WO2023196925A2 - Méthodes et systèmes de typage d'allèles - Google Patents

Méthodes et systèmes de typage d'allèles Download PDF

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WO2023196925A2
WO2023196925A2 PCT/US2023/065469 US2023065469W WO2023196925A2 WO 2023196925 A2 WO2023196925 A2 WO 2023196925A2 US 2023065469 W US2023065469 W US 2023065469W WO 2023196925 A2 WO2023196925 A2 WO 2023196925A2
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hla
sequence
allele
determining
sequences
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PCT/US2023/065469
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WO2023196925A3 (fr
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Sante GNERRE
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Guardant Health, Inc.
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Publication of WO2023196925A2 publication Critical patent/WO2023196925A2/fr
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6881—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers

Definitions

  • HLA Human leukocyte antigen
  • A, B, and C present intracellular antigens originating from viruses or tumors to cytotoxic T lymphocytes.
  • Class II HLA proteins DR, DQ, and DP
  • HLA genes are highly polymorphic and play an important role in immune-mediated diseases, tumor-development processes, transplanted organ or tissue survival determination, and drug hypersensitivity.
  • HLA genotyping is a complex procedure due to the extreme degree of polymorphism in the major histocompatibility complex family.
  • the most polymorphic regions, known as the core exons, are exons 2 and 3 in HLA class I genes and exon 2 in HLA class II genes.
  • the sequences of the core exons are the most popular targets for genotyping as they are believed to be essential determinants of antigen specificity, which is informative for transplantation.
  • many polymorphisms in other exons, introns, and UTRs have been identified and contribute to creating HLA nomenclature.
  • HLA typing is performed using DNA-based methods, including SSP- (sequencespecific primer), SSO- (sequence-specific oligonucleotide), and RFLP-PCR (restriction fragment length polymorphism polymerase chain reaction) and sequence-based typing (SBT).
  • SSP- sequencespecific primer
  • SSO- sequence-specific oligonucleotide
  • RFLP-PCR restriction fragment length polymorphism polymerase chain reaction
  • SBT sequence-based typing
  • Disclosed are methods comprising determining a plurality of known allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of known allele sequences, determining, based on the alignment, for each known allele sequence of the plurality of known allele sequences, a number of sequence reads that aligned to each known allele sequence, and determining, based on the numbers of sequence reads that aligned to each known allele sequence, for the one or more loci, the known allele sequences present at the one or more loci.
  • Disclosed are methods comprising determining a plurality of known allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of known allele sequences, determining, based on the alignment, for each known allele sequence of the plurality of known allele sequences, a number of sequence read families (i.e., number of nucleic acid molecules — a sequence read family may be a group of sequence reads corresponding to a single nucleic acid molecule) that aligned to each known allele sequence, and determining, based on the numbers of sequence read families that aligned to each known allele sequence, for the one or more loci, the known allele sequences present at the one or more loci.
  • a number of sequence read families i.e., number of nucleic acid molecules — a sequence read family may be a group of sequence
  • Disclosed are methods comprising determining a plurality of known human leukocyte antigen (HLA) allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of known HLA allele sequences, determining, based on the alignment, for each known HLA allele sequence of the plurality of know n HLA allele sequences, a number of sequence reads that aligned to each known HLA allele sequence, generating, based on the numbers of sequence reads that aligned to each known HLA allele sequence, one or more supersets of known HLA allele sequences, and determining, based on a number of distinct reads in the one or more supersets of known HLA allele sequences, for the one or more loci, the known HLA allele sequences present at the one or more loci.
  • HLA human leukocyte anti
  • Disclosed are methods comprising determining a plurality of known human leukocyte antigen (HLA) allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of known HLA allele sequences, determining, based on the alignment, for each known HLA allele sequence of the plurality of known HLA allele sequences, a number of sequence read families that aligned to each known HLA allele sequence, generating, based on the numbers of sequence read families that aligned to each known HLA allele sequence, one or more supersets of known HLA allele sequences, and determining, based on a number of distinct read families in the one or more supersets of know n HLA allele sequences, for the one or more loci, the known HLA allele sequences present at the one or more loci.
  • HLA human leukocyte anti
  • Disclosed are methods comprising determining a plurality of known killer cell immunoglobulin-hke receptor (KIR) allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of know n KIR allele sequences, determining, based on the alignment, for each known KIR allele sequence of the plurality of known KIR allele sequences, a number of sequence reads that aligned to each known KIR allele sequence, generating, based on the numbers of sequence reads that aligned to each known KIR allele sequence, one or more supersets of known KIR allele sequences, and determining, based on a number of distinct reads in the one or more supersets of known KIR allele sequences, for the one or more loci, the known KIR allele sequences present at the one or more loci.
  • KIR killer
  • Disclosed are methods comprising determining a plurality of known killer cell immunoglobulin-hke receptor (KIR) allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of known KIR allele sequences, determining, based on the alignment, for each known KIR allele sequence of the plurality of known KIR allele sequences, a number of sequence read families that aligned to each know n KIR allele sequence, generating, based on the numbers of sequence read families that aligned to each known KIR allele sequence, one or more supersets of known KIR allele sequences, and determining, based on a number of distinct read families in the one or more supersets of known KIR allele sequences, for the one or more loci, the known KIR allele sequences present at the one or more loci.
  • KIR killer
  • Disclosed are methods comprising determining a plurality of known allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of known allele sequences, determining, based on the alignment, for each known allele sequence of the plurality of known allele sequences, a number of sequence reads that aligned to each known allele sequence, generating, based on the numbers of sequence reads that aligned to each known allele sequence, one or more supersets of known allele sequences, and determining, based on a number of distinct reads in the one or more supersets of known allele sequences, for the one or more loci, the known allele sequences present at the one or more loci.
  • Disclosed are methods comprising determining a plurality of known allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of known allele sequences, determining, based on the alignment, for each known allele sequence of the plurality of known allele sequences, a number of sequence read families that aligned to each know n allele sequence, generating, based on the numbers of sequence read families that aligned to each known allele sequence, one or more supersets of known allele sequences, and determining, based on a number of distinct read families in the one or more supersets of known allele sequences, for the one or more loci, the known allele sequences present at the one or more loci.
  • the results of the systems and methods disclosed herein are used as an input to generate a report.
  • the report may be in a paper or electronic format.
  • the determination of allele type e.g., allele sequence
  • FIG. 1A is a flow chart that schematically depicts exemplary method steps for allele typing.
  • FIG. IB is a flow chart that schematically depicts another exemplary method steps for allele typing.
  • FIG. 2 shows an example of a system for allele typing.
  • FIG. 3 shows an example k-mer data structure.
  • FIG. 4 shows an example Hasse diagram.
  • FIG. 5 shows an example graph data structure.
  • FIG. 6 shows an example graph data structure.
  • FIG. 7 shows an example graph data structure.
  • FIG. 8 shows an example superset comparison.
  • FIG. 9 shows an example method.
  • FIG. 10 shows an example method.
  • FIG. 11 shows an example of different alleles with high homology.
  • the alleles in FIG. 11 encode distinct products at the protein level, even if most of their genomic content is identical.
  • two alleles coding for different proteins may in practice differ only on one or two bases.
  • FIG. 12 shows example flowcharts of the disclosed methods.
  • the disclosed methods can be configured to run in one or more modes. For example, a “Build index” mode (to generate a kmers map database) and a “Call alleles” mode (to call alleles on an input sample).
  • FIG. 13 shows an example of the hierarchical structure of allele names.
  • An HLA allele name is encoded by a string such as A*01:02:03:04, comprising a gene name (A, for HLA-A), and up to four sets of digits.
  • FIG. 14 shows a screen capture of an Integrative Genomics Viewer showing one allele being called by Kmerizer and the set of all the supporting reads for the called allele. This view only contains sequence reads that perfectly match the called allele (regardless of alignment onto the reference genome)
  • FIG. 15 shows analytical performance of kmerizer on two simulated sets of 100 samples each, matched against the sensitivity of HISAT-genotype.
  • the “Regular” set contains 100 simulated samples, with a simulated error rate of 0.1%, with reads in 14 HLA genes (HISAT-genotype does not call genes in DRB3, DRB4, and DRB5).
  • the “Difficult” set contains 100 simulated samples, with a simulated error rate of 0.1%, with reads in 8 HLA genes, selected from a pool of alleles known to share a high level of homology.
  • FIG. 16 shows analytical performance of Kmerizer on a simulated set of 100 samples. This set contains 100 simulated samples, with a simulated error rate of 0.1%, with reads in 17 KIR genes.
  • FIG. 17 shows analytical performance of Kmerizer on 19 samples with known HLA status on 4 HLA genes (HLA- A, HLA-B, HLA-C, and HLA-DQB1), matched against the performance of HISAT-genotype.
  • FIG. 19 shows a distribution of counts of germlme HLA alleles for which immunogenic neoantigens were predicted.
  • FIG. 20 shows the overall germline HLA allele distribution for all between methylation epigenomic detection samples.
  • FIG. 21 shows shows that germlme allele(s) and homozygous/heterozygous status were randomly assigned based on AFND to generate germline reads using references from the IMGT/HLA database; somatic mutation was then randomly generated at an exonic position and mutant reads were then generated from the altered reference.
  • FIG. 22 shows variant allele frequency (AF) distribution of detected variants.
  • FIG. 23 shows TMB v. predicted neoepitope affinity across genes. Spearman correlations listed for each group.
  • FIG. 24 shows the distribution of neoantigen binding affinities by cancer type.
  • “about” or “approximately” as applied to one or more values or elements of interest refers to a value or element that is similar to a stated reference value or element.
  • the term “about” or “approximately” refers to a range of values or elements that falls within 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less in either direction (greater than or less than) of the stated reference value or element unless otherwise stated or otherwise evident from the context (except where such number would exceed 100% of a possible value or element).
  • Adapter refers to short nucleic acids (e.g., less than about 500, less than about 100 or less than about 50 nucleotides in length) that are typically at least partially double-stranded and used to link to either or both ends of a given sample nucleic acid molecule.
  • Adapters can include nucleic acid primer binding sites to permit amplification of a nucleic acid molecule flanked by adapters at both ends, and/or a sequencing primer binding site, including primer binding sites for sequencing applications, such as various next generation sequencing (NGS) applications.
  • Adapters can also include binding sites for capture probes, such as an oligonucleotide attached to a flow cell support or the like.
  • Adapters can also include a nucleic acid tag as described herein.
  • Nucleic acid tags are typically positioned relative to amplification primer and sequencing primer binding sites, such that a nucleic acid tag is included in amplicons and sequencing reads of a given nucleic acid molecule.
  • Adapters of the same or different sequence can be linked to the respective ends of a nucleic acid molecule. In certain embodiments, the same adapter is linked to the respective ends of the nucleic acid molecule except that the nucleic acid tag differs in its sequence.
  • the adapter is a Y-shaped adapter in which one end is blunt ended or tailed as described herein, for joining to a nucleic acid molecule, which is also blunt ended or tailed with one or more complementary nucleotides.
  • an adapter is a bell-shaped adapter that includes a blunt or tailed end for joining to a nucleic acid molecule to be analyzed.
  • Other exemplary adapters include T-tailed and C-tailed adapters.
  • Administer or “administering” a therapeutic agent (e g., an immunological therapeutic agent, a DNA damage response (DDR) inhibitor (e.g., a poly (ADP-ribose) polymerase (PARP) inhibitor (PARPi)), etc.) to a subject means to give, apply or bring the composition into contact with the subject.
  • Administration can be accomplished by any of a number of routes, including, for example, topical, oral, subcutaneous, intramuscular, intraperitoneal, intravenous, intrathecal, and intradermal.
  • Align As used herein, “align,” alignment,” and “aligning” in the context of nucleic acids refers to arranging sequences of DNA or RNA to identify regions of similarity. Similarity may be related to the nucleotide sequence, structural, functional, and/or evolutionary relationships between the sequences. Alignment of DNA sequences involves alignment of genomic DNA of one sequence to genomic DNA of at least one other sequence. Such alignment may exclude non-genomic DNA, such as a molecular barcode, padding bases, and the like. For example, genomic DNA of a sequence read may be aligned to genomic DNA of a reference DNA sequence, excluding any molecular tag or adapter sequence that may be attached to the sequence read.
  • nucleotides “correspond to” nucleotides in a sequence refers to nucleotides identified upon alignment with the sequence to maximize identify using a standard alignment algorithm, such as the GAP algorithm.
  • sequence identify refers to the number of identical or similar nucleotide bases in an alignment between two or more polynucleotide sequences.
  • at least 90% identical to refers to percent identities from 90 to 100% relative to the reference polynucleotide. Identity at a level of 90% or more is indicative of the fact that, assuming for exemplification purposes a test and reference polynucleotide length of 100 nucleotides are compared, no more than 10% (i.e., 10 out of 100) of nucleotides in the test polynucleotide differs from that of the reference polynucleotide.
  • Such differences can be represented as point mutations randomly distributed over the entire length of a nucleotide sequence or they can be clustered in one or more locations of varying length up to the maximum allowable, e.g., 10/100 nucleotide difference (approximately 90% identity). Differences are defined as nucleic acid substitutions, insertions or deletions.
  • Sequence identity can be determined by sequence alignment of nucleic acid sequences to identify regions of similarity or identity.
  • sequence identity is generally determined by alignment to identify identical bases. The alignment can be local or global. Matches, mismatches and gaps can be identified between compared sequences. Gaps are null nucleotides inserted between the bases of aligned sequences so that identical or similar characters are aligned. Generally, there can be internal and terminal gaps. Sequence identify can be determined by taking into account gaps as the number of identical bases/length of the shortest sequence x 100. When using gap penalties, sequence identity can be determined with no penalty for end gaps (e.g., terminal gaps are not penalized). Alternatively, sequence identity can be determined without taking into account gaps as the number of identical positions/length of the total aligned sequence x 100.
  • a “global alignment” is an alignment that aligns two sequences from beginning to end, aligning each base in each sequence only once. An alignment is produced regardless of whether or not there is similarity or identity between the sequences. For example, 50% sequence identity based on “global alignment” means that in an alignment of the full sequence of two compared sequences each of 100 nucleotides in length, 50% of the bases are the same. It is understood that global alignment also can be used in determining sequence identify even when the length of the aligned sequences is not the same. The differences in the terminal ends of the sequences will be taken into account in determining sequence identify, unless the “no penalty for end gaps” is selected.
  • a global alignment is used on sequences that share significant similarity over most of their length.
  • Exemplary algorithms for performing global alignment include the Needleman-Wunsch algorithm (Needleman et al. J. Mol. Biol. 48: 443 (1970).
  • Exemplary programs for performing global alignment are publicly available and include the Global Sequence Alignment Tool available at the National Center for Biotechnology Information (NCBI) website (ncbi.nlm.nih.gov/), and the program available at deepc2 psi iastate. edu/ aat/ align/ align.html.
  • a “local alignment” is an alignment that aligns two sequences, but only aligns those portions of the sequences that share similarity or identity. Hence, a local alignment determines if sub-segments of one sequence are present in another sequence. If there is no similarity, no alignment will be returned.
  • Local alignment algorithms include BLAST or Smith- Waterman algorithm (Adv. Appl. Math. 2: 482 (1981)). For example, 50% sequence identity based on “local alignment” means that in an alignment of the full sequence of two compared sequences of any length, a region of similarity or identity of 100 nucleotides in length has 50% of the bases that are the same in the region of similarity or identity.
  • allelic variant refers to a specific genetic variant at defined genomic location or locus.
  • An allelic variant is usually presented at a frequency of 50% (0.5) or 100%, depending on whether the allele is heterozygous or homozygous.
  • germline variants are inherited and usually have a frequency of 0.5 or 1.
  • Somatic variants; however, are acquired variants and usually have a frequency of ⁇ 0.5.
  • Major and minor alleles of a genetic locus refer to nucleic acids harboring the locus in which the locus is occupied by a nucleotide of a reference sequence, and a variant nucleotide different than the reference sequence respectively.
  • amplify or “amplification” in the context of nucleic acids refers to the production of multiple copies of a polynucleotide, or a portion of the polynucleotide, typically starting from a small amount of the polynucleotide (e.g., a single polynucleotide molecule), where the amplification products or amplicons are generally detectable. Amplification of polynucleotides encompasses a variety of chemical and enzymatic processes.
  • Barcode in the context of nucleic acids refers to a nucleic acid molecule having a sequence that can serve as an identifier of the molecule (molecular barcode), identifier of a partition (partition barcode) or an identifier of the sample (sample barcode or sample index).
  • molecular barcode molecular barcode
  • partition barcode identifier of a partition
  • sample barcode or sample index identifier of the sample.
  • cell-free nucleic acid refers to nucleic acids not contained within or otherwise bound to a cell. In some embodiments, “cell-free nucleic acid” refers to nucleic acids which are not contained within or otherwise bound to a cell at the point of isolation from the subject. Cell-free nucleic acids can include, for example, all non-encapsulated nucleic acids sourced from a bodily fluid (e g., blood, plasma, serum, urine, cerebrospinal fluid (CSF), etc.) from a subject.
  • a bodily fluid e g., blood, plasma, serum, urine, cerebrospinal fluid (CSF), etc.
  • Cell -free nucleic acids include DNA (cfDNA), RNA (cfRNA), and hybrids thereof, including cfDNA and/or cfRNA derived from genomic DNA, mitochondrial DNA, circulating DNA, siRNA, miRNA, circulating RNA (cRNA), tRNA, rRNA, small nucleolar RNA (snoRNA), Piwi-interacting RNA (piRNA), long non-coding RNA (long ncRNA), and/or fragments of any of these.
  • Cell-free nucleic acids can be double-stranded, single-stranded, or a hybrid thereof.
  • a cell-free nucleic acid can be released into bodily fluid through secretion or cell death processes, e.g., cellular necrosis, apoptosis, or the like. Some cell-free nucleic acids are released into bodily fluid from cancer cells, e.g., circulating tumor DNA (ctDNA). Others are released from healthy cells. ctDNA can be non-encapsulated tumor-derived fragmented DNA. Another example of cell-free nucleic acids is fetal DNA circulating freely in the maternal blood stream, also called cell-free fetal DNA (cffDNA).
  • cffDNA cell-free fetal DNA
  • a cell-free nucleic acid can have one or more epigenetic modifications, for example, a cell-free nucleic acid can be acetylated, 5- methylated, ubiquitylated, phosphorylated, sumoylated, ribosylated, and/or citrullinated.
  • Cellular Origin in the context of cell-free nucleic acids means the cell type from which a given cell-free nucleic acid molecule derives or otherwise originates (e.g., via a apoptotic process, a necrotic process, or the like).
  • a given cell-free nucleic acid molecule may originate from a tumor cell (e.g., a cancerous pulmonary cell, etc.) or a non-tumor or normal cell (e.g., a non- cancerous pulmonary cell, etc.).
  • a tumor cell e.g., a cancerous pulmonary cell, etc.
  • a non-tumor or normal cell e.g., a non- cancerous pulmonary cell, etc.
  • Classifier generally refers to algorithm computer code that receives, as input, test data and produces, as output, a classification of the input data as belonging to one or another class (e.g., having a DNA damage repair deficiency (DDRD) or not having DDRD, tumor DNA or non-tumor DNA).
  • DDRD DNA damage repair deficiency
  • Contiguous Sequence refers to a set of consecutive nucleic acid bases (that can be overlapping), that together represent a consensus of a nucleic acid region. It can refer to any contiguous stretch of sequence data of sequence reads, or reference intervals, or constructs from reads and/or reference segments (such as assembled reads).
  • Copy Number Variant refers to a phenomenon in which sections of the genome are repeated and the number of repeats in the genome varies between individuals in the population under consideration.
  • Coverage As used herein, the terms “coverage”, “total molecule count” or “total allele count” are used interchangeably. They refer to the total number of nucleic acid molecules at a particular genomic position in a given sample.
  • deoxyribonucleic Acid or Ribonucleic Acid refers a natural or modified nucleotide which has a hydrogen group at the 2'- position of the sugar moiety.
  • DNA typically includes a chain of nucleotides comprising deoxyribonucleosides that comprise one of four ty pes of nucleobases, namely, adenine (A), thymine (T), cytosine (C), and guanine (G).
  • ribonucleic acid or RNA refers to a natural or modified nucleotide which has a hydroxyl group at the 2'-position of the sugar moiety.
  • RNA typically includes a chain of nucleotides comprising ribonucleosides that comprise one of four types of nucleobases, namely, A, uracil (U), G, and C.
  • nucleotide refers to a natural nucleotide or a modified nucleotide. Certain pairs of nucleotides specifically bind to one another in a complementary' fashion (called complementary' base pairing).
  • complementary' base pairing complementary' base pairing
  • RNA adenine (A) pairs with uracil (U) and cytosine (C) pairs with guanine (G).
  • nucleic acid sequencing data denotes any information or data that is indicative of the order and identity of the nucleotide bases (e.g., adenine, guanine, cytosine, and thymine or uracil) in a molecule (e.g., a whole genome, whole transcriptome, exome, oligonucleotide, polynucleotide, or fragment) of a nucleic acid such as DNA or RNA.
  • sequence information obtained using all available varieties of techniques, platforms or technologies, including, but not limited to: capillary electrophoresis, microarrays, ligation-based systems, polymerase-based systems, hybridization-based systems, direct or indirect nucleotide identification systems, pyrosequencing, ion- or pH-based detection systems, and electronic signature-based systems.
  • Detect refers to an act of determining the existence or presence of one or more target nucleic acids (e.g., nucleic acids having targeted mutations or other markers) in a sample.
  • target nucleic acids e.g., nucleic acids having targeted mutations or other markers
  • enriched sample refers to a sample that has been enriched for specific regions of interest.
  • the sample can be enriched by amplifying regions of interest or by using single-stranded DNA/RNA probes or double stranded DNA probes that can hybridize to nucleic acid molecules of interest (e.g., SureSelect® probes, Agilent Technologies).
  • an enriched sample refers to a subset or portion of the processed sample that is enriched, where the subset or portion of the processed sample being enriched contains nucleic acid molecules from a sample of cell-free polynucleotides or polynucleotides.
  • Gene refers to any segment of DNA associated with a biological function. Thus, genes include coding sequences and optionally, the regulatory sequences required for their expression. Genes also optionally include non-expressed DNA segments that, for example, form recognition sequences for other proteins.
  • Genomic region means a fixed position on, or section of, a chromosome, such as the position of a gene or a genomic marker.
  • genomic markers include transcriptional factor binding regions (e g., CTCF binding regions, etc.), distal regulatory elements (DREs), repetitive elements (e.g., microsatellites, etc.), intron-exon or exon-intron junctions, transcriptional start sites (TSSs), and the like.
  • Indel refers to mutation that involves the insertion or deletion of nucleotide positions in the genome of a subject.
  • k-mer refers to a string (or sub-string) of length k contained within a genomic sequence.
  • the length k can be 100 bp or less, 150 bp or less, 200 bp or less or 250 bp or less.
  • Match means that at least a first value or element is at least approximately equal to at least a second value or element.
  • the cellular origin of at least the subset of the DNA molecules from a cfDNA sample is determined when there is at least a substantial or approximate match between a test sample distribution of cfDNA fragment properties and a reference sample distribution of cfDNA fragment properties.
  • mutation refers to a variation from a known reference sequence and includes mutations such as, for example, single nucleotide variants (SNVs), copy number variants or variations (CNVs)/aberrations, insertions or deletions (indels), truncation, gene fusions, transversions, translocations, frame shifts, duplications, repeat expansions, and epigenetic variants.
  • SNVs single nucleotide variants
  • CNVs copy number variants or variations
  • indels insertions or deletions
  • truncation gene fusions
  • transversions transversions
  • translocations translocations
  • frame shifts duplications, repeat expansions
  • epigenetic variants e.g., a mutation can be a germline or somatic mutation.
  • a reference sequence for purposes of comparison is a wildtype genomic sequence of the species of the subject providing a test sample, typically the human genome.
  • a mutation or variant is a “tumor-related genetic variant” that causes or at least contributes to oncogenesis.
  • a driver mutation can be a mutation associated with cancer or another abnormal biological condition. The presence of a driver mutation can be indicative of cancer diagnosis, stratification of a subject to a subtype, tumor burden, tumor in a tissue or organ, tumor metastasis, therapeutic efficacy, or resistance to treatment.
  • next generation sequencing or “NGS” refers to massively parallel sequencing technologies having increased throughput as compared to traditional Sanger- and capillary electrophoresis-based approaches, for example, with the ability to generate hundreds of thousands of relatively small sequence reads at a time.
  • next generation sequencing techniques include, but are not limited to, sequencing by synthesis, sequencing by ligation, and sequencing by hybridization.
  • nucleic acid tag refers to a short nucleic acid (e.g., less than about 500, about 100, about 50 or about 10 nucleotides in length), used to label nucleic acid molecules to distinguish nucleic acids from different samples (e.g., representing a sample index), or different nucleic acid molecules in the same sample (e.g., representing a molecular tag), of different types, or which have undergone different processing.
  • Nucleic acid tags can be single stranded, double stranded or at least partially double stranded. Nucleic acid tags optionally have the same length or varied lengths.
  • Nucleic acid tags can also include double-stranded molecules having one or more blunt-ends, include 5’ or 3’ single-stranded regions (e.g., an overhang), and/or include one or more other singlestranded regions at other locations within a given molecule.
  • Nucleic acid tags can be attached to one end or both ends of the other nucleic acids (e.g., sample nucleic acids to be amplified and/or sequenced). Nucleic acid tags can be decoded to reveal information such as the sample of origin, form or processing of a given nucleic acid.
  • Nucleic acid tags can also be used to enable pooling and/or parallel processing of multiple samples comprising nucleic acids bearing different nucleic acid tags and/or sample indexes in which the nucleic acids are subsequently being deconvoluted by reading the nucleic acid tags.
  • Nucleic acid tags can also be referred to as molecular identifiers or tags, sample identifiers, index tags, and/or barcodes. Additionally or alternatively, nucleic acid tags can be used to distinguish different molecules in the same sample. This includes, for example, uniquely tagging different nucleic acid molecules in a given sample, or non-uniquely tagging such molecules.
  • tags with a limited number of different sequences may be used to tag nucleic acid molecules such that different molecules can be distinguished based on, for example, start and/or stop positions where they map to a selected reference genome in combination with at least one nucleic acid tag.
  • a sufficient number of different nucleic acid tags are used such that there is a low probability (e.g., less than about a 10%, less than about a 5%, less than about a 1%, or less than about a 0. 1% chance) that any two molecules will have the same start/ stop positions and also have the same nucleic acid tag.
  • nucleic acid tags include multiple molecular identifiers to label samples, forms of nucleic acid molecules within a sample, and nucleic acid molecules within a form having the same start and stop positions.
  • Such nucleic acid tags can be referenced using the exemplary form “Ali” in which the uppercase letter indicates a sample type, the Arabic numeral indicates a form of molecule within a sample, and the lowercase Roman numeral indicates a molecule within a form.
  • polynucleotide refers to a linear polymer of nucleosides (including deoxyribonucleosides, ribonucleosides, or analogs thereof) joined by intemucleosidic linkages.
  • a polynucleotide comprises at least three nucleosides. Oligonucleotides often range in size from a few monomeric units, e.g. 3-4, to hundreds of monomeric units.
  • a polynucleotide is represented by a sequence of letters, such as “ATGCCTG,” it will be understood that the nucleotides are in 5’ 3’ order from left to right and that in the case of DNA, “A” denotes deoxyadenosine, “C” denotes deoxycytidine, “G” denotes deoxyguanosine, and “T” denotes deoxythymidine, unless otherwise noted.
  • the letters A, C, G, and T may be used to refer to the bases themselves, to nucleosides, or to nucleotides comprising the bases, as is standard in the art.
  • prevalence in the context of nucleic acid variants refers to the degree, pervasiveness, or frequency with which a given nucleic acid variant is or was observed in a given sample (e.g., a given bodily fluid sample, a given non-bodily fluid sample, etc.) or other population (e.g., a given population of bodily fluid samples, a given population of non-bodily fluid samples, etc ).
  • a given sample e.g., a given bodily fluid sample, a given non-bodily fluid sample, etc.
  • other population e.g., a given population of bodily fluid samples, a given population of non-bodily fluid samples, etc .
  • reference sample or “reference cfDNA sample” refers a sample of known composition and/or having or known to have or lack specific properties (e.g., known nucleic acid variant(s), known cellular origin, known tumor fraction, known coverage, and/or the like) that is analyzed along with or compared to test samples in order to evaluate the accuracy of an analytical procedure.
  • a reference sample data set ty pically includes from at least about 25 to at least about 30,000 or more reference samples.
  • the reference sample data set includes about 50, 75, 100, 150, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, 2,500, 5,000, 7,500, 10,000, 15,000, 20,000, 25,000, 50,000, 100,000, 1,000,000, or more reference samples.
  • reference Sequence refers to a known sequence used for purposes of comparison with experimentally determined sequences.
  • a known sequence can be an entire genome, a chromosome, or any segment thereof.
  • a reference sequence typically includes at least about 20, at least about 50, at least about 100, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, at least about 500, at least about 1000, at least about 10,000, at least about 100,000, at least about 1,000,000, at least about 10,000,000, at least about 100,000,000, at least about 1,000,000,000, or more nucleotides.
  • a reference sequence can align with a single contiguous sequence of a genome or chromosome or can include noncontiguous segments that align with different regions of a genome or chromosome.
  • Exemplary reference sequences include, for example, human genomes, such as, hG19 and hG38.
  • samples means any biological sample capable of being analyzed by the methods and/or systems disclosed herein.
  • samples are bodily fluid samples, for example, whole blood or fractions thereof, lymphatic fluid, urine, and/or cerebrospinal fluid, among other bodily fluid types from which cell-free (circulating, not contained within, or otherwise bound to a cell) nucleic acids are sourced.
  • bodily fluid samples are plasma samples, which are the fluid portions of whole blood exclusive of cells, such as red and white blood cells.
  • bodily fluid samples are serum samples, that is, plasma lacking fibrinogen.
  • samples are “non-bodily fluid samples” or “nonplasma samples,” that is, biological samples other than “bodily fluid samples” such as, as cellular and/or tissue samples, from which nucleic acids other than cell-free nucleic acids are sourced.
  • the sample can include body tissue or tissue biopsy.
  • Sensitivity in the context of a given assay or method refers to the ability of the assay or method to detect and distinguish between targeted (e.g., nucleic acid variants) and non-targeted analytes.
  • Sequence fragmentconstructing the allele k- mer data structure may comprise dividing the known allele sequences into a quantity of k- mers.
  • a quantity of k-mers having a length from about 100 nucleotides to about 200 nucleotides.
  • the quantity of k-mers may have a length of 125-175 nucleotides, 130-160 nucleotides, 135-155 nucleotides, 140-150 nucleotides.
  • the quantity of k-mers may have a length of 140, 141, 142, 143, 144, or 145 nucleotides.
  • Constructing the allele k-mer data structure may comprise associating each k- mer with metadata.
  • the metadata may comprise, for example, an indication of a quantity of alleles that contain the k-mer and, for each allele that contains the k-mer, an allele identifier and a start position of the k-mer.
  • Sequence read As used herein, “read,” “sequence read,” or “sequencing read” refers to the sequence of base pairs corresponding to all or a part of a sequence fragment.
  • Sequencing refers to any of a number of technologies used to determine the sequence (e.g., the identity and order of monomer units) of a biomolecule, e.g., a nucleic acid such as DNA or RNA.
  • Exemplary sequencing methods include, but are not limited to, targeted sequencing, single molecule real-time sequencing, exon or exome sequencing, intron sequencing, electron microscopy-based sequencing, panel sequencing, transistor-mediated sequencing, direct sequencing, random shotgun sequencing, Sanger dideoxy termination sequencing, whole-genome sequencing, sequencing by hybridization, pyrosequencing, capillary electrophoresis, duplex sequencing, cycle sequencing, single-base extension sequencing, solid-phase sequencing, high-throughput sequencing, massively parallel signature sequencing, emulsion PCR, co-amplification at lower denaturation temperature-PCR (COLD-PCR), multiplex PCR, sequencing by reversible dye terminator, paired-end sequencing, near-term sequencing, exonuclease sequencing, sequencing by ligation, short-read sequencing, single-molecule sequencing, sequencing-by- synthesis, real-time sequencing, reverse-terminator sequencing, nanopore sequencing, 454 sequencing, Solexa Genome Analyzer sequencing, SOLiDTM sequencing, MS-PET sequencing, and a combination thereof.
  • sequence information in the context of a nucleic acid polymer means the order and/or identity of monomer units (e.g., nucleotides, etc.) in that polymer.
  • Single nucleotide Variant As used herein, “single nucleotide variant” or “SNV” means a mutation or variation in a single nucleotide that occurs at a specific position in a genome or a given genomic sequence.
  • specificity in the context of a diagnostic analysis or assay refers to the extent to which the analysis or assay detects an intended target analyte to the exclusion of other components of a given sample.
  • status in the context of subjects refers to one or more states of a given subject, such as whether or not the subject has cancer.
  • subject refers to an animal, such as a mammalian species (e.g., human) or avian (e.g., bird) species, or other organism, such as a plant. More specifically, a subject can be a vertebrate, e.g., a mammal such as a mouse, a primate, a simian, or a human. Animals include farm animals (e.g., production cattle, dairy cattle, poultry, horses, pigs, and the like), sport animals, and companion animals (e.g., pets or support animals).
  • farm animals e.g., production cattle, dairy cattle, poultry, horses, pigs, and the like
  • companion animals e.g., pets or support animals.
  • a subject can be a healthy individual, an individual that has or is suspected of having a disease or a predisposition to the disease, or an individual that is in need of therapy or suspected of needing therapy.
  • the terms “individual” or “patient” are intended to be interchangeable with “subject.”
  • the subject is a human who has, or is suspected of having cancer.
  • a subject can be an individual who has been diagnosed with having a cancer, is going to receive a cancer therapy, and/or has received at least one cancer therapy.
  • the subject can be in remission of a cancer.
  • the subject can be an individual who is diagnosed of having an autoimmune disease.
  • the subject can be a female individual who is pregnant or who is planning on getting pregnant, who may have been diagnosed with or suspected of having a disease, e.g., a cancer, an auto-immune disease.
  • a “reference subject” refers to a subject known to have or lack specific properties (e.g., known cancer or disease status, known nucleic acid variant(s), known cellular origin, known tumor fraction, known coverage, and/or the like).
  • Threshold refers to a separately determined value used to characterize or classify experimentally determined values. In certain embodiments, for example, “threshold value” refers to a selected value to which a quantitative value is compared in order to determine that a given target nucleic acid variant is absent at a given genetic locus.
  • Value generally refers to an entry in a data set can be anything that characterizes the feature to which the value refers. This includes, without limitation, numbers, words or phrases, symbols (e.g., + or -) or degrees.
  • the nucleic acid sample can be, but is not limited to, cell-free nucleic acid (cfNA), genomic DNA, or RNA.
  • cfNA cell-free nucleic acid
  • the nucleic acid sample may be derived from a specific chromosome and/or from a specific region of a chromosome
  • the nucleic acid sample may be derived from all or a portion of the major histocompatibility complex (MHC) region of human chromosome 6.
  • MHC major histocompatibility complex
  • the nucleic acid sample may be derived from all or a portion of the killer cell immunoglobulin-like receptor (KIR) region of human chromosome 19. In an embodiment, the nucleic acid sample may be derived in whole or in part from both the MHC region of human chromosome 6 and the KIR region of human chromosome 19. a. Major Histocompatibility Complex (MHC)
  • the MHC is a set of cell surface molecules encoded by a large gene family which controls a major part of the immune system in all vertebrates.
  • the major function of major histocompatibility complexes is to bind to peptide fragments derived from pathogens and display them on the cell surface for recognition by the appropriate T-cells.
  • the MHC contains two types of polymorphic MHC genes, the class I and class II genes, which encode two groups of structurally distinct but homologous proteins, and other nonpolymorphic genes whose products are involved in antigen presentation.
  • HLA human leukocyte antigen
  • HLA proteins are encoded by genes of the MHC.
  • HLA class I antigens include HLA-A, HLA-B, and HLA- C.
  • HLA class II antigens include HLA-DR, HLA-DQ, HLA-DP, HLA-DM, HLA- DOA, HLA-DOB.
  • HLA's corresponding to MHC class I (A, B, and C) present peptides from inside the cell.
  • HLA's corresponding to MHC class II (DP, DM, DO A, DOB, DQ, and DR) present antigens from outside of the cell to T-lymphocytes.
  • MHC molecules mediate the binding of a given T cell receptor to a given antigen, so MHC polymorphism across individuals modulates the T cell response to a given antigen.
  • Clinical applications of HLA typing include vaccine trials, disease associations, adverse drug reactions, platelet transfusion, and transplantation of organs and stem cells.
  • HLA gene locus There are numerous alleles at each HLA gene locus (Al, A2, A3, etc.). Each person inherits one complete set of HLA alleles (haplotype) from each parent, and this combination of encoded proteins constitutes a person's HLA type (e.g., different antigens of A23, A31, B7, B44, C7, C8, DR4, DR7, DQ2, DQ7, DP2, DP3). There are more than 50,000 different known HLA types.
  • Certain diseases are associated with particular HLA types.
  • the association level varies among diseases, and there is generally a lack of a strong concordance between the HLA type and the disease.
  • the exact mechanisms underlying the most HLA-disease association are not well understood, and other genetic and environmental factors may play roles as well.
  • narcolepsy with HLA-DQBr0602/HLA-DRBTT 501
  • ankylosing spondylitis with HLA-B27
  • celiac disease with HLA-DQBT02.
  • the HLA-A1, B8, DR17 haplotype is frequently associated with autoimmune disorders.
  • Rheumatoid arthritis is associated with a particular sequence of the amino acid positions 66 to 75 in the DR 1 chain that is common to the major subtypes of HLA-DR4 and DR1.
  • Type I diabetes mellitus is associated with HLA-DR3,4 heterozygotes, and the absence of asparagine at position 57 on the DQ(H chain appears to render susceptibility to this disease.
  • HLA-A, HLA-B, and HLA-DR have long been known as major transplantation antigens. Recent clinical data indicate that HLA-C matching also affects the clinical outcomes of hematopoietic stem cell transplantation. Allogeneic hematopoietic stem cell transplantation is used to treat hematologic malignancy, severe aplastic anemia, severe congenital immunodeficiencies, and selected inherited metabolic diseases.
  • the HLA system is the major histocompatibility barrier in stem cell transplantation, and the degree of HLA matching is predictive of the clinical outcome. HLA mismatch between a recipient and a stem cell donor represents a risk factor for graft rejection/failure and acute graft-versus-host disease (GVHD).
  • GVHD acute graft-versus-host disease
  • T-cell depletion of donor marrow results in lower incidence of acute GVHD, but higher incidence of graft failure, graft rejection, malignant disease relapse (i.e., loss of the graft-versus-leukemia effect), impaired immune recovery, and later complication of Epstein-Barr virus-associated lymphoproliferative disorders.
  • KIR Killer Cell Immunoglobulin-like Receptor
  • NK cells are part of the innate immune system and are specialized for early defense against infection as well as tumors. The NK cells were first discovered as a result of their ability to kill tumor cell targets. Unlike cytolytic T-cells, NK cells can kill targets in anon-major histocompatibility complex (non-MHC)-restricted manner. As an important part of the innate immune system, the NK cells comprise about 10% of the total circulating lymphocytes in the human body.
  • NK cells are normally kept under tight control. All normal cells in the body express the MHC class I molecules on their surface. These molecules protect normal cells from killing by the NK cells because they serve as ligands for many of the receptors found on NK cells. Cells lacking sufficient MHC class I on their surface are recognized as “abnormal” by NK cells and killed. Simultaneously with killing the abnormal cells, the NK cells also elicit a cytokine response.
  • Natural killer cells constitute a rapid-response force against cancer and viral infections. These specialized white blood cells originate in the bone marrow, circulate in the blood, and concentrate in the spleen and other lymphoid tissues. NK cells key their activities on a subset of the HLA proteins that occur on the surfaces of healthy cells but that virus- and cancer-weakened cells shed. When NK cells encounter cells that lack HLA proteins, they attack and destroy them — thus preventing the cells from further spreading the virus or cancer. NK cells are distinguished from other immune system cells by the promptness and breadth of their protective response. Other white blood cells come into play more slowly and target specific pathogens — cancers, viruses, or bacteria — rather than damaged cells in general.
  • the natural killer cell immunoglobulin-like receptor (KIR) gene family is one of several families of receptors that encode important proteins found on the surface of NK cells.
  • a subset of the KIR genes, namely the inhibitory KIR, interact with the HLA class I molecules, which are encoded within the human MHC.
  • Such interactions allow communication between the NK cells and other cells of the body, including normal, virally infected, or cancerous cells.
  • This communication between KIR molecules on the NK cells and HLA class I molecules on all other cells helps determine whether or not cells in the body are recognized by the NK cells as self or non-self. Cells which are deemed to be ‘non-self are targeted for killing by the NK cells.
  • the KIR gene family consists of 16 genes (KIR2DL1, KIR2DL2, KIR2DL3, KIR2DL4, KIR2DL5A, KIR2DL5B, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, KIR3DL1/S1, KIR3DL2, KIR3DL3, KIR2DP1 and KIR3DP1.)
  • the KIR gene cluster is located within a 100-200 kb region of the Leukocyte Receptor Complex (LRC) located on chromosome 19 (19ql3.4)
  • LRC Leukocyte Receptor Complex
  • the gene complex is thought to have arisen by gene duplication events occurring after the evolutionary split between mammals and rodents
  • the KIR genes are arranged in a head-to tail fashion, with only 2.4 kb of sequence separating the genes, except for one 14 kb sequence between 3DP1 and 2DL4.
  • KIR haplotypes See Martin et al. Immunogenetics. (2008) December; 60(12):767-774).
  • the A-haplotype contains no stimulatory genes (2DS and 3DS1) other than 2DS4, no 2DL5 genes and no 2DL2 genes.
  • the B-haplotype is more variable in gene content, with different B-haplotypes containing different numbers of stimulatory genes, either one or two 2DL5 genes, etc.
  • KIR2DL and KIR3DL encode proteins with long cytoplasmic tails that contain immune tyrosinebased inhibitory motifs (ITIM). These KIR proteins can send inhibitory signals to the natural killer cell when the extra-cellular domain has come into contact with its ligand.
  • ITIM immune tyrosinebased inhibitory motifs
  • KIR receptor structure and the identity of the HLA class I ligands for each KIR receptor are described in Parham P. et al., Alloreactive killer cells: hindrance and help for hematopoietic transplants. Nature Rev. Immunology. (2003)3: 108-122.
  • the nomenclature for the killer-cell immunoglobulin-hke receptors (KIRs) describes the number of extracellular immunoglobulin-like domains (2D or 3D) and the length of the cytoplasmic tail (L for long, S for short).
  • Each immunoglobulin-like domain is depicted as a loop, each immunoreceptor tyrosine-based inhibitory motif (ITIM) in the cytoplasmic tail as an oblong shape, and each positively charged residue in the transmembrane region as a diamond.
  • ITIM immunoreceptor tyrosine-based inhibitory motif
  • KIR gene associations with autoimmune disease and recipient survival after allogeneic hematopoietic cell transplantation have been shown (Parham P. (2005) MHC class I molecules and KIRs in human history, health and survival. Nature reviews, March; 5(3):201-214). It is now clear that each KIR gene has more than one sequence; that is, each KIR gene has variable sequence because of single nucleotide polymorphisms (SNPs), and in some instances, insertions or deletions within the coding sequence.
  • SNPs single nucleotide polymorphisms
  • KIR3DL1 polymorphism can affect not only the expression levels of KIR3DL1 on natural killer cells, but also the binding affinity of KIR3DL 1 to its ligand.
  • the methods and systems disclosed may be applied to a nucleic acid sample derived from one or more genes of the MHC region of human chromosome 6.
  • the nucleic acid sample may be derived from one or more genes of the KIR region of human chromosome 19.
  • the nucleic acid sample may be derived in whole or in part may be derived from one or more genes of the MHC region of human chromosome 6 and from one or more genes of the KIR region of human chromosome 19. Essentially any number of genes may be evaluated using the methods and related aspects of the present disclosure.
  • sets of genes targeted for analysis include at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 100, 1,000, 10,000, or more genes.
  • Table 1 A non-exhaustive list of genes, one or more of w hich are optionally selected for evaluation using the methods and related aspects disclosed herein is provided in Table 1.
  • FIG. 1A is a flow chart that schematically depicts an example technique for allele typing in a cell-free nucleic acid (cfDNA) sample obtained from a test subject. Allele typing may be used to determine one or more alleles present at a locus of a chromosome.
  • a method 100 A at step 102A, may comprise obtaining data.
  • the data may comprise sequence data, such as allele sequence data.
  • step 102 A may comprise obtaining (or otherwise determining, retrieving, receiving, etc.) known allele sequences.
  • known allele sequences may be downloaded from the IPD-IMGT/HLA Database and/or the IPD-KIR Database.
  • known HLA allele sequences may be downloaded from ftp.ebi.ac.uk/pub/databases/ipd/imgt/hla and known KIR allele sequences may be downloaded from ftp.ebi.ac.uk/pub/databases/ipd/kir.
  • the known allele sequences may be any type of allele sequence.
  • the known allele sequences may be known human leukocyte antigen (HLA) allele sequences.
  • the known allele sequences may be known killer cell immunoglobulin-like receptor (KIR) allele sequences.
  • step 104A the data may be pre-processed.
  • step 104A may comprise constructing an allele k-mer data structure.
  • the allele k-mer data structure may be a database.
  • the allele k-mer data structure may be a flat file.
  • the allele k-mer data structure may be any form of data structure.
  • Constructing the allele k-mer data structure may comprise dividing the known allele sequences into a quantity of k-mers. For example, a quantity of k-mers having a length from about 100 nucleotides to about 200 nucleotides. In an embodiment, the quantity of k-mers may have a length of 143 nucleotides.
  • Constructing the allele k-mer data structure may comprise associating each k-mer with metadata.
  • the metadata may comprise, for example, an indication of a quantity of alleles that contain the k-mer and, for each allele that contains the k-mer, an allele identifier and a start position of the k-mer.
  • step 106A sequence processing may be performed.
  • step 106A may comprise obtaining (or otherwise determining, retrieving, receiving, etc.) sequence read pairs (e.g., test sequence reads) from a cell-free nucleic acid (cfDNA) sample obtained from a test subject.
  • Step 106 A may comprise performing an alignment between the test sequence reads and the known allele sequences.
  • step 106A may comprise performing an alignment between the test sequence reads and the k-mers in the allele k-mer data structure.
  • the sequence processing may determine an allele(s) supported by a test sequence read(s). An allele may be supported by more than one test sequence read. A test sequence read may support more than one allele.
  • a test sequence read may be found to support an allele if the test sequence read aligns to the allele (e.g., a k-mer of the allele) with over a threshold percent identity.
  • the threshold percent identity may be, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 100%, and the like.
  • the threshold percent identity may be 100%, requiring a “perfect” match between a test sequence read and the allele (e.g., a k-mer of the allele).
  • step 106A may comprise determining a number of test sequence read families that support an allele(s) (e.g., a number of nucleic acid molecules that support an allele(s)).
  • Each test sequence read may comprise a barcode.
  • the barcode may identify the nucleic acid molecule (e.g., test sequence read family) with which the test sequence read is associated.
  • a test sequence read family may be found to support an allele if the test sequence read family aligns to the allele with over a threshold percent identity.
  • the threshold percent identity may be, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 100%, and the like.
  • the threshold percent identity may be 100%, requiring a “perfect” match between a test sequence read family and the allele (e.g., a k-mer of the allele).
  • a clustering operation may be performed.
  • the alleles may be sorted by the number of supporting test sequence reads (or by the number of supporting test sequence read families) and one or more allele supersets may be constructed.
  • An allele superset may be constructed by determining a first allele associated with a highest number of supporting test sequence reads (or associated with a highest number of supporting test sequence read families). The first allele may form the basis of an allele superset. Additional alleles may be added to the allele superset if a given allele is associated with supporting test sequence reads (or supporting test sequence read families) are themselves a subset of the supporting test sequence reads (or supporting test sequence read families) of the first allele. Alleles that are not incorporated into the allele superset of the first allele may be used to construct one or more additional allele supersets in a similar fashion.
  • An allele superset may be a data structure.
  • An allele superset may be a database.
  • An allele superset may be a flat file.
  • An allele superset may comprise a representation of a Hasse diagram.
  • a Hasse diagram is a representation of the relation of elements of a partially ordered set with an implied upward orientation.
  • a point, or node may represent each element of the partially ordered set and nodes may be joined with a line segment according to the following rules: 1) if p ⁇ q in the partially ordered set, then the point corresponding to p appears lower in the drawing than the point corresponding to q; 2) the two points p and q will be joined by a line segment if p is related to q.
  • the Hasse diagram may be represented as a graph data structure, such as a directed acyclic graph (DAG) and/or the like.
  • DAG directed acyclic graph
  • a DAG comprising a line from node A to node B if node A strictly contains node B and there is no node C such that node A strictly contains node C and node C strictly contains node B.
  • an allele may be classified.
  • an allele type may be determined for a given allele. The allele may be classified based on the one or more allele supersets.
  • the first allele of the superset in the event only one allele superset is constructed the first allele of the superset may be classified as the allele present at the locus (e.g., haploid locus) of the chromosome. In an embodiment, in the event that a plurality of allele supersets are constructed, the first alleles of the two supersets having a cumulative largest number of distinct supporting test sequence reads (or a cumulative largest number of distinct supporting test sequence read families) may be classified as the alleles present at the locus (e.g., diploid locus) of the chromosome.
  • the classification of the allele(s) may be used to direct treatment of a subject. It may have been previously unknown whether the subject has a disease or it may be known that the subject has a disease.
  • the disease may be cancer.
  • the methods may comprise administering one or more therapies to the subject to treat the disease.
  • the therapies may comprise administering immunotherapy, administering chemotherapy, administering radiation therapy, or performing surgery to resect all or a portion of the tumor.
  • the methods may comprise assisting in a communication of determination of the classification of the allele(s) to a subject associated with the test sample.
  • the method 100A may be performed using known HLA allele sequences to determine the subject’s HLA genotype and/or the method 100A may be performed using known KIR allele sequences to determine the subject’s KIR genotype.
  • FIG. IB is a flow chart that schematically depicts an example technique for allele typing and/or variant calling in a cell-free nucleic acid (cfDNA) sample obtained from a test subject. Allele typing may be used to determine one or more alleles present at a locus of a chromosome. Variant calling may be used to identify the presence of a known, or unknown variant. Variant calling may be used to characterize cancer progression.
  • a method 100B, at step 102B may comprise obtaining data.
  • the data may comprise sequence data, such as allele sequence data and/or decoy sequence data.
  • step 102B may comprise obtaining (or otherwise determining, retrieving, receiving, etc.) known allele sequences.
  • known allele sequences may be downloaded from the IPD-IMGT/HLA Database and/or the IPD-KIR Database.
  • known HLA allele sequences may be downloaded from ftp.ebi.ac.uk/pub/databases/ipd/imgt/hla and known KIR allele sequences may be downloaded from ffy.ebi
  • the known allele sequences may be any type of allele sequence.
  • the known allele sequences may be known human leukocyte antigen (HLA) allele sequences.
  • the known allele sequences may be known killer cell immunoglobulin-like receptor (KIR) allele sequences.
  • step 102B may comprise obtaining (or otherwise determining, retrieving, receiving, etc.) decoy sequences.
  • the decoy sequences may be any type of decoy sequence.
  • the decoy sequences may be one or more of, “alt” sequences, unprobed known HLA allele sequences, probed known HLA allele sequences (to address small germline inaccuracies, 3 vs 4 sets of digits, combinations thereof, and the like.
  • the decoy sequences are sequences of genomic material (human, in general) similar to the sequences we want to look at (for example, the regions we want to genotype). These are not already part of the reference because they encode an alternate form of a region or gene (hence the name “alt”).
  • the problem for us is that we deploy targeted sequencing, which is a way to select only molecules from portions of genome matching some specified region (these “specified regions” are called probes, or baits, and in our case are 120 bases long): what happens is that sometimes a probe designed to capture molecules from a region of interest, instead captures molecules from one of these “alt” sequences. We can detect this because in these cases the read (or read pair) aligns better on the decoy than on the human reference.
  • the decoy sequences may comprise decoy sequences selected to identify contamination in the test sample.
  • the one or more decoy sequences may comprise one or more non-human reference sequences.
  • the one or more decoy sequences may comprise bovine reference sequences, rat reference sequences, microbial reference sequences, combinations thereof and the like. Any test sequences pairs aligning to a non- human decoy sequence may be used to support a conclusion that the test sample has been contaminated with DNA from sources other than the test subject. The idea is the same as above, only we use here as “decoy” the sequence of our suspected contaminants.
  • step 104B the data may be pre-processed.
  • step 104B may comprise constructing an allele k-mer data structure.
  • the allele k-mer data structure may be a database.
  • the allele k-mer data structure may be a flat file.
  • the allele k-mer data structure may be any form of data structure.
  • Constructing the allele k-mer data structure may comprise dividing the known allele sequences into a quantity of k-mers. For example, a quantity of k-mers having a length from about 100 nucleotides to about 200 nucleotides. In an embodiment, the quantity of k-mers may have a length of 143 nucleotides.
  • Constructing the allele k-mer data structure may comprise associating each k-mer with metadata.
  • the metadata may comprise, for example, an indication of a quantity of alleles that contain the k-mer and, for each allele that contains the k-mer, an allele identifier and a start position of the k-mer.
  • step 104B may comprise constructing a decoy data structure.
  • the decoy data structure may be a database.
  • the decoy data structure may be a flat file.
  • the decoy data structure may be any form of data structure. Structuring the algorithm, like this (ie, with a target sequence plus decoy sequence) allows us to keep some flexibility. The idea is that we can always add to the decoy any number of as-yet unknown “problematic” sequence, where in this case problematic means sequence similar to the one of our targets (in other words, sequence we could accidentally pick-up with our targeted sequencing tech dev, instead of the target region).
  • step 106B sequence processing may be performed.
  • step 106B may comprise obtaining (or otherwise determining, retrieving, receiving, etc.) sequence reads (e.g., test sequence reads) from a cell-free nucleic acid (cfDNA) sample obtained from a test subject.
  • step 106B may comprise performing an alignment between the test sequence reads and the known allele sequences.
  • step 106B may comprise performing an alignment between the test sequence reads and the k-mers in the allele k-mer data structure.
  • the sequence processing may determine an allele(s) supported by a test sequence read(s). An allele may be supported by more than one test sequence read. A test sequence read may support more than one allele.
  • a test sequence read may be found to support an allele if the test sequence read aligns to the allele (e.g., a k-mer of the allele) with over a threshold percent identity.
  • the threshold percent identity may be, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 100%, and the like.
  • the threshold percent identity may be 100%, requiring a “perfect’' match between a test sequence read and the allele (e.g., a k-mer of the allele) indicating no mismatches and no indels.
  • the threshold percent identity may be less than 100%, requiring an “imperfect” match between a test sequence read and the allele (e.g., a k-mer of the allele) indicating at least one mismatch and/or at least one indel.
  • An indication of percent identity may be determined for each alignment and stored for later processing.
  • the results of an alignment may be represented by an alignment score, described in further detail with regard to the alignment component 215.
  • the alignment score may equal the sum of the number of mismatches and the number of indels.
  • step 106B may comprise determining a number of test sequence read families that support an allele(s) (e.g., a number of nucleic acid molecules that support an allele(s)).
  • Each test sequence read may comprise a barcode.
  • the barcode may identify the nucleic acid molecule (e.g., test sequence read family) with which the test sequence read is associated.
  • a test sequence read family may be found to support an allele if the test sequence read family aligns to the allele with over a threshold percent identity.
  • the threshold percent identity may be, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 100%, and the like.
  • the threshold percent identity may be 100%, requiring a “perfect” match betw een a test sequence read family and the allele (e.g., a k-mer of the allele).
  • Step 106B may comprise performing an alignment between the test sequence reads and the decoy sequences.
  • step 106B may comprise performing an alignment between the test sequence reads and the decoy sequences in the decoy data structure.
  • the sequence processing may determine a decoy sequence(s) supported by a test sequence read(s).
  • a test sequence read may be found to support a decoy sequence if the test sequence read aligns to the decoy sequence with over a threshold percent identity.
  • the threshold percent identity may be, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 100%, and the like.
  • the threshold percent identity may be 100%, requiring a “perfect” match between a test sequence read and the decoy sequence) indicating no mismatches and no indels. An indication of percent identity may be determined for each alignment and stored for later processing.
  • one or more test sequence reads that align to one or more decoy sequences with 100% identity may be discarded and not used for further processing.
  • any test sequence reads that match to a non-human decoy sequence with 100% identity' may be used to support identification of the test sample as being contaminated. A notification associated with potential contamination may be generated and/or sent.
  • the results of an alignment may be represented by an alignment score, described in further detail with regard to the alignment component 215.
  • the alignment score may equal the sum of the number of mismatches and the number of indels.
  • a clustering operation may be performed based on alignments between the test sequence reads and the known allele sequences.
  • the known alleles may be sorted by the number of supporting test sequence reads (or by the number of supporting test sequence read families) and one or more allele supersets may be constructed.
  • An allele superset may be constructed by determining a first allele associated with a highest number of supporting test sequence reads (or associated with a highest number of supporting test sequence read families). The first allele may form the basis of an allele superset.
  • Additional alleles may be added to the allele superset if a given allele is associated with supporting test sequence reads (or supporting test sequence read families) are themselves a subset of the supporting test sequence reads (or supporting test sequence read families) of the first allele. Alleles that are not incorporated into the allele superset of the first allele may be used to construct one or more additional allele supersets in a similar fashion.
  • An allele superset may be a data structure.
  • An allele superset may be a database.
  • An allele superset may be a flat file.
  • An allele superset may comprise a representation of a Hasse diagram.
  • a Hasse diagram is a representation of the relation of elements of a partially ordered set with an implied upward orientation.
  • a point, or node may represent each element of the partially ordered set and nodes may be joined with a line segment according to the following rules: 1) if p ⁇ q in the partially ordered set, then the point corresponding to p appears lower in the drawing than the point corresponding to q; 2) the two points p and q will be joined by a line segment if p is related to q.
  • the Hasse diagram may be represented as a graph data structure, such as a directed acyclic graph (DAG) and/or the like.
  • DAG directed acyclic graph
  • a DAG comprising a line from node A to node B if node A strictly contains node B and there is no node C such that node A strictly contains node C and node C strictly contains node B.
  • an allele may be classified.
  • an allele type may be detemiined for a given allele. The allele may be classified based on the one or more allele supersets.
  • the first allele of the superset in the event only one allele superset is constructed the first allele of the superset may be classified as the allele present at the locus (e.g., haploid locus) of the chromosome. In an embodiment, in the event that a plurality of allele supersets are constructed, the first alleles of the two supersets having a cumulative largest number of distinct supporting test sequence reads (or a cumulative largest number of distinct supporting test sequence read families) may be classified as the alleles present at the locus (e.g., diploid locus) of the chromosome.
  • the classification of the allele(s) may be used to direct treatment of a subject. It may have been previously unknown whether the subject has a disease or it may be known that the subject has a disease.
  • the disease may be cancer.
  • the methods may comprise administering one or more therapies to the subject to treat the disease.
  • the therapies may comprise administering immunotherapy, administering chemotherapy, administering radiation therapy, or performing surgery to resect all or a portion of the tumor.
  • the methods may comprise assisting in a communication of determination of the classification of the allele(s) to a subject associated with the test sample.
  • the method 100B may be performed using known HL A allele sequences to determine the subject’s HL A genotype and/or the method 100B may be performed using known KIR allele sequences to determine the subject’s KIR genotype.
  • alignment scores of remaining test sequence reads that did not align with 100% identify to either a known allele sequence or a decoy sequence may be compared.
  • the remaining test sequence reads may have an alignment score associated with the known allele sequences (e.g., a germline alignment score) and an alignment score associated with the decoy sequences (e.g., a decoy alignment score).
  • a test sequence read pair associated with a germline alignment score that is less than a decoy alignment score may be discarded.
  • a test sequence read pair associated with a germline alignment score that is greater than a decoy alignment score may be sent for variant calling at step HOB. If a test sequence read pair aligns to two or more known allele sequences (e.g., aligns to two or more entries in a fasta file), the first entry in the fasta file with the highest alignment score is selected as the alignment.
  • the test sequence read pairs associated with a germline alignment score that is greater than a decoy alignment score may be analyzed to determine and/or identify the test sequence read pairs as a variant.
  • Variant calling is the process of identifying true differences between sequence reads of test samples and a reference sequence. Variant calling may be perfonned as further described with regard to the variant caller component 219 below.
  • the test sequence read pairs may be identified as a somatic variant.
  • the test sequence read pairs may be identified as a variant that is a candidate variant associated with a somatic event.
  • candidate variants may be identified in the test sequence read pairs.
  • the candidate variants may be identified by comparing the test sequence read pairs to a reference sequence of a target region of a reference genome (e.g., human reference genome hg!9). Edges of the test sequence read pairs may be aligned to the reference sequence and the genomic positions of mismatched edges and mismatched nucleotide bases adjacent to the edges recorded as the locations of candidate variants. In some embodiments, the genomic positions of mismatched nucleotide bases to the left and right edges are recorded as the locations of called variants. Additionally, candidate variants may be identified based on the sequencing depth of a target region. In particular, more confidence may be obtained in identifying variants in target regions that have greater sequencing depth, for example, because a greater number of sequence reads help to resolve (e.g., using redundancies) mismatches or other base pair variations between sequences.
  • a reference genome e.g., human reference genome hg!9
  • the reference sequence used for variant calling may comprise one or more reference sequences.
  • the one or more reference sequences may be selected to identify contamination in the test sample.
  • the one or more reference sequences may comprise one or more non-human reference sequences.
  • the one or more reference sequences may comprise a bovine reference sequences, rat reference sequences, microbial reference sequences, combinations thereof, and the like. Any test sequences pairs identified as a non-human variant may be used to support a conclusion that the test sample has been contaminated with DNA from sources other than the test subject.
  • Clinical applications of HLA typing and/or variant calling include vaccine trials, disease associations, adverse drug reactions, platelet transfusion, and transplantation of organs and stem cells.
  • the methods include determining a subject’s HL A type as described herein, and determining that the subject is predisposed to developing rheumatoid arthritis (RA) if it has been determined that one or more of HLA-DRB 1*04:01, *04:04, and *04:08; HLA-DRBl*04:05; HLA-DRB1 *01:01 and *01:02; HLA-DRB1 *14:02; HLA- DRBl*10:01; and/or HLA-DRB 1*01:01, *04:01, *04:04, and *04:05 , is present.
  • RA rheumatoid arthritis
  • the methods include determining a subject’s HLA type as described herein, and determining that the subject is predisposed to developing multiple sclerosis (MS) if it has been detennined that the HLA-DRB1* 15:01, HLA-DQBl*06:02, HLA-DRB1 *01 :08, HLA-DRB1 *03:01, and/or HL A-DRB1* 13:03 alleles are present.
  • MS multiple sclerosis
  • the methods include determining a subject’s HLA type as described herein, and determining that the subject is predisposed to developing systemic lupus erythematosus (SLE) if it has been determined that the HLA-DRB 1, HLA-DR2 (DRBl*15:01), HLA-DR3 (DRBl*03:01), HLA-DRB1 *08:01, and/or HLA-DQA1 *01 :02 alleles are present.
  • SLE systemic lupus erythematosus
  • the methosd include determining a subject’s HLA type as described herein, and determining that the subject is predisposed to developing Type 1 diabetes mellitus (T1D) if it has been determined that HLA-DQBl*03:02 and/or DQBl*02:01 are present.
  • T1D Type 1 diabetes mellitus
  • the methods include determining a subject’s HLA type as described herein, and determining that the subject is predisposed to developing Sjogren’s syndrome (SS) if it has been determined that DQA1*O5:O1, DQBl*02:01, and DRBl*03:01 alleles are present.
  • SS Sjogren’s syndrome
  • the methods include determining a subject’s HLA type as described herein, and determining that the subject is predisposed to developing celiac disease (CD) if it has been determined that HLA-DQ2 (encoded by HLA-DQAl*05:01- DQBl*02:01) and/or HLA-DQ8 (encoded by DQAl*03:01-DQBl*03:02) are present.
  • CD celiac disease
  • the methods include determining a woman’s KIR type as described herein, and determining that the woman is predisposed to developing preeclampsia if it has been determined that a KIR2DL1 allele is present.
  • both HLA and KIR genotypes may be detennined for the same subject.
  • the methods may include determining that the subject is predisposed to clearing an HCV infection, slow progression of HIV infection to AIDS, or Crohn’s disease when certain KIR alleles in combination with certain HLA alleles have been found in the individual.
  • the methods include determining a subject’s KIR type as described herein, and determining that the subject is likely to clear an HCV infection if it has been determined that a KIR2DL3 allele is present.
  • the methods further comprise determining the a subject’s HLA-C genotype and determining that the a subject is likely to clear an HCV infection if it has been determined that a combination of KIR2DL3 allele and HLA-C 1 allele are present.
  • the methods include determining a subject’s KIR type as described herein, and determining that the subject is less likely to progress from HIV infection to AIDS if it has been determined that a KIR3DS1 allele is present. In some embodiments, the methods further comprise determining the subject’s HLA genotype and determining that the subject is less likely to progress from HIV infection to AIDS if it has been determined that a combination of KIR3DS1 allele and HLA-Bw4 allele are present. [0145] In yet other embodiments, the methods include determining a subject’s KIR type as described herein, and determining that the subject is predisposed to developing an autoimmune disease if it has been determined that a KIR2DS1 allele is present.
  • the methods include determining a subject’s KIR type as described herein, and determining that the subject is predisposed to developing Crohn’s disease if it has been determined that KIR2DL2/KIR2DL3 heterozygosity is present. In some embodiments, the methods further comprise determining the subject’s HLA genotype and determining that the subject is predisposed to developing Crohn’s disease if it has been determined that a combination of KIR2DL2/KIR2DL3 heterozy gosity and HLA-C2 allele are present.
  • the methods include determining a subject’s KIR type as described herein, and determining whether the subject is a suitable candidate for a donor in an unrelated hematopoietic cell transplantation if it has been determined that a group B KIR haplotype is present.
  • FIG. 2 illustrates an example of a system 200 for determining an allele type and/or a variant of a test subject 211, according to an embodiment of the present disclosure.
  • the system 200 may process one or more samples 201 from the subject 211 to generate sequence reads.
  • the system 200 may include a laboratory system 202, a computer system 210, and/or other components. It should be noted that the laboratory system 202 and the computer system 210 may be remote from one another, and connected to one another through a computer network (not illustrated).
  • the laboratory system 202 may include a sample collection and preparation pipeline 203, a sequencing pipeline 205, a sequence read datastore 209, and/or other components.
  • the sequencing pipeline 205 may include one or more sequencing devices 207 (illustrated in FIG. 2 as sequencing devices 207a... n).
  • the sample collection and preparation pipeline 203 may include obtaining cfDNA reference samples 201 from one or more reference subjects and a cfDNA test sample 211 from a test subject.
  • a polynucleotide can comprise any type of nucleic acid, such as DNA and/or RNA.
  • a polynucleotide is DNA, it can be genomic DNA, complementary DNA (cDNA), or any other deoxyribonucleic acid.
  • a polynucleotide can also be a cell-free nucleic acid such as cell-free DNA (cfDNA).
  • the polynucleotide can be circulating cfDNA. Circulating cfDNA may comprise DNA shed from bodily cells via apoptosis or necrosis. cfDNA shed via apoptosis or necrosis may originate from normal (e.g., healthy) bodily cells. a. Samples
  • a sample can be any biological sample isolated from a subject Samples can include body tissues, whole blood, platelets, serum, plasma, stool, red blood cells, white blood cells or leucocytes, endothelial cells, tissue biopsies (e.g., biopsies from known or suspected solid tumors), cerebrospinal fluid, synovial fluid, lymphatic fluid, ascites fluid, interstitial or extracellular fluid (e.g., fluid from intercellular spaces), gingival fluid, crevicular fluid, bone marrow, pleural effusions, cerebrospinal fluid, saliva, mucous, sputum, semen, sweat, urine.
  • tissue biopsies e.g., biopsies from known or suspected solid tumors
  • cerebrospinal fluid e.g., biopsies from known or suspected solid tumors
  • synovial fluid e.g., synovial fluid
  • lymphatic fluid e.g., ascites fluid
  • interstitial or extracellular fluid e.
  • Samples are preferably body fluids, particularly blood and fractions thereof, and urine.
  • the nucleic acids can include DNA and RNA and can be in double and single-stranded forms.
  • a sample can be in the form originally isolated from a subject or can have been subjected to further processing to remove or add components, such as cells, enrich for one component relative to another, or convert one form of nucleic acid to another, such as RNA to DNA or single-stranded nucleic acids to double-stranded.
  • a body fluid sample for analysis is plasma or serum containing cell-free nucleic acids, e.g., cell-free DNA (cfDNA)
  • the sample volume of body fluid taken from a subject depends on the desired read depth for sequenced regions.
  • Exemplary volumes are about 0.4-40 ml, about 5-20 ml, about 10-20 ml.
  • the volume can be about 0.5 ml, about 1 ml, about 5 ml, about 10 ml, about 20 ml, about 30 ml, about 40 ml, or more milliliters.
  • a volume of sampled plasma is typically between about 5 ml to about 20 ml.
  • the sample can comprise various amounts of nucleic acid. Typically, the amount of nucleic acid in a given sample is equated with multiple genome equivalents. For example, a sample of about 30 ng DNA can contain about 10,000 (104) haploid human genome equivalents and, in the case of cfDNA, about 200 billion (2x1011) individual polynucleotide molecules. Similarly, a sample of about 100 ng of DNA can contain about 30,000 haploid human genome equivalents and, in the case of cfDNA, about 600 billion individual molecules.
  • a sample comprises nucleic acids from different sources, e.g., from cells and from cell-free sources (e.g., blood samples, etc.).
  • a sample comprises nucleic acids from a region of genomic DNA comprising alleles that allows for allele typing, such as HLA alleles or KIR alleles.
  • Exemplary amounts of cell-free nucleic acids in a sample before amplification typically range from about 1 femtogram (fg) to about 1 microgram (pg), e.g., about 1 picogram (pg) to about 200 nanogram (ng), about 1 ng to about 100 ng, about 10 ng to about 1000 ng.
  • a sample includes up to about 600 ng, up to about 500 ng, up to about 400 ng, up to about 300 ng, up to about 200 ng, up to about 100 ng, up to about 50 ng, or up to about 20 ng of cell-free nucleic acid molecules.
  • the amount is at least about 1 fg, at least about 10 fg, at least about 100 fg, at least about 1 pg, at least about 10 pg, at least about 100 pg, at least about 1 ng, at least about 10 ng, at least about 100 ng, at least about 150 ng, or at least about 200 ng of cell-free nucleic acid molecules.
  • the amount is up to about 1 fg, about 10 fg, about 100 fg, about 1 pg, about 10 pg, about 100 pg, about 1 ng, about 10 ng, about 100 ng, about 150 ng, or about 200 ng of cell-free nucleic acid molecules.
  • methods include obtaining between about 1 fg to about 200 ng cell-free nucleic acid molecules from samples.
  • Cell-free nucleic acids typically have a size distribution of between about 100 nucleotides in length and about 500 nucleotides in length, with molecules of about 110 nucleotides in length to about 230 nucleotides in length representing about 90% of molecules in the sample, with a mode of about 168 nucleotides length and a second minor peak in a range between about 240 to about 440 nucleotides in length.
  • cell- free nucleic acids are from about 160 to about 180 nucleotides in length, or from about 320 to about 360 nucleotides in length, or from about 440 to about 480 nucleotides in length.
  • cell-free nucleic acids are isolated from bodily fluids through a partitioning step in which cell-free nucleic acids, as found in solution, are separated from intact cells and other non-soluble components of the bodily fluid.
  • partitioning includes techniques such as centrifugation or filtration.
  • cells in bodily fluids are lysed, and cell-free and cellular nucleic acids processed together.
  • cell-free nucleic acids are precipitated with, for example, an alcohol.
  • additional clean up steps are used, such as silica-based columns to remove contaminants or salts.
  • Non-specific bulk carrier nucleic acids are optionally added throughout the reaction to optimize certain aspects of the exemplary procedure, such as yield.
  • samples typically include various forms of nucleic acids including double-stranded DNA, single-stranded DNA and/or single-stranded RNA.
  • single stranded DNA and/or single stranded RNA are converted to double stranded forms so that they are included in subsequent processing and analysis steps. Additional details regarding cfDNA partitioning and related analysis of epigenetic modifications that are optionally adapted for use in performing the methods disclosed herein are described in, for example, WO 2018/119452, filed December 22, 2017, which is incorporated by reference. b. Nucleic Acid Tags
  • tags providing molecular identifiers or barcodes are incorporated into or otherwise joined to adapters by chemical synthesis, ligation, or overlap extension PCR, among other methods.
  • the assignment of unique or non-unique identifiers, or molecular barcodes in reactions follows methods and utilizes systems described in, for example, US patent applications 20010053 19, 20030152490, 20110160078, and U.S. Pat. Nos. 6,582,908, 7,537,898, and 9,598,731, which are each incorporated by reference.
  • Tags are linked (e.g., ligated) to sample nucleic acids randomly or non-randomly.
  • tags are introduced at an expected ratio of identifiers (e.g., a combination of unique and/or non-unique barcodes) to microwells.
  • the identifiers may be loaded so that more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 50,000, 100,000, 500,000, 1,000,000, 10,000,000, 50,000,000 or 1,000,000,000 identifiers are loaded per genome sample.
  • the identifiers are loaded so that less than about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 50,000, 100,000, 500,000, 1,000,000, 10,000,000, 50,000,000 or 1,000,000,000 identifiers are loaded per genome sample.
  • the average number of identifiers loaded per sample genome is less than, or greater than, about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 50,000, 100,000, 500,000, 1,000,000, 10,000,000, 50,000,000 or 1,000,000,000 identifiers per genome sample.
  • the identifiers are generally unique or nonunique.
  • One exemplary format uses from about 2 to about 1,000,000 different tags, or from about 5 to about 150 different tags, or from about 20 to about 50 different tags, ligated to both ends of a target nucleic acid molecule. For 20-50 x 20-50 tags, a total of 400-2500 tags are created. Such numbers of tags are typically sufficient for different molecules having the same start and stop points to have a high probability (e.g., at least 94%, 99.5%, 99.99%, 99.999%) of receiving different combinations of tags.
  • identifiers are predetermined, random, or semi-random sequence oligonucleotides.
  • a plurality of barcodes may be used such that barcodes are not necessarily unique to one another in the plurality.
  • barcodes are generally attached (e.g., by ligation or PCR amplification) to individual molecules such that the combination of the barcode and the sequence it may be attached to creates a unique sequence that may be individually tracked.
  • detection of non-uniquely tagged barcodes in combination with sequence data of beginning (start) and end (stop) portions of sequence reads typically allows for the assignment of a unique identity to a particular molecule.
  • the length, or number of base pairs, of an individual sequence read are also optionally used to assign a unique identity to a given molecule.
  • fragments from a single strand of nucleic acid having been assigned a unique identity may thereby permit subsequent identification of fragments from the parent strand, and/or a complementary' strand.
  • the nucleic acid molecules may be tagged with sample indexes and/or molecular barcodes (referred to generally as “tags”).
  • Tags may be incorporated into or otherwise joined to adapters by chemical synthesis, ligation (e.g., blunt-end ligation or sticky-end ligation), or overlap extension polymerase chain reaction (PCR), among other methods.
  • ligation e.g., blunt-end ligation or sticky-end ligation
  • PCR overlap extension polymerase chain reaction
  • Such adapters may be ultimately joined to the target nucleic acid molecule.
  • one or more rounds of amplification cycles are generally applied to introduce sample indexes to a nucleic acid molecule using conventional nucleic acid amplification methods.
  • the amplifications may be conducted in one or more reaction mixtures (e.g., a plurality of microwells in an array).
  • Molecular barcodes and/or sample indexes may be introduced simultaneously, or in any sequential order.
  • molecular barcodes and/or sample indexes are introduced prior to and/or after sequence capturing steps are performed.
  • only the molecular barcodes are introduced prior to probe capturing and the sample indexes are introduced after sequence capturing steps are performed.
  • both the molecular barcodes and the sample indexes are introduced prior to performing probe-based capturing steps.
  • the sample indexes are introduced after sequence capturing steps are performed.
  • molecular barcodes are incorporated to the nucleic acid molecules (e.g. cfDNA molecules) in a sample through adapters via ligation (e.g., blunt-end ligation or sticky-end ligation).
  • sample indexes are incorporated to the nucleic acid molecules (e g. cfDNA molecules) in a sample through overlap extension polymerase chain reaction (PCR).
  • sequence capturing protocols involve introducing a singlestranded nucleic acid molecule complementary to a targeted nucleic acid sequence, e.g., a coding sequence of a genomic region and mutation of such region is associated with a cancer type.
  • the tags may be located at one end or at both ends of the sample nucleic acid molecule.
  • tags are predetermined or random or semi-random sequence oligonucleotides.
  • the tags may be less than about 500, 200, 100, 50, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 nucleotides in length.
  • the tags may be linked to sample nucleic acids randomly or non-randomly.
  • each sample is uniquely tagged with a sample index or a combination of sample indexes.
  • each nucleic acid molecule of a sample or sub-sample is uniquely tagged with a molecular barcode or a combination of molecular barcodes.
  • a plurality of molecular barcodes may be used such that molecular barcodes are not necessarily unique to one another in the plurality (e.g., non-unique molecular barcodes).
  • molecular barcodes are generally attached (e.g., by ligation) to individual molecules such that the combination of the molecular barcode and the sequence it may be attached to create a unique sequence that may be individually tracked.
  • techniques for discriminating true genomic alterations from technical errors may be used as described in Lee, et a/., “Accurate Detection of Rare Mutant Alleles by Target Base-Specific Cleavage with the CRISPR/Cas9 System,” ACS Synth. Biol. 2021, 10, 6, 1451-1464, May 19, 2021, incorporated herein by reference in its entirety.
  • Detection of non-unique molecular barcodes in combination with endogenous sequence information typically allows for the assignment of a unique identity to a particular molecule.
  • endogenous sequence information e.g., the beginning (start) and/or end (stop) genomic location/position corresponding to the sequence of the original nucleic acid molecule in the sample, start and stop genomic positions corresponding to the sequence of the original nucleic acid molecule in the sample, the beginning (start) and/or end (stop) genomic location/position of the sequence read that is mapped to the reference sequence, start and stop genomic positions of the sequence read that is mapped to the reference sequence, sub-sequences of sequence reads at one or both ends, length of sequence reads, and/or length of the original nucleic acid molecule in the sample) typically allows for the assignment of a unique identity to a particular molecule.
  • beginning region comprises the first 1, first 2, the first 5, the first 10, the first 15, the first 20, the first 25, the first 30 or at least the first 30 base positions at the 5' end of the sequencing read that align to the reference sequence.
  • end region comprises the last 1, last 2, the last 5, the last 10, the last 15, the last 20, the last 25, the last 30 or at least the last 30 base positions at the 3' end of the sequencing read that align to the reference sequence.
  • the length, or number of base pairs, of an individual sequence read are also optionally used to assign a unique identity to a given molecule. As described herein, fragments from a single strand of nucleic acid having been assigned a unique identity, may thereby permit subsequent identification of fragments from the parent strand, and/or a complementary strand.
  • the number of different tags used to uniquely identify a number of molecules, z, in a class can be between any of 2*z, 3*z, 4*z, 5*z, 6*z, 7*z, 8*z, 9*z, 10*z, 11 *z, 12*z, 13*z, 14*z, 15*z, 16*z, 17*z, 18*z, 19*z, 20*z or 100*z (e.g., lower limit) and any of 100,000*z, 10,000*z, 1000*z or 100*z (e.g., upper limit).
  • molecular barcodes are introduced at an expected ratio of a set of identifiers (e.g., a combination of unique or non-unique molecular barcodes) to molecules in a sample.
  • a set of identifiers e.g., a combination of unique or non-unique molecular barcodes
  • One example format uses from about 2 to about 1 ,000,000 different molecular barcode sequences, or from about 5 to about 150 different molecular barcode sequences, or from about 20 to about 50 different molecular barcode sequences, ligated to both ends of a target molecule. Alternatively, from about 25 to about 1,000,000 different molecular barcode sequences may be used.
  • 20-50 x 20-50 molecular barcode sequences i.e., one of the 20-50 different molecular barcode sequences can be attached to each end of the target molecule
  • Such numbers of identifiers are typically sufficient for different molecules having the same start and stop points to have a high probability (e.g., at least 94%, 99.5%, 99.99%, or 99.999%) of receiving different combinations of identifiers.
  • about 80%, about 90%, about 95%, or about 99% of molecules have the same combinations of molecular barcodes.
  • Sample nucleic acids flanked by adapters are typically amplified by PCR and other amplification methods using nucleic acid primers binding to primer binding sites in adapters flanking a DNA molecule to be amplified as part of the sample collection and preparation pipeline 203.
  • amplification methods involve cycles of extension, denaturation and annealing resulting from thermocycling, or can be isothermal as, for example, in transcription mediated amplification.
  • Other exemplary amplification methods that are optionally utilized, include the ligase chain reaction, strand displacement amplification, nucleic acid sequence-based amplification, and self-sustained sequence-based replication, among other approaches.
  • One or more rounds of amplification cycles are generally applied to introduce molecular tags and/or sample indexes/tags to a nucleic acid molecule using conventional nucleic acid amplification methods.
  • the amplifications are typically conducted in one or more reaction mixtures.
  • Molecular tags and sample indexes/tags are optionally introduced simultaneously, or in any sequential order.
  • molecular tags and sample indexes/tags are introduced prior to and/or after sequence capturing steps are performed.
  • only the molecular tags are introduced prior to probe capturing and the sample indexes/tags are introduced after sequence capturing steps are performed.
  • both the molecular tags and the sample indexes/tags are introduced prior to performing probe-based capturing steps.
  • the sample indexes/tags are introduced after sequence capturing steps are performed.
  • sequence capturing protocols involve introducing a single-stranded nucleic acid molecule complementary to a targeted nucleic acid sequence, e.g., a coding sequence of a genomic region and mutation of such region associated with a cancer type.
  • the amplification reactions generate a plurality of non-uniquely or uniquely tagged nucleic acid amplicons with molecular tags and sample indexes/tags at size ranging from about 200 nucleotides (nt) to about 700 nt, from 250 nt to about 350 nt, or from about 320 nt to about 550 nt.
  • the amplicons have a size of about 300 nt. In some embodiments, the amplicons have a size of about 500 nt. [0167] In some aspects, amplification can occur pre and/or post enrichment. d. Nucleic Acid Enrichment
  • sequences are enriched prior to sequencing the nucleic acids as part of the sample collection and preparation pipeline 203. Enrichment is optionally performed for specific target regions or nonspecifically (“target sequences”).
  • targeted regions of interest may be enriched with nucleic acid capture probes (“baits”) selected for one or more bait set panels using a differential tiling and capture scheme.
  • targeted regions of interest may be enriched using CRISPR mediated enrichment.
  • a differential tiling and capture scheme generally uses bait sets of different relative concentrations to differentially tile (e.g., at different “resolutions”) across genomic sections associated with the baits, subject to a set of constraints (e.g., sequencer constraints such as sequencing load, utility of each bait, etc.), and capture the targeted nucleic acids at a desired level for downstream sequencing.
  • These targeted genomic sections of interest optionally include natural or synthetic nucleotide sequences of the nucleic acid construct.
  • biotin-labeled beads with probes to one or more sections of interest can be used to capture target sequences, and optionally followed by amplification of those sections, to enrich for the regions of interest.
  • Sequence capture typically involves the use of oligonucleotide probes that hybridize to the target nucleic acid sequence.
  • a probe set strategy involves tiling the probes across a section of interest.
  • Such probes can be, for example, from about 60 to about 120 nucleotides in length.
  • the set can have a depth of about 2x, 3x, 4x, 5x, 6x, 8x, 9x, lOx, 15x, 20x, 50x or more.
  • the effectiveness of sequence capture generally depends, in part, on the length of the sequence in the target molecule that is complementary (or nearly complementary) to the sequence of the probe.
  • a probe can be designed to be specific to the alleles of interest. Thus, different alleles from the same gene have an equal chance to be captured.
  • amplification (as described above) can be performed. e. Nucleic Acid Sequencing
  • the cfDNA may be sequenced via the sequencing pipeline 205 including one or more sequencing devices 207.
  • Sample nucleic acids, optionally flanked by adapters, with or without prior amplification are generally subject to sequencing.
  • Sequencing methods or commercially available formats include, for example, Sanger sequencing, high-throughput sequencing, bisulfite sequencing, pyrosequencing, sequencing-by-synthesis, single-molecule sequencing, nanopore-based sequencing, semiconductor sequencing, sequencing-by-ligation, sequencing- by -hybridization, RNA-Seq (Illumina), Digital Gene Expression (Helicos), next generation sequencing (NGS), Single Molecule Sequencing by Synthesis (SMSS) (Helicos), massively- parallel sequencing, Clonal Single Molecule Array (Solexa), shotgun sequencing, Ion Torrent, Oxford Nanopore, Roche Genia, Maxim-Gilbert sequencing, primer walking, sequencing using PacBio, SOLiD, Ion Torrent, or nanopore platforms. Sequencing reactions can be performed in a variety of sample processing units, which may include multiple lanes, multiple channels, multiple wells, or other means of processing multiple sample sets substantially simultaneously. Sample processing units can also include multiple sample chambers to enable the processing of multiple
  • the sequencing reactions can be performed on one more nucleic acid fragment ty pes or sections known to contain alleles of interest.
  • the sequencing reactions can also be performed on any nucleic acid fragment present in the sample.
  • the sequence reactions may provide for sequence coverage of the genome of at least about 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 99.9% or 100% of the genome. In other cases, sequence coverage of the genome may be less than about 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 99.9% or 100% of the genome.
  • Simultaneous sequencing reactions may be performed using multiplex sequencing techniques.
  • cell-free polynucleotides are sequenced with at least about 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000, or 100,000 sequencing reactions.
  • cell-free polynucleotides are sequenced with less than about 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000, or 100,000 sequencing reactions. Sequencing reactions are typically performed sequentially or simultaneously. Subsequent data analysis is generally performed on all or part of the sequencing reactions.
  • data analysis is performed on at least about 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000, or 100,000 sequencing reactions. In other embodiments, data analysis may be performed on less than about 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000, or 100,000 sequencing reactions.
  • An exemplary read depth is from about 1000 to about 50000 reads per locus (base position).
  • a nucleic acid population is prepared for sequencing by enzymatically forming blunt-ends on double-stranded nucleic acids with single-stranded overhangs at one or both ends.
  • the population is ty pically treated with an enzyme having a 5 ’-3’ DNA polymerase activity and a 3’ -5’ exonuclease activity in the presence of the nucleotides (e.g., A, C, G and T or U).
  • the nucleotides e.g., A, C, G and T or U.
  • Exemplary enzymes or catalytic fragments thereof that are optionally used include KI enow large fragment and T4 polymerase.
  • the enzyme typically extends the recessed 3’ end on the opposing strand until it is flush with the 5’ end to produce a blunt end.
  • the enzyme generally digests from the 3’ end up to and sometimes beyond the 5’ end of the opposing strand. If this digestion proceeds beyond the 5 ’ end of the opposing strand, the gap can be filled in by an enzyme having the same polymerase activity that is used for 5’ overhangs.
  • the formation of blunt-ends on double-stranded nucleic acids facilitates, for example, the attachment of adapters and subsequent amplification.
  • nucleic acid populations are subject to additional processing, such as the conversion of single-stranded nucleic acids to double-stranded and/or conversion of RNA to DNA. These forms of nucleic acid are also optionally linked to adapters and amplified.
  • nucleic acids subject to the process of forming blunt-ends described above, and optionally other nucleic acids in a sample can be sequenced to produce sequenced nucleic acids.
  • a sequenced nucleic acid can refer either to the sequence of a nucleic acid (i.e., sequence information) or a nucleic acid whose sequence has been determined. Sequencing can be performed so as to provide sequence data of individual nucleic acid molecules in a sample either directly or indirectly from a consensus sequence of amplification products of an individual nucleic acid molecule in the sample.
  • double-stranded nucleic acids with single-stranded overhangs in a sample after blunt-end formation are linked at both ends to adapters including barcodes, and the sequencing determines nucleic acid sequences as well as in-line barcodes introduced by the adapters.
  • the blunt-end DNA molecules are optionally ligated to a blunt end of an at least partially double-stranded adapter (e.g., a Y shaped or bell-shaped adapter).
  • blunt ends of sample nucleic acids and adapters can be tailed with complementary' nucleotides to facilitate ligation (e.g., sticky end ligation).
  • the nucleic acid sample is typically contacted with a sufficient number of adapters such that there is a low probability (e g., ⁇ 1 or 0. 1 %) that any two copies of the same nucleic acid receive the same combination of adapter barcodes from the adapters linked at both ends.
  • a sufficient number of adapters such that there is a low probability (e g., ⁇ 1 or 0. 1 %) that any two copies of the same nucleic acid receive the same combination of adapter barcodes from the adapters linked at both ends.
  • the use of adapters in this manner permits identification of families of nucleic acid sequences with the same start and stop points on a reference nucleic acid and linked to the same combination of barcodes. Such a family represents sequences of amplification products of a nucleic acid in the sample before amplification.
  • sequences of family members can be compiled to derive consensus nucleotide(s) or a complete consensus sequence for a nucleic acid molecule in the original sample, as modified by blunt end formation and adapter attachment.
  • the nucleotide occupying a specified position of a nucleic acid in the sample is determined to be the consensus of nucleotides occupying that corresponding position in family member sequences.
  • Families can include sequences of one or both strands of a double-stranded nucleic acid.
  • members of a family include sequences of both strands from a double-stranded nucleic acid, sequences of one strand are converted to their complement for purposes of compiling all sequences to derive consensus nucleotide(s) or sequences.
  • Some families include only a single member sequence. In this case, this sequence can be taken as the sequence of a nucleic acid in the sample before amplification. Alternatively, families with only a single member sequence can be eliminated from subsequent analysis.
  • nucleic acid sequencing includes the formats and applications described herein. Additional details regarding nucleic acid sequencing, including the formats and applications described herein are also provided in, for example, Levy et al., Annual Review of Genomics and Human Genetics, 17: 95-115 (2016), Liu et al., J. of Biomedicine and Biotechnology, Volume 2012, Article ID 251364:1-11 (2012), Voelkerding et al., Clinical Chem, 55: 641-658 (2009), MacLean et al., Nature Rev. Microbiol., 7: 287-296 (2009), Astier et al., J Am Chem Soc., 128(5):1705-10 (2006), U.S. Pat. No. 6,210,891, U.S. Pat. No. 6,258,568, U.S. Pat.
  • primers used for sequencing can be specific to adaptors added to the ends of the DNA fragments or can be specific to the target region of interest. For example, if the target region of interest is an HLA region then primers to specific HLA genes can be used for sequencing. In some embodiments, the primers can be designed to cover an HLA region comprising multiple HLA alleles. In some embodiments, the primers can be designed to cover an HLA region comprising a single HLA allele.
  • the sections of DNA sequenced may comprise a panel of genes or genomic sections that comprise known genomic regions. Selection of a limited section for sequencing (e.g., a limited panel) can reduce the total sequencing needed (e.g., a total amount of nucleotides sequenced).
  • a sequencing panel can target a plurality of different genes or regions, for example, HLA alleles or KIR alleles.
  • DNA may be sequenced by whole genome sequencing (WGS) or other unbiased sequencing method without the use of a sequencing panel. Examples of suitable panel and targets for use in panels can be those comprising allele typing regions such as HLA allele regions and/or KIR allele regions.
  • a panel that targets a plurality of different genes or genomic regions is selected such that a subject can be HLA typed or KIR typed using the panel.
  • the panel may be selected to limit a region for sequencing to a fixed number of base pairs.
  • the panel may be selected to sequence a desired amount of DNA.
  • the panel may be further selected to achieve a desired sequence read depth.
  • the panel may be selected to achieve a desired sequence read depth or sequence read coverage for an amount of sequenced base pairs.
  • the panel may be selected to achieve a theoretical sensitivity, a theoretical specificity, and/or a theoretical accuracy for allele typing in a sample.
  • Genes included in this panel may comprise one or more of the HL A or KIR genes, such as, but not limited to, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-G, HLA-H, HLA-J, HLA-K, HLA-L, HLA-N, HLA-P, HLA-S, HLA-T, HLA-U, HLA-V, HLA-W, HLA-DRA, HLA-DRB1, HLA-DRB2, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DRB6, HLA-DRB7, HLA-DRB8, HLA-DRB9, HLA-DQA1, HLA-DQB1, HLA-DQA2, HLA- DQB2, HLA-DQB3, HLA-DOA, HLA-DOB, HLA-DMA, HLA-DMB, HLA-DPA1, HLA- DPB1, HLA-DPA2,
  • Probes for detecting the panel of regions can include those for detecting genomic regions of interest (allele typing regions) as well as nucleosome-aware probes (e.g., KRAS codons 12 and 13) and may be designed to optimize capture based on analysis of cfDNA coverage and fragment size variation impacted by nucleosome binding patterns and GC sequence composition.
  • genomic locations of interest may be chromosomal positions 6p21 or 19ql 3.
  • genomic locations used in the methods of the present disclosure comprise at least a portion of at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, or at least 50, of the genes of Table 1.
  • the one or more regions in the panel comprise one or more loci from one or a plurality of genes for allele typing.
  • a genomic location may be selected for inclusion in a sequencing panel based on the presence of genes of interest for allele typing, such as HLA typing and KIR typing.
  • Genes included in the panel for sequencing can include the fully transcribed region, the promoter region, enhancer regions, regulatory elements, and/or downstream sequence. In some embodiments, only exons may be included in the panel.
  • the panel can comprise all exons of a selected gene, or only one or more of the exons of a selected gene.
  • the panel may comprise of exons from each of a plurality of different genes.
  • the panel may comprise at least one exon from each of the plurality of different genes.
  • At least one full exon from each different gene in a panel of genes may be sequenced.
  • all of the exons of a gene may be sequenced.
  • the sequenced panel may comprise all or some exons from a plurality of genes.
  • the panel may comprise exons from 2 to 100 different genes, from 2 to 70 genes, from 2 to 50 genes, from 2 to 30 genes, from 2 to 15 genes, or from 2 to 10 genes.
  • a selected panel may comprise a varying number of exons.
  • a selected panel may comprise all of the exons of a gene.
  • the panel may comprise from 2 to 3000 exons.
  • the panel may comprise from 2 to 1000 exons.
  • the panel may comprise from 2 to 500 exons.
  • the panel may comprise from 2 to 100 exons.
  • the panel may comprise from 2 to 50 exons.
  • the panel may comprise no more than 300 exons.
  • the panel may comprise no more than 200 exons.
  • the panel may comprise no more than 100 exons.
  • the panel may comprise no more than 50 exons.
  • the panel may comprise no more than 40 exons.
  • the panel may comprise no more than 30 exons.
  • the panel may comprise no more than 25 exons.
  • the panel may comprise no more than 20 exons.
  • the panel may comprise no more than 15 exons.
  • the panel may comprise no more than 10 exons.
  • the panel may comprise no more than 9 exons.
  • the panel may comprise no more than 8 exons.
  • the panel may comprise no more than 7 exons.
  • the panel may comprise one or more exons from a plurality of different genes.
  • the panel may comprise one or more exons from each of a proportion of the plurality of different genes.
  • the panel may comprise at least two exons from each of at least 25%, 50%, 75% or 90% of the different genes.
  • the panel may comprise at least three exons from each of at least 25%, 50%, 75% or 90% of the different genes.
  • the panel may comprise at least four exons from each of at least 25%, 50%, 75% or 90% of the different genes.
  • the sizes of the sequencing panel may vary.
  • a sequencing panel may be made larger or smaller (in terms of nucleotide size) depending on several factors including, for example, the total amount of nucleotides sequenced or a number of unique molecules sequenced for a particular region in the panel.
  • the sequencing panel can be sized 5 kb to 50 kb.
  • the sequencing panel can be 10 kb to 30 kb in size.
  • the sequencing panel can be 12 kb to 20 kb in size.
  • the sequencing panel can be 12 kb to 60 kb in size.
  • the sequencing panel can be 50kb to 10Mb in size.
  • the sequencing panel can be 500kb to 5Mb in size.
  • the sequencing panel can be at least lOkb, 12 kb, 15 kb, 20 kb, 25 kb, 30 kb, 35 kb, 40 kb, 45 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90 kb, 100 kb, 110 kb, 120 kb, 130 kb, 140 kb, 150 kb, 200 kb, 250 kb, 300 kb, 350 kb, 400 kb, 450 kb, or 500 kb in size.
  • the sequencing panel may be less than 100 kb, 90 kb, 80 kb, 70 kb, 60 kb, or 50 kb in size.
  • the sequencing panel can be at least 1 Mb, 2 Mb,
  • the panel selected for sequencing can comprise at least 1, 5, 10, 15, 20, 25, 30, 40, 50, 60, 80, or 100 genomic locations (e.g., that each include genomic regions of interest).
  • the genomic locations in the panel are selected that the size of the locations are relatively small.
  • the regions in the panel have a size of about 10 kb or less, about 8 kb or less, about 6 kb or less, about 5 kb or less, about 4 kb or less, about 3 kb or less, about 2.5 kb or less, about 2 kb or less, about 1.5 kb or less, or about 1 kb or less or less.
  • the genomic locations in the panel have a size from about 0.5 kb to about 10 kb, from about 0.5 kb to about 6 kb, from about 1 kb to about 11 kb, from about 1 kb to about 15 kb, from about 1 kb to about 20 kb, from about 0. 1 kb to about 10 kb, or from about 0.2 kb to about 1 kb.
  • the regions in the panel can have a size from about 0.1 kb to about 5 kb.
  • the panel can comprise one or more locations comprising genomic regions of interest from each of one or more genes. In some cases, the panel can comprise one or more locations comprising genomic regions of interest from each of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, or 80 genes. In some cases, the panel can comprise one or more locations comprising genomic regions of interest from each of at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, or 80 genes. In some cases, the panel can comprise one or more locations comprising genomic regions of interest from each of from about 1 to about 80, from 1 to about 50, from about 3 to about 40, from 5 to about 30, from 10 to about 20 different genes.
  • the concentration of probes or baits used in the panel may be increased (2 to 6 ng/pL) to capture more nucleic acid molecule within a sample.
  • the concentration of probes or baits used in the panel may be at least 2 ng/pL, 3 ng/ pL, 4 ng/ pL, 5 ng/pL, 6 ng/pL, or greater.
  • the concentration of probes may be about 2 ng/pL to about 3 ng/pL, about 2 ng/pL to about
  • the concentration of probes or baits used in the panel may be 2 ng/pL or more to 6 ng/pL or less. In some instances this may allow for more molecules within a biological to be analyzed thereby enabling lower frequency alleles to be detected.
  • the panel may be subjected to one or more of: whole-genome bisulfite sequencing (WGBS) interrogating genome-wide methylation patterns, whole-genome sequencing (WGS), and/or targeted sequencing approaches interrogating copy -number variants (CNVs) and single-nucleotide variants (SNVs).
  • WGBS whole-genome bisulfite sequencing
  • CNVs copy -number variants
  • SNVs single-nucleotide variants
  • sequence reads and any associated data may be stored in the sequence datastore 209.
  • the sequence reads can be stored in any format.
  • the sequence datastore 209 may be local and/or remote to a location where sequencing is performed. As shown in FIG. 2, the stored reads may be subjected to a sequence analysis pipeline 212. i. Sequence Quality Control
  • the sequence analysis pipeline 212 may include a sequence quality control (QC) component 213 that may filter sequence reads from the laboratory system 102.
  • the sequence QC component 213 may assign a quality score to one or more sequence reads.
  • a quality score may be a representation of sequence reads that indicates whether those sequence reads may be useful in subsequent analysis based on a threshold. In some cases, some sequence reads are not of sufficient quality or length to perform a subsequent mapping step. Sequence reads with a quality score at least 60%, 70%, 80%, 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filtered out of a data set of sequence reads. In other cases, sequence reads assigned a quality scored at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filtered out of the data set.
  • Sequence reads that meet a specified quality score threshold may be mapped to a reference genome by the sequence QC component 213. After mapping alignment, sequence reads may be assigned a mapping score.
  • a mapping score may be a representation of sequence reads mapped back to the reference sequence indicating whether each position is or is not uniquely mappable. Sequence reads with a mapping score at least 60%, 70%, 80%, 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filtered out of the data set. In other cases, sequencing reads assigned a mapping scored less than 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filtered out of the data set. ii. Pre-processor
  • a pre-processor 214 may retrieve/receive data from the analysis datastore 218.
  • the pre-processor 214 may retrieve/receive data representing the plurality of known allele sequences, the plurality of test sequence reads, and/or the plurality of decoy sequences.
  • the pre-processor 214 may also be configured to retrieve sequence data from another source (e.g., an external source).
  • the pre-processor 214 may be configured to download a plurality of known allele sequences, for example from the IPD-IMGT/HLA Database and/or the IPD-KIR Database.
  • the pre-processor 214 may be configured to divide the known allele sequences into a plurality of k-mer sequences.
  • k may be from about 25 to about 250.
  • k may be 135 or 140.
  • k may be 125-175 nucleotides, 130-160 nucleotides, 135-155 nucleotides, 140-150 nucleotides in length.
  • the k may be 140, 141, 142, 143, 144, or 145 nucleotides in length.
  • the pre-processor 214 may create a database comprising the k-mer sequences and additional data.
  • the pre-processor 214 may create a data structure comprising the k-mer sequences and additional data.
  • the data structure may be, for example, a table or a flat file.
  • FIG. 3 shows an example data structure 300.
  • the data structure 300 may comprise an entry for each k-mer sequence.
  • the data structure 300 may indicate the number of alleles the k-mer is associated with and, for each of those alleles, an allele identifier and a starting position where the k-mer sequence is found on that allele.
  • the preprocessor 214 may create a database comprising the decoy sequences and additional data.
  • the pre-processor 214 may create a data structure comprising the decoy sequences and additional data.
  • the data structure may be, for example, a table or a flat file.
  • An alignment component 215 may retrieve/receive data from the analysis datastore 218. For example, the alignment component 215 may retrieve/receive data representing the plurality of known allele sequences, k-mer sequences generated from the plurality of known allele sequences, the plurality of test sequence reads, and/or the plurality of decoy sequences. [0204] In various embodiments, the alignment component 215 may be configured to align a test sequence read to a reference sequence or another test sequence read. The alignment component 215 may be configured to align a test sequence read to one or more k-mer sequences generated from the plurality of known allele sequences. The alignment component 215 may be configured to align a test sequence read (e.g., pair) to one or more decoy sequences.
  • a test sequence read e.g., pair
  • An alignment score is a score indicating a similarity of two sequences determined using an alignment method.
  • an alignment score accounts for number of edits (e.g., deletions, insertions, and substitutions of characters in the string).
  • an alignment score accounts for a number of matches.
  • an alignment score accounts for both the number of matches and a number of edits.
  • the number of matches and edits are equally weighted for the alignment score. For example, an alignment score can be calculated as: # of matches-# of insertions-# of deletions-# of substitutions. In other implementations, the numbers of matches and edits can be weighted differently.
  • an alignment score can be calculated as: # of matches* 5-# of insertions *4-# of deletions*4-# of substitutions *6.
  • Pairwise alignment generally involves placing one sequence along part of target, introducing gaps according to an algorithm, scoring how well the two sequences match, and preferably repeating for various positions along the reference. The best-scoring match is deemed to be the alignment and represents an inference of homology between alignment portions of the sequences.
  • scoring an alignment of a pair of nucleic acid sequences involves setting values for the scores of substitutions and indels.
  • a match or mismatch contributes to the alignment score by a substitution probability, which could be, for example, 1 for a match and -0.33 for a mismatch.
  • An indel deducts from an alignment score by a gap penalty, which could be, for example, -1.
  • Gap penalties and substitution probabilities can be based on empirical knowledge or a priori assumptions about how sequences evolve. Their values affect the resulting alignment. Particularly, the relationship between the gap penalties and substitution probabilities influences whether substitutions or indels will be favored in the resulting alignment.
  • the alignment component 21 may utilize a Burro ws-Wheel er Aligner (BWA).
  • BWA Burro ws-Wheel er Aligner
  • the length of the test sequence read can be substantially less than the length of the k-mer sequences generated from the plurality of known allele sequences.
  • the test sequence read and the k-mer sequences can include a sequence of symbols.
  • the alignment of the test sequence read and the k-mer sequences can include a limited number of mismatches between the symbols of the test sequence read and the symbols of the k-mer sequences.
  • the test sequence read can be aligned to a portion of the k-mer sequences in order to minimize the number of mismatches between the test sequence read and the k-mer sequences.
  • the symbols of the test sequence read and the k-mer sequence can represent the composition of biomolecules.
  • the symbols can correspond to identity of nucleotides in a nucleic acid, such as RNA or DNA.
  • the symbols can have a direct correlation to these subcomponents of the biomolecules.
  • each symbol can represent a single base of a polynucleotide.
  • each symbol can represent two or more adjacent subcomponent of the biomolecules, such as two adjacent bases of a polynucleotide.
  • the symbols can represent overlapping sets of adjacent subcomponents or distinct sets of adjacent subcomponents.
  • each symbol represents two adjacent bases of a polynucleotide
  • two adjacent symbols representing overlapping sets can correspond to three bases of polynucleotide sequence
  • two adjacent symbols representing distinct sets can represent a sequence of four bases.
  • the symbols can correspond directly to the subcomponents, such as nucleotides, or they can correspond to a color call or other indirect measure of the subcomponents.
  • the symbols can correspond to an incorporation or non-incorporation for a particular nucleotide flow.
  • the alignment component 215 may be configured to determine those test sequence reads that have an identical, or substantially identical, alignment to one or more k-mer sequences.
  • nucleic acid sequences or polypeptide sequences are said to be “identical” if the sequence of nucleotides or amino acid residues, respectively, in the two sequences is the same when aligned for maximum correspondence as described herein.
  • the terms “identical” or percent “identity,” in the context of two or more nucleic acids or polypeptide sequences, refer to two or more sequences or subsequences that are the same or have a specified percentage of amino acid residues or nucleotides that are the same, when compared and aligned for maximum correspondence over a comparison window, as measured using one of the following sequence comparison algorithms or by manual alignment and visual inspection.
  • substantially identical used in the context of two nucleic acids or polypeptides, refers to a sequence that has at least 50% sequence identity with a reference sequence.
  • Percent identity can be any integer from 50% to 100%. Some embodiments include at least: 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%, compared to a reference sequence using the programs described herein, e.g., BLAST.
  • sequence comparison typically one sequence acts as a reference sequence, to which test sequences are compared.
  • test and reference sequences are entered into a computer, subsequence coordinates are designated, if necessary, and sequence algorithm program parameters are designated. Default program parameters can be used, or alternative parameters can be designated.
  • sequence comparison algorithm then calculates the percent sequence identities for the test sequences relative to the reference sequence, based on the program parameters.
  • Methods of alignment of sequences for comparison are well-known in the art.
  • Optimal alignment of sequences for comparison can be conducted, e.g., by the local homology algorithm of Smith & Waterman, Adv. Appl. Math. 2:482 (1981), by the homology alignment algorithm of Needleman & Wunsch, J. Mol. Biol.
  • HSPs high scoring sequence pairs
  • T is referred to as the neighborhood word score threshold (Altschul et al, supra).
  • These initial neighborhood word hits acts as seeds for initiating searches to find longer HSPs containing them.
  • the word hits are then extended in both directions along each sequence for as far as the cumulative alignment score can be increased.
  • Cumulative scores are calculated using, for nucleotide sequences, the parameters M (reward score for a pair of matching residues; always >0) and N (penalty score for mismatching residues; always ⁇ 0). For amino acid sequences, a scoring matrix is used to calculate the cumulative score.
  • Extension of the word hits in each direction are halted when: the cumulative alignment score falls off by the quantity X from its maximum achieved value; the cumulative score goes to zero or below, due to the accumulation of one or more negative-scoring residue alignments; or the end of either sequence is reached.
  • the BLAST algorithm parameters W, T, and X determine the sensitivity and speed of the alignment.
  • the BLASTP program uses as defaults a word size (W) of 3, an expectation (E) of 10, and the BLOSUM62 scoring matrix (see Henikoff & Henikoff, Proc. Natl. Acad. Sci. USA 89: 10915 (1989)).
  • the BLAST algorithm also performs a statistical analysis of the similarity between two sequences (see, e.g., Karlin & Altschul, Proc. Nat'l. Acad. Sci. USA 90:5873-5787 (1993)).
  • One measure of similarity provided by the BLAST algorithm is the smallest sum probability (P(N)), which provides an indication of the probability by which a match between two nucleotide or amino acid sequences would occur by chance.
  • P(N) the smallest sum probability
  • a nucleic acid is considered similar to a reference sequence if the smallest sum probability in a comparison of the test nucleic acid to the reference nucleic acid is less than about 0.01, more preferably less than about 10-5, and most preferably less than about 10-20.
  • Nucleic acid or protein sequences that are substantially identical to a reference sequence include “conservatively modified variants ” With respect to particular nucleic acid sequences, conservatively modified variants refers to those nucleic acids which encode identical or essentially identical amino acid sequences, or where the nucleic acid does not encode an amino acid sequence, to essentially identical sequences. Because of the degeneracy of the genetic code, a large number of functionally identical nucleic acids encode any given protein. For instance, the codons GCA, GCC, GCG and GCU all encode the amino acid alanine. Thus, at every position where an alanine is specified by a codon, the codon can be altered to any of the corresponding codons described without altering the encoded polypeptide.
  • nucleic acid variations are “silent variations,” which are one species of conservatively modified variations. Every nucleic acid sequence herein which encodes a polypeptide also describes every possible silent variation of the nucleic acid.
  • each codon in a nucleic acid except AUG, which is ordinarily the only codon for methionine
  • each silent variation of a nucleic acid which encodes a polypeptide is implicit in each described sequence.
  • a list of test sequence reads that aligned to (supported) a k-mer sequence of that allele can be generated for each allele.
  • only test sequence reads that align identically (e.g., no mismatches and no indels) to a k-mer sequence are included in the list.
  • only test sequence reads that align substantially identically (e.g., at least one mismatch and/or at least one indel) to a k-mer sequence are included in the list.
  • the alignment component can discard the actual alignment.
  • a test sequence read may align (identically or substantially identically) to a plurality of alleles. Each test sequence read may be associated with a test sequence read identifier. Accordingly, for each allele, a list of test sequence read identifiers associated with the supporting test sequence reads may be generated. A list of test sequence reads that aligned to a decoy sequence may also be generated. In an embodiment, only test sequence reads that align identically (e.g., no mismatches and no indels) to a decoy sequence are included in the list. In an embodiment, only test sequence reads that align substantially identically (e.g., at least one mismatch and/or at least one indel) to a decoy sequence are included in the list. The alignment component 215 may be configured to discard any test sequence reads that aligned to a decoy sequence with no mismatches and no indels.
  • a cluster component 216 may retrieve/receive data from the analysis datastore 218.
  • the cluster component 21 may retrieve/receive data representing the plurality of known allele sequences, k-mer sequences generated from the plurality of known allele sequences, the plurality of test sequence reads, and results from the alignment component 215.
  • a superset of one or more of the plurality of known allele sequences may be computationally generated by constructing one or more graph data structures.
  • the graph data structure may comprise nodes (also referred to as vertices) representing known allele sequences and edges connecting the nodes indicating that supporting reads of one node are a subset of the supporting reads of the other node.
  • Graph data structure construction may be parallelized given the computationally intensive nature of such construction.
  • the graph data structure is stored in a memory subsystem (e.g., FIG. 2, memory 222), which may include pointers to identify a physical location in the memory 222 where each vertex is stored.
  • a memory subsystem e.g., FIG. 2, memory 222
  • the nodes in a graph data structure each represent an element in a set, while the edges represent relationships among the elements.
  • the graph data structure may comprise a directed graph, a tree, a directed acyclic graph (DAG), and/or the like.
  • a directed graph is one in which the edges have a direction.
  • a tree is a type of directed graph data structure having a root node, and a number of additional nodes that are each either an internal node or a leaf node.
  • the root node and internal nodes each have one or more “child” nodes and each is referred to as the “parent” of its child nodes.
  • Leaf nodes do not have any child nodes.
  • Edges in a tree are conventionally directed from parent to child. In a tree, nodes have exactly one parent.
  • a generalization of trees, known as a directed acyclic graph (DAG) allows a node to have multiple parents, but does not allow the edges to form a cycle.
  • DAG directed acyclic graph
  • the graph data structure may represent a Hasse diagram.
  • the alleles may be sorted by the number of supporting test sequence reads.
  • a graph data structure may be constructed by determining a first allele associated with a highest number of supporting test sequence reads.
  • the first allele may form the basis of the graph data structure (e.g., top level node).
  • the supporting test sequence reads of the first allele may define a set of supporting test sequence reads. Additional alleles may be added to the graph data structure if a given allele is associated with supporting test sequence reads are themselves a subset of the set of supporting test sequence reads of the first allele.
  • Alleles that are not incorporated into the allele superset of the first allele may be used to construct one or more additional allele supersets in a similar fashion.
  • a given allele may have the highest number of supporting test sequence reads and each supporting test sequence read may be associated with a test sequence read identifier.
  • a set may be formed of the test sequence read identifiers of the supporting test sequence reads for the first allele.
  • the first allele may be supported by test sequence reads having identifiers “1,” “2,” “3,” and “4.”
  • the power set of A, P(A) is the set of all subsets of A.
  • the root node 402 of the Hasse diagram 400 is the set that includes all of test supporting reads 1, 2, 3, and 4.
  • the second-level nodes, such as node 404, are each associated with three-member sets of test supporting reads.
  • the third-level nodes, such as node 406, are each associated with sets containing two test supporting reads.
  • the fourth-level nodes, such as node 408, each contain a single test supporting read.
  • the lowest-level node is the empty set 410.
  • FIG. 5 illustrates construction of an example graph data structure 500 as a Hasse diagram to represent a superset.
  • Allele A*03:01:01 :05 represents the known allele sequence with the highest number of supporting test sequence reads.
  • Allele A*03:01 :01 :05 may represent the first allele inserted as node 502 into the graph data structure 500.
  • Allele A*03:01:01:05 may be associated with supporting test sequence reads having test sequence read identifiers: ⁇ 0; 1; 2; 23; 1,030; 2,340; ... ⁇ .
  • the remaining known allele sequences may be analyzed to determine which, if any, of the remaining known allele sequences are associated with supporting test sequence reads having test sequence read identifiers that are a subset of ⁇ 0; 1; 2; 23; 1,030; 2,340; ... ⁇ .
  • allele A*03:01:01:07 is associated with supporting test sequence reads having test sequence read identifiers ⁇ 0; 1; 2; 1,030 ... ⁇ which are a subset of ⁇ 0; 1; 2; 23; 1,030; 2,340; ... ⁇ , resulting in allele A*03:01:01 :07 being inserted as node 504 with an edge connecting to node 502 in the graph data structure 500.
  • allele A*03:01 :01 :01 is associated with supporting test sequence reads having test sequence read identifiers ⁇ 1; 2; 2,340; ... ⁇ which are a subset of ⁇ 0; 1; 2; 23; 1,030; 2,340; resulting in allele A*03:01:01:01 being inserted as node 506 with an edge connecting to node 502 in the graph data structure 500.
  • allele A*03:01:01:23 is associated with supporting test sequence reads having test sequence read identifiers ⁇ 0; 2; ... ⁇ which are a subset of ⁇ 0; 1; 2; 1,030 ... ⁇ , resulting in allele A*03:01:01:23 being inserted as node 508 with an edge connecting to node 504 in the graph data structure 500.
  • allele A*03:01:01 :23 is associated with supporting test sequence reads having test sequence read identifiers ⁇ 0; 1; 2; ... ⁇ which are a subset of ⁇ 0; 1; 2; 1,030 ... ⁇ , resulting in allele A*03:01 :01 :23 being inserted as node 510 with an edge connecting to node 504 in the graph data structure 500.
  • allele A*03:01:01 :47 is associated with supporting test sequence reads having test sequence read identifiers ⁇ 2; 2,340; ... ⁇ which are a subset of ⁇ 1; 2; ; 2,340 ... ⁇ , resulting in allele A*03:01 :01:47 being inserted as node 512 with an edge connecting to node 506 in the graph data structure 500.
  • allele A*03:01:01 :25 is associated with supporting test sequence reads having test sequence read identifiers ⁇ 2; ... ⁇ which are a subset of ⁇ 0; 2; ... ⁇ and ⁇ 0; 1; 2; ... ⁇ , resulting in allele A*03:01:01 :25 being inserted as node 514 with an edge connecting to node 508 and node 510 in the graph data structure 500.
  • allele A*03:01:01:24 is associated with supporting test sequence reads having test sequence read identifiers ⁇ 2,340; ... ⁇ which are a subset of ⁇ 2; 2,340; ... ⁇ resulting in allele A*03:01 :01 :24 being inserted as node 516 with an edge connecting to node 512 in the graph data structure 500.
  • the graph data structure 500 is completed when no further known allele sequences are associated with supporting test sequence reads having test sequence read identifiers that are a subset of node 502. Additional graph data structures may be constructed using the remaining known allele sequences. In an embodiment, once all known allele sequences have been inserted into a graph data structure (superset). The root node (first allele) represents a candidate allele. The candidate allele may be a candidate for the allele present at the locus. [0225] In an embodiment, the graph data structure 500 may be traversed to determine the known allele sequences associated with a given set of test sequence read identifiers.
  • the graph data structure (e.g., representing a superset) is stored in a memory subsystem (e.g., FIG. 2, memory 222) using adjacency techniques, which may include pointers to identify a physical location in the memory 222 where each vertex is stored.
  • the graph data structure is stored in the memory 222 using adjacency lists. In some embodiments, there is an adjacency list for each vertex.
  • FIG. 6 shows a graph data structure 600 that includes vertex objects 605 and edge objects 609.
  • Known allele sequences and associated identifiers of supporting test sequence reads are identified as blocks and those blocks are transformed into objects 605 that are stored in a tangible memory device.
  • the objects 605 are connected to create paths such that there is a path for each known allele sequence and associated subset of identifiers of supporting test sequence reads.
  • a path may indicate the known allele sequences that are associated with a subset of identifiers of supporting test sequence reads.
  • the connections creating the paths can themselves be implemented as objects so that the blocks are represented by vertex objects 605 and the connections are represented by edge objects 609.
  • the directed graph comprises vertex and edge objects stored in the tangible memory device.
  • node object 605 and edge object 609 refer to an object created using a computer system.
  • FIG. 6 further shows an adjacency list 610 for vertices 605.
  • the disclosed methods and systems may use a processor to create a graph data structure 600 that includes vertex objects 605 and edge objects 609 through the use of adjacency, e.g., adjacency lists or index free adjacency.
  • the processor may create the graph data structure 600 using index-free adjacency wherein a vertex 605 includes a pointer to another vertex 605 to which it is connected and the pointer identifies a physical location on a memory device 222 where the connected vertex is stored.
  • the graph data structure 600 may be implemented using adjacency lists such that each vertex or edge stores a list of such objects that it is adjacent to. Each adjacency list comprises pointers to specific physical locations within a memory device for the adjacent objects.
  • the graph data structure 600 will typically be stored on a physical device of memory subsystem 222 in a fashion that provides for very rapid traversals. In that sense, the bottom portion of FIG. 6 represents that objects are stored at specific physical locations on a tangible part of the memory subsystem 222.
  • Each node 605 is stored at a physical location, the location of which is referenced by a pointer in any adjacency list 610 that references that node.
  • Each node 605 has an adjacency list 610 that includes every adjacent node in the graph data structure 600. The entries in the list 610 are pointers to the adjacent nodes.
  • FIG. 7 shows the use of an adjacency list 710 for each vertex 705 and edge 709.
  • the disclosed methods and systems may create the graph data structure 700 using an adjacency list 710 for each vertex and edge, wherein the adjacency list 710 for a vertex 705 or edge 709 lists the edges or vertices to which that vertex or edge is adjacent.
  • Each entry in adjacency list 710 is a pointer to the adjacent vertex or edge.
  • Each pointer identifies a physical location in the memory subsystem at which the adjacent object is stored.
  • the pointer or native pointer is manipulatable as a memory address in that it points to a physical location on the memory and permits access to the intended data by means of pointer dereference. That is, a pointer is a reference to a datum stored somewhere in memory; to obtain that datum is to dereference the pointer.
  • the feature that separates pointers from other kinds of reference is that a pointer’s value is interpreted as a memory address, at a low-level or hardware level. Such a graph representation provides means for fast random access, modification, and data retrieval.
  • index-free adjacency is another example of low-level, or hardware-level, memory referencing for data retrieval. Specifically, index-free adjacency can be implemented such that the pointers contained within elements are references to a physical location in memory.
  • An allele caller 217 may retrieve/receive data from the analysis datastore 218.
  • the allele caller 217 may retrieve/receive data representing the plurality of known allele sequences, k-mer sequences generated from the plurality of know n allele sequences, the plurality of test sequence reads, results from the alignment component 215, and/or one or more graph data structures (supersets) generated by the cluster component 216.
  • the allele caller 217 may be configured to determine an allele type for a given allele.
  • the allele caller 217 may be configured to classify an allele based on the one or more graph data structures (supersets).
  • the allele (the first allele) associated with the root node of the graph data structure may be classified as the allele present at the locus (e.g., haploid locus) of the chromosome.
  • the alleles (the first alleles) associated with the root nodes of the two supersets having a cumulative largest number of distinct supporting test sequence reads may be classified as the alleles present at the locus (e.g., diploid locus) of the chromosome.
  • a set operation may be performed on combinations of root nodes to determine the two root nodes having a cumulative largest number of distinct supporting test sequence reads.
  • a union operation (U) may be used.
  • cluster component 216 constructed three graph data structures and the resulting root nodes from each graph data structure are shown as root node 802, root node 804 and root node 806.
  • a union operation may be performed between root node 802 and root node 804, between root node 802 and root node 806, and between root node 804 and root node 806.
  • root node 802 is depicted as being associated with a set of 9 test sequence read identifiers made up of the set ⁇ 0; 1; 2; 23; 1,030; 1,223; 2,001; 2,300; 2,340 ⁇
  • root node 804 is depicted as being associated with a set of 10 test sequence read identifiers made up of the set ⁇ 1; 3; 5; 6; 7; 21; 1,708; 2,000; 2,001; 2,340 ⁇
  • root node 806 is depicted as being associated with a set of 11 test sequence read identifiers made up of the set ⁇ 0; 6; 7; 9; 10; 11; 21; 58; 1,200; 2,000; 2,900 ⁇ .
  • a union operation between node 802 and node 804 results in a set ⁇ 0; 1; 2; 3; 5; 6; 7; 21; 23; 1,030; 1,223; 1,708; 2,000; 2,001; 2,300; 2,340 ⁇ which includes 16 cumulative distinct test sequence read identifiers, as the union operation discards duplicates.
  • a union operation between node 802 and node 806 results in a set ⁇ 0; 1; 2; 6; 7; 9; 10; 11; 21; 23; 58; 1,030; 1,200; 1,223; 2,000; 2,001; 2,300; 2,340;
  • 2,900 ⁇ which includes 19 cumulative distinct test sequence read identifiers, as the union operation discards duplicates.
  • a union operation between node 804 and node 806 results in a set ⁇ 0; 1; 3; 5; 6; 7; 9; 10; 11; 21; 58; 1,200; 1,708; 2,000, 2,001; 2,340; 2,900 ⁇ which includes 17 cumulative distinct test sequence read identifiers, as the union operation discards duplicates.
  • the union operation between node 802 and node 806 resulted in the largest cumulative number of distinct test sequence read identifiers, and therefore distinct test sequence reads.
  • the allele caller 217 may classify the known allele sequences associated with node 802 (DQBl*03:02:01 :01) and node 806 (DQBl*03:04:03) (e.g., the first alleles) as the alleles present at the locus.
  • the allele caller 217 may continue to process graph data structures/root nodes for any number of loci. vi. Variant Caller
  • a variant caller 219 may retrieve/receive data from the analysis datastore 218.
  • the variant caller 219 may retrieve/receive data representing a plurality of sequence reads.
  • the variant caller 219 may retrieve test sequence reads that aligned to a decoy sequence and to a known allele with at least one mismatch and/or at least one indel and that had a greater alignment score to the known allele.
  • the test sequence reads may be analyzed to determine one or more variants.
  • Variants may include, for example, single nucleotide variants (SNVs), indels, fusions, and/or copy number variation. Any known technique for variant calling may be used.
  • nucleotide variations in sequenced nucleic acids can be determined by comparing sequenced nucleic acids with a reference sequence.
  • the reference sequence is often a known sequence, e.g., a known whole or partial genome sequence from a subject (e.g., a whole genome sequence of a human subject).
  • the reference sequence can be, for example, hG19 or hG38.
  • the sequenced nucleic acids can represent sequences determined directly for a nucleic acid in a sample, or a consensus of sequences of amplification products of such a nucleic acid, as described above. A comparison can be performed at one or more designated positions on a reference sequence.
  • a subset of sequenced nucleic acids can be identified including a position corresponding with a designated position of the reference sequence when the respective sequences are maximally aligned. Within such a subset it can be determined which, if any, sequenced nucleic acids include a nucleotide variation at the designated position, the length of a given cfDNA fragment based upon where its endpoints (i.e., it 5’ and 3’ terminal nucleotides) map to the reference sequence, the offset of a midpoint of a given cfDNA fragment from a midpoint of a genomic region in the cfDNA fragment, and optionally which if any, include a reference nucleotide (i.e., same as in the reference sequence).
  • a variant nucleotide can be called at the designated position.
  • the threshold can be a simple number, such as at least 1, 2, 3, 4, 5, 6, 7, 9, or 10 sequenced nucleic acids within the subset including the nucleotide variant or it can be a ratio, such as a least 0.5, 1, 2, 3, 4, 5, 10, 15, or 20 of sequenced nucleic acids within the subset that include the nucleotide variant, among other possibilities.
  • the comparison can be repeated for any designated position of interest in the reference sequence. Sometimes a comparison can be performed for designated positions occupying at least about 20, 100, 200, or 300 contiguous positions on a reference sequence, e.g., about 20-500, or about 50-300 contiguous positions.
  • the variant caller 417 may be configured to assign a somatic or germline label to a given variant. Additional details regarding classification of a variant as somatic or germline that are optionally adapted for use in performing the methods disclosed herein are described in, for example, U.S. Pat. App Pub. No. US20210265013A1, filed February 17, 2021, which is incorporated by reference.
  • the germline status may be verified using one or more germline variant callers. Germline variant callers include HAPLOTYPE CALLER, samtools, and/or freebayes.
  • the somatic status may bverified using one or more somatic variant callers. Somatic variant callers include SEURAT, STRELKA, and/or MUTECT.
  • any data analyzed, determined, and/or output by the sequence analysis pipeline 212 may be stored in the analysis datastore 218.
  • the processor 220 may implement (be programmed by) various components of the sequence analysis pipeline 212, such as the sequence quality control component 213, the pre-processor 214, the alignment component 215, the cluster component 216, the allele caller 217, the variant caller 219, and/or other components.
  • these components of the sequence analysis pipeline 212 may include a hardware module.
  • sequence quality control component 213, the pre-processor 214, the alignment component 215, the cluster component 21 , the allele caller 217, and/or the variant caller 219 may be integrated with one another.
  • the computer system 210 may exchange data with a computer system 224 using a network 223.
  • the computer system 224 may retrieve data from the analytics datastore 218.
  • the computer system 224 may be configured for determining/classifying alleles present at a locus.
  • a method 900 for allele calling is disclosed.
  • the sequence QC component 213, the pre-processor 214, the alignment component 215, the cluster component 216, the allele caller 217, the variant caller 219, additional components not shown (e.g., a component of the computer system 210) alone and/or in a combination thereof may be configured to access the sequence datastore 209 and/or the analysis datastore 218 and perform the method 900 in whole and/or in part.
  • the method 900 may be performed in whole or in part by a single computing device using parallel computing, in one or more cores, or using a plurality of computing devices, for example using a distributed computer system, and the like.
  • the method 900 may comprise determining a plurality of known allele sequences at 901.
  • the plurality of know n allele sequences may comprise a plurality of known human leukocyte antigen (HLA) allele sequences or a plurality of known killer cell immunoglobulin- like receptor (KIR) region allele sequences.
  • HLA human leukocyte antigen
  • KIR killer cell immunoglobulin- like receptor
  • Determining the plurality of known allele sequences may comprise retrieving sequence data for known alleles, determining, based on the sequence data for known alleles, a plurality of k-mer sequences, and assembling the plurality of k-mer sequences into a data structure comprising a plurality of entries, wherein each entry may comprise a k-mer sequence, a number of alleles containing the k-mer sequence, an allele identifier for each allele containing the k-mer sequence, and a start and stop position of the k-mer sequence in each allele.
  • the method 900 may comprise determining a plurality of sequence reads of a target region of a chromosome of a subject at 902.
  • the target region of the chromosome may comprise one or more loci.
  • the method 900 may further comprise obtaining a sample from the subject and sequencing the sample to obtain the plurality of sequence reads of the target region of the chromosome.
  • the target region may comprise one or more of the following genes: HLA- A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-G, HLA-H, HLA- J, HLA-K, HLA- L, HLA-N, HLA-P, HLA-S, HLA-T, HLA-U, HLA-V, HLA-W, HLA-DRA, HLA-DRB1 , HLA-DRB2, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DRB6, HLA-DRB7, HLA- DRB8, HLA-DRB9, HLA-DQA1, HLA-DQB1, HLA-DQA2, HLA-DQB2, HLA-DQB3, HLA-DOA, HLA-DOB, HLA-DMA, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DPA2, HLA-DPB2, HLA-DPA3, HFE, TA
  • the target region may comprise chromosomal position 6p21 or chromosomal position 19ql3.
  • the target region may comprise at least a portion of a major histocompatibility complex (MHC) region and the chromosome is human chromosome 6.
  • MHC major histocompatibility complex
  • KIR killer cell immunoglobulin-like receptor
  • the method 900 may comprise aligning the plurality of sequence reads to the plurality of known allele sequences at 903. [0248] The method 900 may comprise determining, based on the alignment, for each known allele sequence of the plurality of known allele sequences, a number of sequence reads that aligned to each known allele sequence at 904. In some embodiments, the sequence reads that aligned to each known allele sequence can be grouped into one or more sequence read families (i.e., based on molecular barcode and genomic location). Accordingly, a number of sequence read families that aligned to each known allele sequence may be determined.
  • Determining, based on the alignment, for each known allele sequence of the plurality of known allele sequences, a number of sequence reads that aligned to each known allele sequence may comprise determining the number of sequence reads that aligned to each known allele sequence with 100% identity.
  • the method 900 may comprise determining, based on the numbers of sequence reads that aligned to each known allele sequence, for the one or more loci, the known allele sequences present at the one or more loci at 905.
  • the method 900 may comprise determining, based on the numbers of sequence read families that aligned to each known allele sequence, for the one or more loci, the known allele sequences present at the one or more loci at 905.
  • the known allele sequences present at the one or more loci may comprise a human leukocyte antigen (HLA) type at the locus or a killer cell immunoglobulin-like receptor (KIR) type at the locus
  • Determining, based on the numbers of sequence reads that aligned to each known allele sequence, for the one or more loci, the known allele sequences present at the one or more loci may comprise determining one or more known allele sequences having a highest number of sequence reads aligned. Determining, based on the numbers of sequence read families that aligned to each known allele sequence, for the one or more loci, the known allele sequences present at the one or more loci may comprise determining one or more known allele sequences having a highest number of sequence read families aligned.
  • the method 900 may further comprise determining, based on the alignment, for each read of the plurality of sequence reads, one or more known allele sequences to which each read aligns.
  • the method 900 may further comprise determining a length of a portion of each known allele sequence aligned to two or more sequence reads of the plurality of sequence reads.
  • the method 900 may further comprise sorting, for a locus, the known allele sequences present at the locus by the number of sequence reads that aligned to each known allele sequence, determining, for the locus, a first known allele sequence with a highest number of sequence reads aligned, inserting the first known allele sequence with the highest number of sequence reads aligned into a superset, determining one or more known allele sequences that aligned to reads that are a subset of the reads aligned to the first known allele sequence, and inserting the one or more known allele sequences into the superset.
  • the superset may comprise a graph data structure.
  • the graph data structure may comprise a directed acyclic graph.
  • the graph data structure may represent a Hasse diagram.
  • the method 900 may further comprise determining that the locus is associated with a single superset and determining the first known allele sequence of the single superset as the allele present at the locus.
  • the method 900 may further comprise determining a plurality of supersets for the locus.
  • the method 900 may further comprise determining, based on the plurality of supersets for the locus, two supersets with a cumulative largest number of distinct reads and determining the first known allele sequence of each of the two supersets as the alleles present at the locus.
  • the method 900 may further comprise, assisting in a communication of the know n allele sequences present at the one or more loci to a medical provider.
  • the method 900 may further comprise obtaining a sample from a cell to be transplanted into the subject and sequencing the sample to obtain the plurality of sequence reads of the target region of the chromosome.
  • the method 900 may further comprise determining if an HLA type of the subject matches the HLA type at the locus.
  • the method 900 may further comprise transplanting the cell into the subject if the HLA type of the subject matches the HLA type at the locus.
  • the method 900 may further comprise transplanting the cell into the subject if the HLA type of the subject matches the HLA type at the locus and administering an agent that reduces a likelihood of transplant rejection in the subject.
  • the method 900 may further comprise determining that the subject is predisposed to developing rheumatoid arthritis (RA) wherein the known allele sequences present at the one or more loci may comprise HLA-DRB 1*04:01, *04:04, and *04:08; HLA-DRBl*04:05; HLA-DRB1 *01 :01 and *01 :02; HLA-DRB1 *14:02; HLA-DRB1* 10:01; and HLA- DRBl*01:01, *04:01, *04:04, and *04:05.
  • RA rheumatoid arthritis
  • the method 900 may further comprise determining that the subject is predisposed to developing multiple sclerosis (MS) wherein the known allele sequences present at the one or more loci may comprise HLA-DRBl*15:01, HLA-DQBl*06:02, HLA-DRB1 *01:08, HLA- DRBl*03:01, and/or HLA-DRB 1*13:03.
  • MS multiple sclerosis
  • the method 900 may further comprise determining that the subject is predisposed to developing systemic lupus erythematosus (SLE) wherein the known allele sequences present at the one or more loci may comprise HLA-DRB1, HLA-DR2 (DRBl*15:01), HLA-DR3 (DRBl*03:01), HLA-DRB 1*08: 01, and/or HLA-DQAl*01:02.
  • SLE systemic lupus erythematosus
  • the method 900 may further comprise determining that the subject is predisposed to developing Type 1 diabetes mellitus (T1D) wherein the known allele sequences present at the one or more loci may comprise rise HLA-DQBl*03:02 and/or DQB1 *02:01.
  • T1D Type 1 diabetes mellitus
  • the method 900 may further comprise determining that the subject is predisposed to developing Sjogren’s syndrome (SS) wherein the known allele sequences present at the one or more loci may comprise DQA1*O5:O1, DQBl*02:01, and DRBl*03:01.
  • SS Sjogren’s syndrome
  • the method 900 may further comprise determining that the subject is predisposed to developing that the subject is predisposed to developing celiac disease (CD) wherein the known allele sequences present at the one or more loci may comprise HLA-DQ2 (encoded by HLA-DQAl*05:01-DQBl*02:01) and/or HLA-DQ8 (encoded by DQAl*03:01- DQBl*03:02).
  • CD celiac disease
  • the method 900 may further comprise determining that the subject is predisposed to developing preeclampsia wherein the known allele sequences present at the one or more loci may comprise K1R2DL1.
  • the method 900 may further comprise determining that the subject is less likely to progress from HIV infection to AIDS wherein the known allele sequences present at the one or more loci may comprise KIR3DS1.
  • the method 900 may further comprise determining that the subject is less likely to progress from HIV infection to AIDS wherein the known allele sequences present at the one or more loci may comprise a combination of KIR3DS1 and HLA-Bw4.
  • the method 900 may further comprise determining that the subject is predisposed to developing an autoimmune disease wherein the known allele sequences present at the one or more loci may comprise KIR2DS1.
  • the method 900 may further comprise determining that the subject is predisposed to developing Crohn’s disease wherein the known allele sequences present at the one or more loci may comprise KIR2DL2/KIR2DL3 heterozygosity.
  • the method 900 may further comprise determining that the subject is predisposed to developing Crohn’s disease wherein the known allele sequences present at the one or more loci may comprise a combination of KIR2DL2/KIR2DL3 heterozygosity and HLA-C2 allele. [0272] The method 900 may further comprise determining whether the subject is a suitable candidate for a donor in an unrelated hematopoietic cell transplantation wherein the known allele sequences present at the one or more loci may comprise a group B KIR haplotype. [0273] In an embodiment, shown in FIG. 10, a method 1000 for allele and/or variant calling is disclosed.
  • the sequence QC component 213, the pre-processor 214, the alignment component 215, the cluster component 21 , the allele caller 217, the variant caller 219, additional components not shown (e.g., a component of the computer system 210) alone and/or in a combination thereof may be configured to access the sequence datastore 209 and/or the analysis datastore 218 and perform the method 900 in whole and/or in part.
  • the method 900 may be performed in whole or in part by a single computing device using parallel computing, in one or more cores, or using a plurality of computing devices, for example using a distributed computer system, and the like.
  • the method 1000 may comprise determining a plurality of pairs of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci at 1001.
  • the method 1000 may further comprise obtaining a sample from the subject and sequencing the sample to obtain the plurality of pairs of sequence reads of the target region of the chromosome.
  • the target region may comprise one or more of the following genes: HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-G, HLA-H, HLA-J, HLA-K, HLA-L, HLA-N, HLA-P, HLA-S, HLA-T, HLA-U, HLA-V, HLA-W, HLA-DRA, HLA-DRB1, HLA-DRB2, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DRB6, HLA-DRB7, HLA-DRB8, HLA-DRB9, HLA-DQA1, HLA-DQB1, HLA-DQA2, HLA- DQB2, HLA-DQB3, HLA-DOA, HLA-DOB, HLA-DMA, HLA-DMB, HLA-DPA1, HLA- DPB1, HLA-DPA2, HLA-DPB2, HLA-DPA3, HFE, TAPI,
  • the target region may comprise chromosomal position 6p21 or chromosomal position 19ql3.
  • the target region may comprise at least a portion of a major histocompatibility complex (MHC) region and the chromosome is human chromosome 6.
  • MHC major histocompatibility complex
  • KIR killer cell immunoglobulin-like receptor
  • the method 1000 may comprise generating a germline alignment of the plurality of pairs of sequence reads to a plurality of known allele sequences at 1002.
  • the plurality of known allele sequences may comprise a plurality of know n human leukocyte antigen (HLA) allele sequences or a plurality of known human killer cell immunoglobulin-like receptor (KIR) region allele sequences.
  • HLA human leukocyte antigen
  • KIR human killer cell immunoglobulin-like receptor
  • the method 1000 may comprise generating a decoy alignment of the plurality of pairs of sequence reads to a plurality of decoy allele sequences at 1003.
  • the plurality of decoy sequences may comprise a plurality of non-human sequences.
  • the plurality of non-human sequences may comprise one or more of a plurality of bovine sequences, a plurality of rat sequences, or a plurality of microbial sequences.
  • Generating the germline alignment of the plurality of pairs of sequence reads to a plurality of know n allele sequences may comprise determining, based on the germline alignment, for a pair of sequence reads of the plurality' of pairs of sequence reads, one or more known allele sequences to which each read of the pair of sequence reads aligns with no mismatch or indel.
  • the method 1000 may further comprise determining, based on the germline alignment, for each known allele sequence of the plurality' of known allele sequences, a number of pairs of sequence reads of the plurality of pairs of sequence reads that aligned to each known allele sequence and determining, based on the numbers of pairs of sequence reads that aligned to each known allele sequence, for the one or more loci, the known allele sequences present at the one or more loci.
  • Generating the decoy alignment of the plurality of pairs of sequence reads to a plurality of decoy allele sequences may comprise determining, based on the decoy alignment, for the pair of sequence reads of the plurality of pairs of sequence reads, one or more decoy allele sequences to which each read of the pair of sequence reads aligns with no mismatch or indel and discarding the pair of sequence reads.
  • Generating the decoy alignment of the plurality of pairs of sequence reads to a plurality of decoy allele sequences may comprise determining, based on the decoy alignment, for the pair of sequence reads of the plurality of pairs of sequence reads, one or more non- human decoy sequences to which each read of the pair of sequence reads aligns with no mismatch or indel and identifying the plurality of pairs of sequence reads as originating from a contaminated sample.
  • Generating the germline alignment of the plurality of pairs of sequence reads to a plurality of know n allele sequences may comprise determining, based on the germline alignment, for a pair of sequence reads of the plurality' of pairs of sequence reads, one or more known allele sequences to which each read of the pair of sequence reads aligns with at least one mismatch or indel and generating the germline alignment score.
  • Generating the decoy alignment of the plurality of pairs of sequence reads to a plurality of decoy allele sequences may comprise determining, based on the decoy alignment, for the pair of sequence reads of the plurality of pairs of sequence reads, one or more decoy allele sequences to which each read of the pair of sequence reads aligns with at least one mismatch or indel and generating the decoy alignment score.
  • Generating a germline alignment of the plurality of pairs of sequence reads to a plurality of known allele sequences may comprise determining a pair of sequence reads aligns to at least two allele sequences of the plurality of known allele sequences and selecting one known allele sequence of the at least two allele sequences.
  • Generating a decoy alignment of the plurality of pairs of sequence reads to a plurality of decoy allele sequences may comprise determining a pair of sequence reads align to at least two decoy allele sequences of the plurality' of decoy allele sequences and selecting one decoy allele sequence of the at least two decoy allele sequences.
  • the method 1000 may comprise determining a pair of sequence reads of the plurality of pairs of sequence reads with both a germline alignment with at least one mismatch and/or indel and a decoy alignment with at least one mismatch and/or indel, wherein the pair of sequence reads is associated with a germline alignment score greater than a decoy alignment score at 1004.
  • the method 1000 may comprise identifying the pair of sequence reads as a candidate somatic variant at 1005. Identifying the pair of sequence reads as a candidate somatic variant may comprise identifying, based on aligning the pair of sequence reads to one or more reference variants, a reference variant of the one or more reference variants as the candidate variant. Identifying the pair of sequence reads as a candidate somatic variant may comprise identify ing, based on aligning the pair of sequence reads to one or more non-human reference variants, a non-human reference variant of the one or more non-human reference variants as the candidate variant and identifying the plurality of pairs of sequence reads as originating from a contaminated sample.
  • the present methods can be computer-implemented, such that any or all of the operations described in the specification or appended claims other than wet chemistry steps can be performed in a suitable programmed computer.
  • the computer can be a mainframe, personal computer, tablet, smart phone, cloud, online data storage, remote data storage, or the like.
  • the computer can be operated in one or more locations.
  • Various operations of the present methods can utilize information and/or programs and generate results that are stored on computer-readable media (e.g., hard drive, auxiliary memory, external memory, server; database, portable memory device (e.g., CD-R, DVD, ZIP disk, flash memory cards), and the like.
  • computer-readable media e.g., hard drive, auxiliary memory, external memory, server; database, portable memory device (e.g., CD-R, DVD, ZIP disk, flash memory cards), and the like.
  • the present disclosure also includes an article of manufacture for analyzing a nucleic acid population that includes a machine-readable medium containing one or more programs which when executed implement the steps of the present methods.
  • the disclosure can be implemented in hardware and/or software. For example, different aspects of the disclosure can be implemented in either client-side logic or serverside logic.
  • the disclosure or components thereof can be embodied in a fixed media program component containing logic instructions and/or data that when loaded into an appropriately configured computing device cause that device to perform according to the disclosure.
  • a fixed media containing logic instructions can be delivered to a viewer on a fixed media for physically loading into a viewer's computer or a fixed media containing logic instructions may reside on a remote server that a viewer accesses through a communication medium to download a program component.
  • the processor 220 may include a single core or multi core processor, or a plurality of processors for parallel processing.
  • the storage device 222 may include random-access memory , read-only memory, flash memory , a hard disk, and/or other type of storage.
  • the computer system 210 may include a communication interface (e.g., network adapter) for communicating with one or more other systems, and peripheral devices, such as cache, other memory , data storage and/or electronic display adapters.
  • the components of the computer system 210 may communicate with one another through an internal communication bus, such as a motherboard.
  • the storage device 222 may be a data storage unit (or data repository) for storing data.
  • the computer system 210 may be operatively coupled to a network 223 (“network”) with the aid of the communication interface.
  • the network 223 may be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet.
  • the network 223 in some cases is a telecommunication and/or data network.
  • the netw ork 223 may include a local area network.
  • the network 23 may include one or more computer servers, which can enable distributed computing, such as cloud computing.
  • the network 223, in some cases with the aid of the computer system 210, may implement a peer-to-peer network, which may enable devices coupled to the computer system 220 to behave as a client or a server.
  • the computer system 210 may exchange data with a computer system 224 using the network 223. For example, the computer system 224 may retrieve data from the analytics datastore 218.
  • the processor 220 may execute a sequence of machine-readable instructions, w hich can be embodied in a program or software.
  • the instructions may be stored in a memory location, such as the storage device 222.
  • the instructions can be directed to the processor 220, which can subsequently program or otherwise configure the processor 220 to implement methods of the present disclosure. Examples of operations performed by the processor 220 may include fetch, decode, execute, and writeback.
  • the processor 220 may be part of a circuit, such as an integrated circuit. One or more other components of the system 200 may be included in the circuit. In some cases, the circuit may include an application specific integrated circuit (ASIC).
  • ASIC application specific integrated circuit
  • the storage device 222 may store files, such as drivers, libraries, and saved programs.
  • the storage device 222 can store user data, e.g., user preferences and user programs.
  • the computer system 210 in some cases may include one or more additional data storage units that are external to the computer system 210, such as located on a remote server that is in communication with the computer system 210 through an intranet or the Internet.
  • the computer system 210 can communicate with one or more remote computer systems through the network.
  • the computer system 210 can communicate with a remote computer system of a user.
  • remote computer systems include personal computers (e.g., portable PC), slate or tablet PC's (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants.
  • the user can access the computer system 210 via the network.
  • Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 210, such as, for example, on the storage device 222.
  • the machine executable or machine readable code can be provided in the form of software (e.g., computer readable media).
  • the code can be executed by the processor 220.
  • the code can be retrieved from the storage device 222 and stored on the storage device 222 for ready access by the processor 220.
  • the code may be pre-compiled and configured for use with a machine having a processer adapted to execute the code, or can be compiled during runtime.
  • the code can be supplied in a programming language that can be selected to enable the code to execute in a precompiled or as-compiled fashion.
  • aspects of the systems and methods provided herein can be embodied in programming.
  • Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and/or associated data that is carried on or embodied in a type of machine readable medium.
  • Machine-executable code can be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk.
  • Storage type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server.
  • another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across phy sical interfaces between local devices, through wired and optical landline networks and over various air-links.
  • media may include other types of (intangible) media.
  • Storage media terms such as computer or machine “readable medium” refer to any tangible (such as phy sical), non-transitory, medium that participates in providing instructions to a processor for execution.
  • a machine readable medium such as computer-executable code
  • a tangible storage medium such as computer-executable code
  • Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings.
  • Volatile storage media include dynamic memory, such as main memory of such a computer platform.
  • Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system.
  • Carrier-wave transmission media may 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.
  • RF radio frequency
  • IR infrared
  • Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and/or data.
  • Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.
  • the computer system 210 can include or be in communication with an electronic display 935 that comprises a user interface (UI) for providing, for example, a report.
  • UI user interface
  • Examples of UI's include, without limitation, a graphical user interface (GUI) and web-based user interface.
  • Methods and systems of the present disclosure can be implemented by way of one or more algorithms.
  • An algorithm can be implemented by way of software upon execution by the processor 220.
  • HLA and KIR genotypes show great promise as emerging biomarkers for immune checkpoint inhibitors (ICIs) and understanding patient prognosis.
  • ICIs immune checkpoint inhibitors
  • Multiple studies have shown that HLA-I heterozygosity and high sequence divergence across alleles positively correlates with response to ICIs.
  • the high degree of polymorphism and allele sequence similarities in HLA and KIR present a challenge to accurate allele calling.
  • Kmerizer a novel allele caller optimized for short fragments, such as germline reads from a cfDNA assay, was developed.
  • HLA and KIR typing using Kmerizer can include an HLA and KIR panel coverage comprising HLA locus: A, B, C, DPA1, DPB1, DQA1, DQB1, DRB1, DRB3, DRB4, DRB5, E, F, G; and KIR locus: 2DL1, 2DL2, 2DL3, 2DL4, 2DL5, 2DP1, 2DS1, 2DS2, 2DS3, 2DS4, 2DS5, 3DL1, 3DL2, 3DL3, 3DP1, 3DS1.
  • HLA and KIR sequences exhibit a high level of homolog ⁇ 7 between allele sets (FIG. 11).
  • Kmerizer calls HLA and KIR alleles in a sample based on a set of known alleles.
  • the module kmerizer can be run in two modes: build index mode or call alleles mode (FIG. 12).
  • build index mode running without a sample’s bam file in input will cause the algorithm to generate a kmers map index file.
  • the call alleles mode running with a sample’s bam file in input will cause the algorithm to call alleles, based on the kmers map index file generated with the “build index” mode.
  • the build index generates a kmers map based on a given kmer size and known alleles fasta file.
  • the call alleles is based on a sample bam file and a kmers map is generated as done in the build index.
  • Kmerizer encode alleles as a tree using existing the HLA nomenclature system, where each leaf node represents an allele sequence and each leaf node propagates its count onto its parent’s node.
  • FIG. 13 is an example of the hierarchical structure of allele names.
  • An HLA allele name is encoded by a string such as A*01:02:03:04, comprising a gene name (A, for HLA- A), and up to four sets of digits.
  • Kmerizer has been validated in HLA typing (FIG. 15) and KIR typing (FIG. 16) with simulated samples. Simulation randomly picked 2 alleles in each locus from IMGT database and simulated the allele reads from NGS with sequencing errors. In a separate evaluation simulation challenged Kmerizer with allele pairs with sequence identity over 95%. We also executed HISAT-genotype on the same set of samples for method comparison.
  • kmerizer on gDNA sourced from cell lines validated with orthogonal methods was also performed.
  • HLA typing performance of kmerizer using 19 cell line samples sourced from Fred Hutch Philosophy Reference Panel (FIG. 17) were evaluated, where 15 samples sourced from sonicated gDNA and 4 samples sourced from cell line media.
  • ty ping concordance of kmerizer was evaluated on plasma cfDNA collected from healthy donors as compared with huffy coat gDNA.
  • kmenzer delivered 100% sensitivity with 98% specificity.
  • Kmerizer achieved 99% sensitivity and specificity on all the MHC class 1 and class 2 loci, and 90% sensitivity and specificity 7 on all KIR loci, for both homozygous and heterozygous pairs.
  • the allele caller kmerizer also demonstrated a lighter footprint on computational resource need: one deep-sequencing plasma sample on average can be processed in less than 2 minutes which is about 15 times faster than the most commonly used HLA t ping tool HISAT-genotype, which does not support KIR typing.
  • Fragment size in cfDNA is shorter than in genomic DNA, and existing state of the art methods like HISAT-genotype have limited capability 7 in calling alleles accurately.
  • Kmerizer a novel algorithm, was developed and it is capable of typing HLA and KIR alleles with cfDNA like fragments.
  • Kmerizer achieves high sensitivity and specificity on both simulated data and well- characterized cell lines, and it achieves high concordance between plasma and buffy coat DNA
  • HLA germline genotypes and somatic mutations show 7 great promise as emerging biomarkers for immune checkpoint inhibitors (ICIs) and understanding patient prognosis. Multiple studies have shown that HLA homozygosity or loss-of-function somatic mutations negatively correlate with ICI response rate. High HLA fidelity can be used to accurately model patient neoantigen landscapes and provide further prognostic insight for these patients, and identify those who may benefit particularly from IO-treatment.
  • Kmerizer configured to perform HLA germline typing and/or somatic mutation detection from cfDNA input material. These results can be used for in silico neoantigen and patient outcome prediction.
  • Kmerizer may leverage the high depth coverage of targeted sequencing to rapidly identify germline alleles by matching k-mers from the input reads to the k-mers of known HLA and KIR alleles. Realignment of reads onto the called germlines may be followed by somatic variant calling.
  • MHC-I gemiline allele calls may be combined with patient mutation data to generate in silico TCR binding affinity predictions using, for example, netMHC-4.01. These predictions may be compared across cohorts to assess how cancer type and bTMB vary with the predicted neoantigens and TCR binding affinity.
  • 21 shows shows that germline allele(s) and homozygous/heterozygous status were randomly assigned based on AFND to generate germline reads using references from the IMGT/HLA database; somatic mutation was then randomly generated at an exonic position and mutant reads were then generated from the altered reference.
  • the Kmerizer somatic caller achieved 100% specificity and 97.5% aggregated sensitivity on class I genes for both variant detection and mutant molecule recovery (Table 3).
  • Table 3 shows Kmerizer somatic allele calling performance on simulated data. Detected variants have observed AF range [0.08%,2.3%] and median of 0.18% (FIG. 22).
  • FIG. 22 shows variant allele frequency (AF) distribution of detected variants.
  • neoantigens were generated with netMHC-4.0 from 533 clinical cancer samples using matched germline allelle calls and mutation data.
  • HLA-A*02:01 HLA-A*03:01
  • HLA-A*l l:01 HLA-A*31:01.
  • FIG. 24 shows the distribution of neoantigen binding affinities by cancer type. Comparison of the immunogenic neoantigen binding affinities across cancer types identified bladder and melanoma cancer samples as those with the highest immunogenic potential and colorectal cancers as immunologically cold, on average (ks-test, p ⁇ 0.005; FIG. 24; stronger binding denoted by smaller ic50 score).
  • a cumulative density plot of the immunogenic neoantigens by cancer type (FIG. 24, right) demonstrates the immuno-stimulating potential of each group (affinity is represented by ic50, meaning lower values represent stronger TCR binding potential).
  • Kmerizer demonstrated that HLA germline prevalence is correlated with public data.
  • Kmerizer demonstrated HLA somatic specificity in 48 normal samples, sensitivity in simulation.
  • Kmerizer demonstrated neoantigen prediction analysis in clinical samples.
  • Embodiment 1 A method comprising determining a plurality of known allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of known allele sequences, detennining, based on the alignment, for each known allele sequence of the plurality of known allele sequences, a number of sequence reads that aligned to each known allele sequence, and determining, based on the numbers of sequence reads that aligned to each known allele sequence, for the one or more loci, the known allele sequences present at the one or more loci.
  • Embodiment 2 The method of embodiment 1, wherein the plurality of known allele sequences comprise a plurality of known human leukocyte antigen (HLA) allele sequences or a plurality of known killer cell immunoglobulin-like receptor (KIR) region allele sequences.
  • Embodiment 3 The method of embodiment 1, further comprising obtaining a sample from the subject and sequencing the sample to obtain the plurality of sequence reads of the target region of the chromosome.
  • Embodiment 4 The method of embodiment 1, further comprising determining, based on the alignment, for each read of the plurality of sequence reads, one or more known allele sequences to which each read aligns.
  • Embodiment 5 The method of embodiment 1 , wherein the target region comprises one or more of the following genes: HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-G, HLA-H, HLA- J, HLA-K, HLA-L, HLA-N, HLA-P, HLA-S, HLA-T, HLA-U, HLA-V, HLA- W, HLA-DRA, HLA-DRB1, HLA-DRB2, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA- DRB6, HLA-DRB7, HLA-DRB8, HLA-DRB9, HLA-DQA1, HLA-DQB1, HLA-DQA2, HLA-DQB2, HLA-DQB3, HLA-DOA, HLA-DOB, HLA-DMA, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DPA2, H
  • Embodiment 6 The method of embodiment 1, wherein the target region comprises chromosomal position 6p21 or chromosomal position 19ql 3.
  • Embodiment 7 The method of embodiment 1, wherein the target region comprises at least a portion of a major histocompatibility complex (MHC) region and the chromosome is human chromosome 6.
  • MHC major histocompatibility complex
  • Embodiment 8 The method of embodiment 1, wherein the target region comprises at least a portion of a killer cell immunoglobulin-like receptor (KIR) region and the chromosome is human chromosome 19.
  • KIR killer cell immunoglobulin-like receptor
  • Embodiment 9 The method of embodiment 1, wherein determining the plurality of known allele sequences comprises retrieving sequence data for known alleles, determining, based on the sequence data for know n alleles, a plurality of k-mer sequences and assembling the plurality of k-mer sequences into a data structure comprising a plurality of entries, wherein each entry comprises a k-mer sequence, a number of alleles containing the k-mer sequence, an allele identifier for each allele containing the k-mer sequence, and a start and stop position of the k-mer sequence in each allele.
  • Embodiment 10 The method of embodiment 1, wherein determining, based on the alignment, for each known allele sequence of the plurality of known allele sequences, a number of sequence reads that aligned to each known allele sequence comprises determining the number of sequence reads that aligned to each know n allele sequence with 100% identity.
  • Embodiment 11 The method of embodiment 1, wherein determining, based on the numbers of sequence reads that aligned to each known allele sequence, for the one or more loci, the known allele sequences present at the one or more loci comprises determining one or more known allele sequences having a highest number of sequence reads aligned.
  • Embodiment 12 The method of embodiment 1, wherein the reads that aligned to each known allele sequence are grouped into read families, and the method further comprises, determining a number of sequence read families that are aligned to each known allele sequence.
  • Embodiment 13 The method of embodiment 12, wherein determining, for the one or more loci, the known allele sequences present at the one or more loci based on the numbers of sequence read families that aligned to each known allele sequence.
  • Embodiment 14 The method of embodiment 1, further comprising determining a length of a portion of each known allele sequence aligned to two or more sequence reads of the plurality of sequence reads.
  • Embodiment 15 The method of embodiment 1 , further comprising sorting, for a locus, the known allele sequences present at the locus by the number of sequence reads that aligned to each known allele sequence, determining, for the locus, a first known allele sequence with a highest number of sequence reads aligned, inserting the first known allele sequence with the highest number of sequence reads aligned into a superset, determining one or more known allele sequences that aligned to reads that are a subset of the reads aligned to the first known allele sequence, and inserting the one or more know n allele sequences into the superset.
  • Embodiment 16 The method of embodiment 15, wherein the superset comprises a graph data structure.
  • Embodiment 17 The method of embodiment 16, wherein the graph data structure comprises a directed acyclic graph.
  • Embodiment 18 The method of embodiment 16, wherein the graph data structure represents a Hasse diagram.
  • Embodiment 19 The method of embodiment 15, further comprising determining that the locus is associated with a single superset and determining the first known allele sequence of the single superset as the allele present at the locus.
  • Embodiment 20 The method of embodiment 15, further comprising determining a plurality of supersets for the locus.
  • Embodiment 21 The method of embodiment 20, further comprising determining, based on the plurality of supersets for the locus, two supersets with a cumulative largest number of distinct reads and determining the first known allele sequence of each of the two supersets as the alleles present at the locus.
  • Embodiment 22 The method of embodiment 1, further comprising, assisting in a communication of the known allele sequences present at the one or more loci to a medical provider.
  • Embodiment 23 The method of embodiment 1 , wherein the known allele sequences present at the locus comprise a human leukocyte antigen (HLA) type at the locus or a killer cell immunoglobulin-like receptor (KIR) type at the locus.
  • HLA human leukocyte antigen
  • KIR killer cell immunoglobulin-like receptor
  • Embodiment 24 The method of embodiment 1, further comprising obtaining a sample from a cell to be transplanted into the subject and sequencing the sample to obtain the plurality of sequence reads of the target region of the chromosome.
  • Embodiment 25 The method of embodiment 24, further comprising determining if an HLA type of the subject matches the HLA type at the locus.
  • Embodiment 26 The method of embodiment 25, further comprising transplanting the cell into the subject if the HLA type of the subject matches the HLA type at the locus.
  • Embodiment 27 The method of embodiment 25, further comprising transplanting the cell into the subject if the HLA type of the subject matches the HLA type at the locus and administering an agent that reduces a likelihood of transplant rejection in the subject.
  • Embodiment 28 The method of embodiment 1, further comprising determining that the subject is predisposed to developing rheumatoid arthritis (RA) wherein the known allele sequences present at the one or more loci comprise HLA-DRB 1*04:01, *04:04, and *04:08; HLA-DRB 1*04: 05; HLA-DRBl*01 :01 and *01 :02; HLA-DRB 1*14:02; HLA-DRB1* 10:01; and HLA-DRBl*01:01, *04:01, *04:04, and *04:05.
  • RA rheumatoid arthritis
  • Embodiment 29 The method of embodiment 1, further comprising determining that the subject is predisposed to developing multiple sclerosis (MS) wherein the known allele sequences present at the one or more loci comprise HLA-DRBl*15:01, HLA-DQBl*06:02, HLA-DRB1 *01 :08, HLA-DRBl*03:01, and/or HLA-DRB 1*13:03.
  • MS multiple sclerosis
  • Embodiment 30 The method of embodiment 1, further comprising determining that the subject is predisposed to developing systemic lupus erythematosus (SLE) wherein the known allele sequences present at the one or more loci comprise HLA-DRB1, HLA-DR2 (DRBl*15:01), HLA-DR3 (DRBl*03:01), HL A-DRB1 *08:01, and/or HLA-DQA1 *01 :02.
  • SLE systemic lupus erythematosus
  • Embodiment 31 The method of embodiment 1 , further comprising determining that the subject is predisposed to developing Type 1 diabetes mellitus (T1D) wherein the known allele sequences present at the one or more loci comprise HLA-DQBl*03:02 and/or DQBl*02:01.
  • T1D Type 1 diabetes mellitus
  • Embodiment 32 The method of embodiment 1, further comprising determining that the subject is predisposed to developing Sjogren’s syndrome (SS) wherein the known allele sequences present at the one or more loci comprise DQAl*05:01, DQBl*02:01, and DRBl*03:01.
  • SS Sjogren’s syndrome
  • Embodiment 33 The method of embodiment 1, further comprising determining that the subject is predisposed to developing that the subject is predisposed to developing celiac disease (CD) wherein the known allele sequences present at the one or more loci comprise HLA-DQ2 (encoded by HLA-DQAl*05:01-DQBl*02:01) and/or HLA-DQ8 (encoded by DQAl*03:01-DQBl*03:02).
  • CD celiac disease
  • Embodiment 34 The method of embodiment 1, further comprising determining that the subject is predisposed to developing preeclampsia wherein the known allele sequences present at the one or more loci comprise KIR2DL1.
  • Embodiment 35 The method of embodiment 1 , further comprising determining that the subject is less likely to progress from HIV infection to AIDS wherein the known allele sequences present at the one or more loci comprise KIR3DS1.
  • Embodiment 36 The method of embodiment 1, further comprising determining that the subject is less likely to progress from HIV infection to AIDS wherein the known allele sequences present at the one or more loci comprise a combination of KIR3DS 1 and HLA- Bw4.
  • Embodiment 37 The method of embodiment 1, further comprising determining that the subject is predisposed to developing an autoimmune disease wherein the known allele sequences present at the one or more loci comprise KIR2DS1.
  • Embodiment 38 The method of embodiment 1, further comprising determining that the subject is predisposed to developing Crohn’s disease wherein the known allele sequences present at the one or more loci comprise KIR2DL2/KIR2DL3 heterozygosity.
  • Embodiment 39 The method of embodiment 1, further comprising determining that the subject is predisposed to developing Crohn’s disease wherein the known allele sequences present at the one or more loci comprise a combination of KIR2DL2/KIR2DL3 heterozygosity and HLA-C2 allele.
  • Embodiment 40 The method of embodiment 1, further comprising determining whether the subject is a suitable candidate for a donor in an unrelated hematopoietic cell transplantation wherein the known allele sequences present at the one or more loci comprise a group B KIR haploty pe.
  • Embodiment 41 A method comprising determining a plurality of known human leukocyte antigen (HLA) allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of known HLA allele sequences, determining, based on the alignment, for each known HLA allele sequence of the plurality of known HLA allele sequences, a number of sequence reads that aligned to each known HLA allele sequence, generating, based on the numbers of sequence reads that aligned to each known HLA allele sequence, one or more supersets of known HLA allele sequences, and determining, based on a number of distinct reads in the one or more supersets of known HLA allele sequences, for the one or more loci, the known HLA allele sequences present at the one or more loci.
  • HLA human leukocyte anti
  • Embodiment 42 A method comprising determining a plurality of known killer cell immunoglobulin-like receptor (KIR) allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of know n KIR allele sequences, determining, based on the alignment, for each known KIR allele sequence of the plurality of known KIR allele sequences, a number of sequence reads that aligned to each known KIR allele sequence, generating, based on the numbers of sequence reads that aligned to each known KIR allele sequence, one or more supersets of known KIR allele sequences, and determining, based on a number of distinct reads in the one or more supersets of known KIR allele sequences, for the one or more loci, the known KIR allele sequences present at the one or more loci.
  • KIR
  • Embodiment 43 A method comprising determining a plurality of known allele sequences, determining a plurality of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, aligning the plurality of sequence reads to the plurality of known allele sequences, determining, based on the alignment, for each known allele sequence of the plurality of known allele sequences, a number of sequence reads that aligned to each known allele sequence, generating, based on the numbers of sequence reads that aligned to each known allele sequence, one or more supersets of known allele sequences, and determining, based on a number of distinct reads in the one or more supersets of known allele sequences, for the one or more loci, the known allele sequences present at the one or more loci.
  • Embodiment 44 One or more non- transitory computer-readable media storing processor-executable instructions thereon that, when executed by a processor, cause the processor to perform the methods of any of embodiments 1-43.
  • Embodiment 45 A system comprising a computing device configured to perform the methods of any of embodiments 1-43 and an output device configured to output an indication of the known allele sequences present at the one or more loci.
  • Embodiment 46 An apparatus, comprising one or more processors and memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to perform the methods of any of embodiments 1-43.
  • Embodiment 47 A method comprising determining a plurality of sequence reads or read pairs of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, generating alignments of the plurality of sequence reads or read pairs to a plurality of known sequences, generating alignments of the plurality of sequence reads or read pairs to a plurality of decoy sequences, comparing the alignments of a sequence read or read pair to the known sequences against the alignments of the sequence read or read pair against the decoy sequences, wherein the sequence read or read pair is associated with a known sequence when an alignment score of the sequence read or read pair against a known sequence is greater than an alignment score of the sequence read or read pair against a decoy sequence, and identifying the sequence read or read pair as a supporting sequence read or read pair a sequence of interest.
  • Embodiment 48 The method of embodiment 47, further comprising obtaining a sample from the subject and sequencing the sample to obtain the plurality of sequence reads or read pairs of the target region of the chromosome.
  • Embodiment 49 The method of embodiment 47, wherein the target region comprises one or more of the following genes: EIL A- A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-G, HLA-H, HLA-J, HLA-K, HLA-L, HLA-N, HLA-P, HLA-S, HLA-T, HLA-U, HLA-V, HLA- W, HLA-DRA, HLA-DRB1, HLA-DRB2, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA- DRB6, HLA-DRB7, HLA-DRB8, HLA-DRB9, HLA-DQA1, HLA-DQB1, HLA-DQA2, HLA-DQB2, HLA-DQB3, HLA-DOA, HLA-DOB, HLA-DMA, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DPA2, HLA-DPA2,
  • Embodiment 50 The method of embodiment 47, wherein the target region comprises chromosomal position 6p21 or chromosomal position 19ql 3.
  • Embodiment 51 The method of embodiment 47, wherein the target region comprises at least a portion of a major histocompatibility complex (MHC) region and the chromosome is human chromosome 6.
  • MHC major histocompatibility complex
  • Embodiment 52 The method of embodiment 47, wherein the target region comprises at least a portion of a killer cell immunoglobulin-like receptor (KIR) region and the chromosome is human chromosome 19.
  • KIR killer cell immunoglobulin-like receptor
  • Embodiment 53 The method of embodiment 47, wherein the plurality of known sequences comprise a plurality of known human leukocyte antigen (HLA) allele sequences or a plurality of known human killer cell immunoglobulin-like receptor (KIR) region allele sequences.
  • HLA human leukocyte antigen
  • KIR human killer cell immunoglobulin-like receptor
  • Embodiment 54 The method of embodiment 47, wherein the plurality of decoy sequences comprises a plurality of non-human sequences.
  • Embodiment 55 The method of embodiment 54, wherein the plurality of non-human sequences comprises one or more of a plurality of bovine sequences, a plurality of rat sequences, or a plurality of microbial sequences.
  • Embodiment 56 The method of embodiment 47, wherein generating alignments of the plurality of sequence reads or read pairs to the plurality of known sequences comprises determining, based on the alignments, for a sequence read or a read pair of the plurality of sequence reads or read pairs, one or more known sequences to which each sequence read or each read of the read pair aligns with no mismatch or indel.
  • Embodiment 57 The method of embodiment 56, further comprising determining, based on the alignments, for each known sequence of the plurality of known sequences, a number of sequence reads or a number of read pairs that aligned to each known sequence and determining, based on the number of sequence reads or the number of read pairs that aligned to each known sequence, for the one or more loci, the known sequences present at the one or more loci.
  • Embodiment 58 The method of embodiment 47, wherein generating alignments of the plurality of sequence reads or read pairs to a plurality of decoy sequences comprises determining, based on the alignments, for the sequence read or the read pair, one or more decoy sequences to which each sequence read or read pair aligns with no mismatch or indel and discarding the sequence read or read pair.
  • Embodiment 59 The method of embodiment 47, wherein generating alignments of the plurality of sequence reads or read pairs to a plurality of decoy sequences comprises determining, based on the alignments, for the sequence read or read pair, one or more nonhuman decoy sequences to which each sequence read or read pair aligns with no mismatch or indel and identifying the sequence read or read pair as originating from a contaminated sample.
  • Embodiment 60 The method of embodiment 47, wherein generating the alignments of the plurality of sequence reads or read pairs to a plurality of known sequences comprises determining, based on the alignments, for a sequence read or read pair, one or more known sequences to which each sequence read or read pair aligns with at least one mismatch or indel and generating the alignment score against the known sequence.
  • Embodiment 61 The method of embodiment 47, wherein generating the alignments of the plurality of sequence reads or read pairs to a plurality of decoy sequences compnses determining, based on the alignments, for the sequence read or read pair, one or more decoy sequences to which each sequence read or read pair aligns with at least one mismatch or indel and generating the alignment score against the decoy sequence.
  • Embodiment 62 The method of embodiment 47 wherein the alignment score against the known sequence and the alignment score against the decoy sequence each comprise at least one mismatch and/or indel.
  • Embodiment 63 The method of embodiment 47, wherein identify ing the sequence read or read pair as a supporting sequence read or read pair of a sequence of interest comprises identifying, based on aligning the sequence read or read pair to one or more reference variants, a reference variant of the one or more reference variants as the candidate variant.
  • Embodiment 64 The method of embodiment 47, wherein identify ing the sequence read or read pair as a supporting sequence read or read pair of a sequence of interest comprises identifying, based on aligning the sequence read or read pair to one or more nonhuman reference variants, a non-human reference variant of the one or more non-human reference variants as the candidate variant and identifying the sequence read or read pair as originating from a contaminated sample.
  • Embodiment 65 The method of embodiment 47, wherein generating alignments of the plurality sequence reads or read pairs to a plurality of known sequences comprises determining a read pair aligns to at least two sequences of the plurality of known sequences and selecting one known sequence of the at least two known sequences.
  • Embodiment 66 The method of embodiment 47, wherein generating alignments of the plurality sequence reads or read pairs to a plurality of decoy sequences comprises determining a read pair align to at least two decoy sequences of the plurality of decoy sequences and selecting one decoy sequence of the at least two decoy sequences.
  • Embodiment 67 A method comprising determining a plurality of sequence reads or read pairs of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, generating alignments of the plurality of sequence reads or read pairs to a plurality of known allele sequences, generating alignments of the plurality of sequence reads or read pairs to a plurality of decoy allele sequences, comparing the alignments of a sequence read or read pair to the known alleles against the alignments of the sequence read or read pair against the decoy allele sequences, wherein the sequence read or read pair is associated with a germline allele when an alignment score of the sequence read or read pair against a known allele sequence is greater than an alignment score of the sequence read or read pair against a decoy allele sequence, and identifying the sequence read or read pair as a supporting sequence read or read pair for a candidate somatic variant.
  • Embodiment 68 The method of embodiment 67, further comprising obtaining a sample from the subject and sequencing the sample to obtain the plurality of sequence reads or read pairs of the target region of the chromosome.
  • Embodiment 69 The method of embodiment 67, wherein the target region comprises one or more of the following genes: HL A- A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-G, HLA-H, HLA-J, HLA-K, HLA-L, HLA-N, HLA-P, HLA-S, HLA-T, HLA-U, HLA-V, HLA- W, HLA-DRA, HLA-DRB1, HLA-DRB2, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA- DRB6, HLA-DRB7, HLA-DRB8, HLA-DRB9, HLA-DQA1, HLA-DQB1, HLA-DQA2, HLA-DQB2, HLA-DQB3, HLA-DOA, HLA-DOB, HLA-DMA, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DPA2,
  • Embodiment 70 The method of embodiment 67, wherein the target region comprises chromosomal position 6p21 or chromosomal position 19ql 3.
  • Embodiment 71 The method of embodiment 67, wherein the target region comprises at least a portion of a major histocompatibility complex (MHC) region and the chromosome is human chromosome 6.
  • MHC major histocompatibility complex
  • Embodiment 72 The method of embodiment 67, wherein the target region comprises at least a portion of a killer cell immunoglobulin-like receptor (KIR) region and the chromosome is human chromosome 19.
  • KIR killer cell immunoglobulin-like receptor
  • Embodiment 73 The method of embodiment 67, wherein the plurality of known allele sequences comprises a plurality of known human leukocyte antigen (HLA) allele sequences or a plurality of known human killer cell immunoglobulin-like receptor (KIR) region allele sequences.
  • HLA human leukocyte antigen
  • KIR human killer cell immunoglobulin-like receptor
  • Embodiment 74 The method of embodiment 67, wherein the plurality of decoy allele sequences comprises a plurality of non-human sequences.
  • Embodiment 75 The method of embodiment 74, wherein the plurality of non-human sequences comprises one or more of a plurality of bovine sequences, a plurality of rat sequences, or a plurality of microbial sequences.
  • Embodiment 76 The method of embodiment 67, wherein generating alignments of the plurality of sequence reads or read pairs to the plurality of known allele sequences comprises determining, based on the alignments, for a sequence read or a read pair of the plurality of sequence reads or read pairs, one or more known allele sequences to which each sequence read or each read of the read pair aligns with no mismatch or indel.
  • Embodiment 77 The method of embodiment 76, further comprising determining, based on the alignments, for each known allele sequence of the plurality of known allele sequences, a number of sequence reads or a number of read pairs that aligned to each known allele sequence and determining, based on the number of sequence reads or the number of read pairs that aligned to each known allele sequence, for the one or more loci, the known allele sequences present at the one or more loci.
  • Embodiment 78 The method of embodiment 67, wherein generating alignments of the plurality of sequence reads or read pairs to a plurality of decoy allele sequences comprises determining, based on the alignments, for the sequence read or the read pair, one or more decoy allele sequences to which each sequence read or read pair aligns with no mismatch or indel and discarding the sequence read or read pair.
  • Embodiment 79 The method of embodiment 67, wherein generating alignments of the plurality of sequence reads or read pairs to a plurality of decoy allele sequences comprises determining, based on the alignments, for the sequence read or read pair, one or more non- human decoy allele sequences to which each sequence read or read pair aligns with no mismatch or indel and identifying the sequence read or read pair as originating from a contaminated sample.
  • Embodiment 80 The method of embodiment 67, wherein generating the alignments of the plurality of sequence reads or read pairs to a plurality of known allele sequences comprises determining, based on the alignments, for a sequence read or read pair, one or more known allele sequences to which each sequence read or read pair aligns with at least one mismatch or indel and generating the alignment score against the know n allele sequence.
  • Embodiment 81 The method of embodiment 67, wherein generating the alignments of the plurality of sequence reads or read pairs to a plurality of decoy allele sequences comprises determining, based on the alignments, for the sequence read or read pair, one or more decoy allele sequences to which each sequence read or read pair aligns with at least one mismatch or indel and generating the alignment score against the decoy allele sequence.
  • Embodiment 82 The method of embodiment 67 wherein the alignment score against the known allele sequence and the alignment score against the decoy allele sequence each comprise at least one mismatch and/or indel.
  • Embodiment 83 The method of embodiment 67, wherein identifying the sequence read or read pair as a supporting sequence read or read pair of a sequence of interest comprises identifying, based on aligning the sequence read or read pair to one or more reference variants, a reference variant of the one or more reference variants as the candidate variant.
  • Embodiment 84 The method of embodiment 67, wherein identify ing the sequence read or read pair as a supporting sequence read or read pair of a sequence of interest comprises identifying, based on aligning the sequence read or read pair to one or more nonhuman reference variants, a non-human reference variant of the one or more non-human reference variants as the candidate variant and identifying the sequence read or read pair as originating from a contaminated sample.
  • Embodiment 85 The method of embodiment 67, wherein generating alignments of the plurality sequence reads or read pairs to a plurality of known allele sequences comprises determining a read pair aligns to at least two sequences of the plurality of known allele sequences and selecting one known allele sequence of the at least two known allele sequences.
  • Embodiment 86 The method of embodiment 67, wherein generating alignments of the plurality sequence reads or read pairs to a plurality of decoy allele sequences comprises determining a read pair align to at least two decoy allele sequences of the plurality of decoy allele sequences and selecting one decoy allele sequence of the at least two decoy allele sequences.
  • Embodiment 89 A method comprising determining a plurality of pairs of sequence reads of a target region of a chromosome of a subject, wherein the target region of the chromosome comprises one or more loci, generating a germline alignment of the plurality of pairs of sequence reads to a plurality of known allele sequences, generating a decoy alignment of the plurality of pairs of sequence reads to a plurality of decoy allele sequences, determining a pair of sequence reads of the plurality of pairs of sequence reads with both a germline alignment with at least one mismatch and/or indel and a decoy alignment with at least one mismatch and/or indel, wherein the pair of sequence reads is associated with a germline alignment score greater than a decoy alignment score, and identifying the pair of sequence reads as a candidate somatic variant.
  • Embodiment 90 The method of embodiment 89, further comprising obtaining a sample from the subject and sequencing the sample to obtain the plurality 7 of pairs of sequence reads of the target region of the chromosome.
  • Embodiment 91 The method of embodiment 89, wherein the target region comprises one or more of the following genes: HL A- A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-G, HLA-H, HLA-J, HLA-K, HLA-L, HLA-N, HLA-P, HLA-S, HLA-T, HLA-U, HLA-V, HLA- W, HLA-DRA, HLA-DRB1, HLA-DRB2, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA- DRB6, HLA-DRB7, HLA-DRB8, HLA-DRB9, HLA-DQA1, HLA-DQB1, HLA-DQA2, HLA-DQB2, HLA-DQB3, HLA-DOA, HLA-DOB, HLA-DMA, HLA-DMB, HLA-DPA1 , HLA-DPB1, HLA-
  • Embodiment 92 The method of embodiment 89, wherein the target region comprises chromosomal position 6p21 or chromosomal position 19ql 3.
  • Embodiment 93 The method of embodiment 89, wherein the target region comprises at least a portion of a major histocompatibility complex (MHC) region and the chromosome is human chromosome 6
  • MHC major histocompatibility complex
  • Embodiment 94 The method of embodiment 89, wherein the target region comprises at least a portion of a killer cell immunoglobulin-like receptor (KIR) region and the chromosome is human chromosome 19.
  • KIR killer cell immunoglobulin-like receptor
  • Embodiment 95 The method of embodiment 89, wherein the plurality of known allele sequences comprise a plurality of known human leukocyte antigen (HLA) allele sequences or a plurality of known human killer cell immunoglobulin-like receptor (KIR) region allele sequences.
  • HLA human leukocyte antigen
  • KIR human killer cell immunoglobulin-like receptor
  • Embodiment 96 The method of embodiment 89, wherein the plurality of decoy sequences comprises a plurality of non-human sequences.
  • Embodiment 97 The method of embodiment 96, wherein the plurality of non-human sequences comprises one or more of a plurality of bovine sequences, a plurality of rat sequences, or a plurality of microbial sequences.
  • Embodiment 98 The method of embodiment 89, wherein generating the germline alignment of the plurality of pairs of sequence reads to a plurality of known allele sequences comprises determining, based on the germline alignment, for a pair of sequence reads of the plurality of pairs of sequence reads, one or more known allele sequences to which each read of the pair of sequence reads aligns with no mismatch or indel.
  • Embodiment 99 The method of embodiment 98, further comprising determining, based on the germline alignment, for each known allele sequence of the plurality of known allele sequences, a number of pairs of sequence reads of the plurality of pairs of sequence reads that aligned to each known allele sequence and determining, based on the numbers of pairs of sequence reads that aligned to each known allele sequence, for the one or more loci, the known allele sequences present at the one or more loci.
  • Embodiment 100 The method of embodiment 89, wherein generating the decoy alignment of the plurality of pairs of sequence reads to a plurality of decoy allele sequences comprises determining, based on the decoy alignment, for the pair of sequence reads of the plurality of pairs of sequence reads, one or more decoy allele sequences to which each read of the pair of sequence reads aligns with no mismatch or indel and discarding the pair of sequence reads.
  • Embodiment 101 The method of embodiment 89, wherein generating the decoy alignment of the plurality of pairs of sequence reads to a plurality of decoy allele sequences comprises determining, based on the decoy alignment, for the pair of sequence reads of the plurality of pairs of sequence reads, one or more non-human decoy sequences to which each read of the pair of sequence reads aligns with no mismatch or indel and identifying the plurality of pairs of sequence reads as originating from a contaminated sample.
  • Embodiment 102 The method of embodiment 89, wherein generating the germline alignment of the plurality of pairs of sequence reads to a plurality of known allele sequences comprises determining, based on the germline alignment, for a pair of sequence reads of the plurality of pairs of sequence reads, one or more known allele sequences to which each read of the pair of sequence reads aligns with at least one mismatch or indel and generating the germline alignment score.
  • Embodiment 103 The method of embodiment 102, wherein generating the decoy alignment of the plurality of pairs of sequence reads to a plurality of decoy allele sequences comprises determining, based on the decoy alignment, for the pair of sequence reads of the plurality of pairs of sequence reads, one or more decoy allele sequences to which each read of the pair of sequence reads aligns with at least one mismatch or indel and generating the decoy alignment score
  • Embodiment 104 The method of embodiment 89, wherein identifying the pair of sequence reads as a candidate somatic variant comprises identifying, based on aligning the pair of sequence reads to one or more reference variants, a reference variant of the one or more reference variants as the candidate variant.
  • Embodiment 105 The method of embodiment 89, wherein identifying the pair of sequence reads as a candidate somatic variant comprises identifying, based on aligning the pair of sequence reads to one or more non-human reference vanants, a non-human reference variant of the one or more non-human reference variants as the candidate variant and identifying the plurality of pairs of sequence reads as originating from a contaminated sample.
  • Embodiment 106 The method of embodiment 89, wherein generating a germline alignment of the plurality of pairs of sequence reads to a plurality of known allele sequences comprises determining a pair of sequence reads aligns to at least two allele sequences of the plurality of know n allele sequences and selecting one known allele sequence of the at least two allele sequences.
  • Embodiment 107 The method of embodiment 89, wherein generating a decoy alignment of the plurality of pairs of sequence reads to a plurality of decoy allele sequences comprises determining a pair of sequence reads align to at least two decoy allele sequences of the plurality of decoy allele sequences and selecting one decoy allele sequence of the at least two decoy allele sequences.

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Abstract

L'invention concerne des méthodes et des systèmes de typage d'allèles et d'appel de variants.
PCT/US2023/065469 2022-04-07 2023-04-06 Méthodes et systèmes de typage d'allèles WO2023196925A2 (fr)

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