Nucleic acid rearrangement and integration analysis

Abstract

Provided herein are methods and systems for identifying chimeric nucleic acid fragments (e.g., organism-pathogen chimeric nucleic acid fragments and chromosomal rearrangement chimeric nucleic acid fragments). Also provided herein are methods and systems for determining a pathogen integration spectrum or chromosomal rearrangement in a biological sample and determining a classification of a pathology based at least in part on the pathogen integration spectrum or chromosomal rearrangement in the biological sample. In certain aspects of the present disclosure, cell-free nucleic acid molecules from a biological sample are analyzed.

Description

[0001] Cross-references

[0002] This application claims the benefit of U.S. Provisional Application No. 62 / 691,890, filed June 29, 2018, which is hereby incorporated by reference in its entirety. Background Art

[0003] Many diseases and conditions can be associated with chromosomal rearrangements or integrations of pathogen (e.g., viral) nucleic acids into the genome of a host organism (e.g., human). For example, inter- and intrachromosomal rearrangements, such as translocations, can be associated with cancer. Additionally, viruses may be associated with approximately 20% of human cancer cases, and approximately 70% of cervical cancers and precancerous cervical lesions may be caused by infection with two human papillomavirus (HPV) types (16 and 18). Currently, for most solid tumors, chromosomal rearrangements or pathogen integration are primarily detected using tumor tissue samples obtained through tissue biopsy (which can involve large needles, endoscopy, or open surgery) and can be invasive, risky, expensive, and painful. Improved methods, systems, and computer-readable media are needed to identify chromosomal rearrangement and nucleic acid integration events using, for example, cell-free nucleic acid molecules from biological samples. Summary of the Invention

[0004] In one aspect, described herein is a method of analyzing a biological sample of an organism to determine a classification of a pathology, the method comprising: (a) analyzing a plurality of cell-free nucleic acid molecules from the biological sample to identify organism-pathogen chimeric nucleic acid fragments, wherein analyzing each of the plurality of cell-free nucleic acid molecules comprises: identifying a first end of each of the cell-free nucleic acid molecules as being from a first genome, identifying a second end of each of the cell-free nucleic acid molecules as being from a second genome, and identifying the organism-pathogen chimeric nucleic acid fragment when the first genome is a genome of a pathogen and the second genome is a genome of the organism, wherein the organism and the pathogen are different; and (b) determining the classification of the pathology based at least in part on the organism-pathogen chimeric nucleic acid fragments.

[0005] In some cases, identifying the first end as being from the first genome comprises obtaining a read sequence for each of the cell-free nucleic acid molecules and aligning at least a portion of the first end of the read sequence to a reference genome of the pathogen. In some cases, identifying the second end as being from the second genome comprises obtaining a read sequence for each of the cell-free nucleic acid molecules and aligning at least a portion of the second end of the read sequence to a reference genome of the organism. In some cases, analyzing each of the plurality of cell-free nucleic acid molecules comprises obtaining a read sequence for each of the cell-free nucleic acid molecules and identifying the organism-pathogen chimeric nucleic acid fragment when at least a portion of the first end of the read sequence aligns to a reference genome of the pathogen and at least a portion of the second end of the read sequence aligns to a reference genome of the organism. In some embodiments, the method further comprises obtaining a read sequence for the plurality of cell-free nucleic acid molecules by paired-end sequencing, wherein the paired-end sequencing generates a pair of read sequences for each of the plurality of cell-free nucleic acid molecules. In some cases, the pair of read sequences comprises a first read sequence for the first end of each of the cell-free nucleic acid molecules and a second read sequence for the second end of each of the cell-free nucleic acid molecules. In some cases, identifying the organism-pathogen chimeric nucleic acid fragment comprises aligning the first read sequence or a portion thereof to a reference genome of a pathogen, and aligning the second read sequence or a portion thereof to a reference genome of the organism. In some cases, identifying the organism-pathogen chimeric nucleic acid fragment comprises aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 consecutive nucleotides of the first read sequence to the reference genome of the pathogen, and aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 consecutive nucleotides of the second read sequence to the reference genome of the organism. In some cases, identifying the organism-pathogen chimeric nucleic acid fragment comprises aligning at least 50 consecutive nucleotides of the first read sequence to the reference genome of the pathogen, and aligning at least 50 consecutive nucleotides of the second read sequence to the reference genome of the organism. In some cases, the method further comprises determining a pathogen integration index based on the amount of organism-pathogen chimeric nucleic acid fragments from the biological sample; and determining the classification of the pathology based at least in part on the pathogen integration index. In some cases, determining the pathogen integration index comprises determining the amount of the plurality of cell-free nucleic acid molecules comprising a first end of the genome from the pathogen and a second end of the genome from the pathogen.In some cases, determining the pathogen integration index comprises comparing the amount of the organism-pathogen chimeric nucleic acid fragment to the amount of the plurality of cell-free nucleic acid molecules comprising a first end from the genome of the pathogen and a second end from the genome of the pathogen. In some cases, the comparison comprises determining a ratio of the amount of the organism-pathogen chimeric nucleic acid fragment to the amount of the plurality of cell-free nucleic acid molecules comprising a first end from the genome of the pathogen and a second end from the genome of the pathogen. In some cases, identifying the organism-pathogen chimeric nucleic acid fragment further comprises analyzing an amplification reaction of the plurality of cell-free nucleic acid molecules from the biological sample. In some cases, the amplification reaction comprises a first primer complementary to a first target sequence in the genome of the pathogen and a second primer complementary to a second target sequence in the genome of the organism. In some cases, the amplification reaction comprises a polymerase chain reaction (PCR). In some cases, the method further comprises analyzing the sequence of an amplicon produced by the amplification reaction. In some cases, the pathogen comprises a virus. In some cases, the virus comprises Epstein-Barr virus DNA, human papillomavirus DNA, hepatitis B virus DNA, hepatitis C virus nucleic acid, or fragments thereof. In some cases, the virus is human papillomavirus. In some cases, the pathological classification comprises the presence of cancer. In some cases, the cancer is selected from the group consisting of bladder cancer, bone cancer, brain tumor, breast cancer, cervical cancer, colorectal cancer, esophageal cancer, gastrointestinal cancer, hematopoietic malignancy, head and neck squamous cell carcinoma, leukemia, liver cancer, lung cancer, lymphoma, myeloma, nasal cancer, nasopharyngeal cancer, oral cancer, oropharyngeal cancer, ovarian cancer, prostate cancer, sarcoma, gastric cancer, and thyroid cancer. In some cases, the pathological classification comprises the type of cancer. In some cases, the type of cancer comprises cervical cancer or head and neck squamous cell carcinoma. In some cases, the plurality of cell-free nucleic acid molecules comprises deoxyribonucleic acid molecules. In some cases, the organism is an animal. In some cases, the animal is a mammal. In some cases, the mammal is a human. In some cases, the biological sample is plasma, serum, or urine. In some cases, the biological sample is plasma. In some cases, analyzing the plurality of cell-free nucleic acid molecules from the biological sample is performed by a computer system.

[0006] In one aspect, described herein is a method of identifying organism-pathogen chimeric cell-free nucleic acid fragments in a biological sample from an organism, the method comprising: (a) determining a first end of a cell-free nucleic acid molecule as being from a first genome and determining a second end of the cell-free nucleic acid molecule as being from a second genome; and (b) identifying the organism-pathogen cell-free nucleic acid fragment when the first genome is a genome of a pathogen and the second genome is a genome of the organism, wherein the organism and the pathogen are different.

[0007] In some cases, determining the first end as being from the first genome comprises obtaining a read sequence of the cell-free nucleic acid molecule and aligning at least a portion of the first end of the read sequence to a reference genome of the pathogen. In some cases, determining the second end as being from the second genome comprises obtaining a read sequence of the cell-free nucleic acid molecule and aligning at least a portion of the second end of the read sequence to a reference genome of the organism. In some cases, the method further comprises obtaining a read sequence of the cell-free nucleic acid molecule and identifying the organism-pathogen chimeric nucleic acid fragment when at least a portion of the first end of the read sequence aligns to a reference genome of the pathogen and at least a portion of the second end of the read sequence aligns to a reference genome of the organism. In some cases, the method further comprises obtaining a read sequence of the cell-free nucleic acid molecule by paired-end sequencing, wherein the paired-end sequencing generates a pair of read sequences for the cell-free nucleic acid molecule. In some cases, the pair of read sequences comprises a first read sequence of the first end of the cell-free nucleic acid molecule and a second read sequence of the second end of the cell-free nucleic acid molecule. In some cases, identifying the organism-pathogen chimeric nucleic acid fragment comprises aligning the first read sequence or a portion thereof to a reference genome of the pathogen, and aligning the second read sequence or a portion thereof to a reference genome of the organism. In some cases, identifying the organism-pathogen chimeric nucleic acid fragment comprises aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 consecutive nucleotides of the first read sequence to the reference genome of the pathogen, and aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 consecutive nucleotides of the second read sequence to the reference genome of the organism. In some cases, identifying the organism-pathogen chimeric nucleic acid fragment comprises aligning at least 50 consecutive nucleotides of the first read sequence to the reference genome of the pathogen, and aligning at least 50 consecutive nucleotides of the second read sequence to the reference genome of the organism. In some cases, identifying the organism-pathogen chimeric nucleic acid fragment further comprises analyzing an amplification reaction of cell-free nucleic acid molecules from the biological sample. In some cases, the amplification reaction comprises a first primer complementary to a first target sequence in the genome of the pathogen, and a second primer complementary to a second target sequence in the genome of the organism. In some cases, the amplification reaction comprises a polymerase chain reaction (PCR). In some cases, the method further comprises analyzing the sequence of the amplicon produced by the amplification reaction.In some cases, the pathogen comprises a virus. In some cases, the virus comprises Epstein-Barr virus DNA, human papillomavirus DNA, hepatitis B virus DNA, hepatitis C virus nucleic acid, or fragments thereof. In some cases, the virus is human papillomavirus. In some cases, the cell-free nucleic acid molecule is deoxyribonucleic acid. In some cases, the organism is an animal. In some cases, the animal is a mammal. In some cases, the mammal is human. In some cases, the biological sample is plasma, serum, or urine. In some cases, the biological sample is plasma. In some cases, determining the first end of the cell-free nucleic acid molecule as being from the first genome and determining the second end of the cell-free nucleic acid molecule as being from the second genome is performed by a computer system.

[0008] In one aspect, described herein is a method of analyzing cell-free nucleic acid molecules from a biological sample from an organism to determine a type of pathology, the method comprising: (a) analyzing the cell-free nucleic acid molecules from the biological sample to determine a pathogen integration profile, the pathogen integration profile comprising the locations of breakpoints in the genome of a pathogen integrated in the genome of the organism; and (b) determining the type of pathology based on the pathogen integration profile.

[0009] In some cases, the pathogen integration profile is determined by detecting organism-pathogen chimeric nucleic acid fragments in cell-free nucleic acid molecules from the biological sample when the cell-free nucleic acid molecules include genomic sequences from the pathogen and genomic sequences from the organism. In some cases, the detection includes identifying a first end of the cell-free nucleic acid molecule as being from the genome of the pathogen and identifying a second end of the cell-free nucleic acid molecule as being from the genome of the organism. In some cases, the detection includes obtaining read sequences of the cell-free nucleic acid molecules from the biological sample and analyzing the read sequences to detect the organism-pathogen chimeric nucleic acid fragments. In some cases, the read sequences are obtained by performing paired-end sequencing on the cell-free nucleic acid molecules, wherein the paired-end sequencing generates a pair of read sequences for each of the cell-free nucleic acid molecules. In some cases, the pair of read sequences includes a first read sequence from the first end of each of the cell-free nucleic acid molecules and a second read sequence from the second end of each of the cell-free nucleic acid molecules. In some cases, detecting the organism-pathogen chimeric nucleic acid fragment comprises aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 consecutive nucleotides of the first read sequence to a reference genome of the pathogen, and aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 consecutive nucleotides of the second read sequence to a reference genome of the organism. In some cases, determining the pathogen integration spectrum further comprises determining a pathogen integration index based on the amount of organism-pathogen chimeric nucleic acid fragment. In some cases, detecting the organism-pathogen chimeric nucleic acid fragment comprises analyzing an amplification reaction of a cell-free nucleic acid molecule from the biological sample. In some cases, the amplification reaction includes a first primer complementary to a first target sequence in the genome of the pathogen and a second primer complementary to a second target sequence in the genome of the organism. In some cases, the amplification reaction includes a polymerase chain reaction (PCR). In some cases, the method further includes analyzing the sequence of the amplicon produced by the amplification reaction. In some cases, the pathogen includes a virus. In some cases, the virus includes Epstein-Barr virus DNA, human papillomavirus DNA, hepatitis B virus DNA, hepatitis C virus nucleic acid, or fragments thereof. In some cases, the virus is human papillomavirus. In some cases, the pathology classification includes a cancer type. In some cases, the cancer type includes cervical cancer or head and neck squamous cell carcinoma. In some cases, the cell-free nucleic acid molecule includes a deoxyribonucleic acid molecule. In some cases, the organism is an animal.In some cases, the animal is a mammal. In some cases, the mammal is a human. In some cases, the biological sample is plasma, serum, or urine. In some cases, the biological sample is plasma. In some cases, analyzing the cell-free nucleic acid molecules is performed by a computer system.

[0010] In one aspect, described herein is a method of analyzing cell-free nucleic acid molecules from a biological sample from an organism to determine a pathogen integration profile, the method comprising analyzing cell-free nucleic acid molecules from the biological sample to determine a pathogen integration profile, the pathogen integration profile comprising the locations of integration breakpoints in the genome of the pathogen integrated in the genome of the organism.

[0011] In some cases, the pathogen integration profile is determined by detecting organism-pathogen chimeric nucleic acid fragments in cell-free nucleic acid molecules from the biological sample when the cell-free nucleic acid molecules include genomic sequences from the pathogen and genomic sequences from the organism. In some cases, the detection includes identifying a first end of the cell-free nucleic acid molecule as being from the genome of the pathogen and identifying a second end of the cell-free nucleic acid molecule as being from the genome of the organism. In some cases, the detection includes obtaining read sequences of the cell-free nucleic acid molecules from the biological sample and analyzing the read sequences to detect the organism-pathogen chimeric nucleic acid fragments. In some cases, the read sequences are obtained by performing paired-end sequencing on the cell-free nucleic acid molecules, wherein the paired-end sequencing generates a pair of read sequences for each of the cell-free nucleic acid molecules. In some cases, the pair of read sequences includes a first read sequence from the first end of each of the cell-free nucleic acid molecules and a second read sequence from the second end of each of the cell-free nucleic acid molecules. In some cases, detecting the organism-pathogen chimeric nucleic acid fragment comprises aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 contiguous nucleotides of the first read sequence to a reference genome of the pathogen, and aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 contiguous nucleotides of the second read sequence to the reference genome of the organism. In some cases, determining the pathogen integration spectrum further comprises determining a pathogen integration index based on the amount of organism-pathogen chimeric nucleic acid fragment. In some cases, detecting the organism-pathogen chimeric nucleic acid fragment comprises analyzing an amplification reaction of a cell-free nucleic acid molecule from the biological sample. In some cases, the amplification reaction includes a first primer complementary to a first target sequence in the genome of the pathogen and a second primer complementary to a second target sequence in the genome of the organism. In some cases, the amplification reaction includes a polymerase chain reaction (PCR). In some cases, the method further includes analyzing the sequence of the amplicon produced by the amplification reaction. In some cases, the pathogen includes a virus. In some cases, the virus includes Epstein-Barr virus DNA, human papillomavirus DNA, hepatitis B virus DNA, hepatitis C virus nucleic acid, or fragments thereof. In some cases, the virus is human papillomavirus. In some cases, the cell-free nucleic acid molecule includes a deoxyribonucleic acid molecule. In some cases, the organism is an animal. In some cases, the animal is a mammal. In some cases, the mammal is a human. In some cases, the biological sample is plasma, serum, or urine.In some cases, the biological sample is plasma. In some cases, analyzing the cell-free nucleic acid molecules is performed by a computer system. In some cases, the analyzing comprises sequencing the cell-free nucleic acid molecules. In some cases, the analyzing comprises: identifying read sequences of cell-free nucleic acid molecules from the biological sample that include the same potential integration breakpoint; and detecting the integration breakpoint based on the read sequences. In some cases, detecting the integration breakpoint based on the read sequences comprises: evaluating the variability of the sequence length of each of the read sequences aligned with the genomic region flanking the potential integration breakpoint; and detecting the integration breakpoint in the organism based on the evaluation. In some cases, the analyzing comprises: identifying read sequence pairs of cell-free nucleic acid molecules from the biological sample that include the same potential integration breakpoint; and detecting the integration breakpoint based on the read sequence pairs. In some cases, detecting the integration breakpoints based on the read sequence pairs includes: determining the strand orientation of the first read sequence and the second read sequence of each of the read sequence pairs; filtering out the following read sequence pairs: the strand orientation of the first read sequence and the second read sequence included in the read sequence pairs is inconsistent with the strand orientation of the first read sequence and the second read sequence of the majority of the read sequence pairs; and after the filtering, detecting the integration breakpoints based on the read sequence pairs.

[0012] In some cases, detecting the integration breakpoint based on the read sequence pairs includes: evaluating the variability of sequence length of read sequences of the read sequence pairs aligned with genomic regions flanking the potential integration breakpoint; and detecting the integration breakpoint based on the evaluation. In some cases, identifying the read sequence pairs includes: identifying organism-pathogen chimeric read sequence pairs generated by double-end sequencing of cell-free nucleic acid molecules from the biological sample, the organism-pathogen chimeric read sequence pairs including a first read sequence aligned with a reference genome of the organism and a second read sequence aligned with a reference genome of the pathogen, thereby identifying type A organism-pathogen chimeric read sequence pairs; grouping from the type A organism-pathogen chimeric read sequence pairs, the type A organism-pathogen chimeric read sequence pairs including a first read sequence and a second read sequence, the first read sequence overlapping or separated by a predetermined distance in the reference genome of the organism, and the second read sequence. Sequences are taken that overlap in the reference genome of the pathogen or are separated by a predetermined distance, thereby identifying candidate organism-pathogen integration regions in the reference genomes of the organism and the pathogen; and organism-pathogen chimeric read sequence pairs generated by paired-end sequencing of cell-free nucleic acid molecules from the biological sample, the organism-pathogen chimeric read sequence pairs comprising a first read sequence and a second read sequence, the first read sequence being aligned with the candidate organism-pathogen integration region, the second read sequence comprising a first sequence aligned with the reference genome of the organism and a second sequence aligned with the reference genome of the pathogen, thereby identifying type B organism-pathogen chimeric read sequence pairs. In some cases, the predetermined distance is at most 10, at most 50, at most 75, at most 100, at most 120, at most 150, at most 175, at most 200, at most 225, at most 250, at most 275, at most 300, at most 325, at most 350, at most 375, at most 400, at most 425, at most 450, at most 475, or at most 500 bases. In some cases, there are at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 50, 100, 200, 1000, 10,000, 50,000, 100,000 or more type A organism-pathogen chimeric read sequence pairs in the organism-pathogen candidate integration region.In some cases, detecting the integration breakpoints comprises: determining the strand orientation of the first read sequence and the second read sequence for each of the type A organism-pathogen chimeric read sequence pairs and the type B organism-pathogen chimeric read sequence pairs; filtering out from the type B organism-pathogen chimeric read sequence pairs the type B organism-pathogen chimeric read sequence pairs having the first read sequence and the second read sequence strand orientation that is inconsistent with the strand orientation of the first read sequence and the second read sequence of the majority of the type A organism-pathogen chimeric read sequence pairs within the organism-pathogen candidate integration region; and after filtering out, detecting the integration breakpoints based on the type B organism-pathogen chimeric read sequence pairs. In some cases, detecting the integration breakpoints comprises: determining a diversity score for the type B organism-pathogen chimeric read sequence pairs; wherein the diversity score is calculated as. , wherein σ1 is the standard deviation of the length of the first sequence of the type B organism-pathogen chimeric read sequence pair as aligned to the reference genome of the organism, and wherein σ2 is the standard deviation of the length of the second sequence of the type B organism-pathogen chimeric read sequence pair as aligned to the reference genome of the pathogen; and detecting the integration breakpoint based on the type B organism-pathogen chimeric read sequence pair if the diversity score is equal to or higher than a predetermined cutoff value. In some cases, the predetermined cutoff value is at least 1, at least 1.6, at least 2.0, at least 2.4, at least 2.8, at least 3.0, at least 3.4, at least 3.8, at least 4.0, at least 4.2, at least 4.4, at least 4.6, at least 4.8, at least 5.0, at least 5.4, at least 5.8, at least 6.0, at least 6.5, at least 7.0, at least 8, at least 9, at least 10, at least 20, at least 50, or at least 100.

[0013] In one aspect, described herein is a method of analyzing a biological sample from an organism to detect a chromosomal rearrangement, the method comprising: identifying reads of cell-free nucleic acid molecules from the biological sample, the reads comprising sequence of the same potential chromosomal rearrangement; assessing the variability of sequence length of each of the reads aligned to genomic regions flanking the potential chromosomal rearrangement; and detecting a chromosomal rearrangement in the organism based on the assessment.

[0014] In some cases, the organism is human. In some cases, the chromosomal rearrangement includes a chromosomal translocation, a chromosomal deletion, a chromosomal inversion, or a chromosomal amplification. In some cases, identifying the read sequence includes: identifying a chromosome mosaic read sequence pair, the chromosome mosaic read sequence pair being generated by double-end sequencing of a cell-free nucleic acid molecule from the biological sample and including a read sequence that includes the potential chromosomal rearrangement. In some cases, identifying the chromosome mosaic read sequence pair includes: identifying a chromosome mosaic read sequence pair generated by the double-end sequencing, the chromosome mosaic read sequence pair including a first read sequence and a second read sequence, the first read sequence being aligned with a first genomic region of a reference genome of the organism, and the second read sequence being aligned with a second genomic region of the reference genome, thereby identifying a type A chromosome mosaic read sequence pair, wherein the relative positioning of the first genomic region and the second genomic region in the reference genome indicates the potential chromosomal rearrangement; grouping from the type A chromosome mosaic read sequence pair, the type A chromosome mosaic read sequence pair including a first read sequence, the The first read sequence overlaps in the first genomic region or is separated within a predetermined distance, and includes a second read sequence, the second read sequence overlaps in the second genomic region or is separated within a predetermined distance, thereby identifying candidate rearrangement regions in the first genomic region and the second genomic region; and identifying a chromosome chimeric read sequence pair generated by the double-end sequencing, the chromosome chimeric read sequence pair including a first read sequence and a second read sequence, the first read sequence is aligned with the candidate integration region, the second read sequence includes a first sequence aligned with the first genomic region and a second sequence aligned with the second genomic region, thereby identifying a type B chromosome chimeric read sequence pair. In some cases, evaluating the variability includes: determining a diversity score for the type B chromosome chimeric read sequence pair, wherein the diversity score is calculated as , wherein σ1 is the standard deviation of the length of the first sequence of the type B chromosome mosaic read sequence pair aligned with the first genomic region, and wherein σ2 is the standard deviation of the length of the second sequence of the type B chromosome mosaic read sequence pair aligned with the second genomic region. In some cases, detecting the chromosome rearrangement includes detecting the chromosome rearrangement based on the type B chromosome mosaic read sequence pair if the diversity score is equal to or higher than a predetermined cutoff value. In some cases, the predetermined cutoff value is at least 1, at least 1.6, at least 2.0, at least 2.4, at least 2.8, at least 3.0, at least 3.4, at least 3.8, at least 4.0, at least 4.2, at least 4.4, at least 4.6, at least 4.8, at least 5.0, at least 5.4, at least 5.8, at least 6.0, at least 6.5, at least 7.0, at least 8, at least 9, at least 10, at least 20, at least 50 or at least 100. In some cases, the distance between the first genomic region and the second genomic region is at least 140 bases, at least 180 bases, at least 250 bases, at least 350 bases, at least 450 bases, at least 550 bases, at least 750 bases, at least 900 bases, at least 1100 bases, at least 1250 bases, at least 1800 bases, at least 2500 bases, at least 3500 bases, at least 5500 bases, at least 7500 bases, at least 9000 bases, or at least 10 4 In some cases, the 5' to 3' relative relationship of the first genomic region and the second genomic region in the reference genome is opposite to the 5' to 3' relative relationship of the first read sequence and the second read sequence in each cell-free nucleic acid molecule of each of the type A chromosome chimeric read sequence pairs.

[0015] In one aspect, described herein is a method of analyzing a biological sample of an organism to detect a chromosome rearrangement, the method comprising: identifying read sequence pairs of cell-free nucleic acid molecules from the biological sample that comprise the same potential chromosome rearrangement; determining the strand orientation of a first read sequence and a second read sequence for each of the read sequence pairs; filtering out read sequence pairs comprising the first read sequence and the second read sequence whose strand orientation is inconsistent with the strand orientation of the first read sequence and the second read sequence of a majority of the read sequence pairs; and, after the filtering out, detecting the chromosome rearrangement in the organism based on the read sequence pairs.

[0016] In some cases, the organism is human. In some cases, the chromosomal rearrangement comprises a chromosomal translocation, a chromosomal deletion, a chromosomal inversion, or a chromosomal amplification. In some cases, identifying the read sequence pairs comprises: identifying a chromosome mosaic read sequence pair generated by double-end sequencing of a cell-free nucleic acid molecule from the biological sample, the chromosome mosaic read sequence pair comprising a first read sequence and a second read sequence, the first read sequence being aligned to a first genomic region of a reference genome of the organism, and the second read sequence being aligned to a second genomic region of the reference genome, thereby identifying a type A chromosome mosaic read sequence pair, wherein the relative positioning of the first genomic region and the second genomic region in the reference genome indicates the potential chromosomal rearrangement; grouping from the type A chromosome mosaic read sequence pairs, the type A chromosome mosaic read sequence pairs comprising a first read sequence and a second read sequence. Read sequences, wherein the first read sequences overlap or are separated by a predetermined distance in the first genomic region, and the second read sequences overlap or are separated by a predetermined distance in the second genomic region, thereby identifying candidate rearrangement regions in the first genomic region and the second genomic region; and identifying chromosome mosaic read sequence pairs generated by double-end sequencing of cell-free nucleic acid molecules from the biological sample, the chromosome mosaic read sequence pairs comprising a first read sequence and a second read sequence, the first read sequence being aligned with the candidate rearrangement region, the second read sequence comprising a first sequence aligned with the first genomic region, and a second sequence aligned with the second genomic region, thereby identifying type B chromosome mosaic read sequence pairs. In some cases, the filtering includes filtering out type B chromosome mosaic read sequence pairs: the type B chromosome mosaic read sequence pairs have a first read sequence and a second read sequence whose strand orientation is inconsistent with the strand orientation of the first read sequence and the second read sequence of the majority of the type A chromosome mosaic read sequence pairs within the candidate rearrangement region. In some cases, the distance between the first genomic region and the second genomic region is at least 140 bases, at least 180 bases, at least 250 bases, at least 350 bases, at least 450 bases, at least 550 bases, at least 750 bases, at least 900 bases, at least 1100 bases, at least 1250 bases, at least 1800 bases, at least 2500 bases, at least 3500 bases, at least 5500 bases, at least 7500 bases, at least 9000 bases, or at least 10 4In some cases, the 5' to 3' relative relationship of the first genomic region and the second genomic region in the reference genome is opposite to the 5' to 3' relative relationship of the first read sequence and the second read sequence in each cell-free nucleic acid molecule of each of the type A chromosome chimeric read sequence pairs.

[0017] In one aspect, described herein is a method comprising determining a classification of a pathology based at least in part on a chromosomal rearrangement determined by any of the methods described herein.

[0018] In one aspect, a computer system is described herein that includes one or more processors and a non-transitory computer-readable medium including instructions that, when executed by the one or more computer processors, are operable to cause the computer system to perform any of the methods described herein.

[0019] In one aspect, described herein is a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a computer system, are operable to cause the computer system to perform any of the methods described herein.

[0020] Incorporation by reference

[0021] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The novel features described herein are set forth with particularity in the appended claims. A better understanding of the features and advantages described herein will be obtained by reference to the following detailed description and accompanying drawings which illustrate exemplary embodiments utilizing the principles described herein, in which:

[0023] Figure 1 is a schematic diagram of a workflow for determining chimeric nucleic acid fragments comprising a nucleic acid sequence from a pathogen (eg, a virus) and a nucleic acid sequence from a host organism (eg, a human).

[0024] Figure 2 is a schematic diagram showing read sequence pairs generated from two types (type A and type B) of nucleic acid fragments by paired-end sequencing.

[0025] Figure 3A Figure 2 is a schematic diagram of an exemplary workflow for human papillomavirus (HPV) viral DNA integration analysis.

[0026] Figure 3Bis a schematic diagram of an exemplary workflow for inferring human-HPV candidate integration regions.

[0027] Figure 4 Schematic diagrams and graphs illustrating an exemplary strandedness-based filtering process are shown.

[0028] Figure 5 is a schematic diagram showing candidate integration breakpoints with low (< 4) and high (>= 4) diversity scores.

[0029] Figure 6 Shown are the distribution patterns of integration breakpoints in the HPV16 genome inferred from human-HPV chimeric fragments in samples from patients with cervical cancer (CaCer) and HPV-positive head and neck squamous cell carcinoma (HNSCC).

[0030] Figure 7 Shown are the integration indices of the entire HPV genome determined from cell-free DNA fragments of samples from patients with cervical cancer (Ca Cervix-) and HPV-positive HNSCC (HPV+veSCC).

[0031] Figure 8 A computer control system is shown that can be programmed or otherwise configured to implement the methods provided herein.

[0032] Figure 9 A diagram illustrating the methods and systems disclosed herein is shown.

[0033] Figure 10 is a schematic diagram showing the workflow for comparing the detection rates of the exemplary algorithm and VIFI on a simulated data set.

[0034] Figure 11 is a graph summarizing the comparison of detection rates of the exemplary algorithm and VIFI on a simulated data set.

[0035] Figure 12 Shown is a table summarizing viral DNA integration analysis of plasma samples from patients with cervical cancer (CaCx), head and neck squamous cell carcinoma (HNSCC) by an exemplary algorithm.

[0036] Figure 13 Shown is a table summarizing viral DNA integration analysis of tumor tissue samples from patients with head and neck squamous cell carcinoma (HNSCC) by an exemplary algorithm. DETAILED DESCRIPTION

[0037] I. Overview

[0038] Provided herein are methods and systems for analyzing a biological sample of an organism to identify chimeric nucleic acid fragments comprising a nucleic acid sequence from a pathogen and a nucleic acid sequence from the organism (organism-pathogen chimeric nucleic acid fragments). The methods and systems can include, for example, analyzing, via a computer system, a plurality of nucleic acid molecules, such as cell-free nucleic acid (e.g., DNA) molecules, from a biological sample (e.g., plasma) to detect chimeric nucleic acid fragments comprising a nucleic acid sequence from a pathogen and a nucleic acid sequence from the organism. Analyzing each of the plurality of nucleic acid molecules (e.g., cell-free nucleic acid (e.g., DNA) molecules) can include identifying a first end of each nucleic acid molecule, such as a cell-free nucleic acid molecule, as being from a first genome, and identifying a second end of each nucleic acid molecule, such as a cell-free nucleic acid molecule, as being from a second genome. When the first genome is the genome of a pathogen (e.g., a virus, such as human papillomavirus (HPV)) and the second genome is the genome of an organism (e.g., a human), chimeric nucleic acid fragments comprising a nucleic acid sequence from the pathogen and a nucleic acid sequence from an organism (e.g., a host organism) can be detected. The methods and systems can further include determining a classification of a pathology (e.g., cancer) based at least in part on a chimeric nucleic acid fragment comprising a nucleic acid sequence from a pathogen (e.g., a virus) and a nucleic acid sequence from an organism (e.g., a host organism, e.g., a human). The classification of the cancer can be a type of cancer, e.g., cervical cancer or head and neck squamous cell carcinoma.

[0039] Also provided herein are methods and systems for analyzing (e.g., by a computer system) nucleic acid molecules (e.g., cell-free nucleic acid molecules) from a biological sample (e.g., plasma) to determine a pathogen integration profile. The pathogen integration profile can include the locations of breakpoints in the genome of a pathogen (e.g., a virus) integrated into the genome of an organism (e.g., a host organism, e.g., a human). The methods and systems can further include determining a type of pathology based on the pathogen integration profile. The type of pathology can be, for example, a type of cancer, e.g., cervical cancer or head and neck squamous cell carcinoma (HNSCC), or a condition of the subject associated with an increased risk of cancer, e.g., cervical neoplasia in situ or cervical intraepithelial neoplasia.

[0040] Also provided herein are methods and systems for detecting chromosomal rearrangements. In some cases, the methods include analyzing nucleic acid molecules, e.g., cell-free nucleic acid molecules, from a biological sample (e.g., plasma) to identify chimeric nucleic acid fragments comprising a chromosomal rearrangement. The methods and systems can include, for example, analyzing, by a computer system, a plurality of nucleic acid molecules, e.g., cell-free nucleic acid (e.g., DNA) molecules, from a biological sample (e.g., plasma) to detect chimeric nucleic acid fragments comprising a chromosomal rearrangement. Analyzing each of the plurality of nucleic acid molecules (e.g., cell-free nucleic acid (e.g., DNA) molecules) can include identifying a first end of each nucleic acid molecule (e.g., cell-free nucleic acid molecule) as a first genomic region from a reference genome of the organism, and identifying a second end of each nucleic acid molecule (e.g., cell-free nucleic acid molecule) as a second genomic region from a reference genome of the organism. A chimeric nucleic acid fragment comprising a chromosomal rearrangement can be detected when the relative positioning of the first genomic region and the second genomic region in the reference genome of the organism is inconsistent with the relative positioning of the first end and the second end of each cell-free nucleic acid molecule. In some cases, the methods and systems described herein comprise analyzing chimeric nucleic acid fragments comprising a chromosomal rearrangement to detect the chromosomal rearrangement. In some cases, the methods and systems comprise determining a classification of a pathology based at least in part on the chromosomal rearrangement.

[0041] The methods provided herein may include steps for increasing the likelihood of capturing nucleic acid fragments from cell-free samples that include pathogen (e.g., viral) sequences. For example, the methods provided herein may utilize hybridization probes that cover the entire pathogen (e.g., viral) genome, or at least 99%, at least 95%, at least 90%, or at least 85% of the entire pathogen (e.g., viral) genome, such as human papillomavirus (e.g., HPV16, HPV18, HPV33); Epstein-Barr virus (EBV); or hepatitis B virus (HBV), for target capture. Such hybridization probes used for target capture can enrich for viral nucleic acids and increase the likelihood of obtaining chimeric nucleic acid fragments that include both host (e.g., human) and viral nucleic acids for viral integration analysis.

[0042] The methods provided herein can provide enhanced sensitivity. For example, the methods and systems provided herein can lack a step for filtering out genomic regions (e.g., human genomic regions), such as repetitive regions, e.g., short tandem repeats, short interspersed nuclear elements (SINEs / Alu), or long terminal repeats / endogenous retroviruses (LTRs / ERV1). Eliminating such filters can increase the sensitivity of the methods provided herein.

[0043] The methods provided herein can provide increased specificity. For example, double-end reads spanning host (e.g., human) and pathogen (e.g., viral) genomes can be used to establish local reference sequences that may include breakpoints for pathogen (e.g., viral) integration. Local realignment of those partially mapped double-end read sequences can be used to determine breakpoints in the local reference sequence. Double-end reads, together with compatibility mapping orientation or strand information, can be used for further downstream analysis of viral integration. Read sequences that are incompatible with the alignment (due to, for example, incompatible mapping orientation or strand information) can be removed to reduce artifacts due to mapping errors or other errors. This analysis can also exclude those chimeric fragments resulting from cross-linking of different molecules during nucleic acid (e.g., DNA) library preparation (see, e.g., Figure 4 ). The methods provided herein can test the compatibility of alignments of organism-pathogen chimeric reads (i.e., reads having a breakpoint between a pathogen (e.g., viral) nucleic acid sequence and a host (e.g., human) nucleic acid) to a reference with the alignment of the majority of reads within a region of interest. For example, if a pathogen (e.g., viral) integration breakpoint in a chimeric read is incompatible with the strand information present in the majority of reads, then such a pathogen (e.g., viral) integration breakpoint can be considered a low confidence call.

[0044] The methods provided herein can utilize chimeric fragments distributed around an integration breakpoint that have sufficient diversity with respect to fragment length, fragment end positions, and relative positioning of the breakpoint within the chimeric nucleic acid fragment (see, e.g., Figure 5 In some cases, for a particular integration breakpoint, the greater the diversity in fragment lengths, fragment end positions, and relative positioning of the breakpoint within the chimeric fragment, the higher the confidence in the integration.

[0045] In some cases, because viral episomal nucleic acids (e.g., DNA) can be shorter than host (e.g., human) nucleic acids (e.g., DNA), chimeric nucleic acid fragments generated by integration of viral nucleic acids (e.g., DNA) into the host (e.g., human) genome can be expected to be longer than viral nucleic acids (e.g., DNA) derived from episomes. Thus, the average or median length of chimeric nucleic acid fragments including pathogen (e.g., viral) integration breakpoints can be greater than other pathogen (e.g., viral) nucleic acids (e.g., DNA) derived from episomes.

[0046] In addition, in some cases, the methods provided herein do not include a training step comprising mathematical modeling (e.g., a hidden Markov model) to establish an ensemble of phylogenetic profiles of available viral genomes. In some cases, the methods provided herein do not include analyzing pathogen (e.g., viral) nucleic acids from tissue samples. The methods provided herein can utilize cell-free biological samples, such as plasma. Nucleic acids (e.g., DNA) in plasma can be naturally fragmented, and the fragmentation pattern can vary depending on the source of the plasma nucleic acid (e.g., DNA, e.g., liver DNA, viral DNA, and tumor DNA). In some cases, the methods provided herein do not include shearing nucleic acids from cell-free biological samples prior to short read sequencing.

[0047] II. Workflow

[0048] The systems and methods provided herein can be used to analyze cell-free nucleic acid molecules that include sequences from the genome of an organism (e.g., a host organism, such as a human) and sequences from the genome of a pathogen (e.g., a virus, such as HPV). In the plasma of a human subject (e.g., a patient with a virus-associated malignancy), some cell-free nucleic acid molecules may include sequences from both the viral genome and the human genome. These cell-free nucleic acid molecules in the plasma can be referred to as chimeric nucleic acid fragments.

[0049] Figure 1 A schematic diagram of an exemplary workflow for identifying chimeric nucleic acid fragments comprising a sequence from a pathogen and a sequence from a biological sample of an organism is shown. As shown, the method provided herein for identifying chimeric nucleic acid fragments may include identifying a first end of each cell-free nucleic acid molecule as being from a first genome (110). The method may further include identifying a second end of each cell-free nucleic acid molecule as being from a second genome (120). The method may further include detecting the chimeric nucleic acid fragment when the first genome is a genome of a pathogen and the second genome is a genome of the organism, wherein the organism and the pathogen are different (130). The nucleic acid molecule may be a cell-free nucleic acid molecule. The nucleic acid molecule may be from a biological sample. The first end of the nucleic acid molecule may be the 5' or 3' end of the nucleic acid molecule, and the second end of the nucleic acid molecule may be the 3' or 5' end of the nucleic acid molecule. The organism may be a host organism of the pathogen, e.g., a human. The pathogen may be a virus, e.g., HPV, EBV, and HBV.

[0050] Also provided herein are methods and systems for determining a pathogen integration profile, for example, the location of one or more pathogen integration breakpoints in the genome of a host organism or the genome of a pathogen, by analyzing nucleic acid molecules from a biological sample. Figure 2 As shown in , in paired-end sequencing, host-organism-pathogen (e.g., human-virus) chimeric fragments can be detected and displayed as two different forms of read sequence pairs (read sequence pairs obtained from the two ends of the same nucleic acid molecule (e.g., cell-free DNA molecule)). The two read sequences of a read sequence pair from a single nucleic acid fragment in paired-end sequencing data can be referred to as matched read sequences (mate reads). One type (type A) can represent a cell-free nucleic acid molecule (e.g., cell-free DNA) in which one read sequence at the first end can be mapped to a host organism reference genome (e.g., a human reference genome) and the other read sequence at the second end can be mapped to a pathogen reference genome (e.g., a virus reference genome), or vice versa.

[0051] In some cases, a read is mapped to, mappable to, or aligned with a reference genome (e.g., a human reference genome) when the read has at least 80%, at least 85%, at least 90%, at least 95%, at least 98%, at least 99%, or 100% sequence identity or complementarity to a particular region of a reference genome (e.g., a human reference genome) over the entire read. In some cases, a read is mapped to, mappable to, or aligned with a reference genome (e.g., a human reference genome) when the read has at least 80% sequence identity or complementarity to a particular region of a reference genome (e.g., a human reference genome) over the entire read. In some cases, a read is mapped to, mappable to, or aligned with a reference genome (e.g., a human reference genome) when the read is identical or complementary to a particular region of a reference genome (e.g., a human reference genome) and has no more than 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 mismatches, or has zero mismatches. The maximum number of mismatches or mismatch percentages, or the minimum number of similarities or similarity percentages can vary with selection criteria that depend on the purpose and application scenarios of the methods and systems provided herein. In some cases, a read sequence is mapped to, mappable to, or aligned with a reference genome (e.g., a human reference genome) when the read sequence is identical or complementary to a specific region of the reference genome (e.g., a human reference genome) and has no more than 2 mismatches. Another fragment type (type B) can represent a cell-free nucleic acid molecule (e.g., cell-free DNA) in which a portion of one read sequence at a first end can be mapped to a host organism (e.g., human) genome and the remainder of the read sequence can be mapped to a pathogen (e.g., virus) genome (a "chimeric read sequence"), and in which another read sequence at a second end can be mapped to the host organism genome or the pathogen genome. As Figure 2As shown in , a type B host organism-pathogen (e.g., human-virus) chimeric fragment read sequence pair can have one read sequence that can be mapped to a host organism genome (e.g., a human genome) or a pathogen genome (e.g., a viral genome) and another read sequence, wherein a portion of the other read sequence can be mapped to the host organism (e.g., human) genome and the remaining portion can be mapped to the pathogen (e.g., viral) genome. In type B read sequence pairs, the exact integration breakpoint can be directly identified from the chimeric read sequence. In one example, to identify type A fragments, the read sequence can be aligned to a reference genome (e.g., a human reference genome) and a viral reference genome (e.g., an HPV reference genome) using the SOAP algorithm with a maximum mismatch number of 2 as a selection criterion. In another example, to identify type B fragments, the read sequence can be aligned to a reference genome (e.g., a human reference genome) and a viral reference genome (e.g., an HPV reference genome) using Bowtie2 and a local alignment program with a maximum mismatch number of 3 as a selection criterion.

[0052] Figure 3A A schematic diagram of an exemplary workflow for analyzing pathogen (e.g., HPV) viral DNA integration into a host organism (e.g., human) genome using cell-free DNA fragments from plasma is shown (310). Read sequences of cell-free DNA fragments from a plasma sample can be obtained by performing paired-end sequencing on the cell-free DNA fragments (320). After obtaining read sequence pairs from both ends of the cell-free DNA fragments from the plasma sample, type A fragments can be identified by aligning one read sequence to a human reference genome and its matching read sequence to an HPV reference genome (330). Candidate integration regions in the human reference genome and the HPV reference genome can be detected by grouping chimeric fragment read sequences that have adjacent coordinates in the human reference genome and the HPV reference genome, respectively (340). Type B chimeric fragment read sequence pairs can then be searched for, where one read sequence covers a potential breakpoint and the other read sequence completely maps to the same candidate region (350). Candidate integration breakpoints can be further filtered (360) by determining the linearity of the type A and type B fragment reads and by determining the diversity score of the chimeric reads of the type B fragments for each candidate integration breakpoint to determine the likelihood of the integration breakpoint.

[0053] A. Identification of Candidate Organism-Pathogen Integration Regions

[0054] The methods and systems provided herein can be used to identify candidate organism-pathogen integration regions in the genome of a pathogen (eg, a virus, eg, HPV) and in the genome of a host organism (eg, a human).

[0055] In some examples, type A organism-pathogen chimeric fragments can be identified by searching for read sequence pairs, where one read sequence from the first end of the fragment maps to the human genome and the other read sequence from the second end of the fragment maps to the pathogen (e.g., virus, e.g., HPV) genome. Type A organism-pathogen chimeric fragment read sequences can be used to determine candidate integration regions in the reference genomes of the host organism and the pathogen, respectively. In some embodiments, when the chimeric fragment read sequences overlap or are adjacent to each other in the reference genomes of the host and the pathogen, respectively, all type A chimeric fragment read sequences can be grouped together to identify candidate integration regions in the reference genomes of both the host and the pathogen. In some embodiments, chimeric fragments with the same start and end outer coordinates can be removed because they can be suspected to be PCR duplicates. In some embodiments, after removing putative PCR duplicates, all remaining type A chimeric fragment read sequences can then be merged together to group nucleotide coordinates that overlap or are adjacent to each other in the reference genomes of the host and the pathogen, respectively, to identify candidate integration regions in the reference genomes of both the host and the pathogen. In such Figure 3A In the embodiment shown in , each candidate integration region on the human genome will have one or more corresponding integration regions on the HPV genome, and vice versa. The distance between read sequences can be the distance between the two closest nucleotides on two adjacent read sequences on the reference genome. Adjacent "host organism read sequences" (read sequences aligned with the reference genome of the host organism) or their corresponding adjacent "pathogen read sequences" (read sequences aligned with the reference genome of the pathogen) within a predetermined distance in the host organism and pathogen reference genome, respectively, can be considered to belong to one candidate integration region. The cutoff value of the predetermined distance can be 300 bases, such as Figure 3AAs shown in . In one embodiment, in a candidate integration region, the distance between any two nearest host read sequences or the distance between any two nearest pathogen read sequences does not exceed 300 bases. In one embodiment, host read sequences that are separated by a distance greater than 300 bases from the nearest read sequence of the host read sequence are not included in the same candidate integration region. In one embodiment, pathogen read sequences that are separated by a distance greater than 300 bases from the nearest read sequence of the pathogen read sequence are not included in the same candidate integration region. In some embodiments, when determining candidate integration regions, after grouping type A chimeric fragment read sequences, the boundaries of the outermost type A fragment read sequences on one side of the candidate breakpoint can be further extended toward the candidate breakpoint, for example, 100 bases, 200 bases, 300 bases, 400 bases, 500 bases or 600 bases, so that in some cases, the candidate integration region on the host organism reference genome, the pathogen reference genome or both can cover the candidate integration breakpoint. The extension as described above can be any appropriate number of bases. Figure 3B An exemplary process for inferring candidate organism-pathogen integration regions (e.g., human-HPV candidate integration regions) by merging candidate host organism integration regions and candidate pathogen integration regions is shown. As shown in the figure, human and HPV candidate integration regions can be inferred by grouping type A chimeric fragment reads according to their coordinates in the human and HPV reference genomes, respectively. In this example, the cutoff value for the predetermined distance between any adjacent human reads or HPV reads within the same candidate integration region is 300 bases. Furthermore, for both human and HPV candidate integration regions, in this example, the boundary is extended by 300 bases toward one side of the candidate integration breakpoint. As shown in the figure, in this case, the 300-base extension allows both human and HPV candidate integration regions to cover the candidate breakpoint. The human-HPV candidate integration region can then be inferred by merging the human and HPV candidate integration regions along the boundary on one side of the candidate breakpoint. In some embodiments, the number of type A chimeric fragment read pairs within the organism-pathogen candidate integration region can also be used to evaluate and determine the candidate integration region.

[0056] 1. Alignment of read sequences with the reference genome

[0057] Alignment of read sequences of cell-free nucleic acid molecules from a sample of a subject can be performed by any suitable bioinformatics algorithm, program, toolbox, or package. For example, the Short Oligonucleotide Analysis Package (SOAP) can be used as an alignment tool for application of the methods and systems provided herein. Examples of short read sequence analysis tools that can be used in the methods and systems provided herein include Arioc, BarraCUDA, BBMap, BFAST, BigBWA, BLASTN, BLAT, Bowtie, Bowtie2, BWA, BWA-PSSM, CASHX, Cloudburst, CUDA-EC, CUSHAW, CUSHAW2, CUSHAW2-GPU, CUSHAW3, drFAST, ELAND, ERNE, GASSST, GEM, Genalice MAP, Geneious Assembler, GensearchNGS, GMAP and GSNAP, GNUMAP, HIVE-hexagon, Isaac, LAST, MAQ, mrFAST, mrsFAST, MOM, MOSAIK, MPscan, Novoalign & NovoalignCS, NextGENe, NextGenMap, Omixon Variant Toolbox, PALMap, Partek Flow, PASS, PerM, PRIMEX, QPalma, RazerS, REAL, cREAL, RMAP, rNA, RTG Investigator, Segemehl, SeqMap, Shrec, SHRiMP, SLIDER, SOAP, SOAP2, SOAP3, SOAP3-dp, SOCS, SparkBWA, SSAHA, SSAHA2, Stampy, SToRM, Subread, Subjunc, Taipan, UGENE, VelociMapper, XpressAlign and ZOOM.

[0058] The number of contiguous nucleotides ("sequence stretch") in a read can be used to align to a reference genome to make a call about the alignment. For example, the alignment can include aligning at least 4, at least 6, at least 8, at least 10, at least 12, at least 14, at least 16, at least 18, at least 20, at least 22, at least 24, at least 25, at least 26, at least 28, at least 30, at least 32, at least 34, at least 35, at least 36, at least 38, at least 40, at least 42, at least 44, at least 45, at least 46, at least 48, at least 50, at least 52, at least 54, at least 55, at least 56, at least 58, at least 60, at least 62, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85 , at least 75, at least 76, at least 78, at least 80, at least 82, at least 84, at least 85, at least 86, at least 88, at least 90, at least 92, at least 94, at least 95, at least 96, at least 98, at least 100, at least 102, at least 104, at least 106, at least 108, at least 110, at least 112, at least 114, at least 116, at least 118, at least 120, at least 122, at least 124, at least 126, at least 128, at least 130, at least 132, at least 134, at least 136, at least 138, at least 140, at least 142, at least 145, at least 146, at least 148, or at least 150 contiguous nucleotides are aligned to a reference genome (e.g., a reference genome of a pathogen or a reference genome of a host organism).In some cases, the alignments described herein can include aligning at most 5, at most 7, at most 9, at most 11, at most 13, at most 15, at most 17, at most 19, at most 21, at most 23, at most 25, at most 27, at most 29, at most 31, at most 33, at most 35, at most 37, at most 39, at most 41, at most 43, at most 45, at most 47, at most 49, at most 51, at most 53, at most 55, at most 57, at most 59, at most 61, at most 63, at most 65, at most 67, at most 68, at most 69, at most 70, at most 71, at most 72, at most 73, at most 74, at most 75, at most 76, at most 78, at most 80, at most 81, , at most 83, at most 85, at most 87, at most 89, at most 91, at most 93, at most 95, at most 97, at most 99, at most 101, at most 103, at most 105, at most 107, at most 109, at most 111, at most 113, at most 115, at most 117, at most 119, at most 121, at most 123, at most 125, at most 127, at most 129, at most 131, at most 133, at most 135, at most 137, at most 139, at most 141, at most 143, at most 145, at most 147, at most 149, or at most 151 contiguous nucleotides are aligned to a reference genome (e.g., a reference genome of a pathogen or a reference genome of a host organism). In some cases, the alignments described herein include aligning about 20, about 22, about 24, about 25, about 26, about 28, about 30, about 32, about 34, about 35, about 36, about 38, about 40, about 42, about 44, about 45, about 46, about 48, about 50, about 52, about 54, about 55, about 56, about 58, about 60, about 62, about 64, about 66, about 67, about 68, about 69, about 70, about 71, about 72, about 73, about 74, about 75, about 76, about 77, about 78, about 79, about 80, about 81, about 82, about 83, about 84, about 85, about 86, about 87, about 88, about 89, about 90, about 91, about 92, about 93, about 94, about 95 about 64, about 65, about 66, about 67, about 68, about 69, about 70, about 71, about 72, about 73, about 74, about 75, about 76, about 78, about 80, about 82, about 84, about 85, about 86, about 88, about 90, about 92, about 94, about 95, about 96, about 98, about 100, about 102, about 104, about 106, about 108 170, about 172, about 174, about 175, about 176, about 178, about 180, about 185, about 190, about 195, or about 200 contiguous nucleotides are aligned to a reference genome, e.g., a reference genome of a pathogen or a reference genome of a host organism.

[0059] In some cases, an alignment is called when a sequence segment has at least 80%, at least 85%, at least 90%, at least 95%, at least 98%, at least 99%, or 100% sequence identity or complementarity with a specific region of a reference genome (e.g., a human reference genome) across the entire read sequence. In some cases, an alignment is called when a sequence segment has at least 80% sequence identity or complementarity with a specific region of a reference genome (e.g., a human reference genome) across the entire read sequence. In some cases, an alignment is called when a sequence segment is identical or complementary to a specific region of a reference genome (e.g., a human reference genome) and has no more than 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 mismatch, or has zero mismatches. In some cases, an alignment is called when a sequence segment is identical or complementary to a specific region of a reference genome (e.g., a human reference genome) and has no more than 2 mismatches. The maximum number of mismatches or mismatch percentages, or the minimum number of similarities or similarity percentages, can vary depending on the selection criteria depending on the purpose and application scenario of the methods and systems provided herein.

[0060] 2. Reference genome type

[0061] The read sequences generated using the methods and systems provided herein can be aligned with one or more reference genomes (e.g., a reference genome of a pathogen and a reference genome of a host organism). In some cases, the reference genome of a pathogen can be a viral genome, such as an HPV viral genome. The reference genome can be a consensus genome, or a reference genome of a specific viral strain. In some cases, the pathogen genome includes RNA, and alignment with the pathogen's reference genome can include converting the sequence information of the read sequence into an RNA sequence, or converting the RNA genome sequence into the sequence of its complementary DNA. The host organism can be a human or a non-human animal. Examples of human reference genomes include GRChg37, GRChg37, NCBI Build 34, NCBI Build 35, and NCBI Build 36.1. The human reference genome can be the genome of an individual human. The human genome can be a consensus sequence.

[0062] 3. Cutoff value of integration region

[0063] Examples of cutoff values ​​for the predetermined distance between a host organism read sequence and a host organism read sequence or the predetermined distance between a pathogen read sequence and a pathogen read sequence in defining a candidate integration region include about 10, about 15, about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 55, about 60, about 65, about 70, about 75, about 80, about 85, about 90, about 95, about 100, about 105, about 110, about 120, about 125, about 130, about 135, about 140, about 145, about 150, about 155, about 160, about 165, about 170, about 175, about 180, about 185, about 190, about 195, about 200, about 205, about 210, about 215, about 220, about 230, about 235, about 240, about 250, about 260, about 270, about 280, about 290, about 300, about 310, about 320, about 330, about 340, about 350, about 360, about 370, about 380, about 390, about 400, about 410, about 420, about 430, about 440, about 450 0, about 225, about 230, about 235, about 240, about 245, about 250, about 255, about 260, about 265, about 270, about 275, about 280, about 285, about 290, about 295, about 300, about 305, about 310, about 315, about 320, about 325, about 330, about 335, about 340, about 345, about 35 400, about 470, about 480, about 490, or about 500 bases.In defining a candidate integration region, the cutoff value for the predetermined distance between a host organism read sequence and a host organism read sequence or the predetermined distance between a pathogen read sequence and a pathogen read sequence can be at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, at least 80, at least 85, at least 90, at least 95, at least 100, at least 105, at least 110, at least 120, at least 125, at least 130, at least 135, at least 140, at least 145, at least 150, at least 155, at least 160, at least 165, at least 170, at least 175, at least 180, at least 185, at least 190, at least 195, at least 200, at least 205, at least 210, at least 215, at least 220, at least 225 5, at least 230, at least 235, at least 240, at least 245, at least 250, at least 255, at least 260, at least 265, at least 270, at least 275, at least 280, at least 285, at least 290, at least 295, at least 300, at least 305, at least 310, at least 315, at least 320, at least 325, at least 330, at least 335, at least 340, at least 345, at least 350, At least 355, at least 360, at least 365, at least 370, at least 375, at least 380, at least 385, at least 390, at least 395, at least 400, at least 405, at least 410, at least 415, at least 420, at least 425, at least 430, at least 435, at least 440, at least 445, at least 450, at least 460, at least 470, at least 480, at least 490, or at least 500 bases.In some cases, the predetermined distance between a host organism read sequence and a host organism read sequence or the predetermined distance between a pathogen read sequence and a pathogen read sequence in defining a candidate integration region can be at most 10, at most 15, at most 20, at most 25, at most 30, at most 35, at most 40, at most 45, at most 50, at most 55, at most 60, at most 65, at most 70, at most 75, at most 80, at most 85, at most 90, at most 95, at most 100, at most 110, at most 120, at most 130, at most 140, at most 150, at most 160, at most 170, at most 180, at most 190, at most 200, at most 210, at most 220, at most 230, at most 240, at most 250, at most 260, at most 270, at most 280, at most 290, at most 300, at most 310, at most 320, at most 330, at most 340, at most 350 0, up to 95, up to 100, up to 105, up to 110, up to 120, up to 125, up to 130, up to 135, up to 140, up to 145, up to 150, up to 155, up to 160, up to 165, up to 170, up to 175, up to 180, up to 185, up to 190, up to 195, up to 200, up to 205, up to 210, up to 215, up to 220, up to 225, at most 230, at most 235, at most 240, at most 245, at most 250, at most 255, at most 260, at most 265, at most 270, at most 275, at most 280, at most 285, at most 290, at most 295, at most 300, at most 305, at most 310, at most 315, at most 320, at most 325, at most 330, at most 335, at most 340, at most 345, at most 350 , at most 355, at most 360, at most 365, at most 370, at most 375, at most 380, at most 385, at most 390, at most 395, at most 400, at most 405, at most 410, at most 415, at most 420, at most 425, at most 430, at most 435, at most 440, at most 445, at most 450, at most 460, at most 470, at most 480, at most 490 or at most 500 bases.

[0064] The number of chimeric fragment read sequence pairs within a candidate organism-pathogen integration region can be used to evaluate and screen candidate integration regions. For example, each candidate organism-pathogen integration region can have at least two pairs of organism-pathogen (e.g., human-HPV) chimeric fragment read sequence pairs that are not PCR duplicates, e.g., read sequences with the same start and end external coordinates. In some cases, each candidate organism-pathogen integration region can have at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 50, 100, 200, 1000, 10,000, 50,000, 100,000 or more organism-pathogen (e.g., human-HPV) chimeric fragment read sequence pairs that are not PCR duplicates, e.g., read sequences with the same start and end external coordinates.

[0065] B. Chain Orientation and Inferred Integration Breakpoints

[0066] The number of chimeric fragment read sequence pairs within the candidate organism-pathogen integration region can be used to evaluate and screen candidate integration regions. For example, each candidate organism-pathogen integration region can have at least two pairs of organism-pathogen (e.g., human-HPV) chimeric fragment read sequence pairs that are not PCR duplicates, e.g., read sequences with the same start and end external coordinates. In some cases, each candidate organism-pathogen integration region can have at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 50, 100, 200, 1000, 10,000, 50,000, 100,000 or more organism-pathogen (e.g., human-HPV) chimeric fragment read sequence pairs that are not PCR duplicates, e.g., read sequences with the same start and end external coordinates.

[0067] The strand orientation of reads generated by paired-end sequencing can be used to determine the likelihood of integration breakpoints. After identifying potential candidate integration regions, the strand orientation of the host organism read and pathogen read in the organism-pathogen chimeric fragment read pairs can be used to eliminate organism-pathogen chimeric fragments generated in vitro (e.g., by in vitro ligation). Integration regions in the host organism genome and pathogen genome can be inferred upstream or downstream of the host organism and pathogen reads based on their strand orientation. Type B chimeric fragment read pairs whose chimeric reads have inconsistent orientations compared to the type A chimeric fragment read pairs used to infer candidate integration regions can be filtered out. When type A chimeric fragment read pairs are grouped to determine candidate integration regions, there may be type A read pairs with different strand orientations (or strand orientation patterns) that are grouped together. In some cases, up to four possible strand orientations may exist in a given sample: "+ / +," "- / -," "+ / -," and "- / +," where + represents the forward strand when the read matches a sequence in a reference genome, and - represents the reverse strand when the read is complementary to a sequence in the reference genome; and in expressions such as "+ / +," "- / -," "+ / -," and "- / +," reads aligning to the host organism's genome precede reads aligning to the pathogen's genome. In some cases, the number of type A chimeric fragment read pairs with each of the four possible strand orientations is counted. In some cases, within a group, the type A chimeric fragment read pairs with the majority strand orientation are used to infer candidate integration regions. In some cases, the number of type A chimeric fragment read pairs with the majority strand orientation does not exceed 50% of the total number of type A chimeric fragment read pairs in the group. In some cases, the number of type A chimeric fragment read pairs with the majority strand orientation exceeds that of any other strand orientation. Figure 4 An exemplary analysis of the strand orientation of read pairs of cell-free DNA fragments is shown. In this example, human-HPV chimeric read pairs were analyzed. Figure 4As shown in the schematic diagram of FIG, when the majority of type A fragment read sequence pairs (three-quarters) have a + strand of a human read sequence and a − strand of an HPV read sequence, the remaining type A fragment read sequence pairs having a + strand of a human read sequence and a + strand of an HPV read sequence can be determined as "false" read sequence pairs and filtered out. Therefore, type B chimeric fragment read sequence pairs can be filtered out based on their strand orientation. In this embodiment, type B read sequence pairs can be filtered out as "false" read sequences, where the chimeric read sequence of the type B read sequence pair has a strand orientation inconsistent with the strand orientation of the majority of type A fragment read sequence pairs. Figure 4 The table in lists four different scenarios of the strand orientation of most type A human-HPV segment read pairs in candidate integration regions, and the corresponding expected strand orientations of human-HPV chimeric reads in the same candidate integration regions.

[0068] C. Identifying Integration Breakpoints

[0069] Figure 2The exemplified type B chimeric fragment read sequence pairs or chimeric read sequences in the type B chimeric fragment read sequence pairs can be detected by utilizing a short read sequence analysis tool supplemented with a local alignment function. For example, Bowtie2 software can be used for analysis, and the local alignment function can be used to align short nucleotide segments ("bins") to a host organism reference genome or a pathogen reference genome. The short nucleotide segments can include at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 12, or at least 15 bases. In some cases, the short nucleotide segments can include at most 50, at most 30, at most 25, at most 24, at most 23, at most 22, at most 21, at most 20, at most 19, at most 18, at most 17, at most 16, at most 14, at most 13, at most 11, at most 9, at most 8, at most 7, at most 6, at most 5, or at most 4 bases. The short nucleotide stretch can include about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 11, about 12, about 15, about 18, about 20, about 22, about 24, about 25, about 30, or about 50 bases. In some cases, the minimum mapping stretch can be 4 base pairs. In some cases, the alignment can be performed step by step. For example, the alignment can start with a relatively long segment length, for example, about 30 bases or about 22 bases, to locally map the read sequence to the human or HPV reference genome in order to identify chimeric read sequences, each of which has a portion aligned with the human reference genome and the remaining portion aligned with the HPV reference genome. Once a chimeric read sequence is identified, a shorter "portion" of the chimeric read sequence, e.g., a portion of the chimeric read sequence that is mappable to the human genome or the HPV genome and that is shorter than the rest of the chimeric read sequence, can be further aligned to a reference genome of either human or HPV using a relatively short segment length (e.g., about 4 bases) to achieve a finer alignment to either reference genome, thereby inferring candidate integration breakpoints. The short nucleotide segment length can be adjusted based on various parameters, including but not limited to the type of pathogen and host organism, variables in the various steps of the sequencing assay such as amplification, adapter ligation, sequencing by synthesis, sequencing depth, and length of the read sequence, and the expected sensitivity and specificity for determining pathogen integration breakpoints. In some cases, the alignment can be performed in a single step using an appropriate local sequence alignment algorithm. For example, a chimeric read sequence can be detected using a dynamic algorithm based on Smith-Waterman by aligning a portion of its sequence to the host organism genome or another portion to the pathogen genome.

[0070] Human-HPV chimeric read sequences can be identified within candidate integration regions to determine potential integration breakpoints. In this embodiment, the integration breakpoint can be defined as the boundary where the HPV DNA and human DNA are juxtaposed. As described above, the strand orientations of the chimeric read sequences and type A chimeric fragment read sequences used to construct the candidate regions can be consistent. In view of this information, the relative positions of potential breakpoints can be further inferred by combining the strand information of the candidate regions and the chimeric read sequences. Figure 4 The table in further illustrates exemplary situations in which and how the orientation of the chimeric fragment has a strand orientation consistent with that of a chimeric fragment with a human-HPV chimeric read sequence. For each exemplary situation, the expected relative positions of the breakpoints are also given for either the human or HPV genomic sequence.

[0071] When identifying and analyzing type B chimeric reads, if the longer portion of a potential human-HPV chimeric read (either read in paired-end sequencing) can be mapped to the human side of a candidate region, alignment can be tested between the shorter portion of the chimeric read and the HPV side of the candidate region. Similarly, if the longer portion of a potential human-HPV chimeric read (either read in paired-end sequencing) can be mapped to the HPV side of a candidate region, alignment can be tested between the shorter portion of the chimeric read and the human side of the candidate region. Because the minimum mapping length of the shorter portion of a chimeric read can be short, it is possible that a short mapping segment may occasionally map to multiple regions on the human or HPV genome. Therefore, when the mapping position of a short sequence anchor is within a short predetermined distance of a potential breakpoint, that mapping position can be considered a putative hit, and thus, the corresponding read can be considered a chimeric read sequence covering the integration breakpoint.

[0072] D. Screening for high-confidence integration breakpoints

[0073] For breakpoints covered by multiple chimeric read sequences, a diversity test can be used to evaluate the integration breakpoints identified according to the scheme described above and to filter out potential false positive breakpoints. A true breakpoint may be covered by multiple chimeric fragments having different lengths when aligned with the host organism or pathogen reference genome, which therefore has greater diversity. Potential integration breakpoints can be evaluated by examining the diversity of chimeric read sequences covering the integration breakpoint (e.g., the diversity of sequence lengths of chimeric read sequences aligned with the host organism or pathogen reference genome). A diversity score for examining the diversity of chimeric read sequences can be calculated as follows:

[0074] ,

[0075] in

[0076] σ1: standard deviation of the length of the portion of the chimeric read sequence that aligns to the host organism reference genome;

[0077] σ2: standard deviation of the length of the portion of chimeric read sequence that aligns to the pathogen reference genome.

[0078] Integration breakpoints can then be ranked by diversity score. In some cases, the higher the diversity score, the higher the likelihood that the identified integration breakpoint is true (see, e.g., Figure 5In some embodiments, the cutoff diversity score can be set to 4, such that integration breakpoints with a diversity score below 4 are excluded, while the remaining integration breakpoints with a diversity score of at least 4 can be considered to be from actual HPV integration events. In some cases, the cutoff diversity score is set to about 0.1, about 0.2, about 0.4, about 0.6, about 0.8, about 1, about 1.2, about 1.4, about 1.6, about 1.8, about 2.0, about 2.2, about 2.4, about 2.5, about 2.6, about 2.8, about 3.0, about 3.1, about 3.2, about 3.3, about 3.4, about 3.5, about 3.6, about 3.7, about 3.8, about 3.9 , about 4.0, about 4.1, about 4.2, about 4.3, about 4.4, about 4.5, about 4.6, about 4.7, about 4.8, about 4.9, about 5.0, about 5.2, about 5.4, about 5.6, about 5.8, about 6.0, about 6.5, about 7.0, about 7.5, about 8, about 8.5, about 9, about 9.5, about 10, about 20, about 30, about 40, about 50, about 60, about 80 or about 100. In some cases, the cutoff diversity score can be at most 0.1, at most 0.2, at most 0.4, at most 0.6, at most 0.8, at most 1, at most 1.2, at most 1.4, at most 1.6, at most 1.8, at most 2.0, at most 2.2, at most 2.4, at most 2.5, at most 2.6, at most 2.8, at most 3.0, at most 3.1, at most 3.2, at most 3.3, at most 3.4, at most 3.5, at most 3.6, at most 3.7, at most 3.8, at most 3.9, at most 4.0, at most 4.1, at most 4.2, at most 4.3, at most 4.4, at most 4.5, at most 4.6, at most 4.7, at most 4.8, at most 4.9, at most 5.0, at most 5.2, at most 5.4, at most 5.6, at most 5.8, at most 6.0, at most 6.5, at most 7.0, at most 7.5, at most 8, at most 8.5, at most 9, at most 9.5 or at most 10. In other cases, the cutoff diversity score can be at least 1, at least 1.2, at least 1.4, at least 1.6, at least 1.8, at least 2.0, at least 2.2, at least 2.4, at least 2.5, at least 2.6, at least 2.8, at least 3.0, at least 3.1, at least 3.2, at least 3.3, at least 3.4, at least 3.5, at least 3.6, at least 3.7, at least 3.8, at least 3.9, at least 4.0, at least 4.1, at least 4.2, or more. , at least 4.3, at least 4.4, at least 4.5, at least 4.6, at least 4.7, at least 4.8, at least 4.9, at least 5.0, at least 5.2, at least 5.4, at least 5.6, at least 5.8, at least 6.0, at least 6.5, at least 7.0, at least 7.5, at least 8, at least 8.5, at least 9, at least 9.5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 80 or at least 100.

[0079] One or more of the various cutoff values ​​described herein can be adjusted based on a variety of parameters. These variables can be adjusted to achieve a desired assessment outcome, such as the sensitivity and specificity of pathogen integration breakpoints. Various cutoff values ​​that can be adjusted according to the methods provided herein include: alignment lengths used to search for and identify Type A chimeric fragment read pairs; cutoffs defining the distance between reads in integration regions; the length of short nucleotide segments used for alignment during search and identification of Type B chimeric fragment reads; and cutoff diversity scores. One or more of these variables can be adjusted based on a variety of parameters, including: the context of sample analysis (e.g., the types of pathogen and host organisms); the type of cancer being analyzed; the age, sex, and health status of the host organism; variables in various steps of the sequencing assay, such as amplification, adapter ligation, sequencing by synthesis, sequencing depth, and read length; and the expected outcome of the sample analysis, such as the sensitivity and specificity of pathogen integration breakpoints. In some cases, variable adjustments can be evaluated and / or confirmed by other assays. For example, in patients suffering from a certain type of cancer (e.g., cervical cancer), HPV integration breakpoints can be determined by analyzing the cell-free DNA in a plasma sample according to the methods and systems provided herein. Different series of candidate HPV integration breakpoints can be inferred by adjusting one or more of the variables described herein. These series of candidate HPV integration breakpoints can then be compared with HPV integration breakpoints determined by analyzing a tumor tissue sample from the same patient. In some cases, whole genome sequencing or other next generation sequencing technologies can be used for this analysis. In some cases, variables can be optimized by comparison to obtain correct information about the pathogen integration spectrum in the subject.

[0080] E. Pathogen Integration Index

[0081] The pathogen integration profiles described herein may include a pathogen integration index. Some aspects of the present disclosure also provide methods and systems for determining a pathogen integration index by analyzing nucleic acid molecules from a biological sample. The integration index can be an odds ratio of organism-pathogen chimeric nucleic acid molecules relative to the total number of nucleic acid molecules in the biological sample. In some cases, the integration index is determined based on the number of chimeric fragment read sequence pairs, as described above. For example, the number of chimeric read sequence pairs (e.g., type A fragment read sequence pairs, type B fragment read sequence pairs, or both) can be determined during sample analysis (e.g., sequencing analysis). In some embodiments, the pathogen integration index is calculated by dividing the number of organism-pathogen chimeric read sequence pairs (e.g., human-HPV chimeric read sequence pairs) by the number of pathogen fragment read sequence pairs (e.g., HPV fragment read sequence pairs). The HPV fragment read sequence pair can be a pair of double-end read sequences, both of which map to the genome of the pathogen (e.g., HPV).

[0082] In some embodiments, the integration index can include position information. For example, the integration index can be calculated for each position in the genome of the pathogen, the genome of the host organism, or both. In some cases, for a position in the genome of the pathogen (e.g., nucleotide coordinates), the number of organism-pathogen chimeric fragment read sequence pairs at the end at the position can be determined, and the number of pathogen fragment read sequence pairs at the end at the position can also be determined. Therefore, the integration index for the position can be calculated by dividing the number of organism-pathogen chimeric fragment read sequence pairs at the end at the position by the number of pathogen fragment read sequence pairs at the end at the position. Other calculation methods can also be used to determine the integration index, as long as the method reflects the abundance of organism-pathogen chimeric fragments in the biological sample.

[0083] Integration indices can be analyzed at the genome-wide or locus-specific level. At the locus-specific level, the index can be calculated for any site along the viral genome that is at least 1 base pair in size. In one embodiment, the integration index can be used to predict the risk or likelihood of developing cancer. For example, in some cases, the higher the integration index, the more likely cancer is to occur in the subject from which the sample was derived. In another embodiment, the distribution of pathogen integration indices along the viral genome (analysis at the locus-specific level) can vary with different tumor types. For example, the HPV integration index has been shown to differ between patients with cervical cancer and HNSCC ( Figure 7), thus providing information about the origin of the tumor. The integration index can be calculated based on, for example, the ratio of type A viral-human chimeric fragments to total viral DNA alone. The above-described filtering step for identifying integration breakpoints may or may not be applied to calculate the integration index. For example, any paired-end read sequence (one end aligned to the viral genome and the other end aligned to the human genome), e.g., a type A chimeric fragment, can be used to calculate the integration index without any filtering. In another embodiment, the ratio of the total number of type A and type B chimeric fragments can be used to calculate the integration index.

[0084] It should be understood that although the above detailed exemplary workflow focuses on the analysis of read sequences from paired-end sequencing of organism-pathogen chimeric cell-free nucleic acid fragments (particularly human-HPV chimeric cell-free DNA fragments), similar concepts and methods can be applied to the following situations in which other sequencing technologies are used and it is not necessary to obtain only end sequence information, for example, when sequencing technologies such as ion torrent sequencing and nanopore sequencing are used to determine integration regions and / or breakpoints; similar concepts and methods can also be applied to situations in which pathogens and host organisms and / or other (non-cellular or cellular) nucleic acid molecules are involved.

[0085] III. Pathogen Integration Spectrum

[0086] The pathogen integration profile can include the location of one or more pathogen integration breakpoints in the pathogen genome, the host organism's genome, or both. The pathogen integration profile can include the location of one or more integration breakpoints in the pathogen genome or the host organism's genome. The pathogen integration profile can be used to determine the classification of a pathology, e.g., the presence or absence of cancer or precancerous lesions, the type of cancer, and the stage of cancer.

[0087] A pathogen integration profile can include one or more “integration breakpoints.” An integration breakpoint can be a site at which a pathogen nucleic acid molecule (eg, viral DNA) integrates into the genome (eg, the human genome) of a host cell infected by the pathogen.

[0088] Integration of pathogen nucleic acids can compromise the integrity of the host organism's genome and, in some cases, lead to serious downstream consequences in the host cell. The integration breakpoint in the host organism's genome can be an expressed sequence that can be translated into protein. Pathogen integration can lead to disruption of protein production in infected cells. In some other cases, the integration breakpoint in the host organism's genome can be in a transcriptional regulatory region (e.g., an enhancer or promoter). Integration of pathogen nucleic acid sequences can lead to dysregulation of gene transcription normally regulated by the affected transcriptional regulatory region. In some cases, pathogen integration can lead to changes in the transcription / translation profile of normal cells, changes in cellular function, or malignant transformation into cancer cells. The pathogen nucleic acid molecule can be a pathogen genomic DNA molecule. When the pathogen genome comprises an RNA molecule, for example, when the pathogen is an RNA virus (e.g., a retrovirus), the pathogen nucleic acid molecule can be the complementary DNA of the pathogen's genomic RNA molecule. In the blood of subjects infected with HPV, human-HPV chimeric DNA fragments may circulate in the plasma, which have both HPV DNA sequences and human DNA sequences. In the blood of EBV-infected subjects, human-EBV chimeric DNA fragments may circulate in the plasma, which have both EBV DNA sequences and human DNA sequences.

[0089] The pathogen integration profile can represent: the presence or absence of a combination of nucleotide sequences from the pathogen genome and nucleotide sequences from the host organism genome in the nucleic acid molecule of interest, the size or length of the nucleotide sequences from the pathogen genome in the nucleic acid molecule of interest, the contribution of nucleotide sequences from the pathogen genome in the nucleic acid molecule of interest, the sequence pattern of nucleotide sequences from the pathogen genome in the nucleic acid molecule of interest, the characteristics of the pathogen-host organism junction in the nucleic acid molecule of interest, the location of breakpoints in the pathogen genome that integrate into the host organism genome, or the location of breakpoints in the genome of the host organism where the pathogen nucleic acid molecule is inserted. In some cases, the pathogen integration profile includes the location of breakpoints in the pathogen genome that integrate into the genome of the host organism.

[0090] In some embodiments, the method provided herein relates to analyzing a biological sample of an organism to determine a pathogen integration spectrum. In some cases, the method may include analyzing a cell-free nucleic acid molecule from a biological sample to determine a pathogen integration spectrum. The pathogen integration spectrum may include: the position of the integration breakpoint in the genome of the pathogen integrated into the genome of the host organism, the position of the integration breakpoint in the genome of the host organism, or both. A breakpoint can generally represent the position where the nucleic acid sequence from the first organism is parallel to the nucleic acid sequence from the second organism when the nucleic acid sequence from the first organism is integrated into the genome of the second organism. In some cases, the breakpoint described herein can be the position where the nucleic acid sequence from the genome of the pathogen is parallel to the nucleic acid sequence from the genome of the host organism. For example, a breakpoint in the genome of a pathogen can represent the following position in the pathogen genome: when the pathogen genome is integrated into the host genome, the pathogen genomic DNA breaks at said position and joins the host organism genomic DNA. In some cases, the integration process can relate to a breakpoint in the pathogen genome. In other cases, the integration process can involve multiple breakpoints in the pathogen genome, for example, the pathogen genomic DNA is broken into multiple fragments, for example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 100 or more fragments, which are separately inserted into the host organism genome. In some cases, the methods and systems provided herein can achieve identification of one or more integration breakpoints in a nucleic acid molecule from a biological sample. In some cases, the methods and systems provided herein can achieve identification of 2, 3, 4, 5, 6, 7, 8, 9 or even more integration breakpoints in a nucleic acid molecule from a biological sample.

[0091] IV. Chromosomal rearrangements

[0092] A. Detection of chromosomal rearrangements

[0093] In some aspects, the present disclosure provides methods for detecting chromosome rearrangements. The methods and systems may include analyzing, by a computer system, a plurality of nucleic acid molecules (e.g., cell-free nucleic acid (e.g., DNA) molecules) from a biological sample (e.g., plasma) to detect chimeric nucleic acid fragments comprising a chromosome rearrangement (chromosomal rearrangement chimeric nucleic acid fragments). Analyzing each of the plurality of nucleic acid molecules (e.g., cell-free nucleic acid (e.g., DNA) molecules) may include identifying a first end of each nucleic acid molecule (e.g., cell-free nucleic acid molecule) as a first genomic region from a reference genome of the organism, and identifying a second end of each nucleic acid molecule (e.g., cell-free nucleic acid molecule) as a second genomic region from a reference genome of the organism. When the relative positioning of the first genomic region and the second genomic region in the reference genome of the organism is inconsistent with the relative positioning of the first end and the second end in each cell-free nucleic acid molecule, the chimeric nucleic acid fragment comprising a chromosome rearrangement can be detected.

[0094] The relative positioning can represent relative distances or 5' to 3' (or upstream to downstream) relative relationships within a reference genome or cell-free nucleic acid fragment. The relative distance can represent an exact relative distance or a range of relative distances. For example, if the relative distance between a first genomic region and a second genomic region in the reference genome is identical in some cases, or is within the same range (e.g., 100-180 bases or a typical length range for cell-free nucleic acid fragments) in other cases, then the relative positioning can be consistent. If the relative distance between a first genomic region and a second genomic region in the reference genome is not identical in some cases, or is not within the same range (e.g., 100-180 bases or a typical length range for cell-free nucleic acid fragments) in other cases, then the relative positioning can be inconsistent. The relative positioning between a first genomic region and a second genomic region in the reference genome of an organism is inconsistent with the relative positioning between the first and second ends in each cell-free nucleic acid molecule, and this inconsistency can vary depending on the type of chromosomal rearrangement. Chromosomal rearrangements can be translocations, amplifications, deletions, inversions, loss of chromosome arms, and gain of chromosome arms.

[0095] Translocation can include interchromosomal rearrangement or intrachromosomal rearrangement of nucleic acid sequences from two previously separated genomic regions, respectively, on different chromosomes or on the same chromosome. If the cell-free nucleic acid fragment is generated in a genomic region that includes a translocation event, the two ends of the cell-free nucleic acid fragment can be from two previously separated genomic regions in the organism. In some cases, if the first end of the cell-free nucleic acid fragment is aligned with the first genomic region in the reference genome of the organism, and the second end of the cell-free nucleic acid fragment is aligned with the second genomic region in the reference genome of the organism, the first genomic region and the second genomic region in the reference genome can have a distance that is inconsistent with the distance between the first end and the second end in the cell-free nucleic acid fragment. For example, when the two ends of the cell-free nucleic acid fragment span parts of two originally different chromosomes, if the first genomic region and the second genomic region in the reference genome are on different chromosomes, the distance between the first genomic region and the second genomic region in the reference genome may be undeterminable. In some cases, if the cell-free nucleic acid fragment spans a position where a chromosomal translocation has occurred, the distance between the first genomic region and the second genomic region in the reference genome can be longer than the distance between the first end and the second end in the cell-free nucleic acid fragment. In some cases, the relative distance between the first genomic region and the second genomic region in the reference genome is compared to an arbitrary cutoff value to determine whether the cell-free nucleic acid spans a position where a translocation has occurred. For example, the cutoff value can be a distance longer than the expected length of the cell-free nucleic acid fragment. In some cases, the cutoff value can be a distance longer than the expected length of the cell-free nucleic acid fragment minus the length of the first read sequence and the second read sequence. The cutoff value can be about 100 bases, about 200 bases, about 300 bases, about 400 bases, about 500 bases, about 600 bases, about 800 bases, about 1000 bases, about 1200 bases, about 1500 bases, about 2000 bases, about 3000 bases, about 4000 bases, about 5000 bases, about 6000 bases, about 8000 bases, or about 10 4 The cutoff value can be at least 80 bases, at least 140 bases, at least 180 bases, at least 250 bases, at least 350 bases, at least 450 bases, at least 550 bases, at least 750 bases, at least 900 bases, at least 1100 bases, at least 1250 bases, at least 1800 bases, at least 2500 bases, at least 3500 bases, at least 5500 bases, at least 7500 bases, at least 9000 bases, or at least 10 4 bases.

[0096] Inversion can be a chromosomal rearrangement in which a segment of a chromosome is flipped from one end to the other. Inversion can occur when a single chromosome undergoes a break and rearranges within itself. If the cell-free nucleic acid fragment is generated in a genomic region that includes an inversion event, the two ends of the cell-free nucleic acid fragment can come from two genomic regions that originally had a 5' to 3' opposite relationship. In these cases, if the first end of the cell-free nucleic acid fragment is aligned with a first genomic region in a reference genome of an organism, and the second end of the cell-free nucleic acid fragment is aligned with a second genomic region in a reference genome of an organism, the first genomic region and the second genomic region in the reference genome can have an inverted 5' to 3' relationship compared to the first and second ends of the cell-free nucleic acid fragment.

[0097] Amplification can be a chromosomal rearrangement in which a segment of a chromosome is locally duplicated. Cell-free nucleic acid fragments generated from a genomic region comprising an amplification can have two ends comprising the same duplicated sequence. Deletion can be a chromosomal rearrangement in which a segment of a chromosome is deleted. Cell-free nucleic acid fragments generated from a genomic region comprising a deletion can have two ends from two genomic regions previously separated by the deleted segment.

[0098] The method can further include analyzing the chromosome rearrangement chimeric nucleic acid fragment to detect the chromosome rearrangement. The detection of the chromosome rearrangement can include determining the type of chromosome rearrangement, for example, translocation, amplification, deletion, inversion, loss of chromosome arms or increase in chromosome arms. The detection of the chromosome rearrangement can also include determining the chromosome rearrangement breakpoints.

[0099] In some cases, the method includes identifying type A chromosome mosaic read pairs from a plurality of read pairs generated by paired-end sequencing of cell-free nucleic acid molecules from a biological sample. The type A chromosome mosaic read pairs can have a first read sequence that aligns with a first genomic region of a reference genome of the organism and a second read sequence that aligns with a second genomic region of the reference genome of the organism; for each of the type A chromosome mosaic read pairs, the relative positioning of the first genomic region and the second genomic region in the reference genome of the organism can be inconsistent with the relative positioning of the first end and the second end in the respective cell-free nucleic acid molecules. The method can further include grouping first read sequences of the type A chromosome mosaic read pairs that overlap or are separated by a predetermined distance in the reference genome of the organism; and grouping second read sequences of the type A chromosome mosaic read pairs that overlap or are separated by a predetermined distance in the reference genome of the organism, thereby respectively determining candidate rearranged regions comprising the candidate region in the first genomic region and the second genomic region of the reference genome of the organism. The type of chromosome rearrangement (e.g., translocation or inversion) can be inferred based on the type A chromosome mosaic read pairs.

[0100] The method may further include identifying and analyzing type B chromosome mosaic read sequence pairs. Type B chromosome mosaic read sequence pairs may have a first read sequence and a second read sequence, wherein the first read sequence is aligned with the candidate rearrangement region, and the second read sequence is a mosaic read sequence, wherein a portion of the mosaic read sequence is aligned with the first genomic region, and the remainder of the mosaic read sequence is aligned with the second genomic region. Based on the type B chromosome mosaic read sequence pairs, chromosome rearrangement can be determined. For example, the chromosome rearrangement breakpoint can be determined by locally aligning the sequence of the mosaic read sequence with a reference genome. Based at least in part on the chromosome rearrangement breakpoint, the type of chromosome rearrangement can be further determined.

[0101] In some cases, the methods and systems include determining the diversity of chimeric read sequences of type B chromosome chimeric read sequence pairs, thereby screening for chromosome rearrangements based on the diversity. The screening may include determining a diversity score for the type B chromosome chimeric read sequence pairs. The diversity score may be calculated as ,

[0102] where σ1 is the standard deviation of the lengths of the portion of the chimeric read sequence that aligns to the first genomic region, and where σ2 is the standard deviation of the lengths of the remaining portion of the chimeric read sequence that aligns to the second genomic region.

[0103] The method may include comparing the diversity score to a cutoff value. In some cases, if the diversity score is equal to or higher than the cutoff value, the chromosome rearrangement can be determined based on the chimeric read sequence. In some cases, the diversity score is lower than the cutoff value and the candidate chromosome rearrangement region can be excluded as a false positive hit. The cutoff value of the diversity score can be about 0.1, about 0.2, about 0.4, about 0.6, about 0.8, about 1, about 1.2, about 1.4, about 1.6, about 1.8, about 2.0, about 2.2, about 2.4, about 2.5, about 2.6, about 2.8, about 3.0, about 3.1, about 3.2, about 3.3, about 3.4, about 3.5, about 3.6, about 3.7, about 3.8, about 3.9, about 4 .0, about 4.1, about 4.2, about 4.3, about 4.4, about 4.5, about 4.6, about 4.7, about 4.8, about 4.9, about 5.0, about 5.2, about 5.4, about 5.6, about 5.8, about 6.0, about 6.5, about 7.0, about 7.5, about 8, about 8.5, about 9, about 9.5, about 10, about 20, about 30, about 40, about 50, about 60, about 80 or about 100. In some cases, the cutoff diversity score can be at most 0.1, at most 0.2, at most 0.4, at most 0.6, at most 0.8, at most 1, at most 1.2, at most 1.4, at most 1.6, at most 1.8, at most 2.0, at most 2.2, at most 2.4, at most 2.5, at most 2.6, at most 2.8, at most 3.0, at most 3.1, at most 3.2, at most 3.3, at most 3.4, at most 3.5, at most 3.6, at most 3.7, at most 3.8, at most 3.9, at most 4.0, at most 4.1, at most 4.2, at most 4.3, at most 4.4, at most 4.5, at most 4.6, at most 4.7, at most 4.8, at most 4.9, at most 5.0, at most 5.2, at most 5.4, at most 5.6, at most 5.8, at most 6.0, at most 6.5, at most 7.0, at most 7.5, at most 8, at most 8.5, at most 9, at most 9.5 or at most 10.In other cases, the cutoff diversity score can be at least 1, at least 1.2, at least 1.4, at least 1.6, at least 1.8, at least 2.0, at least 2.2, at least 2.4, at least 2.5, at least 2.6, at least 2.8, at least 3.0, at least 3.1, at least 3.2, at least 3.3, at least 3.4, at least 3.5, at least 3.6, at least 3.7, at least 3.8, at least 3.9, at least 4.0, at least 4.1, at least 4.2, or more. , at least 4.3, at least 4.4, at least 4.5, at least 4.6, at least 4.7, at least 4.8, at least 4.9, at least 5.0, at least 5.2, at least 5.4, at least 5.6, at least 5.8, at least 6.0, at least 6.5, at least 7.0, at least 7.5, at least 8, at least 8.5, at least 9, at least 9.5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 80 or at least 100.

[0104] The method may include filtering out "false positive" read pairs by linearity, similar to methods for detecting pathogen integration breakpoints. The method may include filtering out type B chromosome mosaic read pairs, which have a linearity pattern that is inconsistent with the majority linearity pattern, from determining chromosomal rearrangements.

[0105] B. Identifying Gene Fusions in Cancer Genomes

[0106] In some aspects, the methods and systems provided herein can be applied to detect gene fusions in human cancer genomes. Gene fusions can include interchromosomal or intrachromosomal rearrangements of nucleic acid sequences from two previously separated genomic regions on different or identical chromosomes. Gene fusion events can result in hybrid genomic fragments ("fusion genes") that share similarities with hybrid DNA molecules integrated from pathogens. Gene fusions can occur due to translocations, interstitial deletions, or chromosomal inversions. Gene fusions can play a role in cancer progression. Identification of gene fusions can be a diagnostic tool, and fusion genes can be therapeutic targets in anti-cancer treatments. The methods and systems described herein relate to detecting fusion genes from cell-free nucleic acids released from the genome of tumor tissue. The methods provided herein can make this detection painless and non-invasive, which can assist in precise cancer treatment and monitor treatment responses using tumor DNA in real time.

[0107] C. Identifying somatic structural variants

[0108] In some aspects, the method provided herein can be applied to detect somatic structural variations in the genome. Structural variations can include deletions, duplications, copy number variants, insertions, inversions, and translocations, which can contribute to complex disorders (including autism and cancer). Somatic structural variations can cause chimeric DNA molecules to be released into the plasma and can potentially be detected by the integrated detection method described herein. The method provided herein can allow accurate identification of various structural variations and can facilitate exploration of the pathogenic mechanisms and treatment methods of diseases associated with genomic structural variations. Somatic structural variations can be detected non-invasively based on the method provided herein.

[0109] V. Sequencing Methods

[0110] Sequencing analysis of the biological samples described herein can be performed to determine the pathogen integration profile. The methods provided herein may include sequencing nucleic acid molecules (e.g., cell-free nucleic acid molecules, cellular nucleic acid molecules, or both) from the biological sample. In some cases, the methods provided herein include analyzing sequencing results, e.g., sequencing reads of nucleic acid molecules from the biological sample. The methods and systems provided herein may or may not include an effective step of sequencing. The methods and systems may include or provide a device for receiving and processing sequencing data from a sequencer. The methods and systems may also include or provide a device for providing commands (e.g., commands based on analysis of sequencing results) to the sequencer to adjust parameters of the sequencing process.

[0111] Commercially available sequencing equipment can be used in the methods provided by the present disclosure, such as Illumina sequencing platforms and 454 / Roche platforms. Any method known in the art can be utilized to sequence nucleic acids. For example, sequencing can include next-generation sequencing. In some cases, nucleic acid sequencing can be performed using chain termination sequencing, hybridization sequencing, Illumina sequencing (for example, using reversible terminator dyes), ion torrent semiconductor sequencing, mass spectrometry sequencing, massively parallel signature sequencing (MPSS), Maxam-Gilbert sequencing, nanopore sequencing, polony sequencing, pyrosequencing, shotgun sequencing, single molecule real-time (SMRT) sequencing, SOLiD sequencing (using 4 kinds of fluorescently labeled di-base probe hybridization), universal sequencing, or any combination thereof.

[0112] One sequencing method that can be used in the methods provided herein can include paired-end sequencing, for example, using an Illumina "dual-end module" equipped with its genome analyzer. Using this module, after the genome analyzer has completed the first sequencing read, the dual-end module can guide the resynthesis of the initial template and the second round of cluster generation. By utilizing the paired-end reads in the methods provided herein, sequence information can be obtained from both ends of the nucleic acid molecule and mapped to a reference genome (e.g., the genome of a pathogen or the genome of a host organism). After mapping the two ends, the pathogen integration spectrum can be determined according to some embodiments of the methods provided herein.

[0113] During paired-end sequencing, the read sequence from the first end of the nucleic acid molecule can include at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, at least 80, at least 85, at least 90, at least 95, at least 100, at least 105, at least 110, at least 115, at least 120, at least 125, at least 130, at least 135, at least 140, at least 145, at least 150, at least 155, at least 160, at least 165, at least 170, at least 175, or at least 180 consecutive nucleotides. The read sequence from the first end of the nucleic acid molecule may include at most 24, at most 28, at most 32, at most 38, at most 42, at most 48, at most 52, at most 58, at most 62, at most 68, at most 72, at most 78, at most 82, at most 88, at most 92, at most 98, at most 102, at most 108, at most 122, at most 128, at most 132, at most 138, at most 142, at most 148, at most 152, at most 158, at most 162, at most 168, at most 172, or at most 180 consecutive nucleotides. The read sequence from the first end of the nucleic acid molecule can include about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 55, about 60, about 65, about 70, about 75, about 80, about 85, about 90, about 95, about 100, about 105, about 110, about 115, about 120, about 125, about 130, about 135, about 140, about 145, about 150, about 155, about 160, about 165, about 170, about 175, or about 180 consecutive nucleotides. The read sequence from the second end of the nucleic acid molecule can include at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, at least 80, at least 85, at least 90, at least 95, at least 100, at least 105, at least 110, at least 115, at least 120, at least 125, at least 130, at least 135, at least 140, at least 145, at least 150, at least 155, at least 160, at least 165, at least 170, at least 175, or at least 180 consecutive nucleotides. The read sequence from the second end of the nucleic acid molecule may include at most 24, at most 28, at most 32, at most 38, at most 42, at most 48, at most 52, at most 58, at most 62, at most 68, at most 72, at most 78, at most 82, at most 88, at most 92, at most 98, at most 102, at most 108, at most 122, at most 128, at most 132, at most 138, at most 142, at most 148, at most 152, at most 158, at most 162, at most 168, at most 172, or at most 180 consecutive nucleotides.The read sequence from the second end of the nucleic acid molecule can include about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 55, about 60, about 65, about 70, about 75, about 80, about 85, about 90, about 95, about 100, about 105, about 110, about 115, about 120, about 125, about 130, about 135, about 140, about 145, about 150, about 155, about 160, about 165, about 170, about 175 or about 180 consecutive nucleotides. In some cases, the read sequence from the first end of the nucleic acid molecule can include at least 75 consecutive nucleotides. In some cases, the read sequence from the second end of the nucleic acid molecule can include at least 75 consecutive nucleotides. The read sequence from the first end and the second end of the nucleic acid molecule can have the same length or different lengths. The read sequence from the multiple nucleic acid molecules of a biological sample can have the same length or different lengths.

[0114] The order-checking in the method provided in this article can be carried out with various order-checking depths. Order-checking depth can represent that locus is covered with the number of times of reading sequence on this locus comparison. Locus can be as small as Nucleotide, or as large as chromosome arm, or as large as whole genome. The order-checking depth in the method provided in this article can be 1x, 2x, 5x, 10x, 20x, 25x, 30x, 40x, 50x, 75x or 100x etc., wherein the quantity before " x " represents that locus is covered with the number of times of reading sequence. Order-checking depth can also be applied to multiple locus or full genome, and in this case, x can represent that locus or haploid genome or full genome are respectively ordered the average number of times. In some cases, carry out ultra-deep order-checking in the method described herein, it can represent and carry out at least 100x order-checking depth.

[0115] During the sequencing process, a specific nucleotide within a nucleic acid is read, or the average number of times (e.g., sequencing depth) can be a multiple of the length of the nucleic acid being sequenced. In some cases, when the sequencing depth is sufficiently greater than the length of the nucleic acid (e.g., at least 5 times), the sequencing can be referred to as "deep sequencing." In some embodiments, the sequencing depth can be, on average, at least about 5 times, at least about 10 times, at least about 20 times, at least about 30 times, at least about 40 times, at least about 50 times, at least about 60 times, at least about 70 times, at least about 80 times, at least about 90 times, or at least about 100 times the length of the nucleic acid being sequenced. In some cases, the sample can be enriched for a specific analyte (e.g., nucleic acid fragments or cancer-specific nucleic acid fragments).

[0116] The read sequence (or sequencing read) generated in the methods provided herein can represent a string of nucleotides sequenced from any portion or all of a nucleic acid molecule. For example, the read sequence can be a short string (e.g., 20-150) of nucleotides complementary to a nucleic acid fragment, a string of nucleotides complementary to the end of a nucleic acid fragment, or a string of nucleotides complementary to the entire nucleic acid fragment present in a biological sample. The read sequence can be obtained in a variety of ways (e.g., using sequencing technologies).

[0117] VI. Other Determinations

[0118] In some cases, the systems and methods of the present disclosure involve utilizing sequencing technology to subsequently perform bioinformatics analysis on the read sequences to analyze pathogen integration profiles in biological samples, e.g., to analyze integration breakpoints. Cell-free nucleic acids from biological samples can be collected and sequenced, e.g., by double-end sequencing. The read sequences of the cell-free nucleic acids can be analyzed by the methods and systems provided herein to determine the positions of integration breakpoints in the genome of the pathogen, the genome of the host organism, or both. Other techniques can also be used as alternative or additional methods for analyzing integration profiles, including, but not limited to, amplification assays and microarray assays.

[0119] In some embodiments, the methods provided herein may further include an amplification reaction to analyze nucleic acid molecules from a biological sample. The amplification reaction may include a first primer complementary to a first target sequence in the pathogen's genome and a second primer complementary to a second target sequence in the organism's genome. The amplification reaction may be designed to amplify the pathogen's integration region (where a nucleic acid sequence from the pathogen joins with a nucleic acid sequence from the host organism). The knowledge of characteristic integration breakpoints for certain types of cancer can aid in the design of such amplification reactions. In some cases, the amplification reaction comprises a polymerase chain reaction (PCR). The PCR reaction may be initiated with at least one pair of primers, each complementary to a sequence flanking the integration breakpoint in the pathogen's genome or the host organism's genome. Alternatively, the PCR reaction may be initiated with random probes that randomly bind to the template sequence. In some cases, amplification reactions can provide cost-effective and rapid results regarding integration breakpoints. PCR reactions can be quantitative, providing quantitative insights into the contribution of different types of pathogen integration events. In some cases, the sequences of the amplicons produced by the amplification reaction may also be analyzed to confirm the integration breakpoints. Alternatively or additionally, the amplification reaction and sequence analysis of the amplicon can be implemented as a step of target-specific enrichment, thus providing a potentially more robust readout. Different variations of the PCR reaction that can be used for the systems and methods described herein include multiplex-PCR, asymmetric PCR, Klenow-based PCR, nested PCR, quantitative PCR, real-time PCR, hot start PCR, touchdown PCR, assembly PCR, COLD-PCR (lower denaturation temperature co-amplification-PCR), double-tail PCR, connection-mediated PCR, and methylation-specific PCR. Although examples and embodiments have been provided herein, other techniques and embodiments related to, for example, digital PCR and random sequencing can be found in U.S. Patent No. 8,722,334, filed October 28, 2010, U.S. Provisional Application No. 60 / 951,438, filed July 23, 2007, and U.S. Patent No. 9,121,069, filed July 8, 2013, each of which is incorporated herein by reference in its entirety.

[0120] In some other cases, any other sequence assay, such as a microarray assay, can be used to obtain sequence information of nucleic acid molecules (e.g., cell-free nucleic acids) from a biological sample. For example, a microarray can include probes targeting both host genomic sequences and pathogen genomic sequences to detect chimeric fragments as described herein. In other embodiments, a microarray can be designed to detect the location of pathogen integration breakpoints in nucleic acid molecules applied to the microarray.

[0121] In some cases, the sample analysis described herein can be supplemented and / or confirmed by assays using different principles and / or methods. For example, analysis of cell-free DNA in a plasma sample can produce a series of results that indicate preferred pathogen integration breakpoints associated with a particular type of cancer. In some cases, the sensitivity and specificity of these results from a cell-free DNA sample can be assessed by sequencing (e.g., whole-genome sequencing) of transformed cancer cells of a particular type (e.g., from a biopsy tumor sample). Alternatively, relatively simple and cost-effective methods can also be applied. For example, qualitative or quantitative PCR reactions can be designed to assess breakpoints by utilizing sequence information obtained during the identification of candidate breakpoints.

[0122] In some cases, the systems and methods provided herein involve a first assay and a second assay for determining the classification of a pathology. The first assay may comprise a sequencing assay, e.g., sequencing nucleic acid molecules from a biological sample, followed by bioinformatic analysis of the read sequences as described herein. The second assay may comprise an amplification assay, a microarray assay, or another type of assay. In some embodiments, the second assay comprises a sequencing assay, while the first assay comprises an amplification assay, a microarray assay, or another type of assay. In some cases, the first assay, e.g., a sequencing assay, is used to determine a pathogen integration profile, e.g., the location of one or more pathogen integration breakpoints in the genome of the pathogen or the genome of the host organism. The pathogen integration profile from the first assay can provide guidance for the second assay; for example, the location of one or more pathogen integration breakpoints can provide guidance for designing PCR probes or microarray probes for use in the second assay. In some embodiments, a human subject, e.g., a patient suspected of being infected with a pathogen (e.g., HPV), can be tested using the first assay to determine the pathogen integration profile. The pathogen integration profile can be used to determine the classification of the pathology. Based at least in part on the first assay, the patient can be diagnosed with a disease, e.g., cancer, e.g., cervical cancer. Thus, for example, after a therapy is administered to a patient, or after surgery is performed on a subject, a second assay can be performed on the patient that can be used to continue monitoring the pathogen integration profile in a biological sample from the patient. Continued monitoring of the pathogen integration profile can be used to monitor the disease that the patient has been diagnosed with, for example, to monitor the progression or regression of the disease. In some embodiments, the first assay can be used as a screening assay for one or more diseases / conditions, while the second assay can be used as a confirmatory assay for one or more diseases / conditions.

[0123] VII. Pathological Classification

[0124] The analysis of integration breakpoints in the genome of the pathogen integrated in the genome of the organism can be used to determine the classification of pathology. For example, the method provided herein can include analyzing the viral DNA integration spectrum, for example, analyzing the breakpoints in the HPV viral genome integrated in the human host organism genome, and can further include determining the cancer type based on the viral DNA integration spectrum. Pathology can generally represent a human condition or disease that deviates from a healthy state. Non-limiting examples of pathologies associated with pathogen integration and to which the methods and systems provided herein are applicable can include: adult T-cell leukemia, tropical spastic paraparesis, cervical cancer, head and neck cancer, anogenital cancer, Kaposi's sarcoma, Burkitt's lymphoma, hepatocellular carcinoma, Merkel cell carcinoma, AIDS, brain cancer, bone cancer, mesothelioma, prostate cancer, and B-cell lymphoma.

[0125] Cancer or tumor can represent an abnormal mass of tissue, wherein the growth of the mass exceeds and is not coordinated with the growth of normal tissue. Cancer or tumor can be defined as "benign" or "malignant" based on the following characteristics: the degree of cell differentiation, including morphological and functional, growth rate, local invasion and metastasis. "Benign" tumors can be well differentiated, characteristically have slower growth than malignant tumors, and remain confined to the site of origin. In addition, benign tumors do not have the ability to infiltrate, invade or metastasize to distant sites. "Malignant" tumors can be poorly differentiated (anaplastic) and characteristically have rapid growth accompanied by progressive infiltration, invasion and destruction of surrounding tissues. In addition, malignant tumors can have the ability to metastasize to distant sites.

[0126] In some aspects, the methods provided herein can include analyzing chromosomal rearrangements, e.g., copy number variations, deletions, duplications, inversions, and translocations, and can further include determining a pathology associated with the chromosomal rearrangement. Pathologies associated with chromosomal rearrangements to which the methods and systems provided herein are applicable can include virtually all types of cancer as well as various types of genetic diseases, for example, Wolf-Hirschhorn syndrome, Jacobsen syndrome, Charcot-Marie-Tooth disease type 1A, Angelman syndrome, Turner syndrome, 22q11.2 deletion syndrome, Triple X syndrome, Williams syndrome, Cry-a-chat syndrome, cat eye syndrome, 1q21.1 duplication syndrome, 15q13.3 microdeletion syndrome, 16p11.2 deletion syndrome, 17q23.1q23.2 microdeletion syndrome, 1q duplication syndrome, 1q21.1 microdeletion syndrome, 22q11.2 deletion syndrome, 22q11.2 duplication syndrome, 2q23.1 microdeletion syndrome, 2q37 deletion syndrome, 47XXX syndrome, 47, XYY syndrome, 49,XXXXX syndrome, diploid-triploid mosaicism, distal chromosome 18q Deletion syndrome, Emanuel syndrome, Kleefstra syndrome, Koolen de Vries syndrome, Mosaic monosomy 18, Nablus mask-like facies syndrome, Pallister-Killian Mosaic syndrome, Smith-Magenis syndrome, Tetrasomy 9p syndrome, Tetrasomy X syndrome, Trisomy syndrome, Trisomy 13 syndrome, Trisomy 17 mosaicism, and Trisomy 2 mosaicism.

[0127] VIII. Classification of Cancer

[0128] Viral DNA integration breakpoints can serve as a basis for classifying cancer according to the methods described herein.In some cases, other classifications of pathologies (eg, cancers) can also utilize the methods provided in this disclosure.

[0129] The classification of a cancer can include the presence or absence of a cancer. The methods provided herein can be used to determine the classification of any type of cancer, for example, but not limited to, bladder cancer, bone cancer, brain tumor, breast cancer, cervical cancer, colorectal cancer, esophageal cancer, gastrointestinal cancer, hematopoietic malignancies, head and neck squamous cell carcinoma, leukemia, liver cancer, lung cancer, lymphoma, myeloma, nasal cancer, nasopharyngeal cancer, oral cancer, oropharyngeal cancer, ovarian cancer, prostate cancer, sarcoma, gastric cancer, or thyroid cancer.

[0130] The classification of cancer can include the level of cancer or the stage of cancer. The level of cancer or the stage of cancer (e.g., stage I, II, III, or IV) can represent the size of the tumor, the presence or absence of metastases, the total tumor burden of the body, and / or other measures of the severity of the cancer (e.g., recurrence of the cancer). The level of cancer can be a number or other index, such as a symbol, an alphabet letter, and a color. The level can be zero. The level of cancer can also include pre-malignant or pre-cancerous conditions (states) associated with mutations or the number of mutations.

[0131] The classification of cancer can also be used in various ways. In some cases, the classification of cancer includes the progression of cancer or the regression of cancer. In some cases, the classification of cancer is included in the prejudgment or prediction of the progression or regression of cancer when there is or is no treatment for cancer. For example, the methods and systems described herein can be used to determine whether cancer exists in previously unknown individuals with cancer. Assessment can investigate individuals diagnosed with cancer, so as to monitor the progression of cancer over time, study the effectiveness of treatment or determine prognosis. In some cases, the prognosis can be expressed as the possibility that the patient dies from cancer, or the possibility of cancer progression after a specific duration or time, or the possibility of cancer metastasis.

[0132] In some cases, the classification of a cancer includes the type of cancer, eg, cervical cancer (CC) or head and neck squamous cell carcinoma (HNSCC). Figure 6 The distribution of viral integration breakpoints across the HPV16 genome is shown based on analysis of cell-free DNA from plasma samples of patients with cervical cancer and HPV-positive head and neck squamous cell carcinoma, respectively. As shown in the figure, there may be shared preferred integration breakpoints between cervical cancer and HPV-positive head and neck squamous cell carcinoma (region A). On the other hand, there may also be preferred integration breakpoints that are characteristic of a certain type of cancer, for example, region B shows preferred integration breakpoints for HNSCC.

[0133] Viral integration breakpoints characteristic of a certain classification of cancer (e.g., a type of cancer) can be identified using the methods provided herein; in some cases, similar methods can be applied to determine viral integration breakpoints in order to determine the classification of a cancer, where read sequences of nucleic acid molecules from a biological sample are analyzed.

[0134] IX. Pathogen Integration: Viral Integration

[0135] The methods and systems provided herein can be used to detect different types of viral integrations that may be present in the genome of a host organism, such as mandatory integration. Mandatory integration into the host organism's genome can be an obligate event during viral replication for some viruses (e.g., Retroviridae, Pseudoviridae, Transpoviridae, some Myoviridae, and Longiviridae). Integration of viral DNA can result in the permanent insertion of the viral genome into the host organism's chromosomal DNA, referred to as a provirus in the case of retroviruses or a prophage in the case of prokaryotic viruses. The methods and systems provided herein can be used to detect accidental integration. During viral replication, accidental integration may be non-essential for some viruses, but it can confer advantages to the host / virus conjugate. The methods and systems provided herein can be used to detect endogenous viral elements (EVEs). Through rare and sometimes accidental processes, some endogenous viruses can integrate and "fossilize" into the host organism's genome, including endogenous retroviruses, as well as rare RNA virus integrations. An example of an EVE includes an endogenous retrovirus, which integrates into the germline. The integrated genome of an endogenous retrovirus can remain latent and passively replicate along with the host organism's genome and be passed on to subsequent generations of the cell. However, changes in the host organism's environmental conditions can reactivate the virus, leading to viral transcription and production of new infectious viruses (productive infection).

[0136] The methods and systems provided herein can be used to analyze DNA viruses and RNA viruses.

[0137] The methods and systems provided herein can be used to analyze the integration of DNA viruses. The genome of a DNA virus can be a potential substrate for integration into the host organism genome without prior processing. Once a cell is infected, the genome of a DNA virus can be translocated into the nucleus, where it can remain as an episome to ensure viral persistence or integrate into the genome of the host cell.

[0138] The method and system provided herein can be used to investigate the integration of herpesvirus.Herpesvirus can be a DNA enveloped virus and can be categorized into three families based on their sequence system: α, β and γ herpesvirus.They can include linear double-stranded DNA, and once the virus enters and cyclizes, the linear double-stranded DNA is delivered to the nucleus.Herpesvirus can remain as an additional type, for example, as an extrachromosomal circular DNA.Some herpesviruses can integrate their genome into the host chromosome.The example of herpesvirus includes Epstein-Barr virus (EBV) and human herpesvirus-6 (HHV-6).

[0139] The methods and systems provided herein can be used to analyze human papillomavirus (HPV) integration. HPV DNA can integrate into the host cell genome. DNA damage and agents that can induce DNA damage (e.g., double-strand breaks (DSBs)) can play a role in HPV integration.

[0140] The methods and systems provided herein can be used to analyze hepatitis B virus (HBV) integration. HBV can cause or play a role in the pathogenesis of hepatocellular carcinoma. During acute infection, HBV can integrate its genome into host chromosomes and exhibits several similarities to retroviral integration.

[0141] The methods and systems provided herein can be used to analyze the integration of adeno-associated virus type 2 (AAV-2). AAV is a common virus classified in the Parvoviridae family. AAV replication can be strictly controlled by being present in the same cells infected with a helper virus, such as adenovirus, human papillomavirus (HPV), or herpes simplex virus (HSV). In the absence of a helper virus, AAV can integrate its genome in a site-specific manner.

[0142] The method and system provided herein can be used to analyze the integration of RNA viruses. The viral RNA genome of a retrovirus can be reverse transcribed into a linear double-stranded DNA molecule (viral DNA intermediate), which can therefore serve as a substrate for subsequent viral genome integration into the host genome. For retroviruses, integration can be a mandatory step for productive infection. The genomes of other RNA viruses can also be identified in the host genome. In these cases, for example, for lymphocytic choriomeningitis virus (LCMV), integration can occur incidentally. The method and system provided herein can also assist in illustrating the role of RNA virus integration in the viral life cycle and in the pathological development in the host organism.

[0143] X. Viral Integration and Cancer

[0144] In some embodiments, the methods and systems provided herein can be used to determine the classification of a cancer by determining viral integration.

[0145] In some cases, the methods and systems provided herein can be used to analyze tumor viruses (or oncoviruses), which can directly cause cancer in experimental animals or humans. Several families of tumor viruses exist, including: hepatitis B virus (liver cancer), papillomaviruses (cervical and other anogenital cancers), Epstein-Barr virus (Burkitt's lymphoma and nasopharyngeal carcinoma), Kaposi's sarcoma-associated herpesvirus (Kaposi's sarcoma), polyomaviruses (Merkel cell carcinoma), and human T-cell lymphotropic virus (adult T-cell leukemia). Five of the six non-restricted families have DNA genomes and are referred to as DNA tumor viruses, while another family is a retrovirus, which possesses an RNA genome within the viral particle but can replicate in infected cells via DNA proviral synthesis. Additionally, HIV can indirectly cause cancer in AIDS patients due to immune deficiency, and hepatitis C virus (an RNA virus) can be an indirect cause of liver cancer due to chronic tissue damage.

[0146] The methods and systems provided herein can be used to analyze HBV integration to determine HBV-related conditions / diseases such as hepatocellular carcinoma (HCC). HBV can infect liver cells of several species, including ducks, woodchucks, squirrels, and humans. Hepatitis B virus infection can cause acute liver damage. Hepatitis B virus infection can be associated with up to one million cases of liver cancer each year (approximately 10% of the world's cancer incidence). Cellular transformation caused by the hepatitis B virus can be mediated by a viral gene (called the X gene), which affects the expression of various cellular genes, thereby causing abnormal cell proliferation and survival. In addition, the onset of cancer induced by the hepatitis B virus may be caused by the continued proliferation of liver cells (caused by chronic tissue damage). The methods and systems provided herein can be used to diagnose, monitor, or predict pathologies associated with any genotype (e.g., genotypes F, E, A, B, C, and D) or serotype of the hepatitis B virus or its various subtypes.

[0147] HBV can be associated with HCC. HBV is a partially double-stranded, hepatotropic DNA virus with retroviral characteristics. HBV genome integration into the cellular genome is present in over 85%–90% of HBV-associated HCC. However, integrated forms of HBV can also be present in non-tumor tissues of patients with chronic HBV infection. HBV genome integration into hepatocytes can occur during persistent HBV infection and precede the development of HCC. HBV integration can lead to elevated expression of several cellular cancer-associated genes, such as TERT, mixed lineage leukemia 4 (MLL4), and CCNE1 (encoding cyclin E1). HBV integration is also associated with the early onset and adverse outcomes of HCC.

[0148] The methods and systems provided herein can be used to determine papillomavirus integration profiles to assess papillomavirus-associated conditions / diseases such as cervical cancer. Papillomaviruses are small DNA viruses (approximately 8 kb genomes) that can induce benign and malignant tumors in humans and various other animal species. Approximately 60 different types of human papillomavirus have been identified, which can infect epithelial cells of several tissues. Some of these viruses can only cause benign tumors (e.g., warts), while others can be causative agents of malignant cancers (specifically, cervical cancer, head and neck squamous cell carcinoma (HNSCC), and other anogenital cancers). The methods and systems provided herein can be used to distinguish different types of pathology, for example, HPV-associated cancers. The methods and systems provided herein can also be used to analyze any type of papillomavirus, including HPV types 6, 11, 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68, 73, and 82.

[0149] The subclass of HPV can infect the anogenital area, and within this subclass, each type can be classified as high risk or low risk. High risk HPV can cause cancerous lesions, while low risk HPV is not so. Difference between high risk and low risk HPV can be that high risk HPV shows the larger tendency to be integrated into the host genome, causes height lesions and cancer thus, while low risk types can preferably remain as extrachromosomal circular episomes. HPV almost can cause all cases of cervical cancer, and it can be the second most common cancer in the whole world women, and is the fourth most common reason causing cancer death in the whole world women. HPV16, HPV18, HPV31 and HPV33 can account for 90% of all cervical cancer cases. Among these high risk HPV, HPV type 16 is the most general type, and itself can account for more than 50% of all cervical cancer cases. High risk HPV infection can also be associated with some other anogenital cancers and oropharyngeal cancer. For example, it is thought to cause over 90% of anal cancers, 70% of vaginal and vulvar cancers, 60% of penile cancers, and 63% of oropharyngeal cancers. While 95% of patients with cervical precancerous lesions may harbor HPV, only a small fraction of these individuals will ultimately progress to invasive cancer. Three premalignant stages—cervical intraepithelial neoplasia (CIN) 1, CIN2, and CIN3—may precede the onset of invasive cancer. CIN1 lesions may spontaneously regress, with only a small minority progressing to CIN2 / CIN3 and ultimately invasive cancer. Cervical cancer progression in HPV-infected women is closely linked to integration status, and integrated HPV is consistently observed at high frequencies in cervical cancers. For example, 100% of HPV18-positive cancers, 80% of HPV16-positive cancers, and 81% of HPV31-positive cancers demonstrate viral integration. The methods and systems provided herein can be used to predict and monitor cancer, for example, cervical cancer, by determining the HPV integration profile in a subject.

[0150] The methods and systems provided herein can be used to determine herpesvirus integration profiles to assess herpesvirus-related conditions / diseases such as nasopharyngeal carcinoma. Herpesvirus can be one of the most complex animal viruses, with a genome of 100-200 kb. Several herpesviruses can induce tumors in animal species (including frogs, chickens and monkeys). In addition, two members of the herpesvirus family, Kaposi's sarcoma-associated herpesvirus and Epstein-Barr virus, can be associated with human cancers. Kaposi's sarcoma-associated herpesvirus can play a key role in the morbidity of Kaposi's sarcoma, and Epstein-Barr virus may be involved in several human malignancies, including Burkitt's lymphoma, B-cell lymphoma (in AIDS patients and other immunosuppressed individuals), and nasopharyngeal carcinoma. The methods and systems provided herein can be used to analyze the integration of different genera of herpesviruses, such as Iltovirus, Proboscivirus, Cytomegalovirus, Mardivirus, Rhadinovirus, Macavirus, Roseolovirus, Simplexvirus, Scutavirus, Varicellovirus, Percavirus, Lymphocryptovirus, and Muromegalovirus, and human herpesviruses such as herpes simplex virus-1 (HSV-1), herpes simplex virus-2 (HSV-2), varicella-zoster virus (VZV), Epstein–Barr virus, and varicella-zoster virus (Epstein–Barr virus). These viruses include Epstein-Barr virus (EBV), lymphocryptovirus, cytomegalovirus (CMV), roseolovirus, herpes lymphotropic virus, and Kaposi's sarcoma-associated herpesvirus (KSHV), and zoonotic herpesviruses such as simian herpesvirus-1 and murine herpesvirus 68 (MHV-68).

[0151] EBV is a double-stranded DNA herpes virus that can be associated primarily with Burkitt's lymphoma, nasopharyngeal carcinoma, and several lymphoproliferative disorders. Burkitt's lymphoma can present in three main clinical variants: endemic variants, sporadic variants, and immunodeficiency-associated variants. EBV can be detected in 96% of cases of endemic variant Burkitt's lymphoma (involving the chin), which is the most common malignancy in children in certain regions of Central Africa. In contrast, EBV can rarely be associated with sporadic variants of Burkitt's lymphoma, and less commonly involves the chin. EBV-associated Burkitt's lymphoma can be common in individuals lacking effective T-cell function (e.g., AIDS patients or transplant recipients).

[0152] EBV can be maintained in an episomal state with multiple copies of circular DNA, but fragile sites of EBV integration into host chromosomes can be associated with partial deletions of the viral genome and can generate regions of increased chromatin instability in the host cell genome. This genomic instability can cause the loss of host genes, such as BACH2, which is a putative tumor suppressor gene, and this can contribute to lymphomagenesis.

[0153] In addition to Burkitt's lymphoma, EBV can also be associated with nasopharyngeal carcinoma (NPC). The undifferentiated form of NPC, classified as type III by the WHO, has been consistently associated with EBV in certain regions of the world, such as China and Southeast Asia.

[0154] The methods and systems provided herein can be used to determine the Merkel cell polyomavirus (MCV) integration profile to assess MCV-associated conditions / diseases such as Merkel cell carcinoma (MCC). MCV can be a double-stranded DNA polyomavirus that can be associated with MCC. These tumors can show MCV DNA in an integrated form within the tumor cell genome in a clonal pattern. MCV infection and integration can promote clonal expansion of tumor cells. The MCV genome can encode multiple splice variants of the tumor (T) antigen protein complex, which targets several tumor suppressor proteins such as pRB and p53. One of these splice variants, the large tumor antigen, can mutate in MCV-positive MCC tumor cells, and this selective mutation can affect the cellular DNA damage response, thereby preventing autologous activation of integrated viral replication, disrupting host genome integrity, and inhibiting cell proliferation. MCV can be frequently and selectively associated with MCV.

[0155] Members of a family of RNA viruses, the retroviruses, can cause cancer in various animal species, including humans. For example, retroviruses such as human T-cell leukemia virus can cause adult T-cell leukemia. Different retroviruses can differ significantly in their potential to cause cancer. The methods and systems provided herein can be used to distinguish pathologies associated with retroviruses, such as cancer. The methods and systems provided herein can be used to analyze any type of retrovirus, including exogenous retroviruses, such as members of the genera: alpharetrovirus, betaretrovirus, gammaretrovirus, deltaretrovirus, epsilonretrovirus, lentiretrovirus, and spumavirus, as well as various endogenous retroviruses.

[0156] XI. Biological Samples

[0157] The biological sample used in the methods provided herein may include any tissue or material obtained from a living or dead subject. The biological sample may be a cell-free sample. The biological sample may include nucleic acids (e.g., DNA or RNA) or fragments thereof. The nucleic acids in the sample may be cell-free nucleic acids. The sample may be a liquid sample or a solid sample (e.g., a cell or tissue sample). The biological sample may be a body fluid, such as blood, plasma, serum, urine, vaginal fluid, fluid from a hydrocele (e.g., testicular), vaginal washings, pleural fluid, ascites, cerebrospinal fluid, saliva, sweat, tears, sputum, bronchoalveolar lavage fluid, nipple discharge, aspirates from different parts of the body (e.g., thyroid, breast), etc. Stool samples may also be used. In various embodiments, most of the DNA in a biological sample enriched with cell-free DNA (e.g., a plasma sample obtained by a centrifugation scheme) may be cell-free (e.g., greater than 50%, 60%, 70%, 80%, 90%, 95% or 99% of the DNA may be cell-free). Biological samples can be processed to physically disrupt tissue or cellular structures (e.g., centrifugation and / or cell lysis), thereby releasing intracellular components into a solution, which may further contain enzymes, buffers, salts, detergents, and the like for preparing the sample for analysis.

[0158] The methods and systems provided herein can be used to analyze nucleic acid molecules in biological samples. The nucleic acid molecules can be cellular nucleic acid molecules, cell-free nucleic acid molecules, or both. The cell-free nucleic acid molecules used in the methods provided herein can be nucleic acid molecules that are not present in cells in a biological sample. Cell-free nucleic acid molecules can be present in various body fluids (e.g., blood, saliva, semen, and urine). Cell-free DNA molecules can be generated by cell death in various tissues, which can be caused by health conditions and / or diseases (e.g., viral infection and tumor growth). Cell-free nucleic acid molecules can include sequences generated by pathogen integration events.

[0159] The cell-free nucleic acid molecules (e.g., cell-free DNA) used in the methods provided herein can be present in plasma, urine, saliva, or serum. Cell-free DNA can naturally exist in the form of short fragments. Cell-free DNA fragmentation can refer to the process by which high molecular weight DNA (e.g., DNA in the cell nucleus) is cut, broken, or digested into short fragments when cell-free DNA molecules are produced or released. The methods and systems provided herein can be used to analyze cellular nucleic acid molecules, in some cases, for example, cellular DNA from tumor tissue, or cellular DNA from white blood cells when a patient has leukemia, lymphoma, or myeloma. According to some embodiments of the present disclosure, samples collected from tumor tissue can be assayed and analyzed.

[0160] XII. Subjects

[0161] The methods and systems provided herein can be used to analyze samples from subjects (e.g., organisms, e.g., host organisms). The subject can be any human patient, e.g., a cancer patient, a patient at risk for cancer, or a patient with a family or personal history of cancer. In some cases, the subject is at a particular stage of cancer treatment. In some cases, the subject may have or be suspected of having cancer. In some cases, it is unknown whether the subject has cancer.

[0162] The subject may have any type of cancer or tumor. In an embodiment, the subject may have nasopharyngeal cancer, or cancer of the nasal cavity. In another embodiment, the subject may have oropharyngeal cancer, or cancer of the oral cavity. Non-limiting examples of cancer may include, but are not limited to, adrenal cancer, anal cancer, basal cell carcinoma, bile duct cancer, bladder cancer, blood cancer, bone cancer, brain tumors, breast cancer, bronchial cancer, cardiovascular system cancer, cervical cancer, colon cancer, colorectal cancer, digestive system cancer, endocrine system cancer, endometrial cancer, esophageal cancer, eye cancer, gallbladder cancer, gastrointestinal tumors, hepatocellular carcinoma, kidney cancer, hematopoietic organ malignancies, laryngeal cancer, leukemia, liver cancer, lung cancer, lymphoma, melanoma, mesothelioma, muscular system cancer, cancer, myelodysplastic syndrome (MDS), myeloma, nasal cavity cancer, nasopharyngeal cancer, nervous system cancer, lymphatic system cancer, oral cancer, oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumor, prostate cancer, rectal cancer, renal pelvis cancer, reproductive system cancer, respiratory system cancer, sarcoma, salivary gland cancer, skeletal system cancer, skin cancer, small intestine cancer, stomach cancer, testicular cancer, throat cancer, thymus cancer, thyroid cancer, tumor, urinary system cancer, uterine cancer, vaginal cancer, or vulvar cancer. Lymphoma can be any type of lymphoma, including B-cell lymphoma (e.g., diffuse large B-cell lymphoma, follicular lymphoma, small lymphocytic lymphoma, mantle cell lymphoma, marginal zone B-cell lymphoma, Burkitt's lymphoma, lymphoplasmacytic lymphoma, hairy cell leukemia, or primary central nervous system lymphoma) or T-cell lymphoma (e.g., precursor T-lymphoblastic lymphoma or peripheral T-cell lymphoma). Leukemia can be any type of leukemia, including acute leukemia or chronic leukemia. The types of leukemia include: acute myeloid leukemia, chronic myeloid leukemia, acute lymphocytic leukemia, acute undifferentiated leukemia, or chronic lymphocytic leukemia. In some cases, cancer patients do not suffer from a specific type of cancer. For example, in some cases, patients can suffer from a non-breast cancer cancer.

[0163] Examples of cancer include cancers that result in solid tumors, as well as cancers that do not result in solid tumors. In addition, any cancer described herein can be a primary cancer (e.g., a cancer named after the part of the body where the cancer first begins to grow) or a secondary or metastatic cancer (e.g., a cancer that originates in another part of the body).

[0164] 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107 , 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98 or 99 years old, or within a range therein (e.g., between 2 and 20 years old, between 20 and 40 years old, or between 40 and 90 years old). A particular category of patients that may benefit may be patients over 40 years old. Another particular category of patients that may benefit may be pediatric patients. In addition, the subject diagnosed by any of the methods or compositions described herein may be male or female.

[0165] In some embodiments, the methods of the present disclosure can detect a tumor or cancer in a subject, wherein the tumor or cancer has a geographic pattern of disease. In one embodiment, the subject can have an EBV-associated cancer (e.g., nasopharyngeal carcinoma). In another embodiment, the subject can have an HPV-associated cancer (e.g., oropharyngeal cancer). In yet another embodiment, the subject can have a human T-lymphotrophic virus-1 (HTLV-1)-associated cancer (e.g., adult T-cell leukemia / lymphoma).

[0166] Any of the methods disclosed herein can also be practiced on non-human subjects, such as laboratory or farm animals, or cell samples obtained from an organism disclosed herein. Non-limiting examples of non-human subjects include dogs, goats, guinea pigs, hamsters, mice, pigs, non-human primates (e.g., gorillas, apes, orangutans, lemurs, or baboons), rats, sheep, cattle, or zebrafish.

[0167] XIII. Computer Systems

[0168] Any of the methods disclosed herein can be performed and / or controlled by one or more computer systems. In some embodiments, any step of the methods disclosed herein can be performed and / or controlled completely, individually or sequentially by one or more computer systems. Any of the computer systems described herein can adopt any suitable number of subsystems. In some embodiments, the computer system includes a single computer device, wherein the subsystem can be a component of the computer device. In other embodiments, the computer system can include multiple computer devices, each of which is a subsystem with internal components. The computer system can include desktop and portable computers, tablet computers, mobile phones and other mobile devices.

[0169] The subsystems can be connected to each other via a system bus. Additional subsystems include printers, keyboards, storage devices, and monitors that are coupled to a display adapter. Peripheral devices and input / output (I / O) devices coupled to the I / O controller can be connected to the system through any number of connectors known in the art, such as input / output (I / O) ports (e.g., USB, FireWire, etc.). ® )) to connect to the computer system. For example, I / O ports or external interfaces (e.g., Ethernet, Wi-Fi, etc.) can be used to connect the computer system to a wide area network such as the Internet, a mouse input device, or a scanner. The interconnection via the system bus allows the central processor to communicate with each subsystem and control the execution of multiple instructions from the system memory or storage device (e.g., a fixed disk such as a hard drive or optical disk), as well as the exchange of information between subsystems. The system memory or storage device can implement computer-readable media. Another subsystem is a data collection device, such as a camera, microphone, accelerometer, etc. Any data described herein can be output from one component to another and can be output to a user.

[0170] A computer system can include multiple identical components or subsystems that are, for example, connected together via external interfaces or internal interfaces. In some embodiments, the computer system, subsystems, or devices can communicate on a network. In this case, one computer can be considered as a client and another computer can be considered as a server, wherein each computer can be part of the same computer system. The client and server can each include multiple systems, subsystems, or components.

[0171] The present disclosure provides a computer control system programmed to implement the method of the present disclosure. Figure 8A computer system 1101 is shown that is programmed or otherwise configured to detect chimeric nucleic acid fragments, or determine pathogen integration profiles in nucleic acid molecules, or determine pathological classifications, as described herein. Computer system 1101 can implement and / or regulate various aspects of the methods provided herein, such as controlling the sequencing of nucleic acid molecules from biological samples, performing various steps of bioinformatics analysis of sequencing data as described herein, integrating data acquisition, analysis, and result reporting, and data management. Computer system 1101 can be a user's electronic device, or a computer system remotely located relative to the electronic device. The electronic device can be a mobile electronic device.

[0172] Computer system 1101 may include a central processing unit (CPU, also referred to herein as a "processor" and "computer processor") 1105, which may be a single-core or multi-core processor, or multiple processors for parallel processing. Computer system 1101 may also include memory or storage location 1110 (e.g., random access memory, read-only memory, flash memory), electronic storage unit 1115 (e.g., a hard disk), a communication interface 1120 (e.g., a network adapter) for communicating with one or more other systems, and peripherals 1125, such as cache memory, other memory, data storage, and / or an electronic display adapter. Memory 1110, storage unit 1115, interface 1120, and peripherals 1125 may communicate with CPU 1105 via a communication bus (solid lines), such as a motherboard. Storage unit 1115 may be a data storage unit (or data repository) for storing data. Computer system 1101 may be operatively coupled to a computer network ("network") 1130 via communication interface 1120. Network 1130 can be the Internet, the internet and / or an extranet and / or an intranet and / or extranet in communication with the Internet. In some cases, network 1130 is a telecommunications network and / or a data network. Network 1130 can include one or more computer servers that enable distributed computing, such as cloud computing. In some cases, network 1130 can implement a peer-to-peer network with the aid of computer system 1101, enabling devices coupled to computer system 1101 to function as either clients or servers.

[0173] The CPU 1105 can execute a series 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 1110. The instructions can be directed to the CPU 1105, which can then program or otherwise configure the CPU 1105 to implement the methods of the present disclosure. Examples of operations performed by the CPU 1105 can include fetching, decoding, executing, and writing back.

[0174] CPU 1105 may be part of a circuit, such as an integrated circuit, that may include one or more other components of system 1101. In some cases, the circuit is an application-specific integrated circuit (ASIC).

[0175] The storage unit 1115 can store files, such as drivers, libraries, and saved programs. The storage unit 1115 can store user data, such as user preferences and user programs. In some cases, the computer system 1101 may include one or more other data storage units external to the computer system 1101, such as on a remote server that communicates with the computer system 1101 via an intranet or the Internet.

[0176] Computer system 1101 can communicate with one or more remote computer systems via network 1130. For example, computer system 1101 can communicate with a user's remote computer system (e.g., a smartphone with an application installed that receives and displays the results of sample analysis sent from computer system 1101). Examples of remote computer systems include personal computers (e.g., portable PCs), tablet computers or tablet PCs (e.g., Apple® iPads, Samsung® Galaxy Tabs), telephones, smartphones (e.g., Apple® iPhones, Android-enabled devices, Blackberry®), or personal digital assistants. Users can access computer system 1101 via network 1130.

[0177] The methods described herein may be implemented by machine (e.g., computer processor) executable code stored in an electronic storage location (e.g., memory 1110 or electronic storage unit 1115) of computer system 1101. This machine-executable or machine-readable code may be provided in the form of software. During use, the code may be executed by processor 1105. In some cases, the code may be retrieved from storage unit 1115 and stored in memory 1110 for easy access by processor 1105. In some cases, electronic storage unit 1115 may be eliminated, and machine-executable instructions may be stored in memory 1110.

[0178] The code may be precompiled and configured for use with a machine having a processor suitable for executing the code, or may be compiled during runtime. The code may be provided in an alternative programming language to enable the code to be executed in a precompiled or just-in-time compiled manner.

[0179] Aspects of the systems and methods provided herein, such as computer system 1101, can be embodied in programming. Various aspects of the technology can be considered "products" or "articles of manufacture," typically in the form of machine (or processor) executable code and / or associated data carried on or embodied in a machine-readable medium. Machine executable code can be stored on an electronic storage unit, such as a memory (e.g., read-only memory, random access memory, flash memory), or a hard disk. "Storage" type media can include any or all tangible memories of a computer, processor, etc., or their associated modules, such as various semiconductor memories, tape drives, disk drives, etc., that can provide non-transitory storage for software programming at any time. All or part of the software can be communicated at any time via the Internet or various other telecommunications networks. Such communication can, for example, enable software to be loaded from one computer or processor to another, for example, from a management server or host computer platform to an application server. Thus, another type of media that can carry software elements includes optical, radio, and electromagnetic waves, such as those used across physical interfaces between local devices, through wired and optical landline networks, and through various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, etc., can also be considered media that carry software. As used herein, unless restricted to non-transitory, tangible "storage" media, terms such as computer or machine "readable media" refer to any medium that participates in providing instructions to a processor for execution.

[0180] Thus, machine-readable media, such as computer-executable code, can take many forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, such as any storage device in any computer, etc., which can be used to implement the databases shown in the accompanying drawings, etc. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that make up a bus within a computer system. Carrier 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. Thus, common forms of computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM, a DVD or DVD-ROM, any other optical medium, punched card stock tape, any other physical storage medium having a pattern of holes, RAM, ROM, PROM and EPROM, FLASH-EPROM, any other memory chip or memory cartridge, a carrier wave that transmits data or instructions, a cable or link that transmits 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 may participate in carrying one or more sequences of one or more instructions to a processor for execution.

[0181] Computer system 1101 may include or be in communication with an electronic display 1135 that includes a user interface (UI) 1140 for providing, for example, results of sample analysis, such as, but not limited to, a graphical display of pathogen integration profiles, genomic locations of pathogen integration breakpoints, a classification of pathology (e.g., type of disease or cancer and level of cancer), and recommendations for treatment or preventive measures based on the classification of the pathology. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0182] The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented by software when executed by the central processing unit 1105. The algorithms may, for example, control the sequencing of nucleic acid molecules from a sample, guide the collection of sequencing data, analyze the sequencing data, or determine a classification of a pathology based on analysis of sequencing data.

[0183] In some cases, such as Figure 9As shown in , sample 902 can be obtained from a subject 901, such as a human subject. Sample 902 can be subjected to one or more of the methods described herein, such as assays. In some cases, the assays can include hybridization, amplification, sequencing, labeling, epigenetic modification bases, or any combination thereof. One or more results from the methods can be input into a processor 904. One or more input parameters, such as sample identification, subject identification, sample type, references, or other information, can be input into the processor 904. One or more measurements from the assays can be input into the processor 904 so that the processor can generate results, such as a pathological classification (e.g., diagnosis) or a recommended therapy. The processor can send the results, input parameters, measurements, references, or any combination thereof to a display 905, such as a visual display or a graphical user interface. The processor 904 can (i) send the results, input parameters, measurements, or any combination thereof to a server 907, (ii) receive the results, input parameters, measurements, or any combination thereof from the server 907, (iii), or any combination thereof.

[0184] Various aspects of the present disclosure can be implemented in a modular or integrated manner using hardware (e.g., an application specific integrated circuit or a field programmable gate array) and / or computer software in the form of control logic with a general programmable processor. As used herein, a processor may include a single-core processor, a multi-core processor on the same integrated chip, or multiple processing units on a single circuit board or networked. Based on the disclosure and teachings provided herein, one of ordinary skill in the art will know and understand other ways and / or methods of implementing the embodiments described herein using hardware and combinations of hardware and software.

[0185] Any software component or function described in this application can be implemented as a software code using any suitable computer language (e.g., Java, C, C++, C#, Objective-C, Swift, or scripting languages ​​such as Perl or Python) executed by a processor using, for example, conventional or object-oriented techniques. The software code can be stored on a computer-readable medium as a series of instructions or commands for storage and / or transmission. Suitable non-transitory computer-readable media may include random access memory (RAM), read-only memory (ROM), magnetic media such as a hard drive or floppy disk, or optical media such as a compact disc (CD) or DVD (digital versatile disk), flash memory, etc. The computer-readable medium may be any combination of such storage devices or transmission devices.

[0186] Such a program may also be encoded and transmitted using a carrier signal adapted to be transmitted via a wired, optical, and / or wireless network (including the Internet) conforming to various protocols. Thus, a computer-readable medium may be created using a data signal encoded with such a program. A computer-readable medium encoded with program code may be packaged with a compatible device or provided separately from other devices (e.g., downloaded via the Internet). Any such computer-readable medium may be present on or within a single computer product (e.g., a hard drive, a CD, or an entire computer system), and may be present on or within different computer products within a system or network. A computer system may include a monitor, printer, or other suitable display to provide any results described herein to a user.

[0187] Any of the methods described herein can be performed in whole or in part by a computer system, and the computer system includes one or more processors, which can be configured to perform the steps. Therefore, the embodiment can be directed to a computer system, and the computer system is configured to perform the steps of any of the methods described herein, wherein different components perform corresponding steps or corresponding step groups. Although presented as numbered steps, the steps of the method herein can be performed simultaneously or in different orders. In addition, the part in these steps can be used together with the part in other steps from other methods. In addition, all or part of a step can be optional. In addition, any step in the step of any of the methods in the method can be performed by module, unit, circuit or other schemes, so as to perform these steps.

[0188] XIV. Other Implementation Options

[0189] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0190] It should be understood that the methods described herein are not limited to the specific methods, schemes, subjects, and sequencing technologies described herein, and therefore can be changed. It should also be understood that the terms used herein are only for the purpose of describing specific embodiments, and are not intended to limit the scope of the methods and compositions described herein, which scope will only be limited by the appended claims. Although some embodiments of the present disclosure have been shown and described herein, it is obvious to those skilled in the art that such embodiments are only provided by way of example. Without departing from the scope of the present disclosure, those skilled in the art will now expect many variations, changes, and substitutions. It should be understood that the various alternatives of the embodiments of the present disclosure described herein can be used to implement the present disclosure. The claims are intended to define the scope of the present disclosure, and thus cover the methods and structures within the scope of these claims and their equivalents.

[0191] For explanation, several aspects are described with reference to the application of examples. Unless otherwise stated, any embodiment can be combined with any other embodiment. Should be understood that many specific details, relationships and methods have been set forth to provide a comprehensive understanding of the features described herein. But, those skilled in the art will readily recognize that the features described herein can be implemented in the absence of one or more specific details or with other methods. The features described herein are not limited by the shown order of operation or event, because some operations can occur in different orders, and / or occur simultaneously with other operations or events. In addition, all shown operations or events are not required to implement the method according to the features described herein.

[0192] XV. Examples

[0193] The following examples are offered by way of illustration and not limitation.

[0194] Example 1. Analysis of HPV DNA integration

[0195] This example tests an exemplary method for determining HPV integration breakpoints by analyzing read sequences of cell-free DNA from plasma samples. This example also examines the different HPV integration profiles in samples from patients with cervical cancer and HPV-associated head and neck squamous cell carcinoma, as well as the EBV integration profile in samples from patients with EBV infection.

[0196] Plasma samples from patients with HPV infection were collected and processed. Then, cell-free DNA molecules in the plasma samples were collected and sequenced with paired-end sequencing. In the paired-end sequencing data, Figure 2 As shown in , read sequences from type A human-HPV chimeric fragments and type B human-HPV chimeric fragments were selected.

[0197] To identify human-HPV chimeric DNA in cell-free DNA samples, the obtained read sequences were aligned to the reference human genome and the reference HPV genome using the SOAP alignment tool. Type A human-HPV chimeric fragment read sequence pairs were identified by searching for read sequence pairs (read sequence pairs obtained from both ends of the same plasma cell-free DNA molecule) in which one read sequence mapped to the reference human genome and the other read sequence mapped to the reference HPV genome. Type A chimeric fragments with identical start and end external coordinates were removed as suspected PCR duplicates. All remaining type A chimeric read sequences were then overlaid to group the nucleotide coordinates (which overlapped or were adjacent to each other in the human and HPV sequences) to identify candidate integration regions. Each candidate integration region on the human genome will have one or more corresponding candidate integration regions on the HPV genome and vice versa. Adjacent human read sequences within a distance of 300 bases are grouped into one candidate integration region, and adjacent HPV read sequences within a distance of 300 bases are grouped into one candidate integration region (e.g., as Figure 3A ). Any human read sequence that is more than 300 bases away from its nearest neighbor read sequence is not included in the same candidate integration region, and any HPV human read sequence that is more than 300 bases away from its nearest neighbor read sequence is not included in the same candidate integration region. In this embodiment, at least two non-PCR duplicated human-HPV chimeric fragments must be present in each human-HPV candidate integration region.

[0198] Then, using Bowtie2 with a local alignment function, the unmapped read sequences from the first alignment were realigned with the human and HPV genomes at a default minimum mapping length of 22 bases. Their possible mapping positions were then searched for in the candidate integration regions suggested by type A chimeric fragments. A minimum mapping length of 4 bases was then selected to map the unmapped portions of the partially mapped read sequences to the corresponding candidate integration regions at a minimum mapping length of 4 bp. Thus, within the candidate integration regions, human-HPV chimeric read sequences in type B fragment read sequence pairs were identified to determine potential integration breakpoints. Within the candidate integration regions, the expected chain orientation of the human-HPV chimeric read sequences was inferred from the chain information of the type A chimeric fragment read sequence pairs used to construct the candidate integration regions. Only short sequence anchors whose mapping positions were within a 100 bp distance of the potential breakpoint were considered as putative hits for the integration breakpoint. These read sequences were considered as chimeric read sequences covering the integration breakpoint.

[0199] Further excluded were human-HPV chimeric reads whose matching read sequences were incompatible with the library insert size and mapping orientation. Next, candidate integration sites with a diversity score lower than 4 were excluded. The remaining integration breakpoints were considered to be from actual HPV integration events. Table 1 summarizes the analysis of HPV integration breakpoints in samples from patients with cervical cancer and HNSCC with or without diversity score filtering.

[0200] A similar analysis of EBV integration breakpoints was performed using plasma samples from patients with nasopharyngeal carcinoma (NPC) with EBV infection. The analysis of EBV integration breakpoints with and without diversity score filtering is summarized in Table 2.

[0201] Figure 6 The distribution of viral integration breakpoints in the HPV16 genome is shown based on analysis of plasma cell-free DNA from patients with cervical cancer and HPV-positive head and neck squamous cell carcinoma, respectively. As shown in the figure, there are shared preferred integration breakpoints between cervical cancer and HPV-positive head and neck squamous cell carcinoma (region A). On the other hand, there are also preferred integration breakpoints that are characteristic of certain types of cancer. For example, region B shows HNSCC-preferred integration breakpoints.

[0202] Table 1. Summary of HPV Integrated Analysis

[0203]

[0204] Table 2. Summary of EBV integrative analysis

[0205]

[0206] Example 2. Analysis of HPV DNA Integration Index

[0207] This example examines the HPV DNA integration index in cell-free DNA from plasma samples of patients with cervical cancer and HNSCC.

[0208] In order to analyze the cell-free DNA fragments in the plasma (thus showing that HPV is integrated into the human chromosomes in the cancer genome), double-end sequencing was performed using these cell-free DNA fragments. A double-end sequencing scheme was adopted, which reads 75 nucleotides from each end of the cell-free DNA fragments. Cell-free DNA fragments with all 75 nucleotides, in which one of the read sequences was mapped to the HPV genome and the other read sequence was mapped to the human genome, were counted as human-HPV chimeric fragments. In double-end sequencing, cell-free DNA fragments in which two of the read sequences were mapped to the HPV genome were counted as plasma HPV read sequences. Samples from 11 patients with cervical cancer were analyzed.

[0209] Table 3. Plasma Cell-Free Human-HPV Fragment DNA in CC Samples

[0210]

[0211] Using the same protocol to define plasma cell-free human-HPV chimeric fragments, the genomic location of plasma cell-free human-HPV chimeric fragments was analyzed in samples from a patient with cervical cancer (CaCx3485) and a patient with HPV-positive head and neck squamous cell carcinoma (HPV+ve HNSCC) (TBR1989). Figure 7 In the figure, each dot represents the exact end position of a plasma cell-free DNA fragment with HPV integration. The integration index represents the odds ratio of the end position of a plasma cell-free DNA fragment with HPV integration compared to a plasma HPV fragment (reads 1 and 2 both mapped to the HPV genome). The higher the integration index, the more likely those DNA fragment read end positions are to have HPV integration. Differences in the preferred end positions of plasma cell-free DNA reads with HPV integration were observed across the HPV genome between patients with cervical cancer and HPV-positive HNSCC (HPV+ve HNSCC).

[0212] Example 3. Comparison with public software ViFi

[0213] In this example, the performance of an exemplary algorithm according to the present disclosure in HPV integration detection was compared with ViFi (Nguyen et al., Nucleic Acids Res. 2018 Apr 20;46(7):3309-3325. doi: 10.1093 / nar / gky180), a software that detects viral integration sites through a combination of phylogenetic methods and reference-based read alignment. A simulated data set with artificially constructed integration sites and biological data from tissue samples and paired plasma samples from 26 patients with cervical intraepithelial neoplasia (CIN), 26 patients with cervical cancer (CaCx), and 7 patients with head and neck squamous cell carcinoma (HNSCC) was used to evaluate the detection performance of the exemplary algorithm and ViFi. The integration sites identified by the exemplary algorithm were further confirmed by PCR amplification and Sanger sequencing.

[0214] method

[0215] Public software: ViFi

[0216] ViFi was downloaded from github.com / namphuon / ViFi and run under its default settings and default parameters on simulated data as well as real sequencing data from tissue and plasma samples of cervical cancer and HNSCC patients in fastq format.

[0217] Simulated dataset

[0218] To evaluate the exemplary algorithm, 32,500 chimeric fragments containing both type A and type B fragments were constructed in silico to artificially generate 500 HPV integration sites. These in silico chimeric fragments were then mixed with real sequencing reads from a human cervical cancer plasma sample. The sequencing reads from the human cervical cancer plasma sample did not contain HPV integration sites. The detection performance of the exemplary algorithm was compared with ViFi (Nguyen et al., Nucleic Acids Res. 2018 Apr 20;46(7):3309-3325. doi: 10.1093 / nar / gky180).

[0219] Sequencing data from tissue and plasma DNA of patients with cervical cancer, HNSCC, and NPC

[0220] To explore the integration map of HPV16 in HPV-associated tumors, 26 plasma samples from cervical cancer and 7 tissue samples and paired plasma samples from HNSCC patients were sequenced. Both the exemplary algorithm and ViFi were applied to detect HPV integration sites in these samples. Six plasma samples from nasopharyngeal carcinoma (NPC) patients were also sequenced to detect Epstein-Barr virus (EBV) integration events in the human genome.

[0221] result

[0222] Simulated data

[0223] To directly evaluate the detection performance of the exemplary algorithm and compare it with ViFi, 500 integration sites were artificially constructed to be supported by corresponding chimeric fragments. Both the exemplary algorithm and ViFi were run on this simulated data set and compared for detection rate ( Figure 10 ).like Figure 11As shown in Figure 3, ViFi detected 314 simulated integration regions out of a total of 500 sites, for a detection rate of only 62.8%. However, using the exemplary algorithm, 480 simulated integration sites were accurately identified, including not only the integration regions but also the exact breakpoints, with a significantly higher detection rate of 96%. These results demonstrate that the exemplary algorithm has greater accuracy in detecting integration sites in the simulated dataset compared to ViFi.

[0224] Identification of breakpoints from plasma DNA

[0225] The exemplary algorithm was run on plasma DNA sequencing data from 26 CIN patients, 26 cervical cancer patients, and 7 HNSCC patients, of which 6 cervical cancer plasma samples and 5 HNSCC plasma samples were shown to have at least 1 candidate integration region ( Figure 12 After filtering with a cutoff diversity score of 4, 6 and 41 breakpoints identified by the exemplary algorithm were retained for one cervical cancer case (CaCx3485) and one HNSCC case (TBR1989), respectively ( Figure 12 ). Of these breakpoints, 6 and 9 were selected for experimental confirmation in cervical cancer cases and HNSCC cases, respectively. One breakpoint in the cervical cancer case and three breakpoints in the HNSCC case were validated by PCR assay. Simultaneously, ViFi was run on the same plasma sequencing dataset for comparison with the exemplary algorithm. ViFi identified only one case, CaCx3485, in which 491 putative integration breakpoints were triggered by HPV16. However, none of the breakpoints identified by ViFi overlapped with any breakpoints identified by the exemplary algorithm with a diversity score exceeding 4 (including the breakpoint validated in this case).

[0226] Identifying breakpoints from tumor tissue DNA

[0227] In addition, 7 tissue samples from the above HNSCC patients were sequenced.In the exemplary algorithm, a diversity score of 4 was used as a cutoff. Figure 13 It was shown that 4 of the 5 samples included at least one candidate integration region, where at least one identified integration breakpoint remained after applying this cutoff score. These breakpoints were further experimentally validated by PCR and Sanger sequencing.

[0228] In diversity scoring > A total of 56 integration breakpoints were identified from 4 HNSCC samples in 4 cases ( Figure 13). After merging 26 breakpoints with nearby junction sites (1-5 bp), the total number of breakpoints was reduced to 43. PCR assays were designed for 32 of the breakpoints flanking the integration site in amplicons less than 200 bp in size. 23 of the breakpoints were successfully amplified from tissue DNA of the corresponding samples, showing the expected amplicon size, and of these, 22 breakpoints were further validated by Sanger sequencing. Therefore, based on our algorithm, the diversity score > In 4 cases, the overall validation rate of breakpoints was 69.0% (22 / 32).

[0229] If the diversity score cutoff was set to 9, 24 breakpoints were targeted by the designed PCR assay, of which 22 breakpoints were successfully amplified and validated by Sanger sequencing. Therefore, after the cutoff diversity score was increased from 4 to 9, the overall validation rate was significantly improved to 91.7% (22 / 24).

[0230] In summary, the performance of the exemplary algorithm was compared to that of ViFi using tissue sequencing data. No breakpoints were identified by ViFi in any tissue sample.

[0231] Although preferred embodiments of the present disclosure have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. Those skilled in the art will now appreciate that various variations, changes, and substitutions are possible without departing from the present disclosure. It should be understood that various alternatives to the embodiments described herein can be used to practice the present disclosure. The appended claims are intended to define the scope of the present disclosure, thereby covering methods and structures within the scope of these claims and their equivalents.

Claims

1. A non-transitory computer readable medium comprising instructions that cause a computer system to perform a method of analyzing cell-free nucleic acid molecules from a biological sample to identify integration breakpoint locations in the genome of a human subject suspected of including integration of a pathogen at an unknown genomic location, the method comprising: (a) aligning paired-end read sequences for cell-free nucleic acid molecules from the biological sample to (i) a reference human genome and (ii) a reference genome of the pathogen, wherein the aligning uses an alignment algorithm; (b) identifying one or more chimeric read sequence pairs, wherein a chimeric read sequence pair comprises (i) a first read aligned to the reference genome of the pathogen and (ii) a second read aligned to the reference human genome; and (c) identifying the unknown genomic location in the genome of the subject within 500 nucleotides of the second read of the one or more chimeric read sequence pairs as the integration breakpoint location.

2. The computer-readable medium of claim 1, wherein the paired-end read sequences are obtained by performing paired-end sequencing on the cell-free nucleic acid molecules, and wherein the paired-end sequencing generates a pair of read sequences for each of the cell-free nucleic acid molecules.

3. The computer-readable medium of claim 2, wherein the pair of read sequences comprises a first read sequence at a first end of each of the cell-free nucleic acid molecules and a second read sequence at a second end of each of the cell-free nucleic acid molecules.

4. The computer-readable medium of claim 1 , wherein the aligning comprises aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 consecutive nucleotides of the first read to a reference genome of the pathogen and aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 consecutive nucleotides of the second read to the reference human genome.

5. The computer-readable medium of claim 2, wherein obtaining the paired-end read sequence further comprises amplification of the cell-free nucleic acid molecules from the biological sample.

6. The computer-readable medium of claim 5, wherein the amplification is performed using a first primer complementary to a first target sequence in the genome of the pathogen, and a second primer complementary to a second target sequence in the genome of the subject.

7. The computer readable medium of claim 5, wherein the amplifying comprises polymerase chain reaction (PCR).

8. The computer-readable medium of any one of claims 1 to 7, wherein the pathogen comprises a virus.

9. The computer-readable medium of claim 8, wherein the virus comprises Epstein-Barr virus DNA, human papillomavirus DNA, hepatitis B virus DNA, hepatitis C virus nucleic acid, or a fragment thereof.

10. The computer readable medium of claim 8, wherein the virus is human papillomavirus.

11. The computer-readable medium of any one of claims 1-7 and 9-10, wherein the cell-free nucleic acid molecules comprise deoxyribonucleic acid molecules.

12. The computer-readable medium of claim 8, wherein the cell-free nucleic acid molecules comprise deoxyribonucleic acid molecules.

13. The computer readable medium of any one of claims 1-7, 9-10, and 12, wherein the biological sample is plasma, serum, or urine.

14. The computer readable medium of claim 8, wherein the biological sample is plasma, serum, or urine.

15. The computer readable medium of claim 11, wherein the biological sample is plasma, serum, or urine.

16. The computer readable medium of claim 13, wherein the biological sample is plasma.

17. A computer system comprising a computer readable medium according to any one of claims 1 to 16.

18. A method of analyzing cell-free nucleic acid molecules from a biological sample to identify integration breakpoint locations in the genome of a human subject suspected of having integrated a pathogen at an unknown genomic location, the method comprising: (a) sequencing the cell-free nucleic acid molecules from the biological sample of the subject to generate paired-end read sequences; (b) aligning the paired-end read sequences to (i) a reference human genome and (ii) a reference genome of the pathogen, wherein the aligning uses an alignment algorithm; (c) identifying one or more chimeric read sequence pairs, wherein a chimeric read sequence pair comprises (i) a first read aligned to the reference genome of the pathogen and (ii) a second read aligned to the reference human genome; and (d) identifying the unknown genomic location in the genome of the subject within 500 nucleotides of the second read of the one or more chimeric read sequence pairs as the integration breakpoint location.

19. The method of claim 18, wherein identifying the one or more chimeric read sequence pairs comprises (i) aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 consecutive nucleotides of the first read to a reference genome of the pathogen, and (ii) aligning at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 consecutive nucleotides of the second read to the reference human genome.

20. The method of claim 19, wherein identifying one or more chimeric read sequence pairs comprises (i) aligning at least 50 consecutive nucleotides of the first read to the reference genome of the pathogen, and (ii) aligning at least 50 consecutive nucleotides of the second read to the reference human genome.

21. The method of claim 18, wherein sequencing the cell-free nucleic acid molecules comprises amplifying the cell-free nucleic acid molecules from the biological sample.

22. The method of claim 21, wherein the amplification is performed using a first primer complementary to a first target sequence in the genome of the pathogen, and a second primer complementary to a second target sequence in the genome of the subject.

23. The method of claim 21, wherein the amplifying comprises polymerase chain reaction (PCR).

24. The method of any one of claims 18-23, wherein the pathogen comprises a virus.

25. The method of claim 24, wherein the virus comprises Epstein-Barr virus DNA, human papillomavirus DNA, hepatitis B virus DNA, hepatitis C virus nucleic acid, or a fragment thereof.

26. The method of claim 24, wherein the virus is human papillomavirus.

27. The method of any one of claims 18-23 and 25-26, wherein the cell-free nucleic acid molecules comprise deoxyribonucleic acid molecules.

28. The method of claim 24, wherein the cell-free nucleic acid molecules comprise deoxyribonucleic acid molecules.

29. The method of any one of claims 18-23, 25-26 and 28, wherein the biological sample is plasma, serum or urine.

30. The method of claim 24, wherein the biological sample is plasma, serum or urine.

31. The method of claim 27, wherein the biological sample is plasma, serum or urine.

32. The method of claim 29, wherein the biological sample is plasma.

33. The method of any one of claims 18-23, 25-26, 28, and 30-32, wherein step (b), step (c), or step (d) is performed by a computer system.

34. The method of claim 24, wherein step (b), step (c) or step (d) is performed by a computer system.

35. The method of claim 27, wherein step (b), step (c) or step (d) is performed by a computer system.

36. The method of claim 29, wherein step (b), step (c) or step (d) is performed by a computer system.

37. The method of any one of claims 18-23, 25-26, 28, 30-32, and 34-36, wherein step (c) further comprises: A plurality of different chimeric read sequence pairs of cell-free nucleic acid molecules from the biological sample comprising the same potential integration breakpoint are identified.

38. The method of claim 24, wherein step (c) further comprises: A plurality of different chimeric read sequence pairs of cell-free nucleic acid molecules from the biological sample comprising the same potential integration breakpoint are identified.

39. The method of claim 27, wherein step (c) further comprises: A plurality of different chimeric read sequence pairs of cell-free nucleic acid molecules from the biological sample comprising the same potential integration breakpoint are identified.

40. The method of claim 29, wherein step (c) further comprises: A plurality of different chimeric read sequence pairs of cell-free nucleic acid molecules from the biological sample comprising the same potential integration breakpoint are identified.

41. The method of claim 33, wherein step (c) further comprises: A plurality of different chimeric read sequence pairs of cell-free nucleic acid molecules from the biological sample comprising the same potential integration breakpoint are identified.

42. The method of claim 37, wherein step (d) further comprises: determining a strand orientation of a first read sequence and a second read sequence of each of the plurality of different chimeric read sequence pairs; filtering out chimeric read sequence pairs in which the strand orientations of the first read sequence and the second read sequence included in the chimeric read sequence pairs are inconsistent with the strand orientations of the first read sequence and the second read sequence of most of the chimeric read sequence pairs; and After the filtering, the integration breakpoints are detected based on the remaining chimeric read pairs.

43. The method of any one of claims 38-41, wherein step (d) further comprises: determining a strand orientation of a first read sequence and a second read sequence of each of the plurality of different chimeric read sequence pairs; filtering out chimeric read sequence pairs in which the strand orientations of the first read sequence and the second read sequence included in the chimeric read sequence pairs are inconsistent with the strand orientations of the first read sequence and the second read sequence of most of the chimeric read sequence pairs; and After the filtering, the integration breakpoints are detected based on the remaining chimeric read pairs.

44. The method of claim 37, wherein step (d) further comprises: The read pairs are assessed for variability in sequence length of the reads aligned to genomic regions flanking the potential integration breakpoints.

45. The method of any one of claims 38-41, wherein step (d) further comprises: The read pairs are assessed for variability in sequence length of the reads aligned to genomic regions flanking the potential integration breakpoints.

46. ​​The method of claim 37, wherein identifying the plurality of different chimeric read sequence pairs comprises: identifying a human-pathogen chimeric read sequence pair generated by paired-end sequencing of the cell-free nucleic acid molecule from the biological sample, the human-pathogen chimeric read sequence pair comprising a first read sequence aligned to the reference human genome and a second read sequence aligned to a reference genome of the pathogen, thereby identifying a type A human-pathogen chimeric read sequence pair; grouping from the type A human-pathogen chimeric read pairs, the type A human-pathogen chimeric read pairs comprising a first read sequence and a second read sequence, the first read sequence overlapping in the reference human genome or separated within a predetermined distance, the second read sequence overlapping in the reference genome of the pathogen or separated within a predetermined distance, thereby identifying a human-pathogen candidate integration region in the reference genome; and Identify human-pathogen chimeric read sequence pairs generated by paired-end sequencing of the cell-free nucleic acid molecules from the biological sample, the human-pathogen chimeric read sequence pairs comprising a first read sequence and a second read sequence, the first read sequence aligning with the human-pathogen candidate integration region, the second read sequence comprising a first sequence aligning with the reference human genome and a second sequence aligning with the reference genome of the pathogen, thereby identifying type B human-pathogen chimeric read sequence pairs.

47. The method of any one of claims 38-41, wherein identifying the plurality of different chimeric read sequence pairs comprises: identifying a human-pathogen chimeric read sequence pair generated by paired-end sequencing of the cell-free nucleic acid molecule from the biological sample, the human-pathogen chimeric read sequence pair comprising a first read sequence aligned to the reference human genome and a second read sequence aligned to a reference genome of the pathogen, thereby identifying a type A human-pathogen chimeric read sequence pair; grouping from the type A human-pathogen chimeric read pairs, the type A human-pathogen chimeric read pairs comprising a first read sequence and a second read sequence, the first read sequence overlapping in the reference human genome or separated within a predetermined distance, the second read sequence overlapping in the reference genome of the pathogen or separated within a predetermined distance, thereby identifying a human-pathogen candidate integration region in the reference genome; and Identify human-pathogen chimeric read sequence pairs generated by paired-end sequencing of the cell-free nucleic acid molecules from the biological sample, the human-pathogen chimeric read sequence pairs comprising a first read sequence and a second read sequence, the first read sequence aligning with the human-pathogen candidate integration region, the second read sequence comprising a first sequence aligning with the reference human genome and a second sequence aligning with the reference genome of the pathogen, thereby identifying type B human-pathogen chimeric read sequence pairs.

48. The method of claim 46, wherein the predetermined distance is at most 300 bases.

49. The method of claim 47, wherein the predetermined distance is at most 300 bases.

50. The method of claim 46, wherein in the human-pathogen candidate integration region, there are at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 50, 100, 200, 1000, 10,000, 50,000, 100,000 or more type A human-pathogen chimeric read sequence pairs.

51. The method of claim 47, wherein in the human-pathogen candidate integration region, there are at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 50, 100, 200, 1000, 10,000, 50,000, 100,000 or more type A human-pathogen chimeric read sequence pairs.

52. The method of claim 46, wherein identifying the integration breakpoint comprises: determining the strand orientation of the first read sequence and the second read sequence of each of the type A human-pathogen chimeric read sequence pairs and the type B human-pathogen chimeric read sequence pairs; Filtering out from the type B human-pathogen chimeric read sequence pairs the type B human-pathogen chimeric read sequence pairs having the first read sequence and the second read sequence whose strand orientations are inconsistent with the first read sequence and the second read sequence of the majority of the type A human-pathogen chimeric read sequence pairs within the human-pathogen candidate integration region; and After the filtering, the integration breakpoints were detected based on the type B human-pathogen chimeric read pairs.

53. The method of claim 47, wherein identifying the integration breakpoint comprises: determining the strand orientation of the first read sequence and the second read sequence of each of the type A human-pathogen chimeric read sequence pairs and the type B human-pathogen chimeric read sequence pairs; Filtering out from the type B human-pathogen chimeric read sequence pairs the type B human-pathogen chimeric read sequence pairs having the first read sequence and the second read sequence whose strand orientations are inconsistent with the first read sequence and the second read sequence of the majority of the type A human-pathogen chimeric read sequence pairs within the human-pathogen candidate integration region; and After the filtering, the integration breakpoints were detected based on the type B human-pathogen chimeric read pairs.

54. The method of claim 46, wherein detecting the integration breakpoint comprises: determining a diversity score for the type B human-pathogen chimeric read sequence pairs; where the diversity score is calculated as , Wherein σ1 is the standard deviation of the length of the first sequence of the type B human-pathogen chimeric read sequence pair aligned to the reference human genome, and wherein 𝜎2 is the standard deviation of the length of the second sequence of the type B human-pathogen chimeric read sequence pair aligned to the reference genome of the pathogen; and If the diversity score is equal to or above a predetermined cutoff value, the integration breakpoint is detected based on the type B human-pathogen chimeric read sequence pairs.

55. The method of claim 47, wherein detecting the integration breakpoint comprises: determining a diversity score for the type B human-pathogen chimeric read sequence pairs; where the diversity score is calculated as , wherein σ1 is the standard deviation of the length of the first sequence of the type B human-pathogen chimeric read sequence pairs aligned to the reference human genome, and wherein σ2 is the standard deviation of the length of the second sequence of the type B human-pathogen chimeric read sequence pairs aligned to the reference genome of the pathogen; and If the diversity score is equal to or above a predetermined cutoff value, the integration breakpoint is detected based on the type B human-pathogen chimeric read sequence pairs.

56. The method of claim 54 or 55, wherein the predetermined cutoff value is at least 4.

0.

57. A non-transitory computer readable medium comprising instructions operable when executed by one or more processors of a computer system to cause the computer system to perform the method of any one of claims 18 to 56.

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