expected number and size of resulting fragments from DNases digestion.

[0038] FIGs.21A-21C show an example of the expected coverage of each genetic locus shown in FIGs. 20A-20C by sequence reads of nucleosomal (solid line) and subnucleosomal (dashed line) fragment length ranges. FIG.21A (corresponding to the example locus shown in FIG. 20A) shows the coverage is high in nucleosomal fragment length sequence reads and low in subnucleosomal length sequence reads throughout as the DNA is protected by nucleosomes across each locus. FIG. 21B (corresponding to the example locus shown in FIG. 20B) shows a lower level of nucleosomal fragments as the DNA. FIG. 21C (corresponding to the example locus shown in FIG. 20C) shows an increase in sub-nucleosomal length sequence reads covering the TSS.DETAILED DESCRIPTION

[0039] The following passages describe different aspects of the disclosure in greater detail. Each aspect, embodiment, or feature of the disclosure can be combined with any other aspect, embodiment, or feature of the disclosure unless clearly indicated to the contrary.Definitions

[0040] Unless defined otherwise, all technical and scientific terms used herein have the meaning commonly understood by a person skilled in the art to which this disclosure belongs.

[0041] The articles “A,” “an,” and “the”, as used herein, can each include singular or plural references unless expressly limited to one reference, or unless otherwise indicated by context.

[0042] As used herein, the term “or” is used to refer to a nonexclusive “or”; as such, “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated.

[0043] As used in the Examples herein, unless otherwise indicated, the term “about” when referring to a number or a numerical range means that the number or numerical range referred to is an approximation within experimental variability (or within statistical experimental error), and the number or numerical range can vary, for example, from ±10% to 25% of the stated number or numerical range. As used herein throughout the specification, unless otherwise indicated, the term “about” refers to ±15% of a stated number or value. In other examples, the departure from equimolarity in the case of mixes intended to be equimolar, such as but not limited to, some control molecules in the spike-in mixes, is no more than a ten-fold disparity, an eight-fold disparity, a six-fold disparity, a four-fold disparity or a two-fold disparity.Attorney Docket No. 47697-763601

[0044] The term "about" as used herein generally means plus or minus ten percent (10%) of a value, inclusive of the value, unless otherwise indicated by the context of the usage. For example, “about 100” refers to any number from 90 to 110 and includes the number 100, unless otherwise indicated by the context in which the term is used. The term “about” a range refers to that range minus 10% of its lowest value and plus 10% of its greatest value. When the term "about" is used before a non-numerical term that is a stand-in for a numerical value (e.g., horizontal, perpendicular, aligned), the term "about" refers to the value of the non-numerical term (e.g., 90 degrees, 1800 degrees) plus or minus 10% of that value. Numeric ranges are inclusive of the numbers defining the range.

[0045] As used herein, “abundance” refers to the quantity of something, such as, for example, the quantity or number of molecules, such as nucleic acids. As used herein, “relative abundance” is the abundance of a molecule or molecules of interest per abundance of a reference molecule or molecules of interest. For example, relative abundance of target nucleic acid molecules (e.g., microbial cell-free nucleic acids) refers to abundance per reference nucleic acids (e.g., host nucleic acids, synthetic nucleic acid added to the sample, etc.). As used herein, “absolute abundance” is the abundance of molecules per a defined unit of initial sample or sample quantity. For example, absolute abundance of target nucleic acid molecules (e.g., microbial cell-free nucleic acids) refers to the abundance per defined unit of sample quantity (e.g., sample volume, sample mass etc.).

[0046] Whenever the term "at least," "greater than," "greater than or equal to," "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 applies to each of the numerical values in that series of numerical values, unless otherwise specified. 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.

[0047] 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.

[0048] The terms “increased”, “increasing”, or “increase” are used herein to generally mean an increase by a statically significant amount. In some cases, the terms “increased,” or “increase,” mean an increase of at least 10% as compared to a reference level, for example an increase of at least about 10%, at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or atAttorney Docket No. 47697-763601least about 90% or up to and including a 100% increase or any increase between 10-100% as compared to a reference level, standard, or control. Other examples of “increase” include an increase of at least 2-fold, at least 5 -fold, at least 10-fold, at least 20-fold, at least 50-fold, at least 100-fold, at least 1000-fold or more as compared to a reference level.

[0049] The terms, “decreased”, “decreasing”, or “decrease” are used herein generally to mean a decrease by a statistically significant amount. In some cases, “decreased” or “decrease” means a reduction by at least 10% as compared to a reference level, for example a decrease by at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or up to and including a 100% decrease (e.g., absent level or non-detectable level as compared to a reference level), or any decrease between 10-100% as compared to a reference level. In the context of a marker or symptom, by these terms is meant a statistically significant decrease in such level. The decrease can be, for example, at least 10%, at least 20%, at least 30%, at least 40% or more, and is preferably down to a level accepted as within the range of normal for an individual without a given disease.

[0050] Throughout this application, various cases may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[0051] As used herein, “adapter” or “portions of an adapter” refers to a chemically synthesized, single-stranded, or double-stranded oligonucleotide that can be attached, e.g., covalently (e.g., ligation, primer extension) or non-covalently (e.g., hybridization), to the ends of nucleic acid molecules, such as DNA or RNA molecules. Adapter sequences can be of any length. “Adapter” can refer to either a full-length adapter or a portion of the adapter, e.g., partial adapters can be attached in some embodiments before the full-lengths are introduced by e.g., indexing primers in amplification steps. 3'-end adapters and 5'-end adapters can be full-length or a portion of an adapter sequence that are attached to the opposite ends of a target nucleic acid, a copy of a target nucleic acid, or a target nucleic acid complement. 3 '-end adapters and 5 '-end adapters sequences end up being attached to the opposite ends of e.g., a template that can be sequenced that comprises target nucleic acid, a copy of a target nucleic acid, and / or a target nucleic acidAttorney Docket No. 47697-763601complement. The 3 '-end adapter and 5 '-end adapter sequences can be the same or they can be different.

[0052] As used herein, “control” refers to a standard of comparison. A “negative control” refers to a standard of comparison that is used to identify contaminants from samples, or to identify the nature of a signal in the absence of a sample (e.g., a background signal). A “positive control” refers to a standard of comparison that is designed to produce a positive result or signal. Generally, the presence of a substance (e.g., nucleic acid) is detected in a positive control that is run during an assay. Some embodiments of the disclosure comprise a positive and / or negative control. Some embodiments of the disclosure comprise an initial sample or samples without a positive and / or negative control. Some embodiments of the disclosure comprise an initial sample or samples without a positive control. Some embodiments of the disclosure comprise an initial sample or samples without a negative control.

[0053] As used herein, “denaturing” refers to a process in which biomolecules, such as proteins or nucleic acids, lose their native or higher order structure. Native and higher order structure can include, for example, without limitation, quaternary structure, tertiary structure, or secondary structure. For example, a double-stranded nucleic acid molecule can be denatured into two single-stranded molecules.

[0054] As used herein, the term "nucleotides in length" refers to the total number of nucleotides in a single-stranded DNA molecule. For a DNA molecule comprising at least some double stranded sequence the “nucleotides in length” of the molecule would be calculated as: Nucleotides in length = (number of nucleotides in the duplex region / 2) + total number of unpaired nucleotides on either strand.

[0055] As used herein, “detect” refers to quantitative or qualitative detection, including, without limitation, detection by identifying the presence, absence, quantity, frequency, concentration, sequence, form, structure, origin, or amount of an analyte.

[0056] As used herein, “pathogen” refers to a microbe that can cause a disease, ailment, or an infection.

[0057] As used herein, “microbe,” or “microbial,” generally refers to archaea, bacteria, fungi, protists, parasites, viruses, or other entities that are usually detectable using a microscope (e.g., an optical microscope or electron microscopy). As used herein, the term “microorganism” refers to a uni- or multi- cellular organism, such as, for example, a microscopic organism or macroscopic organism including but not limited to bacteria, fungi, protists, and parasites. Microbes herein can be a prokaryote or a eukaryote. Microbes are often pathogens responsible for disease, but can also exist in a non-pathogenic, symbiotic, commensalistic, mutualistic, or amensalistic relationship with a host, such as a human.Attorney Docket No. 47697-763601

[0058] The term “sequencing,” as used herein, generally refers to methods and technologies for determining the sequence of nucleotide bases in one or more polynucleotides. Sequencing can involve basic methods including Maxam-Gilbert sequencing and chain-termination methods, or de novo sequencing methods including shotgun sequencing and bridge PCR, or next-generation sequencing (NGS) methods (or massively-parallel sequencing method) including but not limited to polony sequencing, pyrosequencing, sequencing-by-synthesis, sequencing by ligation, ion semiconductor sequencing, single molecule sequencing, single-molecule real-time sequencing, nanopore sequencing, and others. Sequencing can be performed by various systems currently available, such as, without limitation, a sequencing system by Illumina®, Pacific Biosciences®, Oxford Nanopore®, Genia Technologies®, or Life Technologies® and others. Such devices can provide a plurality of raw genetic data corresponding to the genetic information of a host (e.g., human), a non-host (e.g., a pathogen, an organ donor), a host-derived variant genetic sequence (e.g., a single nucleotide polymorphism), and / or combinations thereof as generated by the device from a sample provided by the subject.

[0059] The term “derived from” encompasses the terms “originated from,” “obtained from,” “obtainable from” and “created from,” and generally indicates that one specified material finds its origin in another specified material or has features that can be described with reference to the specified material. For example, a sample can be derived from a blood draw, a nucleic acid can be derived from a sample, a sequence read can be derived from sequencing a nucleic acid, or any combination thereof.

[0060] As used herein, the phrase “uniformly distributed” refers to a distribution that is continuous or uniform between members of a family such that for each member of a family there is a predictable or symmetric interval between them. The term “non-uniformly distributed” refers to a distribution of members of a family that does not have a predictable or symmetric interval between them.

[0061] The term "machine learning algorithm," as used herein, generally refers to an algorithm, executed by computer, that automates analytical model building, e.g., for clustering, classification or pattern recognition. Machine learning algorithms may be supervised or unsupervised. Learning algorithms include, for example, artificial neural networks (e.g., back propagation networks), discriminant analyses (e.g., Bayesian classifier or Fischer analysis), support vector machines, decision trees (e.g., recursive partitioning processes such as CART -classification and regression trees, or random forests), linear classifiers (e.g., multiple linear regression (MLR), partial least squares (PLS) regression, and principal components regression), hierarchical clustering, and cluster analysis. A dataset on which a machine learning algorithm learns can be referred to as "training data."Attorney Docket No. 47697-763601

[0062] The term "classifier," as used herein, generally refers to algorithm computer code that receives, as input, test data and produces, as output, a classification of the input data as belonging to one or another class. A classifier for a disease or disorder may refer to a classifier indicating the presence, absence, severity, or stage of the disease or disorder.

[0063] As used herein, the term "nucleotides in length" refers to the total number of nucleotides in a single-stranded DNA molecule. For a DNA molecule comprising at least some double stranded sequence the nucleotides in length of the molecule would be calculated as: Nucleotides in length = (number of nucleotides in the duplex region / 2) + total number of unpaired nucleotides on either strand.Overview

[0064] Provided herein are methods of preparing a fragmentomic profile for analyzing gene activation or replication. In some embodiments, the methods described herein can comprise physically enriching a sample for cfDNA fragments. In some embodiments, the methods comprise physically enriching cfDNA fragments that are shorter than nucleosomal cfDNA (e.g., subnucleosomal cfDNA). In some embodiments, the methods can comprise physically enriching cfDNA fragments associated with a regulatory element. In some embodiments, the regulatory elements can regulate an immune response. In some embodiments, the methods can comprise physically enriching cfDNA fragments that can bind a transcription factor. In some embodiments, the fragmentomic profile generated by the methods can comprise a parameter comprising fragment length, frequency or abundance of fragments of specific lengths, fragment genomic position, or any combination thereof. In some embodiments, the methods can comprise identifying the cfDNA fragments associated with a regulatory element. In some embodiments, the methods comprise comparing the fragmentomic profile with a reference fragmentomic profile. In some embodiments, the methods comprise detecting a change in the cfDNA fragmentomic profile compared to the reference fragmentomic profile. In some embodiments, the methods comprise detecting the gene activation or replication based on the change in the cfDNA fragmentomic profile. In some embodiments, the change in the cfDNA fragmentomic profile comprises an increase or decrease in cfDNA fragments within a size range. In some embodiments, the methods can detect gene activation or replication based on an increase in ultrashort cfDNA fragments (about 50 bases in length).

[0065] In some embodiments, provided herein are methods of detecting an infection in a subject. In some embodiments, the methods comprise preparing cfDNA fragments for Next Generation Sequencing (NGS). In some embodiments, the methods comprise sequencing the cfDNA fragments associated with a mammalian regulatory element. In some embodiments, the mammalian regulatory elements can regulate an immune response. In some embodiments, theAttorney Docket No. 47697-763601methods comprise preparing a fragmentomic profile of the cfDNA fragments. In some embodiments, the methods comprise comparing the fragmentomic profile with a reference fragmentomic profile. In some embodiments, the methods can detect an immune response in the subject based on the fragmentomic profile of the cfDNA. In some embodiments, detecting an immune response comprises detecting an increase in ultrashort cfDNA fragments compared to the reference fragmentomic profile. In some embodiments, the methods can comprise performing a genomics analysis of the cfDNA in the sample. In some embodiments, the methods can comprise identifying the microbial cfDNA in the sample. In some embodiments, the methods can comprise identifying a candidate microbe associated with the immunological response in the subject.

[0066] As used herein, “copy number” refers to the number of times a particular gene or genomic region is present in the genome of an organism. In some embodiments, “copy number” refers to the number of times a microbial gene or genomic region (an AMR gene) is present in a microbe. As used herein, “copy number” can also refer to “copy number variations (CNVs)” in eukaryotes.

[0067] As used herein, “cell-free nucleic acids (cfNAs)” refers to nucleic acids that exist freely outside of cells in biological samples such as blood, urine, cerebrospinal fluid, and synovial fluid. cfNAs can be free-floating, such as cfDNA fragments in plasma. cfNAs can also be associated with cells but not contained within intact cells, as seen in vesicle-associated cfNA or nucleosome-associated cfNA. cfNAs can arise from various biological processes, including cell death (apoptosis, necrosis), active secretion, or viral shedding.

[0068] As used herein, “cell-free sample” refers to a sample devoid, or almost devoid, of cells. In some instances, the cell-free sample is devoid of any human cells. In some instances, the cell-free sample is devoid of any microbial cells. In some instances, the cell-free sample is devoid of all types of cells, including eukaryotic or prokaryotic cells. The cell-free sample can be obtained from a biological sample provided herein. In some instances, the cell-free sample is a plasma, which is almost devoid of blood cells. In some instances, the cell-free sample can be obtained by the sample preparation process described herein, such as centrifuging a biological sample.

[0069] As used herein, “nucleic acids” or “nucleic acid molecules” refer to molecules composed of nucleotides, comprising DNA (deoxyribonucleic acid), RNA (ribonucleic acid), or DNA / RNA hybrid. The nucleic acids may comprise natural, synthetic, and / or artificial nucleotide bases or nucleotide analogues. In some embodiments, the nucleotide bases or nucleotide analogues comprise naturally occurring or artificial modifications at one or more of a deoxyribose moiety, ribose moiety, phosphate moiety, nucleoside moiety, or a combination thereof. The modification can comprise an H, OR, R, halo, SH, SR, NH2, NHR, NR2, or CN,Attorney Docket No. 47697-763601wherein R is an alkyl moiety. For example, the nucleotide bases or nucleotide analogues can comprise 5 -methylcytosine (5mC), 5 -hydroxymethylcytosine (5hmC), 5 -formylcytosine (5fC), 5 -carboxyl cytosine (5caC), or a derivative thereof, or any combination thereof.Fragmentomics

[0070] Described herein are methods for fragmentomics analysis of unique populations of cfNA (e.g., ultrashort cfDNA; subnucleosomal cfDNA) and deriving insights from the fragmentome patterns in such fragmentome analysis (e.g., fragmentomics or fragmentome profiling). The predominant cfDNA fragment size can reflect DNA wrapped around the nucleosome core (144-147 bp) and its associated linker region (-10-20 bp). cfDNA can display periodicity in multiples of -160 bp (e.g., 320 bp, 480 bp), indicating cleavage patterns around di- or tri-nucleosome structures. In some embodiments, the methods described herein comprise performing fragmentomics of subnucleosomal cfDNA (<120 bp). In some embodiments, the methods may comprise performing fragmentomics of ultrashort cfDNA (-50 bp). In some embodiments, the subnucleosomal or ultrashort cfDNA can comprise double-stranded cfDNA (dd-cfDNA) or single-stranded cfDNA (ss-cfDNA). In some embodiments, the methods utilize mammalian cfDNA (e.g., human cfDNA). In some embodiments, the methods further utilize non-mammalian cfDNA (e.g., microbial cfDNA). In some embodiments, the method further comprises analyzing microbial cell-free DNA (mcfDNA) sequencing reads, wherein the mcfDNA sequencing reads are derived from mcfDNA from the mammalian subject. In some embodiments, the method further comprises preparing a microbial fragmentomic profile of the mcfDNA sequencing reads.

[0071] In some embodiments, the ultrashort cfDNA comprise dd-cfDNA captured by Process 1, as described in Example 1 and depicted in FIG. 1. In some embodiments, the subnucleosomal cfDNA comprise dd-cfDNA and ss-cfDNA captured by Process 2, as described in Example 1 and depicted in FIG. 1.

[0072] In some embodiments, the methods comprise analyzing cell-free nucleic acid (cfNA) that are at most 30 bp, at most 35 bp, at most 40 bp, at most 45 bp, at most 50 bp, at most 55 bp, at most 60 bp, at most 65 bp, at most 70 bp, at most 75 bp, at most 80 bp, at most 85 bp, at most 90 bp, at most 95 bp, at most 100 bp, at most 105 bp, at most 110 bp, at most 115 bp, at most 120 bp, at most 125 bp, at most 130 bp, at most 135 bp, or at most 140 bp in length.

[0073] In some embodiments, performing fragmentomics of cfNA can be used alone in the methods described herein, or in combination with existing technologies to determine a plurality of outcomes. In some embodiments, the methods may infer information from the fragmentomic analysis and inform clinical decisions. In some embodiments, the information inferred from fragmentomics analysis can comprise the presence or absence of a disease or condition, prognosis of a diagnosed disease or condition, therapeutic treatment of a disease or condition, orAttorney Docket No. 47697-763601predicted treatment outcome for a disease or condition. In some embodiments, the disease or condition can comprise any disease or condition described herein, including an infection, inflammation, an autoimmune disorder, a transplant status (e.g., transplant eligibility; graft rejection), immunocompetency, cancer, or fetal abnormality. In some embodiments, the information inferred from fragmentomics analysis can comprise DNA epigenetic modifications (e.g., gene expression or replication). In some embodiments, the methods can further infer from the fragmentomics analysis distribution of cell types, differentiating features of various related diseases, or differentiating features of various stages of a disease.

[0074] In some embodiments, the methods described herein combines the analysis of microbial cell-free DNA (mcfDNA) sequencing reads with human cfDNA fragmentomics, wherein the mcfDNA sequencing reads are derived from mcfDNA from the human subject. In some embodiments, the methods can comprise preparing a microbial fragmentomic profile of the mcfDNA sequencing reads. In some embodiments, mcfDNA fragmentomics and mammalian cfDNA (e.g., human cfDNA) fragmentomics be complementary. In some embodiments, the methods can integrate data from both sources to provide an improved classifier. In some embodiments, the methods can provide a more comprehensive picture of host-microbe interactions, uncover subtle shifts in microbial cfDNA that may reflect disease states (e.g., infections or non-infectious diseases), and assess the host's physiological response (e.g., immune response). In some embodiments, the methods comprise assessing gene expression leveraging both human and microbial cfDNA fragmentomics.

[0075] In some embodiments, performing fragmentomics of the cfNA described herein can comprise generating a fragment length profile for a nucleic acid library derived from the cfNA. In some embodiments, the fragment length profile comprises one or more characteristics selected from the group consisting of: shape of the distribution, segment amplitude, segment fraction, peak shape, number of peaks, position of a maximum of a peak, the fragment count ratio for two or more segments, the height of helical phasing peaks, fragment count ratio at two different fragment lengths, ratio of fragment counts within two different fragment length ranges, the amount of fragments within a segment, the fragment length range within a segment, the ratio of maximum amplitudes for two or more segments, fragment length distribution within a subset of reads, slope within a segment, peak width, the rate of count decay or increase within a segment, number of peaks, and scaling of the count decay or increase within a segment.

[0076] In some embodiments, performing fragmentomics of cfNA can comprise performing a next generation sequencing (NGS) assay on the cfNA, thereby generating sequence reads from the cfNA. In some embodiments, performing fragmentomics of cfNA can comprise performing bioinformatic analysis of the sequencing reads from the NGS assays. In some embodiments,Attorney Docket No. 47697-763601performing bioinformatic analysis can comprise aligning the sequence reads from the cfNA to one or more reference genomes, aligning the sequence reads from the cfNA to various loci of one or more genes, normalization, calculating an abundance of the sequence reads from the cfNA, generating a fragment length profile of the cfNA, compiling the sequence reads from the cfNA into distinct populations based on their sequences (e.g., compiling sequence reads that align to one locus of a gene), or comparing various fragment length profiles of distinct populations of cfNA.

[0077] In some embodiments, performing fragmentomics of cfNA can comprise obtaining a value or a quantitative measurement for the cfNA fragments described herein, thereby obtaining a fragment profile of the cfNA fragments. In some embodiments, the value can comprise any of the characteristics of the cfNA fragment length profile described herein. In some embodiments, the value can comprise a peak of the cfNA fragment length profile. In some embodiments, the value can be a relative value. In some embodiments, in the same sample comprising cfNA, there exists cfNA fragments that may align to various loci of many genes. In some embodiments, the value for cfNA fragments around a locus of a gene can be calculated based on a value of other cfNA fragments in the sample. In some embodiments, the value for cfNA fragments around a locus of a gene can be calculated based on a value of other cfNA fragments around other loci of the same gene.

[0078] In some embodiments, the methods described herein may comprise performing fragmentomics of both human cfNA and microbial cfNA. In some instances, the combination of microbial and human cfNA fragmentomics might perform better than either analysis conducted separately.

[0079] In some embodiments, the methods described herein may comprise aggregating cfNA sequence reads from one or more subjects. In some embodiments, the methods described herein may comprise aggregating cfNA sequence reads from one or more samples. In some embodiments, the methods described herein may comprise aggregating cfNA sequence reads from one or more genes.Regulatory elements

[0080] In some embodiments, the methods describe herein can comprise sequencing and analyzing cfNA associated with regulatory elements. In some embodiments, the cfNA associated with regulatory elements can form one or more DNA-protein complexes. In some embodiments, the DNA fragments in DNA-protein complexes can be protected from nuclease digestion. In some embodiments, the DNA-protein complexes do not comprise a nucleosome. In some embodiments, the DNA-protein complexes can comprise a transcription factor. In some embodiments, the regulatory element can regulate gene expression or replication. In someAttorney Docket No. 47697-763601embodiments, the methods described herein can comprise generating a fragment length profile of sequencing reads from cfNA associated with a regulatory element. In some embodiments, the regulatory element comprises a mammalian regulatory element.

[0081] In some embodiments, the regulatory elements can bind to a DNA-binding protein. In some embodiments, the DNA-binding protein comprises a transcription factor. In some embodiments, the transcription factor can comprise any mammalian transcription factor (e.g., human transcription factor), including but not limited to DNA-binding proteins in the basal transcription machinery, developmental transcription factors, tumor suppressors, oncogenic transcription factors, hormone -responsive transcription factors, immune-related transcription factors, stem cell and pluripotency transcription factors, or metabolic transcription factors. In some embodiments, the transcription factor can comprise TFIID, TFIIA, TFIIB, TFIIF, TFIIH, FOXP2, SOX2, PAX6, HOX transcription factors, GATA3, OTX2, TP53, MYC, NF-kB, API, STAT3, HIFla, ER, AR, GR, PPARy, FOXP3, NRF2, NF AT, IRF3 / IRF7, T-bet, RFX5, OCT4, NANOG, KLF4, c-MYC, CLOCK / BMAL1, SREBP1, FOXO1, CTCF, E2F, SP1, RUNX1, MEF2, CREB, EGR1, RelA / p65, SMAD, or YAP / TAZ.

[0082] In some embodiments, the methods described herein can comprise generating a fragment length profile of the cfNA sequence reads that aligned to a genomic region associated with a regulatory element. Regulatory elements are DNA sequences that control gene expression by interacting with various molecular factors (e.g., proteins or RNA). In some embodiments, the regulatory element can comprise a promoter, a transcription start site (TSS), a transcription factor binding site, an enhancer, a cis-regulatory regulatory element (CRE), a silencer, an insulator, a polyadenylation signal, an untranslated region (UTR), or a splice site. In some embodiments, the enhancer can comprise a distal enhancer (at least 200 bp away from a TSS) or a proximal enhancer (< 200 bp of a TSS).Samples

[0083] Disclosed herein in some embodiments are samples derived from subjects. In some embodiments, a sample provided herein can comprise a nucleic acid molecule to be sequenced by a method described herein. As used herein, a “sample” generally refers to any material comprising nucleic acids that has been derived from a subject described herein. A sample can comprise a raw biological sample, such as whole blood. As used herein, the phrase “raw biological sample” refers to an unmanipulated or unprocessed sample obtained from a subject, e.g., a host, containing or presumed to contain target nucleic acids. In some embodiments, a raw biological sample has not been subjected to any extraction methods after being obtained from a subject. In some embodiments, a raw biological sample can be processed or manipulated to produce an initial sample. For example, a raw biological sample can comprise whole blood whichAttorney Docket No. 47697-763601is centrifuged to produce an initial sample of plasma for a sequencing assay. As used herein, the term “initial sample” refers to a sample comprising nucleic acids derived from a raw biological sample. In some embodiments, an initial sample can comprise a sample that has been processed or manipulated, such as plasma or serum. In some embodiments, an initial sample can comprise target or desired nucleic acids obtained or extracted from a raw biological sample. In some embodiments, an initial sample can be subjected to a sequencing assay as described herein. In some embodiments, a raw biological sample or an initial sample can be used directly in a sequencing assay as described herein without extraction of a nucleic acid. In some embodiments, a nucleic acid as described herein can be extracted from a raw biological sample or an initial sample for use in a sequencing assay as described herein. In some embodiments, an extraction method can comprise an alcohol-based extraction, a column purification, a filtration, a size separation, or any combination thereof. As used herein, “removal” or “extraction,” and their cognates, of nucleic acids refers to steps prior to the start of generating or preparing a nucleic acid library that separate nucleic acids from at least one component with which they are normally associated. In some embodiments, removal or extraction of nucleic acids can refer to the process of creating an initial sample from a raw biological sample. For example, without limitation, the fractionation of whole blood into its component parts, such as plasma, can be considered to involve removal or extraction. Similarly, purification or isolation of DNA from a sample (e.g., plasma sample) can be considered extraction. In some embodiments, a nucleic acid extracted from a sample can be subjected to a sequencing assay as described herein. In some embodiments, a raw biological sample or an initial sample can comprise a biological sample.Biological fluids

[0084] In some embodiments, a sample can comprise a biological sample obtained or collected from a subject. In some embodiments, a biological sample can comprise cells. In some embodiments, a biological sample can be substantially cell-free. In some embodiments, a biological sample can comprise a biological fluid. In some embodiments, a biological fluid can comprise a bodily fluid of a subject (e.g., blood), a fluid obtained from the subject via a medical procedure (e.g., lavage), or any fluid obtained from processing a biopsy of the subject (e.g., serous fluid). In some embodiments, a biological fluid can comprise a bodily fluid. In some embodiments, a bodily fluid can comprise a whole blood, a plasma, a serum, a lymph, a synovial fluid, a cerebrospinal fluid (CSF), a saliva, a gastric juice, a bile, a pancreatic juice, an intestinal fluid, a respiratory tract mucosal secretion, a semen, a cervical mucus, a vaginal secretion, a urine, a sebum, a breast milk, an amniotic fluid, a pericardial fluid, a pleural fluid, a peritoneal fluid, or any combination thereof. In some embodiments, a biological fluid can be processed from a bodily fluid. For example, blood from a subject can be processed to generate a plasmaAttorney Docket No. 47697-763601sample, a serum sample, or a platelet sample. In some embodiments, a biological fluid can comprise a plasma sample. In some embodiments, a biological fluid can comprise a lavage from diagnosing, treating, or cleaning an area of a body of a subject. In some embodiments, a lavage can comprise a bronchoalveolar lavage (BAL), a gastric lavage, a peritoneal lavage, a nasal lavage, a bladder lavage, a rectal lavage, a wound lavage, a joint lavage (arthrocentesis), an eye lavage, a sinus lavage, or any combination thereof. In some embodiments, a biological fluid can comprise an amniotic fluid. In some embodiments, a biological fluid can comprise a BAL. In some embodiments, a biological fluid can comprise a joint lavage. In some embodiments, a biological fluid can comprise a fluid obtained from processing a biopsy of a subject. In some embodiments, a biological fluid can comprise a needle aspiration fluid, a serous fluid, a microdialysis fluid, an exudate fluid, or any combination thereof. As used herein, “plasma” or “blood plasma” refers to the liquid component or fraction of blood. Plasma is generally obtained by spinning a whole blood sample and removing the liquid component.Process control molecules

[0085] As used herein, the phrase “process control molecules” refers to molecules that are added to a sample before or during nucleic acid library generation to aid in the identification or quantification of nucleic acids in a sample. In some embodiments, process control molecules can comprise nucleic acids. In some embodiments, process control molecules can comprise synthetic nucleic acids. In some embodiments, process control molecules can comprise synthetic nucleic acids. In some embodiments, process control molecules can comprise synthetic nucleic acid sequences. In some embodiments, process control molecules can comprise naturally-occurring nucleic acids. In some embodiments, process control molecules can comprise naturally-occurring nucleic acid sequences. In some embodiments, process control molecules are separate from and not integrated in the target molecules. In some embodiments, process control molecules can have special features such as specific sequences, lengths, GC content, degrees of degeneracy, degrees of sequence diversity, different secondary, tertiary, or quaternary structures, and / or known starting concentrations. In some embodiments, process control molecules can be used for normalizing the signal in a sample to account for variations in sample processing or to control process performance. In some embodiments, process control molecules can include whole assay internal control (WINC) molecules. In some embodiments, at least 10,000, at least 15,000, at least 20,000, at least 25,000, at least 30,000, at least 35,000, at least 40,000, at least 45,000, or at least 50,000 unique WINC molecules are spike in the sample. In some embodiments, process control molecules can include sample identifiers. In some embodiments, process control molecules can comprise dephosphorylation control molecules, denaturation control molecules,Attorney Docket No. 47697-763601and / or ligation control molecules. In some embodiments, multiple different types or sets of control molecules can be added to a sample.

[0086] In some embodiments, the process control molecules comprise DNA (deoxyribonucleic acid), RNA (ribonucleic acid), or DNA / RNA hybrid. In some embodiments, the process control molecules comprise natural, synthetic, and / or artificial nucleotide bases or nucleotide analogues. In some embodiments, the nucleotide bases or nucleotide analogues comprise naturally occurring or artificial modifications at one or more of a deoxyribose moiety, ribose moiety, phosphate moiety, nucleoside moiety, or a combination thereof. The modification can comprise an H, OR, R, halo, SH, SR, NH2, NHR, NR2, or CN, wherein R is an alkyl moiety. For example, the nucleotide bases or nucleotide analogues can comprise 5 -methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), 5 -formylcytosine (5fC), 5 -carboxylcytosine (5caC), or a derivative thereof, or any combination thereof.

[0087] In some embodiments, the process control molecules comprise one or more nucleotide bases or nucleotide analogues selected from the group consisting of: 5 -methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), 5 -formylcytosine (5fC), 5 -carboxylcytosine (5caC), Nl-methylpseudouridine, 5-propynyluridine, 5-propynylcytidine, 6- methyladenine, 6-methylguanine, N, N, -dimethyladenine, 2-propyladenine, 2propylguanine, 2-aminoadenine, 1-methylinosine, 3 -methyluridine, 5 -methyluridine, 5- (2- amino) propyl uridine, 5-halocytidine, 5-halouridine, 4-acetylcytidine, 1- methyladenosine, 2-methyladenosine, 3 -methylcytidine, 6-methyluridine, 2- methylguanosine, 7-methylguanosine, 2, 2-dimethylguanosine, 5-methylaminoethyluridine, 5 -methyloxyuridine, deazanucleotides (such as 7-deaza- adenosine, 6-azouridine, 6-azocytidine, or 6-azothymidine), 5-methyl-2-thiouridine, other thio bases (such as 2-thiouridine, 4-thiouridine, and 2-thiocytidine), dihydrouridine, pseudouridine, queuosine, archaeosine, naphthyl and substituted naphthyl groups, any O-and N-alkylated purines and pyrimidines (such as N6-methyladenosine, 5 -methylcarbonylmethyluridine, uridine 5-oxyacetic acid, pyridine-4-one, or pyridine-2-one), phenyl and modified phenyl groups such as aminophenol or 2,4, 6-trimethoxy benzene, modified cytosines that act as G-clamp nucleotides, 8-substituted adenines and guanines, 5-substituted uracils and thymines, azapyrimidines, carboxyhydroxyalkyl nucleotides, carboxyalkylaminoalkyi nucleotides, and alkylcarbonylalkylated nucleotides.

[0088] As used herein, the phrase “adapter attachment control molecule” refers to a control molecule that allows monitoring of the efficiency of an adapter attachment reaction. An adapter attachment reaction can be ligation-based, TdT-based, template-switching-based, primer-extension-based, amplification-based, or a combination thereof.Attorney Docket No. 47697-763601

[0089] As used herein, the phrase “degradation assessment molecules” refers to a control molecule used to evaluate sample and spiked sample integrity during processing.

[0090] As used herein, the phrase “spiked initial sample” refers to an initial sample to which process control molecules (or synthetic spike-ins) have been added prior to the start of generating a sequencing library.

[0091] As used herein, “sequence diversity controls” refers to degenerate pools, or pools of nucleic acids with diverse sequences, which degenerate pools can often be used for diversity assessment, abundance calculation, and / or determination of information transfer efficiency /

[0092] As used herein, “size controls,” “length controls,” “GC Spike-in Panel” or “GC size / length controls” refers to nucleic acids that are size or length or GC-content markers, which can be used for abundance normalization, development, and / or analysis purposes and other purposes.

[0093] As used herein, “ID Spike(s)” refers to identification spikes that can be used, for example without limitation, for sample identification tracking, cross-contamination detection, reagent tracking, and / or reagent lot tracking (See, for example, United States patent 9,976,181).Subjects

[0094] The methods and systems described herein can analyze one or more samples obtained derived from a subject. In some embodiments, the subject can comprise a human or a non-human animal. In some embodiments, a subject can comprise a male or a female. In some embodiments, a subject can be of any age. In some embodiments, a subject can be a child. In some embodiments, a subject can comprise an embryo or a fetus.

[0095] In some embodiments, a subject can comprise a healthy subject. In some embodiments, a subject can have, be suspected of having, or be at risk of having a disease or a disorder. In some embodiments, the subject has an infection by a microbe. In some embodiments, the subject is infected by a pathogenic microbe, a commensal microbe, or a combination thereof. In some embodiments, the subject is infected by a carrier microbe harboring one or more AMR genetic marker provided herein. In some embodiments, the subject has a medical indication related to an infection or an abnormal immune response to an infection. In some embodiments, the subject is wholly or partially immunocompromised or abnormally susceptible to infections. In some embodiments, the subject has a reoccurring infection by a microbe, a secondary infection by another microbe, or a co-infection by two or more microbes. In some embodiments, the subject has a disorder or a disease comprising a cancer, transplantation, a surgery, a bum, an infection, a malnourishment, a chronic kidney disease, diabetes mellitus, an autoimmune disease or disorder, or an immune disorder (e.g., an acquired immunodeficiency syndrome (AIDS)). In some embodiments, a subject can have a medical condition. In some embodiments, a medicalAttorney Docket No. 47697-763601condition can comprise pregnancy, lactation, menopause, frailty, malnutrition, graft or organ rejection, chronic fatigue, or a combination thereof.

[0096] In some embodiments, the subject is taking an immunosuppressant agent. The immunosuppressant agent may comprise chemotherapy, radiation, corticosteroids, transplant medications, or certain biologies.

[0097] In some embodiments, the subject is taking an anti-infective agent provided herein. In some embodiments, the anti-infective agent comprises an antibacterial drug (e.g., an antibiotic), an antiviral drug, an antifungal drug, an antiparasitic drug, or any combination thereof. In some embodiments, the subject is taking one anti- infective agent without improvement of symptoms and needs a different anti-infective regimen. In some embodiments, the subject is taking one anti-infective agent but has developed an infection by the one or more carrier microbes harboring an AMR genetic marker. In some embodiments, the subject has a co-infection of one or more microbes, wherein the one or more microbes comprise one or more carrier microbes harboring an AMR genetic marker.

[0098] In some embodiments, a subject can have or be at an elevated risk for developing an infection. In some embodiments, a subject can have or be at an elevated risk for developing a cancer. In some embodiments, a subject can be eligible as a recipient of transplantation or is an actual recipient of a transplanted organ or graft. In some embodiments, a subject can be an organ donor or preparing to be an organ donor. In some embodiments, a subject can comprise an animal organ donor for use in a xenotransplant or an animal being prepared for organ donation in a xenotransplant.

[0099] The subject can also be referred to as a “host”. As used herein, “host” refers to an organism that harbors another organism or microbe. For example, a living thing e.g., a mammal such as a human being can be a host that harbors a microbe, the microbe being the non-host. As used herein, “host nucleic acids” and all derivative terms such as “host cell-free nucleic acids”, “host cell-free DNA”, etc. refer to nucleic acids derived from the host genome. In some embodiments, a host genome can comprise nucleic acids derived from a nucleus, a mitochondrion, a cytoplasm, an exosome, cell-free nucleic acids derived from any of these, or any combination thereof.

[0100] In some embodiments, the target nucleic acids comprise host nucleic acids. In some embodiments, the host nucleic acids comprise a genetic marker associated with a disease or disorder of the host. In some embodiments, the disease or disorder comprises an infectious disease or a non-communicable disease. For example, the host nucleic acid can comprise a genetic marker associated with a cancer.Attorney Docket No. 47697-763601

[0101] In some embodiments, a subject can comprise a mammal. In some embodiments, the mammal can be a human or an animal. In some embodiments, an animal can comprise a vector for disease transmission from which a sample is being tested to determine a presence or absence of a pathogen in the animal. In some embodiments, a disease vector can comprise an animal that has come into contact with a human subject. In some embodiments, an animal coming into contact with a human subject can comprise an animal biting a human, a human ingesting an animal or a secretion of an animal, or a combination thereof. In some embodiments, an animal can comprise a mammal, a bird, a reptile, an amphibian, a fish, an insect, or an arachnid. In some embodiments, an animal can comprise a research animal, an animal for medical use (e.g., xenotransplant donor), a companion animal, a farm animal, a working animal, a performance animal, or a wild animal. In some embodiments, a mammal can comprise a non-human primate (e.g., a macaque or rhesus monkey), a rodent, a carnivore (e.g., a canine or a feline), a bat, a cetacean (e.g., a dolphin), an ungulate, or an insectivore (e.g., a hedgehog). In some embodiments, an ungulate can comprise a swine, a sheep, a cow, a deer, or a horse.Microbes

[0102] In some embodiments, the methods described herein comprise detecting a microbe or microbial nucleic acids from a microbe. In some embodiments, the microbe comprises a carrier microbe harboring the one or more AMR genetic markers. In some embodiments, the one or more AMR genetic markers are associated with one or more carrier microbes that harbor the one or more AMR genetic markers. In some embodiments, the one or more carrier microbes comprise a virus, a bacterium, a protozoa, a fungus, an archaea, an algae, or any combination thereof. In some embodiments, the one or more carrier microbes comprise a pathogen. In some embodiments, the one or more carrier microbes comprise a commensal microbe.

[0103] In some embodiments, the one or more carrier microbes comprise one or more bacteria. In some embodiments, the one or more bacteria comprise a gram-positive or a gram-negative bacterium. In some embodiments, the one or more bacteria comprise Staphylococcus aureus, S. epidermidis, S. lugdunensis, Enterococcus faecalis, E. faecium, Enterobacter cloacae complex, Escherichia coli, Klebsiella aerogenes, K. pneumoniae, K. oxytoca, Proteus mirabilis, P. vulgaris, Salmonella bongori, S. enterica, Serratia marcescens, Pseudomonas aeruginosa, Acinetobacter baumannii, or A. calcoaceticus. In some embodiments, the one or more carrier microbes comprise a methicillin-resistance carrier. In some embodiments, the methicillin-resistance carrier comprises Staphylococcus pseudintermedius, Staphylococcus fleurettii, Staphylococcus epidermidis, Staphylococcus schleiferi, Staphylococcus lugdunensis, Staphylococcus cohnii, Staphylococcus caprae, Staphylococcus warneri, StaphylococcusAttorney Docket No. 47697-763601saprophyticus, Staphylococcus aureus, Staphylococcus haemolyticus, Staphylococcus capitis, Staphylococcus hominis, Staphylococcus pettenkoferi, or Staphylococcus simulans.

[0104] In some embodiments, the one or more carrier microbes comprise a vancomycin-resistance carrier. In some embodiments, the vancomycin-resistance carrier comprises Enterococcus faecium, Enterococcus raffinosus, Enterococcus casseliflavus, Enterococcus gallinarum, Enterococcus avium, Enterococcus durans, Enterococcus faecalis, or Streptococcus gallolyticus.

[0105] In some embodiments, the one or more carrier microbes comprise a Gram-negative carbapenem or extended-spectrum beta-lactamase (ESBL) resistance carrier. In some embodiments, the Gram-negative carbapenem or extended-spectrum beta-lactamase (ESBL) resistance carrier comprises Enterobacter cloacae complex, Escherichia coli, Klebsiella pneumoniae, Klebsiella oxytoca, Proteus mirabilis, Proteus vulgaris, Salmonella enterica, Salmonella bongori, Serratia marcescens, Enterobacter aerogenes, Pseudomonas aeruginosa, Acinetobacter baumannii, Acinetobacter calcoaceticus, Aeromonas caviae, Aeromonas hydrophila, Citrobacter amalonaticus, Citrobacter freundii, Citrobacter koseri, Pluralibacter gergoviae, Enterobacter hormaechei, Morganella morganii, Pantoea agglomerans, Providencia rettgeri, Providencia stuartii, Shigella flexneri, Shigella sonnei, Serratia liquefaciens, Stenotrophomonas maltophilia, Vibrio cholerae, Kluyvera georgiana, Kluyvera cryocrescens, Kluyvera ascorbata, Klebsiella variicola, Raoultella planticola, Raoultella ornithinolytica, Citrobacter braakii, Kluyvera intermedia, Pantoea sp. PSNIH2, Pantoea sp. PSNIH1, or Clavibacter cf. michiganensis LMG 26808.

[0106] In some embodiments, a confounding microbe comprises a carrier microbe. A confounding microbe can refer to any microbe that can harbor nucleic acids that are homologous to the target nucleic acids (e.g., AMR gene or housekeeping gene). In some embodiments, the confounding microbe is a carrier microbe harboring an AMR cassette or an AMR genetic marker.

[0107] As used herein, a “microbe” can refer to a living microorganism or a non-living microscopic entity. In some embodiments, a living microorganism can comprise a bacterium, a protozoa, a fungus, an archaea, an algae, a parasite, or any other living microorganism. In some embodiments, a non-living microscopic entity can comprise a virus, a live virus, a replicating virus, or an attenuated virus. In some embodiments, non-host nucleic acids can be derived from a microbe. In some embodiments, target nucleic acids can be derived from a plurality of microbes.

[0108] In some embodiments, a microbe can be pathogenic to a subject (e.g., a pathogen), a commensal microbe of a subject, or a microbe present in a general environment. In some embodiments, a pathogen can comprise any pathogenic or virulent microbe. In someAttorney Docket No. 47697-763601embodiments, a commensal microbe of a subject can comprise a microbe that inhabits any location in or on a subject without causing any symptom of a disease or disorder. In some embodiments, a microbe of a general environment can comprise a microbe at or near a sample collection site or a microbe at or near a location of a subject. In some embodiments, a microbe of a general environment comprises a commensal microbe. In some embodiments, a commensal microbe of a subject may become a pathogen to the subject. In some embodiments, a commensal microbe of a first subject may be a pathogen to a second subject. In some embodiments, a microbe of a general environment of a subject may become a pathogen to a subject. In some embodiments, a microbe of a general environment of a first subject may be a pathogen to a second subject.

[0109] In some embodiments, a pathogen can cause an infection or disease comprising gastrointestinal infections (e.g., Escherichia coli, Salmonella spp., Clostridioides difficile), urinary infections (e.g., Escherichia coli), skin infections (e.g., Staphylococcus aureus, including MRS A), strep throat (or scarlet fever, rheumatic fever) (e.g., Streptococcus pyogenes), tuberculosis (e.g., Mycobacterium tuberculosis), gonorrhea (e.g., Neisseria gonorrhoeae), cholera (e.g., Vibrio cholerae), Lyme disease (e.g., Borrelia burgdorferi), ulcers or stomach cancer (e.g., Helicobacter pylori), syphilis (e.g., Treponema pallidum), anthrax (e.g., Bacillus anthracis), seasonal flu (e.g., Influenza viruses), acquired immunodeficiency syndrome (AIDS) (e.g., Human Immunodeficiency Virus (HIV)), liver infections (e.g., Hepatitis B and Hepatitis C viruses), cervical cancer (e.g., Human Papillomavirus (HPV)), respiratory infections (e.g., SARS-CoV-2), oral or genital herpes, (e.g., Herpes Simplex Virus (HSV-1 and HSV-2)), chickenpox or shingles (e.g., Varicella Zoster Virus), measles (e.g., Measles virus), neurological disorders (e.g., Rabies virus, Trypanosoma brucei), dengue fever (e.g., Dengue virus), Ebola disease (e.g., Ebola virus), candidiasis (e.g., Candida albicans), deep lung infections (e.g., Aspergillus spp., Pneumocystis jirovecii), histoplasmosis (e.g., Histoplasma capsulatum), meningitis (e.g., Cryptococcus neoformans), ringworm (e.g., Trichophyton spp.), malaria (e.g., Plasmodium spp.), intestinal infection (e.g., Ascaris lumbricoides), giardiasis (e.g., Giardia lamblia), amebiasis (e.g., Entamoeba histolytica), toxoplasmosis (e.g., Toxoplasma gondii), leishmaniasis (e.g., Leishmania spp.), schistosomiasis (e.g., Schistosoma spp.), strongyloidiasis (e.g., Strongyloides stercoralis), tapeworm, taeniasis, or cysticercosis (e.g., Taenia solium).

[0110] In some embodiments, commensal microbes can inhabit a gastrointestinal tract, a skin, a respiratory tract, a urogenital tract, or an oral cavity of a subject. In some embodiments, commensal microbes can comprise an endogenous virus (e.g., endogenous retroviruses (ERVs)) of a subject. In some embodiments, a commensal microbe can comprise a Bacteroides fragilis, a Lactobacillus acidophilus, a Bifidobacterium bifidum, an Escherichia coli (non-pathogenicAttorney Docket No. 47697-763601strains), a Staphylococcus epidermidis, a Streptococcus salivarius, a Propionibacterium acnes, a Candida albicans (under normal conditions), a Enterococcus faecalis, a Clostridium difficile (non-toxigenic strains), a Rothia mucilaginosa, a Fusobacterium nucleatum, a Peptostreptococcus anaerobius, a Prevotella melaninogenica, or any combination thereof. In some embodiments, a commensal microbe can comprise a Lactobacillus spp., a Bacteroides spp., a Faecalibacterium prausnitzii, an Escherichia coli, a Clostridium spp., an Enterococcus spp., a Staphylococcus spp., a Candida spp., an Aspergillus spp., a Porcine Endogenous Retroviruses (PERVs), an Eimeria spp., a Bifidobacterium spp., a usobacterium spp., a Simian Immunodeficiency Virus (SIV), a Simian Retrovirus (SRV), a Entamoeba spp, or any combination thereof. In some embodiments, a microbe disclosed herein can comprise one or more microbes comprising: Coniosporium, Hantavirus, Talaromyces, Machlomovirus, Betatetravirus, Raoultella, Aeromonas, Ephemerovirus, Empedobacter, Loa, Macluravirus, Stenotrophomonas, Alfamovirus, Rosavirus, Emmonsia, Aggregatibacter, Orthopneumovirus, Weeks ella, Nairovirus, Salivirus, Weissella, Mosavirus, Gammapartitivirus, Strongyloides, Passerivirus, Erysipelatoclostridium, Bacillarnavirus, lotatorquevirus, Taenia, Trypanosoma, Olsenella, Cladosporium, Rhizobium, Prevotella, Leclercia, Paracoccus, liarvirus, Lagovirus, Rasamsonia, Plasmodium, Acremonium, Chlamydia, Clonorchis, Vibrio, Bartonella, Nakazawaea, Franconibacter, Anisakis, Norovirus, Nocardia, Solobacterium, Parechovirus, Avenavirus, Orthohep evirus, Aphthovirus, Hepandensovirus, Microbacterium, Lichtheimia, Lomentospora, Achromobacter, Ipomovirus, Tsukamurella, Elizabethkingia, Hepevirus, Seadornavirus, Alternaria, Trueperella, Gammatorquevirus, Bifidobacterium, Chrysosporium, Thogotovirus, Curtovirus, Deltatorquevirus, Balamuthia, Mastrevirus, Bdellomicrovirus, Mupapillomavirus, Pseudozyma, Wickerhamiella, Aquamavirus, Alloscardovia, Thielavia, Idaeovirus, Henipavirus, Coxiella, Haemophilus, Gammacoronavirus, Negevirus Brevibacterium, Peptoniphilus, Alphacarmotetravirus, Nosema, Trichovirus, Arenavirus, Thermomyces, Necator, Waikavirus, Blosnavirus, Jonesia, Tetraparvovirus, Emaravirus, Plectrovirus, Sclerodamavirus, Toxocara, Umbravirus, Burkholderia, Chromobacterium, Paracoccidioides, Brugia, Eragrovirus, Macrococcus, Absidia, Colletotrichum, Inovirus, Phycomyces, Wickerhamomyces, Acidaminococcus, Moraxella, Rothia, Phlebovirus, Slackia, Purpureocillium, Betapapillomavirus, Tupavirus, Cryspovirus, Saksenaea, Erysipelothrix, Kobuvirus, Mimoreovirus, Echinococcus, Mannheimia, Bergeyella, Cyclospora, Xylanimonas, Leptospira, Finegoldia, Curvularia, Cryptosporidium, Babuvirus, Pecluvirus, Lambdatorquevirus, Pythium, Carlavirus, Entomobimavirus, Kocuria, Anaplasma, Ampelovirus, Avihepatovirus, Nepovirus, Rhodococcus, Bordetella, Mischivirus, Scedosporium, Gardnerella, Maculavirus, Trichoderma, Aveparvovirus, Salmonella, Avastrovirus,Attorney Docket No. 47697-763601Copiparvovirus, Trachipleistophora, Clostridioides, Nanovirus, Siccibacter, Leptotrichia, Citrivirus, Odoribacter, Sanguibacter, Novirhabdovirus, Acremonium, Hafnia, Chaetomium, Tenuivirus, Yokenella, Rubulavirus, Varicellovirus, Alphamesonivirus, Sicinivirus, Leuconostoc, Microvirus, Gallantivirus, Morbillivirus, Lolavirus, Pantoea, Hepatovirus, Nupapillomavirus, Metschnikowia, Bamavirus, Kytococcus, Tritimovirus, Tannerella, Respirovirus, Pneumocystis, Dirofilaria, Pediococcus, Lactococcus, Blastomyces, Dianthovirus, Actinobacillus, Teschovirus, Oscivirus, Begomovirus, Potyvirus, Byssochlamys, Alphacoronavirus, Molluscipoxvirus, Lymphocryptovirus, Sapelovirus, Parabacteroides, Pyrenochaeta, Listeria, Senecavirus, Brevidensovirus, Potexvirus, Parvimonas, Flavivirus, Recovirus, Toxoplasma, Yatapoxvirus, Opisthorchis, Trichuris, Cyphellophora, Morganella, Perhabdovirus, Micrococcus, Pequenovirus, Mastadenovirus, Anaeroglobus, Tropheryma, Dolosigranulum, Wolbachia, Lelliottia, Mycoplasma Tobravirus, Shewanella, Paeniclostridium, Erythroparvovirus, Sutterella, Sporopachydermia, Namavirus, Nyavirus, Francisella, Arthroderma, Epsilontorquevirus, Sigmavirus, Amdoparvovirus, Actinomyces, Alphapermutotetravirus, Cardiobacterium, Influenzavirus C, Orthopoxvirus, Poacevirus, Phialophora, Lactobacillus, Polyomavirus, Debaryomyces, Foveavirus, Bymovirus, Mycoflexivirus, Grimontia, Mucor, Rhytidhysteron, Quadrivirus, Thermoascus, Aureusvirus, Trichosporon, Myceliophthora, Dermacoccus, Dysgonomonas, Pseudoramibacter, Becurtovirus, Gordonia, Sapovirus, Orthobunyavirus, Spiromicrovirus, Pomovirus, Exophiala, Sneathia, Helicobacter, Photorhabdus, Mogibacterium, Betapartitivirus, Avibirnavirus, Ambidensovirus, Oleavirus, Orientia, Deltacoronavirus, Anulavirus, Trichomonasvirus, Budvicia, Geotrichum, Enamovirus, Lachnoclostridium, Schistosoma, Paecilomyces, Panicovirus, Rhizoctonia, Brevibacillus, Beauveria, Pestivirus, Tombusvirus, Cilevirus, Cokeromyces, Peptostreptococcus, Phanerochaete, Proteus, Idnoreovirus, Aspergillus, Pasteurella, Malassezia, Hanseniaspora, Endornavirus, Azospirillum, Velarivirus, Cystovirus, Avisivirus, Bacteroides, Picobirnavirus, Myroides, Circovirus, Arterivirus, Aquaparamyxovirus, Onchocerca, Cosavirus, Kluyveromyces, Fijivirus, Candida, Hepacivirus, Dermabacter, Ourmiavirus, Allexivirus, Enterobacter, Acidovorax, Bracorhabdovirus, Carmovirus, Pluralibacter, Coltivirus, Fonsecaea, Streptobacillus, Corynebacterium, Macrophomina, Marburgvirus, Comovirus, Fabavirus, Alphanodavirus, Cellulomonas, Enterobius, Catabacter, Moellerella, Nakaseomyces, Cucumovirus, Valsa, Deltapartitivirus, Plesiomonas, Pseudomonas, Torovirus, Cuevavirus, Hypovirus, Trichomonas, Influenzavirus D, Giardiavirus, Crinivirus, Tepovirus, Sakobuvirus, Cyberlindnera, Paenalcaligenes, Baflnivirus, Rymovirus, Pegivirus, Yarrowia, Treponema, Borreliella, Rubivirus, Aureobasidium, Angiostrongylus, Filobasidium, Photobacterium, Rhizopus, Orthoreovirus, Ustilago, Simplexvirus, Aquareovirus,Attorney Docket No. 47697-763601Protoparvovirus, Propionibacterium, Sprivivirus, Iluiivirus, Apophysomyces, Meyerozyma, Alphapapillomavirus, Candida, Brucella, Gallivirus, Dinovernavirus, Anaerobiospirillum, Eubacterium, Tatlockia, Terrisporobacter, Quaranjavirus, Sobemovirus, Dicipivirus, Arcanobacterium, Macanavirus, Atopobium, Vesivirus, Lodderomyces, Dinornavirus, Betatorquevirus, Kerstersia, Aparavirus, Neisseria, Agrobacterium, Edwardsiella, Labyrnavirus, Totivirus, Actinomadura, Tobamovirus, Influenzavirus B, Mandarivirus, Anaerococcus, Kunsagivirus, Naegleria, Campylobacter, Veillonella, Yamadazyma, Filobasidiella, Oerskovia, Penicillium, Anncaliia, Leptosphaeria, Pneumovirus, Psychrobacter, Isavirus, Granulicatella, Torradovirus, Cladophialophora, Influenzavirus A, Ophiostoma, Aerococcus, Ureaplasma, Etatorquevirus, Bocaparvovirus, Megasphaera, Reptarenavirus, Comamonas, Capnocytophaga, Alphatorquevirus, Syncephalastrum, Wallemia, Betacoronavirus, Hyphopichia, Nocardiopsis, Legionella, Trichinella, Paraburkholderia, Mammarenavirus, Echinostoma, Sphingobacterium, Enterovirus, Methanobrevibacter, Ochroconis, Cheravirus, Pasivirus, Enterococcus, Mycoreovirus, Tospovirus, Betanodavirus, Phytoreovirus, Enterocytozoon, Ferlavirus, StemphyliumFilif actor, Leishmaniavirus, Gemella, Bromovirus, Alloiococcus, Cunninghamella, Cronobacter, Oribacterium, Orbivirus, Chrysovirus, Cripavirus, Tatumella, Pandoraea, Ogataea, Dracunculus, Volvariella, flavirus, Benyvirus, Rhadinovirus, Histoplasma, Rahnella, Morococcus, Verticillium, Janibacter, Gyrovirus, Alphapartitivirus, Mycobacterium, Roseomonas, Varicosavirus, Chryseobacterium, Parapoxvirus, Rhizomucor, Aureimonas, Levivirus, Leishmania, Luteovirus, Cypovirus, Ochrobactrum, Microsporum, Piscihepevirus, Ceratocystis, Sporothrix, Vesiculovirus, Cupriavidus, Cryptococcus, Metapneumovirus, Alphanecrovirus, Eikenella, Brevundimonas, Escherichia, Leifsonia, Schizophyllum, Granulibacter, Gordonibacter, Lachancea, Madurella, Ophiovirus, Phellinus, Nebovirus, Acanthamoeba, Fusobacterium, Pichia, Verruconis, Ehrlichia, Tibrovirus, Higrevirus, Wohlfahrtiimonas, Rhinocladiella, Neorickettsia, Sadwavirus, Roseobacter, Sequivirus, Pannonibacter, Rotavirus, Turicella, Cardiovirus, Propionimicrobium, Furovirus, Naumovozyma, Closterovirus, Fluoribacter, Zeavirus, Clavispora, Megrivirus, Gammapapillomavirus, Rickettsia, Polemovirus, Corynespora, Encephalitozoon, Shimwellia, Fusarium, Yersinia, Capronia, Delftia, Victorivirus, Maraflvirus, Kluyvera, Iteradensovirus, Isoptericola, Vitivirus, Roseolovirus, Conidiobolus, Abiotrophia, Babesia, Phoma, Sanguibacteroides, Staphylococcus, Rhodotorula, Zetatorquevirus, Hymenolepis, Fasciola, Cytorhabdovirus, Cardoreovirus, Memnoniella, Trichophyton, Mitovirus, Phaeoacremonium, Providencia, Lysinibacillus, Giardia, Oligella, Streptomyces, Paraclostridium, Ralstonia, Coccidioides, Brambyvirus, Biatriospora, Allolevivirus, Acinetobacter, Starmerella, Omegatetravirus, Porphyromonas, Avulavirus, Streptococcus, Arcobacter, Topocuvirus,Attorney Docket No. 47697-763601Mamastr ovirus, Ancylostoma, Bomavirus, Capillovirus, Alphavirus, Tymovirus, Nucleorhabdovirus, Diaporthe, Chlamydiamicr ovirus, Tumcurtovirus, Saccharomyces, Riemerella, Betanecr ovirus, Clostridium, Mobiluncus, Cercospora, Mamavirus, Mortierella, Aquabimavirus, Xanthomonas, Dependoparvovirus, Ebolavirus, Neofusicoccum, Borrelia, Leminorella, Klebsiella, Blastocystis, Alcaligenes, Citrobacter, Eggerthella, Cedecea, Serratia, Penstyldensovirus, Bacillus, Laribacter, Wuchereria, Hordeivirus, Cytomegalovirus, Actinomucor, Ascaris, Shigella, Vittaforma, Torulaspora, Kingella, Oryzavirus, Polerovirus, Tremovirus, Erbovirus, Entamoeba, Lyssavirus, Paenibacillus, Facklamia, Kappatorquevirus, Metarhizium, Stachybotrys, Okavirus, Botrexvirus, Thetatorquevirus, or Basidiobolus. In some embodiments, a microbe disclosed herein can comprise one or more microbes disclosed in the Drawings or the Examples.Cell-free Nucleic acids

[0111] In some embodiments, the nucleic acids described herein comprise cell-free nucleic acids (cfNAs). Cell-free nucleic acids are generally fragments of nucleic acids that float freely outside of cells in any body fluid of a subject. In some embodiments, cfNA can comprise plasma cfNA, serum cfNA, whole-blood cfNA, cerebrospinal fluid (CSF) cfNA, saliva cfNA, bronchoalveolar lavage (BAL) cfNA, urine cfNA, amniotic cfNA, fetal cfNA, synovial fluid cfNA, lymphatic cfNA, or any combination thereof. In some cases, cfNA comprise circulating cfNA in a subject’s bloodstream. In some embodiments, the nucleic acids comprise circulating cfDNA, circulating cfRNA, cfDNA, cfRNA, circulating DNA, circulating RNA, or any combination thereof. In some embodiments, a sample of a body fluid can comprise a cfNA.

[0112] The cfNA described herein are, in some embodiments, nucleic acids that, when floating within a body fluid of a subject, are not encapsulated by a cell. In some embodiments, a cfNA comprises nucleic acids that are not encapsulated by a human cell, not encapsulated by a microbial cell, or not encompassed by either a human cell or a microbial cell. The cfNAs can be free-floating, such as cfDNA fragments in plasma. In some embodiments, cfNA includes vesicle-associated cfNA, such as cfNA associated with exosomes, extracellular vesicles, microvesicles, apoptotic bodies, or any combination thereof. In some embodiments, cfNA do not comprise vesicle-associated cfNA, such as cfNA associated with exosomes, extracellular vesicles, microvesicles, apoptotic bodies, or any combination thereof. cfNAs can also be associated with proteins or other cellular constituents, outside of an intact cell. For example, cfNA can comprise free-floating nucleosome-associated cfNA. cfNAs can arise from various biological processes, including cell death (apoptosis, necrosis) or active secretion.

[0113] In some embodiments, cfNA can comprise viral nucleic acids that are not encapsulated by a capsid, that are fragmented, or a combination thereof. Of note, the methods provided hereinAttorney Docket No. 47697-763601can, in some embodiments, be practiced using viral nucleic acids derived from whole viruses floating in a bodily fluid.

[0114] In some cases, the removal of intact cells may use a technique targeting a specific cell type. For example, centrifugation at a relative low RPM can remove human or mammalian cells. In some cases, centrifugation at a higher RPM can be used to remove microbial cells such as bacterial cells. In some cases, centrifugation can be performed at an even higher speed (e.g, via ultracentrifugation) in order to remove viral particles. In some cases, a faster spin can be performed after an initial removal (e.g., by centrifugation) of mammalian cells. In some embodiments, cfNA can be alternatively referred to as free-circulating nucleic acids. In some embodiments, a cfNA can originate from cell death and other processes that release fragments of nucleic acids into a bloodstream or other biological fluid. In some embodiments, a cfNA can be derived from any source of nucleic acids provided herein. In some embodiments, cfNA present in a raw biological sample can be isolated from genomic nucleic acid in the raw biological sample by processing the raw biological sample into an initial sample by removing intact cells. In some embodiments, removing intact cells can comprise centrifuging or filtering a raw biological sample to produce a cell-free fraction of a biological fluid comprising cfNA.

[0115] A sample can comprise non-host nucleic acids. In some embodiments, non-host nucleic acids can comprise microbial nucleic acids. In some embodiments, microbial nucleic acids can comprise microbial cell-free nucleic acid (mcfNA). In some embodiments, the phrase “target nucleic acids” as used herein can refer to target cfNA. In some embodiments, the phrase “target nucleic acids” as used herein can refer to target mcfNA. In some embodiments, an mcfNA can be derived from one or more kingdoms, phyla, classes, orders, families, genera, species, and / or strain of microbe. In some embodiments, a mcfNA can be derived from a prokaryotic or a eukaryotic microbe. In some embodiments, an mcfNA can comprise a bacterial cfNA, a fungal cfNA, a viral cfNA, a protozoan cfNA, an archaeal cfNA, an algal cfNA, or any combination thereof. In some embodiments, a sample can comprise a non-microbial nucleic acid (e.g., a non-microbial cell-free nucleic acid). In some embodiments, a sample can comprise mcfNAs from one or more species of microbes. In some embodiments, a sample can comprise mcfNAs from at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, or at least 30 species of microbes.

[0116] In some embodiments, a sample can comprise a mixture of nucleic acids. In some embodiments, a sample can comprise target nucleic acids (e.g., target cfNAs) and / or non-target nucleic acids. In some embodiments, the cfNAs comprise cfNA derived from a subject orAttorney Docket No. 47697-763601microbial genome. In some cases, the cfNAs are derived from a housekeeping gene, e.g., a human or mammalian housekeeping gene, a microbial housekeeping gene, a microbe-specific housekeeping gene, and / or a microbe non-specific housekeeping gene.

[0117] In some embodiments, a sample can further comprise contaminant nucleic acids. In some embodiments, contaminant nucleic acids can comprise nucleic acids from a general environment (e.g., a sample collection site). In some embodiments, contaminant nucleic acids are introduced during any step of sample processing. In some embodiments, the contaminant nucleic acids comprise contaminating microbial nucleic acids, contaminating nucleic acids from a different sample, contaminating host nucleic acids, or any combination thereof.

[0118] In some embodiments, cell-free nucleic acids (cfNAs) can comprise a mixture of cfNAs. In some embodiments, a mixture of cfNAs can comprise cfNAs originated from one or more organisms. In some embodiments, a mixture of cfNAs can comprise microbial nucleic acids (e.g., mcfNAs) originated from one or more species of microbes. In some embodiments, an mcfNA can comprise a bacterial-derived cfNA, a fungal-derived cfNA, a viral-derived cfNA, a protozoan-derived cfNA, an archaeal-derived cfNA, an algal-derived cfNA, or any combination thereof.

[0119] In some embodiments, a cfNA can comprise a double-stranded nucleic acid (dsNA), a single-stranded nucleic acid (ssNA), nicked double-stranded, or a combination thereof. In some embodiments, a cfNA can comprise a cell-free DNA (cfDNA), a cell-free RNA (cfRNA), a cell-free DNA-RNA hybrid (cfDNA-RNA), or a combination thereof. In some embodiments, hcfNA can comprise host cell-free DNA (hcfDNA), host cell-free RNA (hcfRNA), host cell-free DNA-RNA hybrid (hcfDNA-RNA), or a combination thereof. In some embodiments, mcfNA can comprise microbial cell-free DNA (mcfDNA), microbial cell-free RNA (mcfRNA), microbial cell-free DNA-RNA hybrid (mcfDNA-RNA), or any combination thereof.

[0120] In some embodiments, the cfNA comprises a modified nucleotide base or a nucleotide analogue. In some embodiments, the nucleotide base or nucleotide analogue comprises modifications at one or more of a deoxyribose moiety, ribose moiety, phosphate moiety, nucleoside moiety, or a combination thereof. The modification can comprise an H, OR, R, halo, SH, SR, NH2, NHR, NR2, or CN, wherein R is an alkyl moiety. For example, the cfNA can comprise 5 -methylcytosine (5mC), 5 -hydroxymethylcytosine (5hmC), 5 -formylcytosine (5fC), 5 -carboxyl cytosine (5caC), or a derivative thereof, or any combination thereof.

[0121] As used herein, a cell-free samples is generally a sample devoid, or almost devoid, of cells. In some instances, the cell-free sample is devoid of human cells (e.g., intact human cells). In some instances, the cell-free sample is devoid of microbial cells (e.g., intact microbial cells). In some instances, the cell-free sample is devoid of microbial and human cells. In some instances,Attorney Docket No. 47697-763601the cell-free sample is devoid of a particular type of microbial cell or virus, while comprising a different type of microbial cell or virus. For example, the cell-free sample can, in some embodiments, be devoid of intact bacterial cells while containing intact viruses. In some instances, the cell-free sample is devoid of all types of microbes, including eukaryotic or prokaryotic cells. The cell-free sample can be obtained from a biological sample provided herein. In some instances, the cell-free sample is a plasma, which has been processed in order to remove blood cells, subject cells, and / or intact microbes, or fragments thereof. In some instances, the cell-free sample can be obtained by a sample preparation process, such as centrifuging, ultracentrifuging, or filtering a biological sample.

[0122] In some embodiments, a cfNA comprises a host nucleic acids, a non-host nucleic acid, a target nucleic acid, or a combination thereof. In some embodiments, a cfNA can be derived from a host (host cell free nucleic acids or “hcfNA”) or a non-host. In some embodiments, a hcfNA can be derived from nuclear nucleic acids, mitochondria nucleic acids, exosomal nucleic acids, fetal nucleic acids, or any combination thereof. In some embodiments, a sample can comprise a host nucleic acid (e.g., a host cell-free nucleic acid). In some embodiments, a host is any subject provided herein.

[0123] In some embodiments, the nucleic acids from a sample can be extracted and / or enriched to generate enriched nucleic acids. In some embodiments, the enriched nucleic acids comprise degraded nucleic acids, ultra-short nucleic acids, single stranded nucleic acids, double stranded nucleic acids, nicked nucleic acids, microbial cell-free nucleic acids (mcfNA), subject’s cell-free nucleic acids, circulating tumor nucleic acids (cfNA), mitochondrial nucleic acids (mtNA), or any combination thereof. In some embodiments, the methods comprise enriching for degraded nucleic acids, ultra-short nucleic acids, single stranded nucleic acids, or nicked double stranded nucleic acids. As used herein, “degraded nucleic acid” refers to fragments of DNA and RNA that are released into circulation due to cell death, turnover, or pathological processes. Degraded nucleic acids can originate from various sources, including natural sources or artificial sources. In some embodiments, the degraded nucleic acids originate from normal cellular apoptosis, necrosis, or disease-related processes such as cancer or infections. In some embodiments, the degraded nucleic acids originate from factors during laboratory handling, including, but not limited to, sample degradation due to prolonged storage, or temperature fluctuations. In some embodiments, ultrashort nucleic acids comprise nucleic acids less than 100 bp, less than 90 bp, less than 80 bp, less than 70 bp, less than 60 bp, less than 50 bp, less than 40 bp, or less than 30 bp in length. In some embodiments, at least 70%, 75%, 80%, 85%, 90%, or 95% of the mcfNA are degraded nucleic acids.Attorney Docket No. 47697-763601

[0124] In some embodiments, a cfNA can comprise any nucleic acid that is not encapsulated by a cell (e.g., a eukaryotic or microbial cell). In some embodiments, a cfNA can originate from any nucleic acids. In some embodiments, a cfNA can comprise a plurality of chemical forms of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or DNA / RNA hybrid. In some embodiments, nucleic acids can comprise a plurality of structural forms of DNA, RNA, or DNA / RNA hybrid. In some embodiments, a cfNA can comprise linear nucleic acids or circular nucleic acids. In some embodiments, a cfNA can comprise single stranded nucleic acids (ssNA), double strand nucleic acids (dsNA) or hybrid nucleic acids. In some embodiments, nucleic acids can be from a genome of an organism or an organelle of a cell (e.g., an exosome or a mitochondria). In some embodiments, a cfNA can comprise a mitochondrial DNA, an intercellular signal nucleic acid, an exogenous nucleic acid, a DNA enzyme, a RNA enzyme, a food-derived nucleic acid, any metabolic form of nucleic acid-based therapeutic, or any combination thereof. In some embodiments, a cfNA can be derived from a member selected from the group consisting of genomic DNA, cDNA, mRNA, cRNA, tRNA, ribosomal RNA, miRNA, siRNA, nuclear DNA, nuclear RNA, mitochondrial DNA, mitochondrial RNA, exosomal DNA, exosomal RNA, fetal DNA, fetal RNA, plasmids, vectors, and any combination thereof.

[0125] In some embodiments, nucleic acids can comprise a mixture of nucleic acids from various sources. In some embodiments, nucleic acids can be derived from a plurality of biological fluids. In some embodiments, nucleic acids can be from a plurality of organisms. In some embodiments, nucleic acids can be from a subject. In some embodiments, nucleic acids can be from one or more species of microbes. In some embodiments, nucleic acids can comprise environmental nucleic acids. In some embodiments, environmental nucleic acids can comprise any nucleic acid at or near a sample collection site, or any nucleic acid introduced by personnel, equipment or a reagent used in collecting and / or processing a sample from a subject.Sizes of cfNAs

[0126] In some embodiments, cfNAs or fragments thereof are less than about 10 bases, less than about 15 bases, less than about 20 bases, less than about 25 bases, less than about 30 bases, less than about 35 bases, less than about 40 bases, less than about 45 bases, less than about 50 bases, less than about 55 bases, less than about 60 bases, less than about 65 bases, less than about 70 bases, less than about 75 bases, less than about 80 bases, less than about 85 bases, less than about 90 bases, less than about 95 bases, less than about 100 bases, less than about 105 bases, less than about 110 bases, less than about 115 bases, less than about 120 bases, less than about 125 bases, less than about 130 bases, less than about 135 bases, less than about 140 bases, less than about 145 bases, less than about 150 bases, less than about 155 bases, less than about 160 bases, less than about 165 bases, less than about 170 bases, less than about 175 bases, less thanAttorney Docket No. 47697-763601about 180 bases, less than about 185 bases, less than about 190 bases, less than about 195 bases, or less than about 200 bases long. In some embodiments, the cfNAs are less than about 55 bases long. In some embodiments, the cfNA is less than about 60 bases long. In some embodiments, the cfNA is less than about 80 bases long.

[0127] In some embodiments, cfNAs are about 10 bases, about 15 bases, about 20 bases, about 25 bases, about 30 bases, about 35 bases, about 40 bases, about 45 bases, about 50 bases, about 55 bases, about 60 bases, about 65 bases, about 70 bases, about 75 bases, about 80 bases, about 85 bases, about 90 bases, about 95 bases, about 100 bases, about 105 bases, about 110 bases, about 115 bases, about 120 bases, about 125 bases, about 130 bases, about 135 bases, about 140 bases, about 145 bases, about 150 bases, about 155 bases, about 160 bases, about 165 bases, about 170 bases, about 175 bases, about 180 bases, bout 185 bases, about 190 bases, about 195 bases, or about 200 bases long.

[0128] In some embodiments, the cfNAs comprise ultra short cfNAs. As used herein, “ultra short nucleic acids” refer to subnucleosomal nucleic acids or fragments shorter than subnucleosomal nucleic acids. In some embodiments, ultra short cfNAs can be from about 10 bases to about 100 bases long. In some embodiments, the ultra short cfNAs can be from about 30 bases to about 80 bases long. In some embodiments, the ultra short cfNAs can be from about 40 bases to about 60 bases long. In some embodiments, the ultra short cfNAs are about 50 bases long.

[0129] In some embodiments, microbial cfNA (mcfNA) can be present at higher concentrations relative to host cfNA (hcfNA) at lengths that fall outside a nucleosomal interval. In some embodiments, mcfNA can be enriched relative to hcfNA by enriching for cfNA of less than 180 bases, less than 170 bases, less than 160 bases, less than 150 bases, less than 140 bases, less than 130 bases, less than 120 bases, less than 110 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, less than 30 bases, or less than 20 bases. In some embodiments, ultra short hcfNA can be enriched by enriching for cfNA of less than 180 bases, less than 170 bases, less than 160 bases, less than 150 bases, less than 140 bases, less than 130 bases, less than 120 bases, less than 110 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, less than 30 bases, or less than 20 bases.Sample preparation methods

[0130] In some embodiments, a sample comprising nucleic acids can be prepared prior to a sequencing assay. In some embodiments, a raw biological sample comprising whole blood can be processed by centrifugation to generate an initial sample of plasma.Attorney Docket No. 47697-763601

[0131] In some embodiments, whole blood can be collected in a K2-EDTA tube. In some embodiments, whole blood draws are not pooled. In some embodiments, a tube can be gently inverted multiple times after draw. In some embodiments, a tube can be centrifuged for about 1200 RCF (g), about 1400 RCF (g), about 1600 RCF (g), or more after draw in order to separate plasma from the blood. The centrifugation can occur at ambient temperature. In some cases, the centrifugation occurs for greater than 5 minutes, 7 minutes, 10 minutes, 15 minutes or 20 minutes. In some embodiments, for tubes containing less than 4 mL a tube manufacturer’s instruction and centrifugation speed and time can be used. In some embodiments, the plasma fraction can be transferred into a new tube. In some embodiments, the plasma is subjected to centrifugation a second time to remove residual cells (e.g., mammalian cells and microbial cells). The additional centrifugation can be conducted at, e.g., about 1400 RCF (g), about 1600 RCF (g), about 1800 RCF (g), about 2000 RCF (g), or more.

[0132] In some embodiments, at least 0.4 ml, at least 0.5 ml, at least 0.7 ml, or at least 1.0 ml of plasma can be transferred into a sterile polypropylene tube, with care taken to not disturb a huffy coat when transferring. In some embodiments, a tube can be labelled with a patient’s first and last name, a unique identifier (DOB or MRN), and / or a date and time of specimen collection. In some embodiments, if a specimen is unlikely to reach a testing facility within 96 hours of collection it can be frozen directly in K2-EDTA after centrifugation. In some embodiments, if a gel plug does not rise to separate cells from plasma, then a tube can be re-centrifuged at a higher speed. In some embodiments, a specimen tube can be shipped to a testing facility.

[0133] In some embodiments, the methods comprise analyzing a cell- free sample. In some embodiments, the methods comprise performing a sequencing assay on the cell-free sample. In some embodiments, the methods comprise preparing a cell-free sample from a biological sample for the sequencing assay. In some instances, the methods comprise centrifuging a biological sample to generate a cell-free sample. In some embodiments, the cell free sample is plasma. In some instances, the methods comprise centrifuging a biological sample to generate a cell-free sample devoid of or almost devoid of human cells. In some instances, the methods comprise centrifuging a biological sample to generate a cell-free sample devoid of or almost devoid of non-human cells, such as a microbe. In some embodiments, the methods described herein comprise processing the sample comprising nucleic acids to maximize collection of target nucleic acids. In some embodiments, the methods can enrich for or maximize the collection of degraded nucleic acids, ultra-short nucleic acids, single stranded nucleic acids, double stranded nucleic acids, nicked nucleic acids, or rare nucleic acids.

[0134] In some embodiments, a nucleic acid can be extracted from a sample. In some embodiments, an extraction can comprise separating nucleic acids from other cellularAttorney Docket No. 47697-763601components and contaminants that can be present in a sample. In some embodiments, a nucleic acid can be extracted from a sample using a liquid extraction (e.g., a Trizol®, a DNAzol™) technique. In some embodiments, an extraction can be performed by phenol chloroform extraction or precipitation by organic solvents (e.g., ethanol, or isopropanol). In some embodiments, an extraction can be performed using a nucleic acid-binding column, a nucleic acid-binding spin column, or a combination thereof. In some cases, an extraction of a cell-free nucleic acid can involve filtration or ultra-filtration. In some embodiments, a nucleic acid can be extracted or purified by use of magnetic beads that bind nucleic acids. In some embodiments, compositions of a binding buffer can be adjusted to control a strength of bonds between functional groups and a nucleic acid, allowing for controlled and reversible binding. In some embodiments, a nucleic acid can be released from a magnetic particle with an elution buffer.

[0135] In some embodiments, the methods described herein comprise enriching a population of cfNA. In some embodiments, enriching a population of cfNA comprises bioinformatically enriching or physically enriching. In some embodiments, enriching a population of cfNA comprises isolating, extracting, or selectively amplifying a desired population of cfNA (e.g., target cfNA) from the initial sample. In some embodiments, enriching a population of cfNA does not comprise isolating or extracting the desired population of cfNA from the initial sample. In some embodiments, enriching a population of cfNA does not comprise amplifying the desired population of cfNA. In some embodiments, enriching a population of cfNA comprises removing the undesired cfNA population (e.g., non-target cfNA or contaminants) from the initial sample. In some embodiments, enriching cfNA can comprise differentiating and / or selecting the cfNA by one or more characteristics comprising size, sequence, GC content, secondary structure, biological source, or protein-binding.

[0136] In some embodiments, the methods comprise enriching microbial cfNA (mcfNA) in the biological sample. In some embodiments, the methods provided herein comprise enriching for at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% of the mcfNA in the biological sample. In some embodiments, enriching mcfNA comprises enriching cfNA that are less than about 20 bp, 30 bp, 40 bp, 50 bp, 60 bp, 70 bp, 80 bp, 90 bp, 100 bp, 110 bp, 120 bp, 130 bp, 140 bp, 150 bp, 160 bp, 170 bp, 180 bp, 190 bp, 200 bp, 210 bp, 220 bp, 230 bp, 240 bp, or 250 bp in length. In some embodiments, enriching mcfNA comprises amplifying the nucleic acids in the initial sample with primers containing non-human nucleic acid sequences. In some embodiments, enriching mcfNA comprises removing non-microbial cfNA. In some embodiments, enriching mcfNA comprises removing nucleosome-bound cfNA. In some embodiments, the methods comprise enriching non-microbial cfNA (e.g., host cfNA) in theAttorney Docket No. 47697-763601biological sample. In some embodiments, the methods comprise enriching mammalian cfNA in the biological sample.

[0137] In some embodiments, the methods comprise enriching cfNA by size selection. In some embodiments, size selection can comprise removing nucleic acids not in the desired size range. In some embodiments, the desired size range comprises an artificial or engineered threshold. In some embodiments, size selection comprises separating nucleic acids by size via chromatography (e.g., size-exclusion chromatography), electrophoresis (e.g., gel or capillary electrophoresis), centrifugation (e.g., density-gradient centrifugation), filtration (e.g., membrane ultrafiltration), magnetic bead-based methods (e.g., SPRI beads), affinity-based methods (e.g., streptavidin beads), or any combination thereof.

[0138] In some embodiments, the methods described herein can comprise discriminating or differentiating cfNA by size and / or selectively isolating or extracting the cfNA of the desired size range. In some embodiments, the methods comprise differentiating microbial cfNA from the non-microbial cfNA (e.g., host cfNA) in the sample and selectively removing the non-microbial cfNA in the sample by size. In some embodiments, the non-microbial cfNA comprises mammalian cfNA (e.g., human or animal cfNA).

[0139] In some embodiments, the methods can comprise selectively removing nucleic acid fragments greater than 500 bp, about 450 bp, about 400 bp, about 350 bp, about 300 bp, about 250 bp, about 200 bp, about 150 bp, about 140 bp, about 130 bp, about 120 bp, about 110 bp, about 100 bp, about 90 bp, about 80 bp, about 70 bp, or about 60 bp in length. In some embodiments, the methods can comprise selectively enriching nucleic acid fragments at most 20 bp, about 30 bp, about 40 bp, about 50 bp, about 60 bp, about 70 bp, about 80 bp, about 90 bp, about 100 bp, about 110 bp, about 120 bp, about 130 bp, about 140 bp, about 150 bp, about 160 bp, about 170 bp, about 180 bp, about 190 bp, about 200 bp, about 210 bp, about 220 bp, about 230 bp, about 240 bp, or about 250 bp in length.

[0140] In some embodiments, the methods can comprise selectively enriching nucleic acid fragments of about 10 bp to about 20 bp, about 10 bp to about 30 bp, about 10 bp to about 40 bp, about 10 bp to about 50 bp, about 10 bp to about 60 bp, about 10 bp to about 70 bp, about 10 bp to about 80 bp, about 10 bp to about 90 bp, about 10 bp to about 100 bp, about 10 bp to about 110 bp, about 10 bp to about 120 bp, about 10 bp to about 130 bp, about 10 bp to about 140 bp, about 10 bp to about 150 bp, about 10 bp to about 160 bp, about 10 bp to about 170 bp, about 10 bp to about 180 bp, about 10 bp to about 190 bp, about 10 bp to about 200 bp, about 10 bp to about 210 bp, about 10 bp to about 220 bp, about 10 bp to about 230 bp, about 10 bp to about 240 bp, or about 10 bp to about 250 bp in length. In some embodiments, the methods can comprise selectively enriching nucleic acid fragments of about 20 bp to about 250 bp, about 20Attorney Docket No. 47697-763601bp to about 200 bp, about 20 bp to about 150 bp, about 20 bp to about 100 bp, about 20 bp to about 90 bp, about 20 bp to about 80 bp, about 20 bp to about 70 bp, about 20 bp to about 60 bp, about 20 bp to about 50 bp, about 30 bp to about 250 bp, about 30 bp to about 200 bp, about 30 bp to about 150 bp, about 30 bp to about 100 bp, about 30 bp to about 90 bp, about 30 bp to about 80 bp, about 30 bp to about 70 bp, about 30 bp to about 60 bp, about 30 bp to about 50 bp, about 40 bp to about 250 bp, about 40 bp to about 200 bp, about 40 bp to about 150 bp, about 40 bp to about 100 bp, about 40 bp to about 90 bp, about 40 bp to about 80 bp, about 40 bp to about 70 bp, about 40 bp to about 60 bp, or about 40 bp to about 50 bp in length.Exemplary Process 1 - double-stranded cfDNA

[0141] In some embodiments, the methods provided herein comprise performing process 1 to prepare a sample for high throughput sequencing assay. Process 1 (FIG. 15) provides an example of preparing a sequencing library from the double-stranded cfDNA in the original sample. In some embodiments, a control molecule can be added to an initial sample of plasma to generate a spiked plasma sample. In some embodiments, nucleic acid extraction can be performed on a spiked plasma sample to generate purified and concentrated cfDNA.

[0142] In some embodiments, a library preparation process can be performed on a purified and concentrated cfDNA sample. In some embodiments, the library preparation can comprise attaching (e.g., by ligation) double-stranded adapters to double-stranded cfDNA. In some embodiments, library preparation can comprise performing unbiased amplification on the sample.

[0143] In some embodiments, a sample preparation method does not comprise extracting nucleic acids from a raw or initial sample. For example, in some cases, nucleic acids can be extracted during or following library preparation, if at all.

[0144] In some embodiments, an adapter pair can be attached to cfDNA fragments in a sample such as by ligation or PCR amplification. In some embodiments, a pair of adapters can comprise a p5 adapter that is attached to a 5 ’ end of a molecule and a p7 adapter that is attached to a 3 ’ end of a molecule. In some embodiments, a p5 and p7 sequence can allow a nucleic acid library to bind and generate clusters on a flow cell.

[0145] In some cases, the cfDNA can be attached to adapters comprising identifier sequences that can differentiate between multiple samples. In some embodiments, the multiple samples comprise a plurality of patient samples and / or control samples (e.g., positive control, negative control). In some embodiments, samples can be pooled after barcoding, then sequenced, then de-multiplexed to assign each cluster to its sample.Attorney Docket No. 47697-763601Exemplary Process 2- double-stranded cfDNA and single-stranded cfDNA

[0146] In some embodiments, the methods provided herein comprise performing process 2 to prepare a sample for high throughput sequencing assay. Process 2 (FIG. 15) provides an example of preparing a sequencing library from the double-stranded and single-stranded cfDNA in the original sample. In some embodiments, a control molecule can be added to an initial sample of plasma to generate a spiked plasma sample. In some embodiments, nucleic acid extraction can be performed on a spiked plasma sample to generate purified and concentrated cfDNA. In some embodiments, a sample preparation method does not comprise extracting nucleic acids from a raw or initial sample. For example, in some cases, nucleic acids can be extracted during or following library preparation, if at all.

[0147] In some embodiments, Process 2 comprises physically enriching for degraded nucleic acids, ultra-short nucleic acids, single stranded nucleic acids, double stranded nucleic acids, nicked nucleic acids, or rare nucleic acids.

[0148] In some embodiments, the cfDNA can be denatured in order to separate strands of double-stranded cfDNA, converting the double-stranded cfDNA into single-stranded cfDNA. In some embodiments, the resulting sample can comprise single-stranded cfDNA derived from both double-stranded and single-stranded cfDNA in the original sample.

[0149] In some embodiments, the methods described herein can comprise attaching a splint adapter (e.g., a splint oligonucleotide) to the single-stranded cfDNA in the sample. In some embodiments, the splint adapter can comprise dsDNA with a ssDNA overhang. In some cases, the ssDNA overhang can comprise random or degenerate nucleotides (e.g., NNNNNN). In some embodiments, the ssDNA overhang can comprise a specific sequence capable of hybridizing to a target molecule.

[0150] In some embodiments, the splint adapter can randomly hybridize to cfDNA via the single-stranded DNA overhang within the splint adapter. In some embodiments, attaching the splint adapter can further comprise ligating the hybridized adapter to cfDNA, e.g., using an enzyme (e.g., ligase). In some embodiments, the ligase comprises T4 ligase, CircLigase II, CircLigase ssDNA Ligase, Splint ligase, or any engineered variants thereof, any natural variants thereof, or any combination thereof.

[0151] In some embodiments, an adapter pair can be attached to cfDNA fragments in a sample such as by ligation or PCR amplification. In some embodiments, a pair of adapters can comprise a p5 adapter that is attached to a 5 ’ end of a molecule and a p7 adapter that is attached to a 3 ’ end of a molecule. In some embodiments, a p5 and p7 sequence can allow a nucleic acid library to bind and generate clusters on a flow cell.Attorney Docket No. 47697-763601

[0152] In some cases, the cfDNA can be attached to adapters comprising identifier sequences that can differentiate between multiple samples. In some cases, the sample identifiers attached to the cfDNA via ligation or PCR amplification. In some embodiments, the sample identifiers are incorporated into the splint oligonucleotides. In some embodiments, the multiple samples comprise a plurality of patient samples and / or control samples (e.g., positive control, negative control). In some embodiments, multiple samples can be pooled after barcoding, then sequenced, then de-multiplexed to assign each cluster to its sample.Sequencing Methods

[0153] Disclosed herein are methods of sequencing a sample comprising a nucleic acid as disclosed herein. In some embodiments, disclosed herein are methods for sequencing nucleic acids, cell-free nucleic acids (cfNAs), or a combination thereof present in a sample. In some embodiments, a cfNA as disclosed herein can be prepared into a nucleic acid library for use in a sequencing assay. As used herein, a “nucleic acid library” refers to a collection of nucleic acid fragments. In some embodiments, a collection of nucleic acid fragments can be used, for example, for sequencing. Disclosed herein in some embodiments are methods comprising subjecting nucleic acids in a nucleic acid library derived from a sample to a sequencing assay to generate sequence reads. In some embodiments, the methods further comprise performing any necessary steps, methods, or techniques described herein to prepare the sample for the sequencing assay. For example, the methods may comprise adding process control molecules (or synthetic spike-in molecules) to the sample, ligating adapters to the nucleic acids, or generating a nucleic acid library. In some embodiments, the methods further comprise analyzing the sequence reads generated from the sequencing assay. In some embodiments, analyzing the sequence reads comprises performing a bioinformatic analysis described herein. In some embodiments, a bioinformatic analysis can comprise calculating an abundance of sequence reads, generating fragment length profiles of sequence reads, identifying and mapping sequence reads to known references to identify a source organism from which a sequenced nucleic acid was derived, or any combination thereof. In some embodiments, a method can further comprise generating a report of a result of a sequencing assay.

[0154] In some embodiments, a sequencing assay provided herein can be performed by any sequencing methods suitable for sequencing a nucleic acid provided herein. In some embodiments, the sequencing assay herein can comprise massively parallel sequencing. In some embodiments, the massively parallel sequencing can comprise whole genome sequencing. In some embodiments, a massively parallel sequencing can comprise Next Generation Sequencing or a Next Next Generation sequencing. In some embodiments, the methods provided herein comprise determining a concentration or quantity of a mcfDNA. In some embodiments, theAttorney Docket No. 47697-763601methods comprise monitoring a concentration or quantity of a mcfDNA over time. In some embodiments, the method comprises identifying fragments of mcfDNA that vary during a course of treatment. In some embodiments, the sequencing assay or the sequencing method comprises sequencing-by-synthesis. In some embodiments, sequencing methods provided herein comprise Maxam-Gilbert sequencing-based techniques, chain-termination-based techniques, shotgun sequencing, bridge PCR sequencing, single-molecule real-time sequencing, ion semiconductor sequencing, nanopore sequencing, pyrosequencing, sequencing by synthesis, sequencing by ligation, sequencing by electron microscopy, dideoxy sequencing reactions (Sanger method), massively parallel sequencing, polony sequencing, DNA nanoball sequencing and any variation thereof. The term “Next Generation Sequencing (NGS)” herein refers to sequencing methods that allow for massively parallel sequencing of nucleic acid molecules during which a plurality, e.g., millions, of nucleic acid fragments from a single sample or from multiple different samples are sequenced simultaneously. Non-limiting examples of NGS include sequencing-by-synthesis, sequencing-by-ligation, real-time sequencing, and nanopore sequencing. In some embodiments, sequencing involves hybridizing a primer to the template to form a template / primer duplex, contacting the duplex with a polymerase enzyme in the presence of detectably labeled or unlabeled nucleotides under conditions that permit the polymerase to add labeled or unlabeled nucleotides to the primer in a template-dependent manner, detecting a signal from the incorporated labeled nucleotide or detecting a signal resulting from the process of incorporating labeled or unlabeled nucleotide (e.g., proton release), and sequentially repeating the contacting and / or detecting steps at least once, wherein sequential detection of incorporated labeled or unlabeled nucleotide determines the sequence of the nucleic acid. In some embodiments, exemplary detectable labels include radiolabels, fluorescent labels, protein labels, dye labels, enzymatic labels, etc. In some embodiments, the detectable label can be an optically detectable label, such as a fluorescent label. Exemplary fluorescent labels include cyanine, rhodamine, fluorescein, coumarin, BODIPY®, Alexa Fluor®, or conjugated multi-dyes.

[0155] In some embodiments, a method disclosed herein can comprise calculating an abundance of sequence reads generated from a nucleic acid present in a sample. In some embodiments, a method can comprise calculating an abundance of nucleic acids (e.g., cell-free nucleic acids (cfNA)) in a sample. In some embodiments, an abundance of nucleic acids in a sample can be calculated based on an abundance of sequence reads generated from a nucleic acid in a sample. In some embodiments, an abundance described herein can comprise an absolute abundance, a relative abundance, or a normalized abundance. In some embodiments, a relative abundance or a normalized abundance can be calculated based on a reference value. In some embodiments, a reference value can comprise an abundance of sequence reads generated fromAttorney Docket No. 47697-763601any nucleic acids in the sample. For example, the reference value may be an abundance of sequence reads from microbial cell-free DNA (mcfDNA) or of sequence reads from a synthetic spike-in molecule (or process control molecule). In some embodiments, the methods further comprise calculating the relative abundance of a sequence read at least in part by comparing the abundance of the sequence read to the abundance of another sequence read. In some embodiments, the methods further comprise calculating the normalized abundance of a sequence read at least in part by comparing the abundance of the sequence read from mcfDNA in the sample to the abundance of sequence reads from synthetic spike-in molecules.

[0156] In some embodiments, the methods further comprise mapping a sequence read to a reference sequence. In some embodiments, the reference sequence can comprise a microbial sequence. In some embodiments, the reference sequence comprises a non-microbial sequence. In some embodiments, the reference sequence comprises a genomic sequence. In some embodiments, the method further comprise mapping a sequence read to a reference genome. In some embodiments, a method disclosed herein can comprise identifying a species of microbe described herein by mapping a sequence read to a reference genome. In some embodiments, the methods further comprise identifying the source of nucleic acids in the sample from which the sequence read originated. In some embodiments, the methods further comprise performing a bioinformatic analysis. In some embodiments, performing the bioinformatics analysis comprises assembling sequence data, detecting and quantifying sequence reads, distinguish populations of nucleic acids, detecting the presence and measuring the abundance of microbial nucleic acids, comparing sequence reads, comparing abundances of sequence reads, identifying contaminant nucleic acids from the sample collection site, identifying target nucleic acids (e.g., cell-free nucleic acids), identifying host nucleic acids, generating fragment lengths profiles of microbial nucleic acids, generating fragment lengths profiles of control process molecules, comparing fragment lengths profiles of the microbial nucleic acids, detecting site of infection, detecting the state of infection, detecting the risk of organ rejection in a transplant patient, determining the eligibility of a subject for a transplant, detecting potential for drug resistance, or any combination thereof.

[0157] Disclosed herein in some embodiments are methods for identifying sequence reads obtained through sequencing as host or non-host. In some embodiments, a host can comprise a subject described herein. In some embodiments, sequence reads identified as non-host can then be aligned to a nucleotide database. In some embodiments, the nucleotide database comprises microbial reference sequences. In some embodiments, the database can be selected for those microbial sequences known to be associated with the host, e.g., the set of commensal and pathogenic microorganisms of the subject (e.g., animal or human). In some embodiments, theAttorney Docket No. 47697-763601microbial database can be optimized to mask or remove contaminating sequences. For example, many public database entries include artifactual sequences not derived from the microorganism, e.g., primer sequences, host sequences, and other contaminants. In some embodiments, sequence reads can be aligned to a reference sequence comprising artifactual sequences. In some embodiments, regions that show irregularities in read coverage when multiple samples are aligned can be masked or removed as an artifact. In some embodiments, the detection of such irregular coverage can be done by various metrics, such as the ratio between coverage of a specific nucleotide and the average coverage of the entire contig within which this nucleotide is found. In some embodiments, a sequence that is represented as greater than 5x, about 10x, about 25x, about 50x, about 100xthe average coverage of the reference sequence comprising artifactual sequences can be artifactual. In some embodiments, a binomial test can be applied to provide a per-base likelihood of coverage given the overall coverage of the contig. In some embodiments, each high confidence read can align to multiple organisms in the given microbial database. In some embodiments, to correctly assign organism abundance based upon this possible mapping redundancy, an algorithm can be used to compute the most likely organism (for example, see Lindner et al. Nucl. Acids Res. (2013) 41 (1): elO, which is referenced herein in its entirety). For example, GRAMMy or GASiC algorithms can be used to compute the most likely organism that a given read came from. In some embodiments, alignments and assignment to a host sequence or to a non-host (e.g., microbial) sequence can be performed in accordance with art-recognized methods. For example, a read of 50 nt. can be assigned as matching a given genome if there is not more than 1 mismatch, not more than 2 mismatches, not more than 3 mismatches, not more than 4 mismatches, not more than 5 mismatches, etc. over the length of the read. In some embodiments, publicly available algorithms can be used for alignments and identification. A non-limiting example of such an alignment algorithm is the bowtie2 program (Johns Hopkins University). In some embodiments, these assignments of reads to an organism (e.g., host organism, non-host organism, microbe, pathogen, etc.) can then be totaled and used to compute the estimated number of reads assigned to each organism in a given sample, in a determination of the prevalence of the organism in the sample (for example, a cell-free nucleic acid sample). In some embodiments, this information can be used to determine an origin of a pathogen or contaminant. In some embodiments, the analysis described herein can be used to normalize the counts for the size of the microbial genome to provide a calculation of coverage for a microbe. In some embodiments, the normalized coverage for each microbe can be compared to the host sequence coverage in the same sample to account for differences in sequencing depth between samples. In some embodiments, a dataset of microbial organisms represented byAttorney Docket No. 47697-763601sequences in the sample, and the prevalence of those microorganisms can be optionally aggregated and displayed for ready visualization, e.g., in the form of a report.

[0158] In some embodiments, the methods provided herein comprise generating sequence reads (or sequencing reads) from the nucleic acids in the sample. “Sequence read”, “sequenced read” and “sequencing read” are used interchangeably herein. In some embodiments, the methods further comprise detecting, identifying, analyzing, processing, comparing, aligning, or mapping the sequence reads generated from the nucleic acids in the sample. In some embodiments, the sequence reads are generated from the microbial cell-free nucleic acids (mcfNA) in the sample as described herein. In some embodiments, the methods comprise mapping the sequence reads generated from the nucleic acids in the sample to a reference sequence. In some embodiments, the reference sequence can comprise a microbial sequence. In some embodiments, the microbial sequence comprises a region of a microbial genome. In some embodiments, the reference sequence comprises an artifactual sequence.

[0159] In some embodiments, a sequencing can generate at least 100, at least 250, at least 500, at least 750, at least 1000, at least 1500, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, at least 5500, at least 6000, at least 7000, at least 8000, at least 9000, at least 10,000, at least 12,500, at least 15,000, at least 17,500, at least 20,000, at least 30,000, at least 40,000, at least 50,000, at least 60,000, at least 70,000, at least 80,000, at least 90,000, at least 100,000, at least 200,000, at least 300,000, at least 400,000, at least 500,000, at least 600,000, at least 700,000, at least 800,000, at least 900,000, at least 1,000,000, at least 2,000,000, at least 3,000,000, at least 4,000,000, at least 5,000,000, at least 6,000,000, at least 7,000,000, at least 8,000,000, at least 9,000,000, or at least 10,000,000 sequence reads from mcfNA in a sample.Hardware and Software

[0160] Disclosed herein in some embodiments are kits and systems configured to perform a method disclosed herein. In some embodiments, a kit or system can comprise a nucleic acid sequencer for generating DNA or RNA sequence information. In some embodiments, a kit or system can further comprise a computer comprising software that performs a bioinformatics analysis on the DNA or RNA sequence information. In some embodiments, a bioinformatics analysis can include, without limitation, assembling sequence data, detecting and quantifying sequence reads, distinguish populations of nucleic acids, detecting the presence and measuring the abundance of microbial nucleic acids, comparing sequence reads, comparing abundances of sequence reads, identifying contaminant nucleic acids from the sample collection site, identifying target nucleic acids (e.g., cell-free nucleic acids), identifying host nucleic acids, generating fragment lengths profiles of microbial nucleic acids, generating fragment lengths profiles ofAttorney Docket No. 47697-763601control process molecules, comparing fragment lengths profiles of the microbial nucleic acids, detecting site of infection, detecting the state of infection, detecting the risk of organ rejection in a transplant patient, determining the eligibility of a subject for a transplant, and / or detecting potential for drug resistance.

[0161] The kit or system can also include computer control systems with machine-executable instructions (e.g., software) to implement the methods. FIG. 14 shows a computer system 1401 that is programmed or otherwise configured to implement methods of the present disclosure The computer system 1401 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 1405, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 1401 also includes memory or memory location 1410 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 1415 (e.g., hard disk), communication interface 1420 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 1425, such as cache, other memory, data storage and / or electronic display adapters.

[0162] The memory 1410, storage unit 1415, interface 1420, and peripheral devices 1425 are in communication with the CPU 1405 through a communication bus (solid lines), such as a motherboard. The storage unit 1415 can be a data storage unit (or data repository) for storing data.

[0163] The computer system 1401 can be operatively coupled to a computer network (“network”) 1430 with the aid of the communication interface 1420. The network 1430 can be the Internet, an internet and / or extranet, or an intranet and / or extranet that is in communication with the Internet. The network 1430 in some embodiments is a telecommunication and / or data network. The network 1430 can include one or more computer servers, which can enable distributed computing, such as cloud computing.

[0164] The network 1430, in some embodiments with the aid of the computer system 1401, can implement a peer-to-peer network, which can enable devices coupled to the computer system 1401 to behave as a client or a server.

[0165] The CPU 1405 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions can be stored in a memory location, such as the memory 1410. The instructions can be directed to the CPU 1405, which can subsequently program or otherwise configure the CPU 1405 to implement methods of the present disclosure. Examples of operations performed by the CPU 1405 can include fetch, decode, execute, and writeback. The CPU 1405 can be part of a circuit, such as an integrated circuit. One or more other components of the system 1401 can be included in the circuit. In some embodiments, the circuit is an application specific integrated circuit (ASIC).Attorney Docket No. 47697-763601

[0166] The storage unit 1415 can store files, such as drivers, libraries, and saved programs. The storage unit 1415 can store user data, e.g., user preferences and user programs. The computer system 1401 in some embodiments can include one or more additional data storage units that are external to the computer system 1401, such as located on a remote server that is in communication with the computer system 1401 through an intranet or the Internet.

[0167] The computer system 1401 can communicate with one or more remote computer systems through the network 1430. For instance, the computer system 1401 can communicate with a remote computer system of a user. Examples of remote computer systems include personal computers, slate or tablet PC's, telephones, smart phones, or personal digital assistants. The user can access the computer system 1401 via the network 1430.

[0168] The kit or system can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 1401, such as, for example, on the memory 1410 or electronic storage unit 1415. The machine executable or machine -readable code can be provided in the form of software. During use, the code can be executed by the processor 1405. In some embodiments, the code can be retrieved from the storage unit 1415 and stored on the memory 1410 for ready access by the processor 1405. In some situations, the electronic storage unit 1415 can be precluded, and machine-executable instructions are stored on memory 1410. The code can be pre-compiled and configured for use with a machine having a processor adapted to execute the code or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[0169] Parts of the kits and systems, such as the computer system 1401, can be embodied in programming. Various aspects of the technology can be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and / or associated data that is carried on or embodied in a type of machine readable medium. Machine-executable code can be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which can provide non-transitory storage at any time for the software programming. All or portions of the software can at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, can enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that can bear the software elements includes optical, electrical, and electromagnetic waves, such as used acrossAttorney Docket No. 47697-763601physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links, or the like, also can be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[0170] Hence, a machine readable medium, such as computer-executable code, can take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as can be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media can take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD- ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer can read programming code and / or data. Many of these forms of computer readable media can be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0171] The computer system 1401 can include or be in communication with an electronic display 1435 that comprises a user interface (UI) 1440 for providing, an output of a report, which can include a diagnosis of a subject or a therapeutic intervention for the subject. Examples of UI's include, without limitation, a graphical user interface (GUI) and web-based user interface. The analysis can be provided as a report. The report can be provided to a subject, to a health care professional, a lab-worker, or other individual.

[0172] Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 1405. The algorithm can, for example, facilitate the enrichment, sequencing and / or detection of pathogen or microbe or other target nucleic acids.Attorney Docket No. 47697-763601

[0173] Information about a patient or subject can be entered into a computer system, for example, patient background, patient medical history, or medical scans. The computer system can be used to analyze results from a method described herein, report results to a patient or doctor, or come up with a treatment plan.Examples of Machine Learning Methodologies

[0174] In some embodiments, machine learning (ML) may be applied to the methods and the systems disclosed herein. For example, ML may be used in predicting a risk of a certain medical condition in a user. Further, for example, ML may be used in predicting symptoms in a user. Further, for example, ML may be used in predicting an effective treatment for a user. To accomplish these example predictions, ML may analyze one or more of: research data, genealogical data, medical data, demographic data, geographic data, assay data, etc.

[0175] In some cases, ML may generally involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data. ML may include a ML model (which may include, for example, a ML algorithm). Machine learning, whether analytical or statistical in nature, may provide deductive or abductive inference based on real or simulated data. The ML model may be a trained model. ML techniques may comprise one or more supervised, semi-supervised, self-supervised, or unsupervised ML techniques. For example, an ML model may be a trained model that is trained through supervised learning (e.g., various parameters are determined as weights or scaling factors). ML may comprise one or more of regression analysis, regularization, classification, dimensionality reduction, ensemble learning, meta learning, association rule learning, cluster analysis, anomaly detection, deep learning, or ultra-deep learning. ML may comprise: k-means, k-means clustering, k-nearest neighbors, learning vector quantization, linear regression, non-linear regression, least squares regression, partial least squares regression, logistic regression, stepwise regression, multivariate adaptive regression splines, ridge regression, principal component regression, least absolute shrinkage and selection operation (LASSO), least angle regression, canonical correlation analysis, factor analysis, independent component analysis, linear discriminant analysis, multidimensional scaling, non-negative matrix factorization, principal components analysis, principal coordinates analysis, projection pursuit, Sammon mapping, t-distributed stochastic neighbor embedding, AdaBoosting, boosting, gradient boosting, bootstrap aggregation, ensemble averaging, decision trees, conditional decision trees, boosted decision trees, gradient boosted decision trees, random forests, stacked generalization, Bayesian networks, Bayesian belief networks, naive Bayes, Gaussian naive Bayes, multinomial naive Bayes, hidden Markov models, hierarchical hidden Markov models, support vector machines, encoders, decoders, auto-encoders, stacked autoencoders, perceptrons, multi-layer perceptrons, artificial neural networks, feedforward neuralAttorney Docket No. 47697-763601networks, convolutional neural networks, recurrent neural networks, residual neural networks, physics-informed neural networks, long short-term memory, deep belief networks, deep Boltzmann machines, deep convolutional neural networks, deep recurrent neural networks, large language models, transformer models, vision transformers, or generative adversarial networks.

[0176] Training the ML model may include, in some cases, selecting one or more untrained data models to train using a training data set. The selected untrained data models may include any type of untrained ML models for supervised, semi-supervised, self-supervised, or unsupervised machine learning. The selected untrained data models may be specified based upon input (e.g., user input) specifying relevant parameters to use as predicted variables or other variables to use as potential explanatory variables. For example, the selected untrained data models may be specified to generate an output (e.g., a prediction) based upon the input. Conditions for training the ML model from the selected untrained data models may likewise be selected, such as limits on the ML model complexity or limits on the ML model refinement past a certain point. The ML model may be trained (e.g., via a computer system such as a server) using the training data set. In some cases, a first subset of the training data set may be selected to train the ML model. The selected untrained data models may then be trained on the first subset of training data set using appropriate ML techniques, based upon the type of ML model selected and any conditions specified for training the ML model. In some cases, due to the processing power requirements of training the ML model, the selected untrained data models may be trained using additional computing resources (e.g., cloud computing resources). Such training may continue, in some cases, until at least one aspect of the ML model is validated and meets selection criteria to be used as a predictive model.

[0177] In some cases, one or more aspects of the ML model may be validated using a second subset of the training data set (e.g., distinct from the first subset of the training data set) to determine accuracy and robustness of the ML model. Such validation may include applying the ML model to the second subset of the training data set to make predictions derived from the second subset of the training data. The ML model may then be evaluated to determine whether performance is sufficient based upon the derived predictions. The sufficiency criteria applied to the ML model may vary depending upon the size of the training data set available for training, the performance of previous iterations of trained models, or user-specified performance requirements. If the ML model does not achieve sufficient performance, additional training may be performed. Additional training may include refinement of the ML model or retraining on a different first subset of the training dataset, after which the new ML model may again be validated and assessed. When the ML model has achieved sufficient performance, in some cases, the ML may be stored for present or future use. The ML model may be stored as sets of parameterAttorney Docket No. 47697-763601values or weights for analysis of further input (e.g., further relevant parameters to use as further predicted variables, further explanatory variables, further user interaction data, etc.), which may also include analysis logic or indications of model validity in some instances. In some cases, a plurality of ML models may be stored for generating predictions under different sets of input data conditions. In some cases, the ML model may be stored in a database (e.g., associated with a server).

[0178] In some embodiments, the methods and the systems disclosed herein improve predictive power of ML by using a combination of features to more robustly form predictions. An example is depicted in FIG. 19. For example, the first feature may be an expanded training set to train a neural network. This expanded training set may be developed by applying mathematical transformation functions on an acquired set of training data. These transformations can include affine transformations, for example, rotating, shifting, or mirroring or filtering transformations, for example, smoothing or contrast reduction. The neural networks may then be trained with this expanded training set (e.g., using stochastic learning with backpropagation, which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network). The introduction of an expanded training set may however increase false positives when classifying data outside the training data. Accordingly, the second feature of the methods and the systems disclosed herein is the minimization of these false positives by performing an iterative training algorithm, in which the ML model may be retrained with an updated training set containing the false positives produced after prediction has been performed on a set of non-training data. This combination of features provides a robust prediction model with limited false positives. Accordingly, training the ML model may comprise: (A) obtaining a first set of training data; (B) training a machine learning model on the first set of training data; (C) obtaining a second set of training data; and (D) training the machine learning model on the second set of training data. For example, the second training data may comprise or correspond to at least some of the first training data. Further, for example, one or both of the first set of training data or the second set of training data may be transformed, such as by one or more operations: mirroring, rotating, smoothing, filtering, shifting, thresholding, contrast adjusting, etc.Applications

[0179] In some embodiments, the methods and systems provided here are for detecting a clinically-relevant genetic marker. In some embodiments, the genetic marker is associated with an infection or a non-communicable disease. In some embodiments, the methods are useful for detecting an anti-microbial resistance (AMR) marker. In some embodiments, the methods areAttorney Docket No. 47697-763601useful for detecting a cancer marker. In some embodiments, the methods are useful for detecting a disease site.

[0180] In some embodiments, the methods and systems provided herein are useful for informing diagnostic and treatment decisions for infectious diseases in clinical settings. The methods and systems can be useful for rapid detection of the microbe causing the infection without performing microbial culture. In some embodiments, the methods and systems can be useful for detecting an AMR genetic marker, an AMR gene cassette, and / or a carrier microbe harboring the AMR genetic marker. In some embodiments, the methods comprise generating a report listing the microbes detected by the methods and systems provided here. In some embodiments, the methods comprise generating a report listing the microbes and probabilities of the microbes being resistant or susceptible to antimicrobial treatment for clinical use. In some embodiments, the methods comprise generating a report on the microbes and their resistance or susceptibility to antimicrobial treatment.

[0181] In some embodiments, the methods and systems provided herein are useful for linking the AMR genetic marker to carrier microbes harboring the AMR genetic marker. In some embodiments, by detection of the AMR genetic marker can provides additional phenotypic characteristics of the carrier microbe. In some embodiments, a phenotypic characteristic is resistance to a particular AMR drug or class of AMR drugs. In some embodiments, the methods comprise performing a first sequencing assay to detect one or more carrier microbes harboring the AMR genetic marker and based on this detection, perform a second sequencing assay to link the AMR genetic marker to a specific carrier microbe. For example, many carrier microbes can harbor the same AMR gene, making it important to identify the exact microbes in the infection that carry the gene to better inform treatment. For instance, the mecA gene can be carried by multiple Staphylococcus species, such as S. aureus and S. epidermidis, so mecA could be present in either or both microbes in the infection. The methods and systems provided herein can be useful for determining the likely etiology of the infection and establishing the microbial species containing the mecA gene. In some embodiments, the methods further comprise administering a therapeutic or developing a treatment plan based on the identification of a microbial species, such as a microbial species carrying an AMR marker.

[0182] In some embodiments, the methods and systems are useful for guiding the selection of antimicrobial treatment. For example, these methods can help determine the appropriate class of antimicrobial drugs for an infection based on the results of AMR detection. Once a link is established between an AMR genetic marker and the detected carrier microbe, the treatment plan for the infection can be adjusted accordingly based on the results generated by the methods or systems.Attorney Docket No. 47697-763601

[0183] In some embodiments, the methods comprise modifying the antimicrobial drug class administered to a subject when microbes harboring resistance genes are detected. When such resistance genes are detected, in some embodiments, the methods comprise administering an alternative regimen to the subject. For example, if the methods detect that Staphylococcus aureus infecting the patient harbors the SCCmec cassette, mecA gene, and / or mecC gene, this indicates that the infection is caused by methicillin-resistant Staphylococcus aureus (MRSA), which is resistant to all [l-lactams except ceftaroline and ceftobiprole. As a result, the clinical treatment can shift from [l-lactams to alternative primary regimens such as vancomycin, linezolid, daptomycin, or ceftobiprole. For example, if the methods detect that Enterococcus infecting the patient harbors the VanA or VanB gene, this indicates that the infection may be caused by vancomycin-resistant Enterococcus (VRE). VRE is often resistant to penicillins, aminoglycosides, and glycopeptides. As a result, the clinical treatment would shift from vancomycin or penicillin-based therapies to alternative regimens such as linezolid or daptomycin. For example, if the methods detect that the carrier microbes harbor the blaCTX-M gene, this indicates the presence of an extended-spectrum [l-lactamasc (ESBL)-producing strain. ESBL-producing bacteria are generally resistant to penicillins, penicillin-BLI combinations, most cephalosporins (except cephamycins), and aztreonam. As a result, the clinical treatment would shift from [l-lactam antibiotics to alternative regimens such as ceftolozane-tazobactam, ertapenem, imipenem-cilastatin, meropenem, or aminoglycosides. For example, if the methods detect that the infecting bacteria harbor the blaKPC gene, this indicates the presence of Klebsiella pneumoniae carbapenemase (KPC)-producing bacteria. KPC producers are resistant to penicillins, penicillin-BLI combinations, cephalosporins, carbapenems, and aztreonam. As a result, the clinical treatment would shift from traditional [I-lactam therapies to alternative regimens such as ceftazidime-avibactam, meropenem-vaborbactam, imipenem-cilastatin-relebactam, cefiderocol, or certain aminoglycosides.

[0184] In some embodiments, the methods and systems provided herein are useful for identification of multiple species involved in an infection or a co-infection. In some embodiments, the methods comprise non-biased sequencing of microbial nucleic acids and can identify many microbes present in an infection. This is particularly useful for diagnosing and treating patients susceptible to multiple infections, including co-infections and complex infections, such as those occurring in immunocompromised individuals. In some embodiments, the methods comprise identifying microbes from different kingdoms within an infection, such as a viral, bacterial, and fungal infection occurring simultaneously. For example, immunocompromised patients, such as those with human immunodeficiency virus (HIV), may experience co-infections involving multiple pathogens. In some embodiments, the methodsAttorney Docket No. 47697-763601identify multiple resistant bacteria in difficult-to-treat infections. For instance, chronic infections in cystic fibrosis patients are particularly challenging to manage due to the persistence of multidrug-resistant bacteria. In some embodiments, the methods and systems are useful for identifying resistance markers across a broad range of microbes. For example, the methods can detect clinically relevant AMR genes in Cytomegalovirus (CMV) and other DNA viruses, the rpoB gene in Mycobacterium tuberculosis, additional classes of carbapenemase and ESBL genes in Gram-negative bacteria, and antifungal resistance genes in Aspergillus spp. and other molds.

[0185] In some embodiments, the methods and systems provided herein are useful for rapid diagnosis of life-threatening infections, particularly in immunocompromised or critically ill patients, such as those undergoing chemotherapy, organ transplants, or long-term immunosuppressive therapy. For example, these methods can be used to diagnose endocarditis, pneumonia in immunocompromised hosts (ICH), invasive fungal infections, febrile neutropenia, and / or fever of unknown origin (FUO). The methods described herein offer a rapid and comprehensive approach to identifying causative pathogens, improving targeted treatment, and minimizing the use of broad-spectrum antimicrobials.

[0186] In some embodiments, the methods and systems provided herein can be used independently of microbial culturing or can be used to confirm the results of a microbial culture. The methods can offer various advantages over traditional culturing, including faster turnaround times and the ability to detect microbes that are difficult to culture. In some embodiments, the methods described herein are more sensitive and / or more accurate than microbial culturing. For example, even when a patient’s blood culture fails to show any organism, the methods and systems provided herein can detect microbial nucleic acids from pathogens causing the infection in the patient’s blood sample, thereby identifying the etiology of the infection. In some embodiments, the methods and systems provided herein can be used independently of other detection methods, such as method that depend solely on PCR. The methods and systems provided herein can offer various advantages over methods and systems that solely use PCR. For example, the methods and systems provided herein can detect multiple microbial nucleic acids in a biological sample in a high-throughput manner, providing unique insights into the patient's microbiome, infectome, and resistome that may be unavailable with other detection methods. In some embodiments, the methods and systems described herein more accurately detect the microbe infecting the patient and more accurately link the detection of an AMR genetic marker to its carrier microbe.

[0187] In some embodiments, the methods and systems provided herein are useful for monitoring the development of AMR genetic markers in an infected patient or a population of patients over time, providing longitudinal data on microbial resistance. In some embodiments,Attorney Docket No. 47697-763601the methods and systems monitor AMR genetic marker development at clinical locations, such as hospital wards. The methods and systems may be useful for preventing the spread of antimicrobial-resistant infections in clinical settings. In some instances, the methods and systems can help reduce the acquisition and spread of antimicrobial resistance due to the inappropriate use of antimicrobials, both in individual patients and among populations.

[0188] In some embodiments, the methods and systems provided herein are useful for treating a patient who has a cancer, wherein the cancer harbors a cancer marker or multiple markers for cancer. For example, a cancer marker can be detected by high throughput sequencing; followed by a targeted assay from a different aliquot of a sample in order to detect additional cancer markers. In some embodiments, the methods comprise determining, adjusting, or administering a cancer therapy provided herein to the patient.

[0189] In some embodiments, an mcfNA in a subject’s bodily fluid can originate from a microbe living in or on a subject. In some embodiments, a mcfNA can be detected in a subject that has been exposed to an infectious disease. In some embodiments, an mcfNA can be detected in a healthy individual. In some embodiments, detection of mcfNA by the methods and systems disclosed herein can be performed as a biomarker of infection. In some embodiments, mcfNAs can be associated with communicable and / or non-communicable diseases. In some embodiments, mcfNAs can be associated with a range of diseases and conditions of a subject described herein, including an infection, an inflammatory bowel disease (IBD), a Kawasaki disease (KD), a human immunodeficiency virus (HIV), a cardiovascular disease (CVD), a cystic fibrosis (CF), a pneumonia, a sepsis, a cancer, a gastric cancer (GC), a hepatocellular carcinoma (HCC), a melanoma, or any combination thereof.

[0190] In some embodiments, provided herein comprise detecting, diagnosing, treating, monitoring, staging, or prognosing a disease or a disorder provided herein. In some embodiments, the disease or disorder is an infection, or another medical indication related to an infection. In some embodiments, the methods and systems are for determining an infection site or identifying a source of an infection. In some embodiments, the methods and systems are for determining the biological relationship between a microbe and a host (e.g., the subject herein). In some embodiments, the methods and systems are for detecting or identifying a commensal microbe of the subject. In some cases, a commensal microbe of a first subject may be a potential pathogen to a second subject. For example, a commensal microbe of an animal may be a potential pathogen to a human. In some embodiments, methods and systems disclosed herein can be used in conjunction with one or more medical tests.

[0191] In some embodiments, the methods and systems can be used for determining an eligibility of a subject in transplantation. The subject described herein can be a donor or aAttorney Docket No. 47697-763601recipient of the transplantation. In some embodiments, the transplantation comprises organ transplantation, tissue transplantation, composite tissue transplantation, living donor transplantation, or xenotransplantation. In some embodiments, the methods and systems are for determining the eligibility of an animal donor for xenotransplant. In some embodiments, the methods and systems are for determining the eligibility of a human donor for organ transplant. In some embodiments, the methods and systems are for determining the eligibility of a human transplant recipient.

[0192] In some embodiments, the methods and systems described herein can be used for individualized treatment for an infected subject or a subject who is susceptible or at risk for infections (e.g., immunosuppressed, immunocompromised, living conditions, or genetic variations resulting in increased susceptibility for infection). In some embodiments, the subject described herein comprises a human or an animal. In some embodiments, individualized treatment can include predicting if an infection will progress to an invasive disease stage, monitoring the efficacy of a therapy in a subject, modifying a therapeutic regimen depending on the subject's response to the therapy, and determining a pathogen's resistance to a particular therapeutic.

[0193] In some embodiments, the methods and systems disclosed herein can be used to detect, diagnose, predict, or prognose a pathogen's resistance to a particular therapeutic. In some cases, the methods and systems disclosed herein can further comprise sequencing of the subject's DNA for genetic variations that are associated with therapeutic resistance to therapeutics or to a particular therapeutic.

[0194] The methods provided herein can comprise, in some embodiments, determining a subject’s response to a particular treatment. In some cases, samples can be collected serially at various times before or during the course of the infection to determine the pathogen’s and subject's response to a treatment. In some cases, a treatment plan is individually tailored to the subject. In some embodiments, samples can be collected at various timepoints following administration of a treatment. In some embodiments, samples can be collected at various timepoints following discontinuation of a treatment or adjustment of a treatment. In some cases, serially collected samples are compared to each other to determine whether the infection is improving or worsening in the subject. In some embodiments, after a response to treatment is identified, the methods can comprise maintaining, discontinuing, modifying, adjusting the dose of a particular treatment. In some cases, modifying the treatment may comprise replacing a therapeutic drug with a different or alternative therapeutic drug, particularly if an AMR marker is detected following treatment. In some cases, the methods comprise discontinuing an antimicrobial treatment if an AMR gene or cassette is identified. In some cases, if an anti-Attorney Docket No. 47697-763601microbial resistance is detected, the methods comprise changing a treatment to a different therapy, or alternative regimen. In some cases, the methods can comprise adjusting a dose of current treatment, either up or down, based on detection of an AMR marker.

[0195] In some embodiments, the methods and systems can be used to adjust a therapeutic regimen. For example, the subject can be administered a drug to treat an infection. In some embodiments, methods and systems can be used to track or monitor the efficacy of the drug treatment. In some cases, the therapeutic regimen can be adjusted, depending on upward or downward course of the infection. For example, if the methods provided herein indicate that an infection is not improving with drug treatment, the therapeutic regimen can be adjusted by changing the type of drug or treatment, discontinuing the use of the drug, continuing the use of the drug, increasing the dose of the drug, or adding a new drug or treatment to the subject’s therapeutic regimen.

[0196] In some embodiments, the methods and systems are useful for treating a patient who has an infection by one or more carrier microbes harboring an AMR marker. In some embodiments, the methods comprise determining, adjusting, changing, or administering an antimicrobial treatment of a subject based on identification of a multiple microbes.

[0197] In some embodiments, a treatment can involve administering a drug or other therapy to reduce or eliminate the colonization or invasive disease associated with an infection. In some cases, the subject can be treated prophylactically to prevent the development of an infection. The methods provided herein can comprise performing procedures or administering a treatment to improve or reduce the symptoms of an infection. Some nonlimiting exemplary drugs that can be used are antibiotics (such as ampicillin, sulbactam, penicillin, vancomycin, gentamycin, aminoglycosides, clindamycin, cephalosporin, metronidazole, timentin, ticarcillin, clavulanic acid, cefoxitin), antiretroviral drugs (e.g., highly active antiretroviral therapy (HAART), reverse transcriptase inhibitors, nucleoside / nucleotide reverse transcriptase inhibitors (NRTIs), Nonnucleoside RT inhibitors, and / or protease inhibitors), immunoglobulins, or any variant or combination thereof.

[0198] In some embodiments, the methods and systems described herein are for detection, monitoring, diagnosis, prognosis, treatment, prediction, or prevention of colonization by the microbes disclosed herein. The disclosure also provides methods and systems to detect, monitor, diagnose, prognose, treat, predict, or prevent invasive disease caused by the microbes described herein. The methods and systems of the disclosure can be applied to any pathogen that has various stages of infection. The methods and systems can be especially useful for pathogens that have a colonization stage and an invasive disease stage. In some cases, the invasive disease stageAttorney Docket No. 47697-763601can be caused by the pathogen infection. In some cases, the invasive disease stage can be associated with the pathogen infection.

[0199] In some embodiments, the methods and systems can be used for to distinguish populations of nucleic acids or for detecting a microbe in a subject. In some embodiments, the methods and systems provide a more comprehensive view of the state and diversity of the infection or symbiotic microbes in a subject. For example, the identification of both RNA and DNA in a sample can be useful to detect RNA and DNA type viruses, or to detect bacterial, protist, parasitic or fungal genomic DNA and / or gene expression products, e.g., mRNA. Such process can also be able to differentiate between latent infection (e.g., which might be indicated by the presence of integrated retroviral DNA) versus active infection (e.g., which might be indicated by the presence of viral RNA from intact viral particles). Such processes can also be able to detect drug resistance and / or the origin of infection. Such processes can also be used to analyze host response. Such analyses can include analysis of cell-free, circulating nucleic acids, e.g., for microbial or viral infection identification.Diagnosis and Treatment

[0200] In some embodiments, the methods and systems and systems are for detecting, diagnosing, treating, monitoring, staging, or prognosing an infectious disease or an infection. The infection may be caused by any microbe, including but not limited to pathogenic or commensal microbes. In some embodiments, the infection is caused by a carrier microbe harboring the one or more AMR genetic markers.

[0201] In some embodiments, the methods and systems and systems are for detecting, diagnosing, treating, monitoring, staging or prognosing a non-communicable disease or disorder in a subject. In some embodiments, non-communicable disease or disorder is associated with altered or abnormal gene expression. In some embodiments, the non-communicable disease or disorder is associated with a genetic alteration (e.g., genetic marker). In some embodiments, the non-communicable disease is associated with a cancer genetic marker. In some embodiments, a non-communicable disease or disorder comprises a cancer, an autoimmune disease, a neurodegenerative disease, diabetes, arthritis, a heart disease, hepatitis, a kidney disease, multiple sclerosis, a disease of the integumentary system, an anemia, Duchenne muscular dystrophy, hemophilia, Down syndrome, Angelman syndrome, Prader-Willi syndrome, Rett syndrome, Fragile X syndrome, Li-Fraumeni syndrome, a cardiomyopathy, obesity, an inflammatory bowel disease, celiac disease, an autism spectrum disorder, a prion disease, asthma, a genetic defect, osteoporosis, neurofibromatosis, beta-thalassemia, Marfan syndrome, and cystic fibrosis.

[0202] In some embodiments, the methods and systems and systems are for detecting, diagnosing, treating, monitoring, staging, or prognosing a medical indication related to anAttorney Docket No. 47697-763601infection. In some embodiments, a medical indication related to an infection comprises any disease, disorder or procedure (e.g., medical or surgical) that renders a subject immunocompromised or susceptible to infections. In some embodiments, the medical indication related to an infection may cause a new or reoccurring infection in a subject. For example, the medical indication related to an infection can comprise any disease for which an immunosuppressant (e.g., chemotherapy, radiation, corticosteroids, transplant medications, or certain biologies) or an anti-infective agent is required as a treatment. In some embodiments, a medical indication related to an infection may comprise cancers, transplantation, surgeries, burns, infections, malnourishment, chronic kidney diseases, diabetes mellitus, autoimmune diseases, or immune disorders (e.g., acquired immunodeficiency syndrome (AIDS)).

[0203] In some embodiments, the methods and systems further comprise administering a treatment to the subject. The treatment can comprise a pharmacological treatment or a non-pharmacological treatment for the disease or disorder that the subject has. In some embodiments, the pharmacological treatments may comprise a small molecule drug or a biologic drug. In some embodiments, the biologic drug comprises a peptide, a protein, an antibody or an antigen binding fragment thereof, nucleic acids, a cell-based therapy, or any combination thereof. The drug treatment may comprise medications to be administered via a plurality of routes. In some embodiments, the drug treatment is administered orally, topically, or by injections. Topical administration may comprise intranasal administration, intravaginal administration, transdermal administration, or inhalation. Injections may comprise intravenous, subcutaneous, intramuscular, or intrathecal administration. In some embodiments, non-pharmacological treatment comprises a medical device (e.g., laser or photodynamic therapy, radiofrequency or thermal ablation, or cryotherapy), a surgical intervention (e.g., invasive, minimally invasive, laparoscopic, or others), radiotherapy, radioisotopic / nuclear therapy, physical therapy, occupational therapy, phonoaudiological therapy, a rehabilitation intervention, a digital health intervention, a behavioral intervention, a psychological intervention, or any combination thereof.

[0204] In some embodiments, the treatment comprises an antimicrobial or an anti-infective agent. In some embodiments, the antimicrobial agent comprises an antibacterial drug (e.g., an antibiotic), an antiviral drug, an antifungal drug, an antiparasitic drug, or any combination thereof.

[0205] In some embodiments, the antibiotic drug comprises a β-Lactam drug (e.g., carbapenems, methicillin), an aminoglycoside drug (e.g., gentamicin), a Macrolide-Lincosamide-Streptogramin (MLS) drug (e.g., clindamycin), a tetracycline drug (e.g., doxycycline), a fluoroquinolone drug (e.g., ciprofloxacin), a glycopeptide drug (e.g., vancomycin), a polymyxin drug (e.g., colistin), rifamycin (e.g., rifampin), a sulfonamide drugAttorney Docket No. 47697-763601(e.g., trimethoprim-sulfamethoxazole), or any combination thereof. In some embodiments, the antibiotic drug is a broad-spectrum antibiotic drug. In some embodiments, the antibiotic drug is a narrow-spectrum or targeted antibiotic drug.

[0206] In some embodiments, the antiviral drug comprises a nucleoside / nucleotide analogue (e.g., Acyclovir, Tenofovir, Sofosbuvir), a reverse transcriptase inhibitor (RTI) (e.g., Zidovudine, Efavirenz), a protease inhibitor (PI) (e.g., Ritonavir, Glecaprevir), an integrase inhibitor (e.g., Raltegravir, Dolutegravir), an entry inhibitor (e.g., Maraviroc, Enfuvirtide), a neuraminidase inhibitor (e.g., Oseltamivir, Zanamivir), an RNA polymerase inhibitor (e.g., Remdesivir, Sofosbuvir), a broad-spectrum antiviral drug (e.g., Ribavirin, Nitazoxanide), an antibody (e.g., Palivizumab, Inmazeb), or any combination thereof. In some embodiments, the antiviral drug comprises remdesivir, favipiravir, galidesivir, nirmatrelvir, or molnupiravir.

[0207] In some embodiments, the antifungal drug comprises a polyene (e.g., Amphotericin B, Nystatin), an azole (e.g., Fluconazole, Voriconazole, Posaconazole), an echinocandin (e.g., Caspofungin, Micafungin), a pyrimidine analog (e.g., flucytosine), an allylamine (e.g., terbinafine), Griseofulvin, Ciclop irox, Tavaborole, or any combination thereof.

[0208] In some embodiments, the methods and systems described herein further comprise adjusting a treatment that the subject has received at the time of sample collection.

[0209] In some embodiments, the treatment comprises a cancer treatment. In some embodiments, the cancer treatment comprises surgery, radiation, chemotherapy, targeted therapy, immunotherapy, hormone therapy, gene therapy, or any combination thereof.

[0210] In some embodiments, the treatment may comprise a digital therapy. For example, a digital therapy may be a software-based intervention configured to prevent, manage, or treat a medical condition. Examples of digital therapies may include behavioral therapies (e.g., cognitive behavioral therapy), cognitive training, virtual or augmented reality, biofeedback, neurofeedback, medication management instructions, remote patient monitoring, speech therapy, etc. The digital therapy may be initiated at least in part in response to determining a patient satisfies a risk threshold for a certain medical condition. For example, the digital therapy may be initiated automatically in response to the determination the patient satisfies the risk threshold (e.g., provided a care giver or the patient consents).Data Management

[0211] In some embodiments, the methods and systems and the systems disclosed herein may incorporate one or more data management techniques. For example, these techniques may include standardizing data across different patients, different studies, different data types, etc. Further, for example, these techniques may include sharing data (e.g., automatically) with different users in a network. These users may include, for example, the patient, a care providerAttorney Docket No. 47697-763601(e.g., a doctor, a nurse, a parent, etc.), a pharmacy, a research study, etc. The data may, for example, be shared real-time (or near real-time) across these users.

[0212] Patients may often visit various care providers (e.g., doctors, nurses, etc.), pharmacies, researchers, etc. for diagnosis and treatment. It may be difficult for all these different individuals / entities to share updated information about a patient’s condition with each providers using patient management systems, due to, for example, issues with inconsistent data formatting, data types, data sizes, etc. This can lead to problems with managing treatments, research studies, prescriptions or having patients duplicate tests, for example.

[0213] Further, individuals / entities often continually monitor a patient’s medical records (e.g., biological, genealogical, phonological, demographic, etc.) for updated information, which is often- times incomplete as records across different individuals / entities are often not shared timely in useful data types, formats, sizes, etc.

[0214] To address these challenges, the methods and systems and the systems disclosed herein provide a network-based patient management that collects, converts, and consolidates patient information from various care providers, research studies, pharmacies, etc. into a standardized format for network-based storage and sharing.

[0215] In some embodiments, the methods and systems and the systems disclosed herein provide a graphical user interface (GUI) by a content server, which is hardware or a combination of both hardware and software. A user, such as a care provider, a pharmacy, a researcher, or a patient, is given remote access through the GUI to view or update information about a patient’s medical condition using the user’s own local device (e.g., a personal computer or wireless handheld device). When a user wants to update the records, the user can input the update in any format used by the user’s local device. Whenever the patient information is updated, it may be converted into the standardized format and then stored in a collection of medical records on one or more of the network-based storage devices. After the updated information about the patient’s condition has been stored in the collection, the content server, which is connected to the networkbased storage devices, a message may be generated containing the updated information about the patient’s condition. This message may be transmitted in a standardized format over the computer network to users (e.g., care providers) in a network that have access to the patient’s information (e.g., to a care provider the updated information about the patient’s medical condition) so that the users can quickly be notified of any changes (e.g., without having to manually look up or consolidate all of the updates of patient condition). This helps ensure each of a group of care providers is provided real-time notice and access to changes so they can readily adapt their own medical diagnostic and treatment strategy in accordance with other care providers’ actions. The message can be in the form of an email message, text message, a push notification, etc.Attorney Docket No. 47697-763601

[0216] Accordingly, in some embodiments, data management may comprise: (A) obtaining, from a first source, first data corresponding to a patient condition for a patient; (B) obtaining, from a second source, second data corresponding to a patient condition for the patient; (C) standardizing the first data and the second data to generate standardized data; (D) generating (e.g., automatically) a message corresponding to the standardized data; and (E) transmitting the message to a plurality of users (e.g., care providers of the patient, pharmacists, researchers, the patient, etc.) over a computer network (e.g., in real time), so that each user of the plurality of users has access to the standardized data. For example, the first data and the second data may be of a different data format, different data size, different data type, etc.

[0217] In some embodiments, the methods and systems and the systems disclosed herein help reduce data size. For example, this data size may correspond to data used in assays, research, medical, etc. contexts. In reducing data size, the methods and systems and the systems disclosed herein may improve efficiency. This improved efficiency may help reduce the amounts of physical resources consumed, e.g., chemicals, devices, testing kits, etc. Further, this improved efficiency may help reduce computing resources, e.g., electricity consumption, heat loss, computing power etc.

[0218] Still further advantageously, improved efficiency from the methods and systems and the systems disclosed herein may help reduce traffic over a network by reducing data size. Accordingly, the methods and systems and the systems disclosed herein may be useful in optimizing network performance, resolving network issues, and improving network security through reducing network traffic and burden on the network.

[0219] In some embodiments, the methods and systems and the systems disclosed herein help reduce network traffic. In some embodiments, the methods and systems and the systems disclosed herein help reduce network traffic by varying (e.g., reducing) the amount of network data. For example, the methods and systems and the systems disclosed herein may reduce data based at least in part on a threshold. This threshold may correspond to a risk threshold (e.g., a risk of a patient having a medical condition), an accuracy threshold (e.g., an accuracy threshold needed for a conclusion in a research study, assay, test, etc.), or a data threshold (e.g., a data size threshold, a network performance threshold, etc.). Accordingly, the methods and systems and the systems disclosed herein may perform operations comprising: (A) collecting (e.g., over a network) data corresponding to a patient; (B) analyzing the data with respect to a threshold (e.g., risk threshold, accuracy threshold, data threshold, etc.); and (C) transmitting a subset of the data over the network, the subset of the data determined based at least in part on the threshold.Attorney Docket No. 47697-763601EXAMPLES

[0220] The following examples are provided to further illustrate some embodiments of the present disclosure, but are not intended to limit the scope of the disclosure; it will be understood by their exemplary nature that other procedures, methodologies, or techniques known to those skilled in the art may alternatively be used.Example 1. Inferring gene expression from human cell- free nucleic acid fragmentation patterns

[0221] It is known that apoptosis digests chromatin at the nucleosomal boundaries. Double stranded DNA (dsDNA), protected by the histone against further endonuclease digestion, results in the well-known nucleosomal peak (-140-160 bp in length) measured in cell-free DNA (cfDNA). Hematopoietic cells are thought to be the predominant population generating cfDNA, which are mostly double-stranded. Fragmentation profiles of the nucleosomal peak cfDNA can be used to infer the nucleosomal positioning of the originating cell populations, and thus, gene expression can be inferred (FIGs. 16-18). Fragmentomics may utilize granular information such as size, length, genomic location, and split ends to deduce cell population statistics indirectly via nucleosomal positioning and other epigenetic factors. There are a few proposed metrics that attempt to capture such features, including Nucleosome Depleted Region (NDR) coverage, Windowed Protection Score (WPS), Promoter Fragmentation Entropy (PFE), Motif Diversity Score (MDS), and Orientation Aware cfDNA Fragmentomic Features (OCF). These metrics are known in the art.

[0222] Actively expressed genes can have open chromatin at the transcriptional start site (TSS). Thus, such genes can be less shielded by nucleosomes and, therefore, more susceptible to endonuclease cleavage. Consequently, the depth of cfDNA originating from the TSS of active genes can be shallower compared to that of inactive genes. NDR can quantify the normalized depth within each 2-kilobase window surrounding each TSS. A lower NDR of a gene TSS site can be associated with higher levels of gene expression.

[0223] PFE can involve the measurement of fragmentation patterns of promoter regions of cfDNA. cfDNA fragments originating from active promoters, which can be less shielded by nucleosomes and thus can be more susceptible to endonuclease cleavage, can display more erratic cleavage patterns compared to fragments from inactive promoters, which are better protected by nucleosomes.

[0224] WPS can measure the balance between cfDNA fragments that span a genomic window and those that terminate within a genomic window. This can provide a readout of local DNA protection. Regions bound by nucleosomes or protein complexes can exhibit a high WPS due toAttorney Docket No. 47697-763601fewer fragment endpoints, whereas accessible regions exhibit low or negative WPS. WPS can reflect chromatin organization and nucleosome positioning encoded in cfDNA fragmentation patterns.

[0225] MDS can quantify the diversity of short nucleotide motifs (e.g., 4-6 bp) at cfDNA fragment ends using Shannon entropy. A high MDS can reflect a broad, heterogeneous distribution of end motifs, while low MDS can indicate enrichment of specific cleavage signatures associated with particular nucleases or chromatin states. Changes in MDS can capture shifts in cfDNA fragmentation mechanisms and can distinguish disease-associated cfDNA from background cell-free DNA. A high MDS can indicate many different ends (uniform), while a low MDS can indicate skewed ends (few types), which can hint at the presence of cancer or other disease. MDS can serve as a powerful fragmentomics biomarker for diagnosis and prognosis in liquid biopsies.

[0226] OCF can leverage the genomic orientation of cfDNA fragment ends by classifying them as upstream (U) or downstream (D) based on their alignment coordinates. DNA wrapped around nucleosomes can produce characteristic paired U and D ends at the upstream and downstream nucleosome boundaries, whereas linker or open chromatin regions can show the opposite U / D flanking pattern. These orientation-specific end signals can enable inference of nucleosome positioning and chromatin accessibility, which can provide biologically informative fragmentomic features for cfDNA analysis.

[0227] Despite the progress in human fragmentomics, the fragmentation profiles of shorter human cfDNA have not been extensively studied. The methods and systems presented herein focus on analyzing short cfDNA fragments of both dsDNA and ssDNA, a unique population of human cfDNA that are typically overlooked by conventional cfDNA lab protocols. As depicted in FIG. 1, the cfDNA used in the methods and systems described herein comprise dsDNA and ssDNA and are largely devoid of nucleosomes, coinciding with an enriched representation of subnucleosomal (<120 bp) and ultra-short (~50 bp) cfDNA fragments. Process 1 and process 2 capture different populations of short cfDNA. Process 1 captures mostly dsDNA, whose fragment profile has a broad ultrashort peak at around 50 bp, with weak nucleosomal peaks (mono-, di-, and tri-nucleosomal peaks). Process 2 may capture a mix of ssDNA and dsDNA, whose fragment profile has a sharp nucleosomal peak (—160 bp) and a sharper ultrashort (50 bp) peak.

[0228] FIGs. 20A-20C show schematic diagrams demonstrating a cause of the difference in fragment lengths between open and closed chromatin. The numbers of fragments and ratios in these figures are provided for exemplary purposes only. FIG.20 A shows a stretch of DNA bound in four nucleosomes in a closed chromatin state at the transcription start site (TSS). The digestionAttorney Docket No. 47697-763601of this region by DNases creates four times the number of fragments that are approximately nucleosomal length relative to FIG. 20C as there is four times the amount of DNA that is protected from digestion by the nucleosomes. FIG. 20B shows the DNA opening into a more open chromatin state at the TSS with only two sections bound to nucleosomes. In this state the digestion of this region by DNases creates two times the number of fragments that are approximately nucleosomal length relative to FIG. 20C. FIG. 20C shows the binding of RNA Polymerase II Complex (Pol II) to the open chromatin state at the TSS with only one section of the DNA bound to a single nucleosome. In this state the digestion of this region by DNases creates four times the number of fragments that are approximately Pol II length relative to the number of fragments that are approximately nucleosomal length, as there are four times as many stretches of DNA bound to Pol II as there are stretches bound to the nucleosome.

[0229] FIGs.21A-21C show an example of the expected coverage of each genetic locus shown in FIGs 20A-20C by sequence reads of nucleosomal (solid line) and subnucleosomal (dashed line) fragment length ranges. FIG.21A (corresponding to the example locus shown in FIG. 20A) shows the coverage is high in nucleosomal fragment length sequence reads and low in subnucleosomal length sequence reads throughout as the DNA is protected by nucleosomes across each locus. FIG. 21B (corresponding to the example locus shown in FIG. 20B) shows that the loci containing the transcription start sites have a lower level of nucleosomal fragments as the DNA has been unwound from the nucleosomes across these loci. FIG. 21C (corresponding to the example locus shown in FIG. 20C) shows an increase in sub-nucleosomal length sequence reads covering the TSS as these loci have been bound to RNA polymerase II Complex (Pol II) resulting in an increase in fragment lengths associated with the lengths of DNA bound to Pol II.

[0230] It is well characterized that the cell population that is the dominant source of cfDNA is hematopoietic cells. In particular, cfDNA has been strongly associated to PBMC leukocytes in healthy individuals (Esfahani 2022). As such, we utilize PBMC scRNAseq data obtained from the human cell atlas to rank order genes by expression of this cell type. It is common to attempt “peak” detection of various fragmentomic metrics, e.g. NDR, to infer nucleosomal positioning de-novo. We do not have sufficient coverage for this. As such, we will leverage gene annotations on the human genome to compute various metrics per gene. Nucleosomes arrange themselves around transcription start sites (TSS) based upon the gene expression of the associated gene, i.e., if a gene is expressed, the TSS must be accessible by DNA-b inding proteins and thus is unassociated to a nucleosome. We utilize the refTSS database for such annotations.

[0231] By analyzing the cfDNA captured by process 2, we demonstrated that promoters show signatures of nucleosomal positioning based upon PBMCs. Specifically, the genes were grouped into different sets based upon the RNAseq expression of each gene and plotted eachAttorney Docket No. 47697-763601fragmentomic metric in a 5 k bp window around each TSS. As depicted in FIG. 2A, it was observed that positions around promoter TSS of highly expressed genes demonstrated lower Windowed Protection Score (WPS) (left), and the effect becomes quantitatively diminished for genes with low expression (right). This observation can be utilized as a biomarker for gene expression. (See Example 2 for more detail). Additionally, this effect was only observed for promoter TSS but not any other TSS (e.g., exon TSS). FIG. 2B shows the WPS metric around the exon TSS regions of highly (left) and lowly (right) expressed genes.

[0232] In contrast, the cfDNA (dsDNA) captured by process 1 demonstrated a reversed trend at promoter TSS, as depicted in FIG. 2C. A higher WPS (or NDR) was observed at promoter TSS. In other words, promoters associated with highly expressed genes within PBMCs show higher coverage of ultrashort ds-cfDNA. This result has never been observed in literature before. We then tested if the promoter enrichment of ultrashort cfDNA can be utilized as a biomarker for gene expression. Given the high WPS observed at promoter TSS, the ds-cfDNA enriched in process 1 may comprise dsDNA molecules protected by small DNA-binding proteins, e.g. transcription factors.

[0233] Another important observation that fragment size distribution around promoter regions varies quantitatively based upon the putative expression of PBMC. (FIGs.3A-3B). The fragment size distribution is shown over all promoters, partitioned by the expression level of the associated gene. For cfDNA captured by process 2, promoters of high expressed genes are covered by an excess 50 bp fragments and are depleted by nucleosomes (167 bp). Conversely, promoters of low expression genes are enriched in nucleosomal fragments at the expense of the ultra-short population. (FIG.3A). This is different for cfDNA in process 1. (FIG.3B). Promoters associated with open chromatin / highly expressed genes have higher coverage of 50 bp dsDNA than low expressed / closed chromatin. However, once controlled for by the total coverage, the shape is invariant as shown by the cumulative function on the right.

[0234] Quantitative correlation between RNAseq and fragmentomic metrics was observed at promoters. (FIGs. 4A-4B). The comparison was done on a single sample for data from process 2. A composite sample out of 5 production results for process 1 was used to simulate similar coverage on the human genome.

[0235] As shown in FIG. 4C, preliminary results suggest that human ultra-short cfDNA fragmentomics can be used to deconvolve cell populations. As an initial and simplistic approach, we aimed to assess the feasibility of deconvolving a mixture of multiple distinct cell populations. Specifically, while the results above indicate that a significant proportion of cfDNA is derived from PBMCs, we sought to determine whether the presence of another cell type could be detected. To this end, we applied a small set of biomarkers for colorectal cancer (CRC) andAttorney Docket No. 47697-763601advanced adenomas (AA) from the Indivumed studies using the fragmentomic pipeline. To avoid the complexity of solving the full deconvolution problem a priori, we instead focused on identifying a small subset of genes that are minimally expressed in PBMCs but highly expressed in CRC cells, as determined by this study. We then calculated the average of the previously defined fragmentomic metrics over this reduced gene set. We chose only genes that were lowly expressed in PBMCs but highly expressed in CRC cells, as obtained by the human cell atlas. This is depicted graphically as the orange box in FIG.4C (left). This resulted in 76 gene features.FIG. 4C (right) depicts the NDR coverage and PFE metrics averaged over the small set of genes for AA and CRC stage III. As depicted by the hand-drawn dashed line, there appears to be separation between both sets of points. However, we note that this procedure was manually performed.

[0236] FIGs. 20A-20C show schematic diagrams demonstrating a cause of the difference in fragment lengths between open and closed chromatin. The numbers of fragments and ratios in these figures are provided for exemplary purposes only. FIG.20 A shows a stretch of DNA bound in four nucleosomes in a closed chromating state at the transcription start site (TSS). The digestion of this region by DNases creates four times the number of fragments that are approximately nucleosomal length relative to FIG.20C as there is four times the amount of DNA that is protected from digestion by the nucleosomes. FIG. 20B shows the DNA opening into a more open chromatin state at the TSS with only two sections bound to nucleosomes. In this state the digestion of this region by DNases creates two times the number of fragments that are approximately nucleosomal length relative to FIG. 20C. FIG. 20C shows the binding of RNA Polymerase II Complex (Pol II) to the open chromatin state at the TSS with only one section of the DNA bound to a single nucleosome. In this state the digestion of this region by DNases creates four times the number of fragments that are approximately Pol II length relative to the the number of fragments that are approximately nucleosomal length, as there are four times as many stretches of DNA bound to Pol II as there are stretches bound to the nucleosome.

[0237] FIGs. 21A-21C show an example of the expected coverage of each genetic locus by sequence reads of nucleosomal (solid line) and subnucleosomal (dashed line) fragment length ranges. FIG. 21A shows the coverage is high in nucleosomal fragment length sequence reads and low in sub-nucleosomal length sequence reads throughout as the DNA is protected by nucleosomes across each locus. FIG. 21B shows that the loci containing the transcription start sites have a lower level of nucleosomal fragments as the DNA has been unwound from the nucleosomes across these loci. FIG.21C shows an increase in sub-nucleosomal length sequence reads covering the TSS as these loci have been bound to RNA polymerase II Complex (Pol II) resulting in an increase in fragment lengths associated with the lengths of DNA bound to Pol II.Attorney Docket No. 47697-763601Example 2. Fragmentomics of shorter cfDNA align with existing in vivo MNase-seq results

[0238] It was demonstrated that fragmentomic signal is observed at TSS (specifically promoters) in cfDNA captured in both process 1 and process 2. Interestingly, both assays were observed to have inverted / complimentary signal with respect to each other; Process 2 exhibits the expected nucleosomal depletion around open regions while Process 1 exhibits enriched cfDNA at open chromatin regions. This has been leveraged to produce useful features for biomarker discovery, as well as predicting the positivity of Karius tests.

[0239] One major challenge in applying these findings in a production setting is the low coverage of each sample (median human coverage is 0.1, each TSS is -IKbp, each read is ~50 bp => ~2 reads per promoter); Process 2 biomarker runs are sequenced to 20X depth while the Process 1 results are processed by coarse graining. Additionally, promoter chromatin availability is highly correlated across cell-types, which in conjunction with low coverage complicates celldeconvolution. Therefore, we aimed to find additional loci of interest that exhibit strong fragmentomic signatures in this example.

[0240] Experimental design: In order to boost signal to noise ratio, 8 production sequencingruns were aggregated into a meta-sample. All alignments were collected into one bam, with the sample identifier stored as a read group tag to perform enrichment analysis, e.g., coverage peaks in positive versus negative production Karius tests. Similarly, the Biomarker Foundation study (NC- 13280, NC- 13295) were aggregated to obtain the composite Process 2 sample used in the below analysis. All alignments / fragmentomic processing was performed with the exact same alignment protocol. The only variation is on the samples chosen.

[0241] Host cfDNA patterned at ENCODE cCREs

[0242] 1. Overview of candidate cis-regulatory elements (cCRE).

[0243] The ENCODE project goal is to “build a comprehensive parts list of functional elements in the human genome”. As a core part of this effort, ENCODE has compiled a registry of candidate cis-regulatory elements (cCRE) which are the subset of DNase hypersensitive sites found within the ENCODE sample-set that are further supported by either i) histone modifications (H3K4me3, H3K27ac) or CTCF CHiP-seq experiments.

[0244] cCREs are then classified into 4 possible regulatory elements: 1) Promoter-like signature (PLS): Loci that fall within 200 bp of an annotated TSS and have high DNase and H3K4me3 signals. Enhancer-like signature (ELS): Have high DNase and H3K27ac with low H3K4me3 signal. If locus is within 200 bp of a TSS, it is labeled as 2) a proximal ELS. Otherwise it is labelled as 3) a distal ELS. 4) CTCF-only: ENCODE locus not supported by either H3K4me3 orAttorney Docket No. 47697-763601H3K27ac. At present, there are over 1,000,000 cCREs annotated by the ENCODE project, a substantial increase in feature-space dimension.

[0245] As shown in FIG.5A, dominant peaks (green dotted line) in the average coverage profile of Process 1 correspond nicely to ENCODE loci (red dotted line). Peaks were detected using scipy.signal.find_peaks under the constraint that the prominence was 20% of the average coverage to filter out noise-induced peaks. FIGs. 5A-5B shows the sequencing depth peaks from Process 1 cluster near the ENCODE loci. In FIG. 5A, the black line shows the coverage profile of the aggregated sample, averaged over 1 kbp window using a first order savgol filter. The red lines denote the ENCODE cCREs, and the green lines denote our de -novo peaks detected. FIG.5B shows that the de-novo coverage peaks (left) are statistically closer to ENCODE than randomly chosen loci (right).

[0246] We note that there appears to be other, subdominant peaks, we could use as additional features. However, due to their lack of annotation / characterization, we will focus only upon coverage patterns centered on cCREs and will use the ENCODE loci as our regions of interest.

[0247] 2. Patterns of coverage differ substantially across Process 1 and Process 2 assays. Process 1 cfDNA: consistent with small protein bound duplex.

[0248] Promoter enrichment is observed from Process 1, which recapitulates earlier analyses in Example 1. Averaged across all promoter sites, there is a 60% enrichment of coverage at the center of a promoter than its neighboring region. Moreover, similar enrichments were observed at promoters (FIG. 6A) and CTCF-only domains (FIG. 6D). FIGs. 6A-6D depict that the Process 1 coverage is enriched at ENCODE loci. (FIG. 6A: Promoter loci; FIG. 6B: Proximal enhancers; FIG. 6C: distal enhancers; FIG. 6D: CTCF-only). We note that the CTCF-only sites show a remarkable (~150 bp) periodicity within the < 120 bp cfDNA fraction, consistent with protein binding between nucleosomes. This complementary pattern was orthogonally confirmed.Process 2 cfDNA: consistent with nucleosomal bound DNA.

[0249] Process 2 cfDNA exhibits promoter depletion, which recapitulates the earlier analyses in Example 1. Additionally, qualitatively similar pattern is also observed in the short (<75 bp) cfDNA, suggesting that these are not the same short fragments we measure in Process 1. Patterns at promixal / distal enhancers, as well as CTCF-only sites have qualitatively similar patterns to Process 1. FIGs.7A-7D depict that the Process 2 coverage is depleted at promoters and enriched at other ENCODE cCREs. (FIG. 7A: Promoter loci; FIG. 7B: Proximal enhancers; FIG. 7C: distal enhancers; FIG. 7D: CTCF-only).

[0250] 3. cfDNA coverage agrees quantitatively with DNase experiments

[0251] Beyond simply observing that any promoter can be expected to exhibit more cfDNA than neighboring genomic regions, we would like to assess the hypothesis more quantitativelyAttorney Docket No. 47697-763601and ask whether open chromatin regions exhibit systematically more enrichment than closed regions. To this end, we leveraged the atlas of ENCODE DNase Z-scores for different cell-types. Specifically, ENCODE provides a normalized estimate for how sensitive each ENCODE accession is, across 1,518 cell types. For the below analysis, we used the CD4 T-cell background but note that all PBMC cells exhibited highly correlated z-scores. Furthermore, for all plots below, z-scores (for T-cells) were partitioned into 5 percentiles of open / closed, with yellow denoting the most “open” cCREs and blue denoted the most “closed”.

[0252] Process 1: consistent at all ENCODE loci.

[0253] The longer fragments of Process 1 (>120 bp) appeared to have recapitulated the “depletion” of nucleosomes around open chromatin regions that is observed in Process 2. Moreover, the short fragments exhibit subdominant coverage in the “very open” regions (yellow). The odd shape of the proximal enhancer regions is easier to interpret under open / closed grouping; very open proximal enhancers show enriched “CTCF” like punctuated coverage at the center and two neighboring peaks. Closed regions show a similar enrichment but also a depletion of the neighboring peaks. FIG.8 A shows the Process 1 coverage pattern at promoters, partitioned by open / closed chromatin in T-cells. FIG. 8B shows the Process 1 coverage pattern at proximal enhancers, partitioned by open / closed chromatin in T-cells. FIG. 8C shows the Process 1 coverage pattern at distal enhancers, partitioned by open / closed chromatin in T-cells. FIG. 8D shows the Process 1 coverage pattern at CTCF-only loci, partitioned by open / closed chromatin in T-cells. The Z-scores were partitioned into 5 percentiles of open / closed, with yellow denoting the most “open” cCREs and blue denoted the most “closed”.

[0254] The results also hold at a more granular scale; as shown in FIG. 8E with the scatter ENCODE z-score (how open the chromatin of each region is thought to be) to coverage ratio at each promoter. The correlation for each classification is shown in FIG.8E. Specifically, the ratio used was:22 reads G [—500, +500]x = - 22 reads G [-1500, -1000] + 22readse [+1000, +1500]

[0255] Process 2: consistent predominately at promoters; marginally at proximal enhancers.

[0256] The short fragments from Process 2 (<75 bp) do not recapitulate the results from Process 1. As depicted in FIG. 9A, it looks to be qualitatively similar to the “nucleosomal” pattern seen in longer fragments (<125 bp). This is consistent with our hypothesis that the 50 bp peak in Process 2 is dominated by ssDNA that is generated by dissociated nucleosomal fragments. FIG.9A shows the Process 2 coverage pattern at promoter loci, partitioned by open / closed chromatinAttorney Docket No. 47697-763601in T-cells. The Z-scores were partitioned into 5 percentiles of open / closed, with yellow denoting the most “open” cCREs and blue denoted the most “closed”. Signal is also observed in proximal enhancers that is consistent with that observed in Process 1; more “open” proximal enhancer regions show nucleosomal depletion in the two neighboring peaks flanking left and right of center. FIG. 9B shows the Process 2 coverage pattern at proximal enhancer loci, partitioned by open / closed chromatin in T-cells. Again, short fragments more-or-less mirror this pattern and exhibit none of the signal seen in Process 1.

[0257] Distal enhancers and CTCF show no obvious separation between the different open / closed partitions. In other words, we don’t see a systematic depletion of nucleosomes in these regions. FIG. 9C shows the Process 2 coverage pattern at distal enhancer loci, partitioned by open / closed chromatin in T-cells. FIG. 9D shows the Process 2 coverage pattern at CTCF-only loci, partitioned by open / closed chromatin in T-cells.

[0258] As with Process 1, the results suggested by the above plots also hold at a more granular scale with Process 2; as shown in FIG. 9E with the scatter ENCODE z-score (how open the chromatin of each region is thought to be) to coverage ratio at each promoter. The correlations for each classification also shown in FIG. 9E.

[0259] Additionally, we have demonstrated that all such coverage signals are observed even at the individual sample level and do not rely on aggregation, though the coverage profiles are considerably noisier. FIG. 10A shows that the Process 1 cfDNA coverage is enriched at ENCODE loci within a single sample. Additionally, we can still observe patterns as a function of open / closed states within each ENCODE cCRE at the individual sample level, as depicted in FIG. 10B.Example 3. Human short cfDNA fragmentation patterns reveal infection and site of infection

[0260] The inclusion of enhancers may assist in identifying a site of infection. As a proof of concept, we analyzed one commercial sample with ICD-10 code T86.13, Kidney transplant infection and compared to ICD-10 code R50.9, Fever, unspecified. Process 1 was used to capture the cfDNA analyzed in this experiment. We found that the distal enhancers show the most celltype to cell-type variability.

[0261] Using the ENCODE z-score atlas, we formed a strong prior as to what feature space allows us to disambiguate cell-specific signal the easiest. Under the assumption that the predominant background of cfDNA we measure arises from PBMCs, we ask what cCREs provide us the clearest signal of kidney cell apoptosis. As shown in FIG. 11, distal enhancers showed the most variation (the least correlation). In order to control for the expected correlationAttorney Docket No. 47697-763601between kidney cell chromatin state and T-cell chromatin state, we focused our attention on just the enhancers that fall within the bottom 20% of chromatin z-score. In other words, all enhancers are expected to be closed if PBMCs were the only contribution to the cfDNA population. We then partitioned the remaining enhancers into 5 high / medium / low z-score percentiles. The resulting plots are shown in FIGs. 12A-12B. The enhancer coverage shows enrichment for kidney cells in transplant, as depicted in FIG. 12A (left). Conversely, cfDNA from the unspecified fever sample showed no quantitative enrichment in “open” kidney enhancers (yellow) over “closed” blue, as depicted in FIG. 12A (right). FIG. 12B shows the result from the aggregated samples.

[0262] The results from Process 1 suggest that DNA-protein complexes involving transcription factors are enriched, and gene expression can be inferred from the coverage patterns of ultra-short cfDNA (~50 bp) at various regulatory elements (e.g., enhancers and CTCF sites). Additional information can also be extracted from these coverage patterns. For instance, in this example, human fragmentomics of ultra-short cfDNA were leveraged to infer the site of infection in a patient. Among the various regulatory elements, enhancers emerge as the strongest candidates for deriving site-specific signals. According to DNase experiments obtained from ENCODE, enhancers have the greatest variability across cell types. Conversely, promoters are the most correlated. This is true across many cell type comparisons. There are also many more enhancers than promoters (an order of magnitude more). At production coverage, this greatly amplifies the signal by allowing us to “coarse-grain” longitudinally along the genome. However, this complicates the underlying model. The connections between enhancers and genes are complicated, although databases like PEREGRINE exist. Additionally, Factorbook has predictions of TF binding sites for each ENCODE accession, allowing us to coarse-grain our many features by this.

[0263] We have demonstrated that ultra-short cfDNA fragments are likely associated with transcription factor binding and / or RNA polymerase activity. There is a positive quantitative correlation between chromatin accessibility, as measured by ENCODE DNase experiments, and cfDNA fragmentomic biomarkers. Chromatin accessibility, in turn, directly influences gene expression. This finding sharply contrasts with the conventional nucleosome depletion model employed by others. Furthermore, this quantitative relationship was observed not only at well-established transcription start sites but also at other transcription factor-bound regulatory elements, such as CTCF sites and distal enhancers. As a result, Karius' fragmentomics assay incorporates all known regulatory elements and transcription factor binding sites into its fragmentomic biomarker set.Attorney Docket No. 47697-763601

[0264] We also investigated whether promoter enrichment of human ultrashort cfDNA can predict infection state of the subjects. Preliminary results identified transcription factors and regulatory elements that are enriched in infections, which are mostly transcription factors associated with immune responses. See FIG. 13A. Additionally, samples from subjects who have received xenograft transplants (heart) were also analyzed. Human cfDNA fragmentomic profiles (both genomic and mitochondrial) were monitored over time, as depicted in FIG. 13B.Expression of various genes were inferred based on the fragmentomic results.

Claims

Attorney Docket No. 47697-763601CLAIMS WHAT IS CLAIMED IS:

1. A method for preparing a nucleic acid library from a subject useful for a fragmentomics analysis, the method comprising:a) providing a substantially cell-free sample comprising cell-free DNA (cfDNA) from a subject, wherein the cfDNA comprises double-stranded cell-free DNA (dscfDNA) and single-stranded cell-free DNA (sscfDNA);b) producing a fraction of the cfDNA in (a) by:i. size discrimination of the cfDNA; andii. selectively removing cfDNA fragments less than approximately 25 nucleotides in length and greater than approximately 120 nucleotides in length to enrich for cfDNA fragments between 25-120 nucleotides in length; andc) analyzing a fragment length profile of the cfDNA that is between 25-120 nucleotides in length to determine gene expression.

2. The method of claim 1, wherein the cfDNA fraction after (b) comprises:i) at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least 90% of cfDNA fragments with a length range between 25 nucleotides in length and 120 nucleotides in length;ii) a greater proportion of cfDNA fragments that are less than 120 nucleotides in length than cfDNA fragments that are greater than 120 nucleotides in length; or iii) a combination of i) and ii).

3. The method of claim 2, wherein the cfDNA fraction after (b) comprises at least 50% of fragments with a length range between 25 nucleotides in length and 120 nucleotides in length.

4. A method of analyzing gene activation or replication comprising:a) obtaining a sample comprising fragments of cell-free DNA (cfDNA) derived from a mammalian subject;b) physically enriching the sample for cfDNA fragments below about 120 nucleotides in length, thereby producing a fraction of cfDNA comprising cfDNA fragments associated with one or more mammalian regulatory elements;c) sequencing the fraction of cfDNA;d) identifying a subset of cfDNA fragments of the fraction of cfDNA associated with the one or more mammalian regulatory elements;Attorney Docket No. 47697-763601e) preparing a fragmentomic profile of the subset of cfDNA fragments, wherein the fragmentomic profile comprises a parameter selected from the group consisting of:i) fragment length,ii) frequency or abundance of fragments of specific lengths, iii) fragment genomic position, andiv) any combination thereof; andf) comparing the fragmentomic profile with a reference fragmentomic profile and detecting the gene activation or replication based on an increase in cfDNA fragments of 40 to 60 nucleotides in length compared to the reference fragmentomic profile.

5. A method of analyzing cell-free DNA (cfDNA) to detect gene expression, the method comprising:quantifying a first number of cell-free DNA (sscfDNA) sequence reads with a length range between 25-120 nucleotides in length that span a first genetic locus and comparing the quantity to a second number of sscfDNA sequence reads with a length range between 25-120 nucleotides in length that span a second genetic locus; and a) detecting gene expression associated with the first locus based in part on the first number of sscfDNA sequence reads being greater than the second number of sscfDNA sequence reads; orb) detecting an absence of gene expression associated with the first locus based in part on the first number of sscfDNA sequence reads being equal to or less than the second number of sscfDNA sequence reads.

6. A method for analyzing cell-free DNA (cfDNA) from a biological sample, the method comprising:a) obtaining sequencing data from cfDNA fragments from the biological sample, wherein the cfDN A fragments comprise nucleic acid fragments having a length between 24 nucleotides and 120 nucleotides;b) mapping the cfDNA fragments to a reference genome to determine genomic positions of the cfDNA fragments;c) computing a coverage metric at a plurality of candidate cis-regulatory elements by quantifying a number of cfDNA fragments mapping to each candidate cis- regulatory element relative to flanking genomic regions;d) correlating the coverage metric at each candidate cis-regulatory element with a chromatin accessibility measurement for a plurality of cell types to generate a cell-type specific fragmentomic signature; andAttorney Docket No. 47697-763601e) determining a relative contribution of each cell type to the cfDNA in the biological sample based in part on the cell-type specific fragmentomic signature.

7. The method of claim 6, wherein the candidate cis-regulatory elements comprise at least one of promoter-like signature elements, enhancer-like signature elements, or CTCF-binding elements.

8. The method of claim 6, wherein the enhancer-like signature elements comprise proximal enhancer elements located within 200 nucleotides of a transcription start site and distal enhancer elements located more than 200 nucleotides from a transcription start site.

9. The method of claim 6, wherein the chromatin accessibility measurement comprises DNase hypersensitivity data from an ENCODE database.

10. The method of claim 6, wherein the coverage metric is computed as a ratio of cfDNA fragment coverage within a region of interest centered on each candidate cis-regulatory element to cfDNA fragment coverage in flanking regions outside the region of interest.

11. The method of claim 10, wherein the region of interest comprises a window of approximately 500 nucleotides on each side of a center of the candidate cis-regulatory element, and wherein the flanking regions comprise windows of approximately 500 nucleotides located between 1000 and 1500 nucleotides from the center of the candidate cis-regulatory element.

12. The method of claim 6, further comprising aggregating the coverage metric across multiple candidate cis-regulatory elements associated with a common gene or functional annotation to generate an aggregated fragmentomic feature.

13. The method of claim 12, wherein the functional annotation comprises at least one of a gene ontology term, a biological pathway, and a transcription factor binding site.

14. The method of any one of the preceding claims, further comprising normalizing the sequencing data to correct for sample-specific biases in fragment length and GC content using spike-in control molecules.

15. The method of any one of the preceding claims, wherein the nucleic acid fragments having a length between 24 nucleotides and 120 nucleotides comprise transcription factor-bound DNA fragments.

16. The method of any one of claims 1-3, wherein the analyzing the fragment length profile of the cfDNA that is between 25-120 nucleotides in length to determine gene expression comprises the method of detecting gene expression of any one of claims 4-15.

17. The method of any one of the preceding claims, wherein the analyzing comprises analyzing cfDNA fragments sequence reads that are less than 90 nucleotides in length.Attorney Docket No. 47697-76360118. The method of any one of the preceding claims, wherein the analyzing comprises analyzing cfDNA fragments sequence reads that are less than 80 nucleotides in length.

19. The method of any one of the preceding claims, further comprising classifying a disease state of a subject from which the biological sample was obtained based on the relative contribution of each cell type to the cfDNA.

20. The method of any one of the preceding claims, wherein the cfDNA fragments 40-60 nucleotides in length are double-stranded cell-free DNA (dscfDNA).

21. The method of any one of the preceding claims, further comprising analyzing microbial cell-free DNA (mcfDNA) sequencing reads, wherein the mcfDNA sequencing reads are derived from mcfDNA from the mammalian subject.

22. The method of any one of the preceding claims, further comprising preparing a microbial fragmentomic profile of the mcfDNA sequencing reads.

23. The method of any one of the preceding claims, further comprising preparing a library by attached adapters comprising an overhang to single-stranded cfDNA fragments within the sample.

24. The method of any one of the preceding claims, wherein the cfDNA fragments 40-60 nucleotides in length comprise single-stranded cfDNA or a mixture of single-stranded and double-stranded cfDNA.

25. The method of any one of the preceding claims, further comprising preparing a library that preferentially captures single-stranded cfDNA or both single-stranded and double-stranded cfDNA.

26. The method of any one of the preceding claims, wherein the mammalian regulatory element is an enhancer, a cis-regulatory element, or distal regulatory element.

27. The method of any one of the preceding claims, wherein the mammalian regulatory element is a distal regulatory element that is a silencer or an insulator.

28. The method of any one of the preceding claims, wherein the distal regulatory element is at least 500 nucleotides in length or 1000 nucleotides in length away from a transcription start site (TSS) regulated by the distal regulatory element or from a gene regulated by the distal regulatory element.

29. The method of any one of the preceding claims, wherein the mammalian regulatory element is a promoter.

30. The method of any one of the preceding claims, wherein the fragments 40-60 nucleotides in length are evident as a distinct peak when fragment length is graphically compared with fragment quantity.Attorney Docket No. 47697-76360131. The method of claim 30, wherein the distinct peak is about 20 to about 120 bases in width.

32. The method of any one of the preceding claims, further comprising physically enriching the cfDNA for cfDNA fragments that are at most about 120 nucleotides in length.

33. The method of any one of the preceding claims, wherein the mammalian regulatory element binds a transcription factor selected from the group consisting of: TFIID, TFIIA, TFIIB, TFIIF, TFIIH, FOXP2, SOX2, PAX6, HOX, GATA3, OTX2, TP53, MYC, NF-kB, API, STAT3, HIFla, ER, AR, GR, PPARy, FOXP3, NRF2, NFAT, IRF3 / IRF7, T-bet, RFX5, OCT4, NANOG, KLF4, c-MYC, CLOCK / BMAL1, SREBP1, FOXO1, CTCF, E2F, SP1, RUNX1, MEF2, CREB, EGR1, RelA / p65, SMAD, and YAP / TAZ.

34. The method of any one of the preceding claims, wherein the cfDNA comprises human cell-free DNA (cfDNA).

35. The method of any one of the preceding claims, wherein the cfDNA comprises human non-fetal cfDNA.

36. The method of any one of the preceding claims, wherein the mammalian regulatory element controls expression of a gene preferentially expressed in non-hematopoietic cells over hematopoietic cells.

37. The method of any one of the preceding claims, wherein the mammalian regulatory element controls expression of a gene preferentially expressed in hematopoietic cells over non-hematopoietic cells.

38. The method of claim 37, further comprising preparing a profile of fragment lengths associated with multiple mammalian regulatory elements.

39. The method of claim 38, wherein the multiple mammalian regulatory elements are associated with regulatory factors or transcription factors correlated with immune system activation.

40. The method of claim 39, wherein the regulatory factors or transcription factors comprise one or more transcription factors selected from the group consisting of: ZNF776, STNECTC1-STN1-TEN1, SQSTM1, RUVBL2, FOXCI, PAF1, PAX-5, andNR4A2.

41. The method of any one of the preceding claims, wherein the regulatory elements are not bound to a Transcription Start Site (TSS).

42. The method of any one of the preceding claims, wherein the sample is a bodily fluid.

43. The method of any one of the preceding claims, wherein the sample is a plasma sample.

44. The method of any one of the preceding claims, wherein the sample comprises a whole blood, a plasma, a serum, a lymph, a synovial fluid, a cerebrospinal fluid (CSF), a saliva, a gastric juice, a bile, a pancreatic juice, an intestinal fluid, a respiratory tract mucosal secretion,Attorney Docket No. 47697-763601a semen, a cervical mucus, a vaginal secretion, a urine, a sebum, a breast milk, an amniotic fluid, a pericardial fluid, a pleural fluid, a peritoneal fluid, a bronchoalveolar lavage (BAL), a gastric lavage, a peritoneal lavage, a nasal lavage, a bladder lavage, a rectal lavage, a wound lavage, a joint lavage (arthrocentesis), an eye lavage, a sinus lavage, or any combination thereof.

45. The method of any one of the preceding claims, wherein no extraction of nucleic acids has been performed on the sample prior to adding sequencing adaptors to the cfDNA.

46. The method of any one of the preceding claims, wherein the sample is a sample of purified cfDNA.

47. The method of any one of the preceding claims, wherein the reference profile of fragment lengths relates to fragments associated with a mammalian regulatory element associated with a gene with inactive or low expression.

48. The method of any one of the preceding claims, wherein the sequencing is conducted at a sequencing depth of 10x-30x.

49. The method of any one of the preceding claims, wherein the cfDNA is not digested with micrococcal nuclease.

50. The method of any one of the preceding claims, wherein the size discrimination or physical enriching of the cfDNA is accomplished by using gel electrophoresis, automated gel electrophoresis, chromatography, filtration, centrifugation, magnetic beads, a spin column, or any combination thereof.

51. The method of any one of the preceding claims, wherein the analyzing further comprises generating a cell-type deconvolution output indicating proportions of cfDNA originating from different cell populations.

52. The method of claim 51, further comprising detecting a site of an infection based on the proportions of cfDNA originating from different cell populations.

53. A method of detecting an infection in a subject comprising:a) preparing a sequencing library by attaching adapters to cfDNA fragments within a sample from the subject;b) performing Next Generation Sequencing (NGS) on the sequencing library and producing sequencing reads for cfDNA fragments associated with one or more mammalian regulatory elements that regulate an immune response; c) preparing a fragmentomic profile for the cfDNA fragments associated with one or more mammalian regulatory elements that regulate an immune response wherein the fragmentomic profile comprises fragment length, quantity ofAttorney Docket No. 47697-763601fragments of a specified length, fragment genomic position, or a combination thereof;d) comparing the fragmentomic profile with a reference fragmentomic profile and detecting an immune response in the subject based on an increase in fragments 40-60 nucleotides in length compared to the reference fragmentomic profile; e) after detecting the immune response in the subject, performing a genomics analysis of a sample of cfDNA from the subject to identify microbial cell-free nucleic acids; andf) based on the microbial cell-free nucleic acids, identifying a candidate microbe associated with the immunological response in the subject.

54. The method of claim 53, further comprising analyzing microbial cell-free DNA (mcfDNA) sequencing reads, wherein the mcfDNA sequencing reads are derived from mcfDNA from the mammalian subject.

55. The method of claim 54, further comprising preparing a microbial fragmentomic profile of the mcfDNA sequencing reads.

56. The method of any one of claims 53 to 55, wherein the regulatory factors or transcription factors comprise one or more transcription factors selected from the group consisting of: ZNF776, STN1: CTC1-STN1-TEN1, SQSTM1, RUVBL2, FOXCI, PAF1, PAX-5, and NR4A2.

57. The method of any one of claims 53 to 56, further comprising detecting a disease or disorder in the subject based on a relative level of gene activation or gene expression compared to a reference value.

58. The method of claim 57, further comprising administering a treatment to the subject to treat the disease or disorder.

59. The method of claim 58, wherein the disease or disorder comprises a cancer.

60. The method of claim 59, wherein the cancer comprises a lung cancer, a colorectal cancer, a breast cancer, a prostate cancer, a liver cancer, or pancreatic cancer.

61. The method of claim 58, wherein the disease or disorder comprises an autoimmune disease or inflammatory disease.

62. The method of claim 61, wherein the autoimmune disease or inflammatory disease comprises an inflammatory bowel disease, systemic lipid erythematosus, rheumatoid arthritis, or psoriatic arthritis.

63. The method of claim 58, wherein the disease or disorder comprises a liver disease, a kidney disease, a cardiovascular disease, a neurodegenerative or neurological disease, a pulmonary disease, a fibrotic disease, or a combination thereof.Attorney Docket No. 47697-76360164. The method of claim 58, wherein the disease or disorder comprises an infectious disease.

65. The method of claim 58, wherein the disease or disorder comprises a pregnancy-related condition.

66. The method of claim any of claims 53 to 65, wherein the fragments 40-60 nucleotides in length are double-stranded.

67. The method of any one of the preceding claims, further comprising preparing a DNA library by attaching double-stranded adapters to double-stranded cfDNA within the sample.

68. The method of any one of the preceding claims, wherein the fragments 40-60 nucleotides in length comprise single-stranded cfDNA or a mixture of single-stranded and double-stranded cfDNA.

69. The method of claim 53, wherein the mammalian regulatory element is an enhancer, a cis-regulatory element, or distal regulatory element.

70. The method of claim 53, wherein the mammalian regulatory element is a distal regulatory element that is a silencer or an insulator.

71. The method of claim 53, wherein the fragments 40-60 nucleotides in length are evident as a distinct peak when fragment length is graphically compared with fragment quantity.

72. The method of claim 71, wherein the distinct peak is about 20 to about 120 nucleotides in length in width.

73. The method of any one of the preceding claims, wherein the cfDNA is human cell-free DNA (cfDNA).

74. The method of any one of the preceding claims, wherein the cfDNA is human non-fetal cfDNA.

75. The method of any one of the preceding claims, wherein the mammalian regulatory element controls expression of a gene preferentially expressed in hematopoietic cells over non-hematopoietic cells.

76. The method of any one of the preceding claims, wherein the mammalian regulatory element controls expression of a gene preferentially expressed in peripheral blood mononuclear cells (PBMCs).

77. The method of any one of the preceding claims, wherein the sample is a sample of purified cfDNA.

78. The method of any one of the preceding claims, wherein the reference profile of fragment lengths relates to fragments associated with a mammalian regulatory element associated with a gene with inactive or low expression.Attorney Docket No. 47697-76360179. The method of any one of the preceding claims, wherein the NGS is conducted at a sequencing depth of 10x-30x.

80. The method of any one of the preceding claims, wherein the cfDNA is not digested with micrococcal nuclease.

81. A method for identifying and analyzing cell-free deoxyribonucleic acid (cfDNA) fragments from a human subject, the method comprising:(a) subjecting the cfDNA fragments to library preparation and high-throughput sequencing to generate sequence information representative of cfDNA fragments from the human subject, wherein the cfDNA fragments have not been subjected to micrococcal nuclease digestion; (b) performing multi-parametric analysis of the aligned sequence information, thereby generating a multi-parametric model representative of the cfDNA fragments, wherein the multi-parametric model comprises one or more parameters selected from the group consisting of: (i) length of cfDNA fragments that align with one or more mammalian regulatory elements; (ii) quantity of cfDNA fragments that align with genomic positions within the one or more mammalian regulatory elements; and (iii) quantity of cfDNA fragments that align with mcfDNA sequences; and(c) performing, with a computer, statistical analysis with a trained classifier to classify the multi-parametric model as being indicative of gene activation or replication based on projected binding of the cfDNA fragments to non-nucleosomal regulatory elements.

82. A method for analyzing gene activation or replication in a subject comprising:a) sequencing cell-free DNA (cfDNA) obtained from the subject;b) identifying and quantifying fragment lengths of the cfDNA at genomic regions associated with a mammalian regulatory element, thereby obtaining a profile of fragment lengths associated with the mammalian regulatory element; andc) detecting the gene activation or replication when the profile of fragment lengths comprises an increase of fragments 40-60 nucleotides in length compared to a profile of fragment lengths of a mammalian regulatory element associated with a gene with inactive or low expression.

83. A method of assessing gene activation or replication comprising:a) physically enriching a sample for cfDNA fragments below about 200 nucleotides in length to produce an enriched sample;b) sequencing the enriched sample to produce a set of fragment sequences;c) preparing a fragmentomic profile of a subset of the fragment sequences associated with one or more mammalian regulatory elements;Attorney Docket No. 47697-763601d) comparing the fragmentomic profile with a reference fragmentomic profile; and e) assessing the gene activation or replication based on an increase in cfDNA fragments 40-60 nucleotides in length compared to the reference fragmentomic profile.

84. The method of any one of claims 81 to 83, further comprising analyzing microbial cell-free DNA (mcfDNA) sequencing reads, wherein the mcfDNA sequencing reads are derived from mcfDNA from the mammalian subject.

85. The method of claim 84, further comprising preparing a microbial fragmentomic profile of the mcfDNA sequencing reads.

86. The method of any one of claims 81 to 83, wherein the fragments 40-60 nucleotides in length are evident as a distinct peak when fragment length is graphically compared with fragment quantity.

87. The method of claim 86, wherein the distinct peak is about 20 to about 120 nucleotides in length in width.

88. The method of any one of the preceding claims, wherein the subject is a mammal.

89. The method of claim 81, wherein the subject is not pregnant or suspected of being pregnant.

90. The method of claim 81, wherein the subject does not have cancer or is not suspected of having cancer.

91. The method of any one of the preceding claims, wherein the subject is a human.

92. The method of any one of the preceding claims, wherein the method further comprises analyzing cfDNA that has been marked as sscfDNA.

93. The method of claim 92, wherein the marking comprises contacting the ssDNA in the sample with an enzyme that preferentially converts cytosine residues to uracil residues in ssDNA.

94. The method of claim 93, wherein the contacting with the enzyme that preferentially converts cytosine residues to uracil residues in ssDNA is performed after the contacting the ssDNA in the sample with the methylcytosine dioxygenase TET2.

95. The method of claim 92 or 93, wherein the enzyme that preferentially converts cytosine residues to uracil residues in ssDNA is an APOBEC enzyme.

96. The method of claim 92, wherein the marking comprises ligating an adapter specific to single-stranded DNA (ssDNA) to the sscfDNA or incorporating an adapter specific to ssDNA into the sscfDNA via primer extension.Attorney Docket No. 47697-76360197. The method of any one of the preceding claims, wherein the method further comprises digesting double stranded DNA (dsDNA).

98. The method of claim 97, wherein the digesting dsDNA comprises adding a dsDNA specific nuclease to the sample.

99. The method of any one of the preceding claims, wherein the method further comprises adding process control molecules to the sample.

100. The method of any one of the preceding claims, wherein the analyzing sequence reads comprises analyzing at least 1000, at least 10,000, at least 100,000, or at least 1,000,000 sequence reads.

101. The method of any one of the preceding claims, wherein the analyzing sequence reads comprises aligning at least 1000, at least 10,000, at least 100,000, or at least 1,000,000 sequence reads.

102. A system configured to perform the method of any one of the preceding claims.

103. The system of claim 102, wherein the system comprises a computer readable memory operatively coupled to a processor, wherein the computer readable memory comprises instructions to perform the method of any one of the preceding claims.

104. A system for determining cell-type contributions from cell- free DNA, comprising:a) a processor; andb) a memory storing instructions that, when executed by the processor, cause the system to:i. receive sequencing data representing cfDNA fragments from a biological sample, wherein the cfDNA fragments are enriched for nucleic acid fragments having a length between 24 nucleotides and 120 nucleotides; ii. align the cfDNA fragments to genomic coordinates corresponding to candidate cis-regulatory elements;iii. calculate a coverage ratio for each candidate cis-regulatory element, wherein the coverage ratio comprises a ratio of cfDNA fragment coverage within a central window of the candidate cis-regulatory element to cfDNA fragment coverage in flanking regions;iv. compare the coverage ratio to reference chromatin accessibility data for a plurality of cell types; andv. output an estimate of cell-type proportions contributing to the cfDNA based on the comparison.

105. The system of any one of claims 102-104, wherein the system comprises:Attorney Docket No. 47697-763601a) a flowcell comprising a glass surface with surface-bound oligonucleotides, lanes or patterned nanowells, inlet and outlet ports for reagents, and sealed microfluidic channels;b) a reagent cartridge;c) a peristaltic or syringe pump;d) a waste reservoir; ande) an optical imaging system comprising a laser, an excitation optic, an emission filter, a high-sensitivity camera, and optical housing.

106. The system of any one of claims 102-104, wherein the system comprises:a) a nanopore array comprising a Complementary Metal Oxide Semiconductor (CMOS) sensor platform;b) a fluidics manifold and reagents channel;c) a temperature control unit;d) a reagent reservoir; ande) a waste reservoir.

107. The system of claim any one of claims 102-106, wherein the nucleic acid fragments comprise at least one of single-stranded DNA fragments and double-stranded DNA fragments.

108. The system of any one of claims 102-107, wherein the nucleic acid fragments have a length of less than 90 nucleotides.

109. The system of any one of claims 102-108, wherein the nucleic acid fragments have a length of less than 80 nucleotides.

110. The system of any one of claims 102-109, wherein the instructions further cause the system to normalize the sequencing data to correct for fragment length bias and GC content bias using spike-in control molecules having known concentrations and fragment lengths.

111. The system of any one of claims 102-110, wherein normalizing the sequencing data comprises fitting a response function to recovery rates of the spike-in control molecules and applying a weight to each cfDNA fragment based on the response function.

112. The system of any one of claims 102-111, wherein the reference chromatin accessibility data comprises DNase hypersensitivity z-scores from an ENCODE database for each of the plurality of cell types.

113. The system of any one of claims 102-112, wherein the candidate cis-regulatory elements comprise at least one of promoter-like signature elements, proximal enhancer elements located within 200 nucleotides of a transcription start site, distal enhancerAttorney Docket No. 47697-763601elements located more than 200 nucleotides from a transcription start site, and CTCF- binding elements.

114. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising:a) receiving fragment length and genomic position data for a plurality of cfDNA fragments obtained from a subject;b) selecting cfDNA fragments having a length between 24 nucleotides and 120 nucleotides;c) computing fragmentomic features at genomic loci corresponding to candidate cis-regulatory elements, wherein the fragmentomic features comprise coverage enrichment relative to flanking regions;d) correlating the fragmentomic features with cell-type specific chromatin accessibility profiles; ande) generating a cell-type deconvolution output indicating proportions of cfDNA originating from different cell populations.

115. The non-transitory computer-readable medium of claim 114, wherein the cfDNA fragments comprise at least one of single-stranded nucleic acid fragments and doublestranded nucleic acid fragments.

116. The non-transitory computer-readable medium of claim 114 or 115, wherein selecting cfDNA fragments comprises selecting fragments having a length of less than 90 nucleotides.

117. The non-transitory computer-readable medium of any one of claims 114-116, wherein selecting cfDNA fragments comprises selecting fragments having a length of less than 80 nucleotides.

118. The non-transitory computer-readable medium of any one of claims 114-117, wherein the candidate cis-regulatory elements comprise at least one of promoter-like signature elements, proximal enhancer elements located within 200 nucleotides of a transcription start site, distal enhancer elements located more than 200 nucleotides from a transcription start site, and CTCF-binding elements.

119. The non-transitory computer-readable medium of any one of claims 114-118, wherein the operations further comprise aggregating the fragmentomic features across multiple candidate cis-regulatory elements associated with a common functional annotation comprising at least one of a gene ontology term, a biological pathway, and a cell-type specific marker gene set.Attorney Docket No. 47697-763601120. The non-transitory computer-readable medium of any one of claims 114-119, wherein the operations further comprise classifying a disease state of the subject based on the cell-type deconvolution output, wherein the disease state comprises at least one of an inflammatory bowel disease, a colorectal cancer, and an organ transplant rejection.

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