Methods and systems for identifying genetic variants

Abstract

Aspects disclosed herein are systems, devices, and methods for identifying recombinant variants (e.g., gene transformation variants), copy number of recombinant variants, and gene variant status (e.g., heterozygote or homozygote) of genes such as RHD and RHCE genes. In some embodiments, the disclosed systems, devices, and methods include the steps of: receiving a sequence read that is aligned to an RHD gene or an RHCE gene; estimating the combined copy number of the RHD gene and the RHCE gene; estimating the copy number of an RHD-specific base and an RHCE-specific base at each of a plurality of predetermined differentiation sites of the RHD gene and the RHCE gene; and calculating the probability of RHCE * CE-D (2)-CE gene transformation in the nucleic acid sample.

Description

[0001] Incorporation by reference into any priority application

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 349,993, filed on June 7, 2022, which is hereby incorporated by reference in its entirety. Background Art Technical Field

[0003] The disclosed technology relates to the field of nucleic acid sequencing. More specifically, the disclosed technology relates to detecting RHCE*CE-D(2)-CE gene conversion events in nucleic acid samples. Background Art

[0005] Rhesus (Rh) antigens play an important role in the red blood cell (RBC) antigenic phenotype. There are more than 330 RBC antigens. Variation in RBC antigens can be caused by variation in the RHD gene (Rh blood group D antigen) and the RHCE gene (Rh blood group CcEe antigen). Many different duplications, deletions, translocations, and gene conversion events within the RHD and RHCE genes have been documented in populations, including the RHCE*CE-D(2)-CE gene conversion event. Summary of the invention

[0006] In one aspect, disclosed herein are systems and computer-implemented methods for detecting RHCE*CE-D(2)-CE gene conversion events in a nucleic acid sample. In some embodiments, the method includes receiving sequence reads aligned to an RHD gene or an RHCE gene, estimating the combined copy number of the RHD gene and the RHCE gene in the nucleic acid sample, estimating the copy number of the RHD-specific base and the RHCE-specific base at each of a plurality of predetermined differentiation sites of the RHD gene and the RHCE gene, and calculating the probability of RHCE*CE-D(2)-CE gene conversion in the nucleic acid sample based on the estimated combined copy number of the RHD gene and the RHCE gene and the estimated copy number of the RHD-specific base and the RHCE-specific base at each of a plurality of predetermined differentiation sites.

[0007] In some embodiments, the RHCE*CE-D(2)-CE gene conversion results in a first breakpoint. In some embodiments, the plurality of predetermined differentiation sites includes at least two predetermined differentiation sites flanking the first breakpoint. In some embodiments, the method further includes identifying one or more sequence reads that encompass the first breakpoint and include RHD-specific bases at a first predetermined differentiation site flanking the first breakpoint and RHCE-specific bases at a second predetermined differentiation site flanking the first breakpoint.

[0008] In some embodiments, the RHCE*CE-D(2)-CE gene conversion results in a second breakpoint. In some embodiments, the plurality of predetermined differentiation sites includes at least two predetermined differentiation sites flanking the second breakpoint. In some embodiments, the method includes identifying one or more sequence reads that encompass the second breakpoint and include RHD-specific bases at a first predetermined differentiation site flanking the second breakpoint and RHCE-specific bases at a second predetermined differentiation site flanking the second breakpoint.

[0009] In some embodiments, estimating the copy number of RHD-specific bases and RHCE-specific bases at each of multiple predetermined differentiation sites of the RHD gene and the RHCE gene includes counting sequence reads including the RHD-specific base at a predetermined differentiation site among the multiple predetermined differentiation sites, and counting sequence reads including the RHCE-specific base at the predetermined differentiation site.

[0010] In some embodiments, calculating the probability of RHCE*CE-D(2)-CE gene conversion includes estimating the gene-specific copy number at each predetermined differentiation site in a plurality of predetermined differentiation sites based on the proportion of sequence reads comprising the RHD-specific or RHCE-specific base at the predetermined differentiation site multiplied by the estimated combined copy number of the RHD gene and the RHCE gene. In some embodiments, calculating the probability of RHCE*CE-D(2)-CE gene conversion includes detecting changes in gene-specific copy number in consecutive predetermined differentiation sites.

[0011] In some embodiments, estimating the combined copy number of the RHD gene and the RHCE gene comprises counting sequence reads aligned to the RHD gene or the RHCE gene. In some embodiments, estimating the combined copy number comprises normalizing the counts of sequence reads aligned to the RHD gene or the RHCE gene and applying a Gaussian mixture model. In some embodiments, the method accounts for the opposite orientation of the RHD gene and the RHCE gene.

[0012] In some embodiments, a plurality of predetermined differentiation sites are identified by a method comprising the steps of: identifying a single base difference between the sequences of the RHD gene and the RHCE gene in a reference sequence, and selecting the single base difference that is fixed in the population as the differentiation site. In some embodiments, selecting the single base difference that is fixed in the population as the differentiation site comprises: for a plurality of nucleic acid samples, receiving a plurality of sequence reads aligned to RHD and RHCE, for each of the plurality of nucleic acid samples, estimating a gene-specific copy number of the RHD gene and a copy number of the RHCE gene, selecting a nucleic acid sample subset of the plurality of nucleic acid samples, wherein the nucleic acid sample subset comprises nucleic acid samples estimated to be diploid of the RHD gene and diploid of the RHCE gene, and selecting a single base difference having a copy number consistent with diploid of the RHD gene and the RHCE gene in at least 90% of the nucleic acid samples in the nucleic acid sample subset.

[0013] In some embodiments, the method further comprises constructing one or more candidate haplotypes. In some embodiments, the one or more candidate haplotypes cover the breakpoint region of the RHCE*CE-D(2)-CE gene conversion. In some embodiments, constructing one or more candidate haplotypes comprises phasing the predetermined differentiation site using sequence reads aligned with the RHD gene or the RHCE gene. In some embodiments, phasing the predetermined differentiation site comprises constructing one or more candidate haplotypes based on all sequenced bases at the first predetermined differentiation site, and extending the one or more candidate haplotypes to the second predetermined differentiation site by aligning the sequence reads of the RHD gene or the RHCE gene. In some embodiments, the first and second predetermined differentiation sites are located on the sides of the RHCE*CE-D(2)-CE gene conversion breakpoint.

[0014] In some embodiments, the methods disclosed herein further comprise performing variant calls at a predetermined differentiation site in a plurality of predetermined differentiation sites. In some embodiments, the methods disclosed herein further comprise performing variant calls for RHCE*CE-D(2)-CE gene conversion. In some embodiments, the variant calls comprise homozygous or heterozygous variant calls. In some embodiments, the method further comprises creating a file comprising variant calls.

[0015] In some embodiments, the predetermined differentiation site comprises a site corresponding to a position selected from chr1:25405587, chr1:25405596, chr1:25409676, or chr1:25409958 of the reference genome hg38. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By referring to the following detailed description and accompanying drawings, the features of the examples of the present disclosure will become apparent, wherein like reference numerals correspond to similar but possibly not identical parts. For the sake of brevity, reference numerals or features having previously described functions may or may not be described in conjunction with other drawings in which they appear.

[0017] Figure 1A Schematic illustration of the RHCE*CE-D(2)-CE gene transformation event.

[0018] Figure 1B Schematic illustration of the differentiation site between the RHD gene and the RHCE gene.

[0019] Figure 1C Schematic illustration of sequence reads that align to the RHD gene or the RHCE gene and cover the differentiation site.

[0020] Figure 2 is a block diagram schematically illustrating a method for detecting RHCE*CE-D(2)-CE gene conversion events in a nucleic acid sample.

[0021] Figure 3A is a block diagram of an exemplary sequencing system that can be used to perform the disclosed methods.

[0022] Figure 3B Can be combined Figure 3A Block diagram of an exemplary computing device used with an exemplary sequencing system.

[0023] Figure 4 is a graph illustrating a reduction in false negatives (FN) after implementing embodiments of the methods described herein. DETAILED DESCRIPTION

[0024] All patents, patent applications and other publications mentioned herein, including all sequences disclosed in these references, are expressly incorporated herein by reference to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. The relevant portions of all references are incorporated herein by reference in their entirety for the purposes indicated by the context in which they are cited herein. However, the citation of any document shall not be construed as an admission of it as prior art to the present disclosure.

[0025] RHCE*CE-D(2)-CE

[0026] Accurate blood typing is necessary for safe blood transfusion. Basic blood typing by serology is the current standard of care (ABO / Rh+ or Rh-), and is usually sufficient to avoid most complications of transfusion. However, patients who require repeated transfusions (e.g., patients with cancer, sickle cell disease, or A-thalassemia) can benefit from a more comprehensive evaluation of their blood antigens. Although serology can be used for this extended blood typing, it relies on the availability of antibodies specific to each blood type and can become cumbersome and expensive. Molecular blood typing based on the patient's DNA can be an alternative to a more complete blood antigen spectrum.

[0027] The rhesus (Rh) factor is a widely used protein-based blood group system, second only to the ABO blood group. The antigens of the Rh blood group are derived from two genes, RHD and RHCE, which are parallel genes with about 97% identity to each other. Although most people are either Rh+ (have an active copy of RHD) or Rh- (have no copy of RHD), a gray area exists in the form of an excess of RHD variants: the so-called weak D, partial D, and DEL phenotypes. In addition to changes in the number of copies of the complete RHD gene or the RHCE gene, two mechanisms can lead to the formation of the D variant: small variants that result in amino acid changes, and gene conversion, in which part of one gene is replaced by another.

[0028] Detection of variants in RHD / RHCE can be complicated by the high sequence similarity observed between the two genes and the variable total copy number observed in such genes. In some cases, gene reads for the RHD / RHCE gene may not correspond to the wrong gene or may map to both genes with equal confidence, resulting in low mapping quality. The RHCE*CE-D(2)-CE gene conversion event is a gene conversion of exon 2 of the RHCE gene. In the RHCE*CE-D(2)-CE gene conversion event, exon 2 of the RHCE gene is replaced by a copy of exon 2 of the RHD gene, as shown in Figure 2. Figure 1A shown.

[0029] like Figure 1B and 1CAs shown, the RHD gene and the RHCE gene are paralogous genes, oriented in opposite directions in the patient's genome. In addition, the RHCE*CE-D(2)-CE gene conversion event is not the only potential mutation in these genes. Other duplications, deletions, translocations, and gene conversion events in the RHD gene and the RHCE gene have been observed in the population. Therefore, when sequencing the RHD gene and the RHCE gene in a nucleic acid sample, it may be difficult to detect the RHCE*CE-D(2)-CE gene conversion event due to the high homology between the RHCE and RHD genes. For example, the RHCE*CE-D(2)-CE gene conversion may not be detected, resulting in a false negative when calling a SNP variant in a nucleic acid sample from a patient. Embodiments of the present invention overcome these challenges, as described more fully below.

[0030] Overview

[0031] Described herein are methods and systems for detecting RHCE*CE-D(2)-CE gene conversion events in a nucleic acid sample taken from a patient. The disclosed systems and methods for detecting RHCE*CE-D(2)-CE gene conversion events in a nucleic acid sample were found to improve the specificity and sensitivity of detecting RHCE*CE-D(2)-CE gene conversions in a nucleic acid sample and the specificity and sensitivity of variant calling in the RHD and / or RHCE regions.

[0032] In some embodiments, the disclosed systems and methods include receiving sequence reads aligned to a RHD gene or a RHCE gene. Once the sequence reads are received, the combined copy number of the RHD gene and the RHCE gene in the nucleic acid sample can be estimated. Estimating the combined copy number can include counting sequence reads aligned to the RHD or RHCE region.

[0033] The disclosed systems and methods can then estimate the copy number of RHD-specific bases and RHCE-specific bases at each of a plurality of predetermined differentiation sites of the RHD gene and the RHCE gene. These predetermined differentiation sites can include positions in the nucleic acid sequence of the RHD gene or the RHCE gene, which include at least one base that differs between the RHD gene and the RHCE gene, and the difference is predetermined to be fixed in the population. Therefore, these predetermined differentiation sites can be used to determine whether a particular sequence read is from the RHD gene or the RHCE gene, including an RHCE*CE-D(2)-CE gene conversion event.

[0034] Figure 1BAn example of one such site that differs between the RHD gene and the RHCE gene is illustrated. In some embodiments, the differentiation sites are "predetermined," meaning that they have been identified (e.g., through population studies) prior to performing the methods described herein or implementing the systems described herein to detect RHCE*CE-D(2)-CE gene conversion events. In some embodiments, a process for detecting RHCE*CE-D(2)-CE gene conversion events includes counting sequence reads that include RHD-specific bases at predetermined differentiation sites and counting sequence reads that include RHCE-specific bases at predetermined differentiation sites. Sequence read counts can be used to estimate RHD-specific and RHCE-specific copy numbers at each predetermined differentiation site.

[0035] In some embodiments, the disclosed systems and methods include a process for calling variants associated with RHCE*CE-D(2)-CE gene conversion in a nucleic acid sample based on the copy number support of each observed base predetermined differentiation site. For example, the method can include calculating the probability of RHCE*CE-D(2)-CE gene conversion in a nucleic acid sample based on the estimated copy number support of RHD-specific bases or RHCE-specific bases at each of a plurality of predetermined differentiation sites, and based on the estimated combined copy number of RHD genes and RHCE genes. For example, the probability of RHCE*CE-D(2)-CE gene conversion in a nucleic acid sample can be inferred by observing changes in the estimated copy number of RHD-specific bases and RHCE-specific bases at consecutive predetermined differentiation sites in sequenced nucleic acids from a patient.

[0036] To further detect the RHCE*CE-D(2)-CE gene conversion event, one or more candidate haplotypes can be constructed, including candidate haplotypes covering the breakpoint region of the RHCE*CE-D(2)-CE gene conversion. Candidate haplotypes can be constructed by, for example, phasing the predetermined differentiation site using sequence reads aligned to the RHD gene or the RHCE gene.

[0037] To further detect the RHCE*CE-D(2)-CE gene conversion event, the methods and systems disclosed herein may include identifying one or more sequence reads that cover the breakpoint of the RHCE*CE-D(2)-CE gene conversion event and include RHD-specific bases at a first predetermined differentiation site flanking the breakpoint and RHCE-specific bases at a second predetermined differentiation site flanking the breakpoint.

[0038] The disclosed systems and methods can improve the recall (also known as sensitivity, the percentage of true variants correctly detected) of single nucleotide polymorphisms (SNPs) resulting from the RHCE*CE-D(2)-CE gene conversion event by 20%, 50%, 80%, 100% or more, for example by reducing false negatives.

[0039] definition

[0040] Unless otherwise defined, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs. See, for example, Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd Edition, J. Wiley & Sons (New York, NY 1994); Sambrook et al., "Molecular Cloning, A Laboratory Manual", Cold Spring Harbor Press (Cold Spring Harbor, NY 1989). For the purposes of this disclosure, the following terms are defined as follows.

[0041] As used herein, "nucleotide" includes a nitrogenous heterocyclic base, a sugar, and one or more phosphate groups. Nucleotides are monomeric units of nucleic acid sequences. Examples of nucleotides include, for example, ribonucleotides or deoxyribonucleotides. In ribonucleotides (RNA), the sugar is ribose, and in deoxyribonucleotides (DNA), the sugar is deoxyribose, i.e., a sugar lacking a hydroxyl group present at the 2' position in the ribose. The nitrogenous heterocyclic base can be a purine base or a pyrimidine base. Purine bases include adenine (A) and guanine (G) and their modified derivatives or analogs. Pyrimidine bases include cytosine (C), thymine (T) and uracil (U) and their modified derivatives or analogs. The C-1 atom of deoxyribose is bonded to the N-1 of pyrimidine or the N-9 of purine. The phosphate group can be in the form of monophosphate, diphosphate or triphosphate. These nucleotides can be natural nucleotides, but it should be further understood that non-natural nucleotides, modified nucleotides or analogs of the aforementioned nucleotides can also be used.

[0042] As used herein, "base" or "nucleobase" is a heterocyclic base such as adenine, guanine, cytosine, thymine, uracil, inosine, xanthine, hypoxanthine, or a heterocyclic derivative, analog or tautomer thereof. Nucleobases may be naturally occurring or synthetic. Non-limiting examples of nucleobases are adenine, guanine, thymine, cytosine, uracil, xanthine, hypoxanthine, 8-azapurine, a purine substituted with methyl or bromine at the 8 position, 9-oxo-N6-methyladenine, 2-aminoadenine, 7-deazaxanthine, 7-deazaguanine, 7-deaza-adenine, N4-ethanolylcytosine, 2,6-diaminopurine, N6-ethanolyl-2,6-diaminopurine, 5-methylcytosine , 5-(C3-C6)-alkynylcytosine, 5-fluorouracil, 5-bromouracil, thiouracil, pseudoisocytosine, 2-hydroxy-5-methyl-4-triazolopyridine, isocytosine, isoguanine, inosine, 7,8-dimethylalloxazine, 6-dihydrothymine, 5,6-dihydrouracil, 4-methyl-indole, ethanoladenine, and U.S. Patent Nos. 5,432,272 and 6,150,510, and PCT Application No. WO 92 / 002258, WO 93 / 10820, WO 94 / 22892 and WO 94 / 24144, and non-naturally occurring nucleobases described in Fasman (Practical Handbook of Biochemistry and Molecular Biology, pp. 385-394, 1989, CRC Press, Boca Raton, LO), all of which are incorporated herein by reference in their entirety.

[0043] The term "nucleic acid" or "polynucleotide" refers to a deoxyribonucleotide or ribonucleotide polymer in single-stranded or double-stranded form, and unless otherwise limited, encompasses known analogs of natural nucleotides that hybridize to nucleic acids in a manner similar to naturally occurring nucleotides, such as peptide nucleic acids (PNA) and thiophosphate DNA. Unless otherwise specified, a specific nucleic acid sequence includes its complementary sequence. Nucleotides include, but are not limited to, ATP, dATP, CTP, dCTP, GTP, dGTP, UTP, TTP, dUTP, 5-methyl-CTP, 5-methyl-dCTP, ITP, dITP, 2-amino-adenosine-TP, 2-amino-deoxyadenosine-TP, 2-thiothymidine triphosphate, pyrrolopyrimidine triphosphate and 2-thiocytidine, as well as α-thiotriphosphates of all of the above substances, and 2'-O-methyl-ribonucleotide triphosphates of all of the above bases. Modified bases include, but are not limited to, 5-Br-UTP, 5-Br-dUTP, 5-F-UTP, 5-F-dUTP, 5-propynyl dCTP, and 5-propynyl-dUTP.

[0044] As used herein, the term "chromosome" refers to the hereditary gene carrier of a living cell, which is derived from a chromatin chain containing DNA and protein components (especially histones). The conventional internationally recognized individual human genome chromosome numbering system is used herein.

[0045] "Genome" refers to the complete genetic information of an organism or virus expressed as nucleic acid sequences.

[0046] As used herein, the term "reference genome" or "reference sequence" refers to any specific known genomic sequence, whether partial or complete, of any organism or virus that can be used to reference an identification sequence from a subject. For example, reference genomes for human subjects, as well as many other organisms, can be found at the National Center for Biotechnology Information at ncbi.nlm.nih.gov. In various embodiments, the reference sequence is significantly larger than the reads to which it is compared. For example, the reference sequence can be at least about 100 times larger, or at least about 1000 times larger, or at least about 10,000 times larger, or at least about 10,000 times larger than the compared reads. 5 times larger, or at least about 10 6 times larger, or at least about 10 7times larger. In one example, the reference sequence is the sequence of the full-length genome. Such sequences may be referred to as genome reference sequences. For example, the reference sequence may be a reference human genome sequence, such as hg19 or hg38. In another example, the reference sequence is limited to a specific human chromosome, such as chromosome 13. In some embodiments, the reference chromosome is a Y chromosome sequence from human genome version hg19. Such sequences may be referred to as chromosome reference sequences. Other examples of reference sequences include genomes of other species, and chromosomes, subchromosome regions (such as chains) of any species, etc. In various embodiments, the reference sequence is a consensus sequence or other combination derived from multiple individuals. However, in certain applications, the reference sequence may be taken from a specific individual.

[0047] The term "nucleic acid sample" herein refers to a sample generally derived from a biological fluid, a cell, a tissue, an organ or an organism, and the sample comprises a nucleic acid or a nucleic acid mixture, and the nucleic acid or nucleic acid mixture comprises at least one nucleic acid sequence of a copy number variation to be screened. In certain embodiments, the nucleic acid sample includes at least one nucleic acid sequence in which the copy number is suspected to have varied. Such samples may include but are not limited to sputum / oral fluid, amniotic fluid, blood, blood fractions or fine needle biopsy samples (e.g., surgical biopsy, fine needle biopsy, etc.), urine, peritoneal fluid, pleural fluid, etc. Although the sample is usually taken from a human subject (e.g., a patient), the sample may be from any mammal, including but not limited to dogs, cats, horses, goats, sheep, cattle, pigs, etc. The sample may be used directly after being obtained from a biological source or after pretreatment to change the characteristics of the sample. For example, such pretreatment may include preparing plasma from blood, diluting viscous fluids, etc. The method of pretreatment may also include but is not limited to filtering, precipitation, dilution, distillation, mixing, centrifugation, freezing, freeze-drying, concentration, amplification, nucleic acid fragmentation, interfering component inactivation, adding reagents, cracking, etc. If such pretreatment methods are used for a sample, such pretreatment methods generally result in the retention of the nucleic acid of interest in the test sample, sometimes at a concentration proportional to that in an untreated test sample (e.g., a sample that has not been subjected to any such pretreatment methods). For purposes of the methods described herein, such "treated" or "treated" samples are still considered biological "test" samples.

[0048] The term "read" or "sequence read" (or sequencing read) refers to a sequence obtained from a portion of a nucleic acid sample. A read can be represented by a nucleotide string sequenced from any portion or all of a nucleic acid molecule. Typically, although not necessarily, a read represents a short sequence of adjacent base pairs in a sample. A read can be symbolically represented by a base pair sequence (in A, T, C, or G) of a sample portion. The read can be stored in a memory device and processed as appropriate to determine whether the read matches a reference sequence or meets other criteria. The read can be obtained directly from a sequencing device or indirectly from stored sequence information about a sample. In some cases, a read is a DNA sequence of sufficient length (e.g., at least about 25 bp) that can be used to identify a larger sequence or region, for example, it can be aligned and specifically assigned to a chromosome or genomic region or gene. For example, a sequence read can be a short nucleotide string (e.g., 20-150 bases) sequenced from a nucleic acid fragment, a short nucleotide string at one end or both ends of a nucleic acid fragment, or sequencing of an entire nucleic acid fragment present in a biological sample. Sequence reads can be obtained by any method known in the art. For example, sequence reads can be obtained in a variety of ways, such as using sequencing techniques or using probes (e.g., in hybridization arrays or capture probes), or amplification techniques, such as polymerase chain reaction (PCR) or linear amplification or isothermal amplification using a single primer. Sequence reads can be generated by techniques such as sequencing by synthesis, sequencing by binding, or sequencing by ligation. Sequence reads can be generated using instruments such as MINISEQ, MISEQ, NEXTSEQ, HISEQ, and NOVASEQ sequencing instruments from Illumina (San Diego, California).

[0049] As used herein, the term "sequencing depth" generally refers to the number of times a locus is covered by sequence reads aligned to the locus. A locus may be as small as one nucleotide, or as large as a chromosome arm, or as large as an entire genome. Sequencing depth may be expressed as 50×, 100×, etc., where "×" refers to the number of times a locus is covered by sequence reads. Sequencing depth may also be applied to multiple loci or whole genomes, in which case x may refer to the average number of times a locus or haploid genome or whole genome is sequenced, respectively. When citing an average depth, the actual depths of the different loci included in the data set span a range of values. Ultra-deep sequencing may refer to a sequencing depth of at least 100×.

[0050] As used herein, the term "aligned, alignment or aligning" refers to comparing a read or tag to a reference sequence to determine the possibility that the reference sequence includes the read sequence. If the reference sequence contains the read, the read can be mapped to the reference sequence, or in some embodiments, to a specific position in the reference sequence. For example, the alignment of the read with the reference sequence of human chromosome 13 will inform the possibility that the read exists in the reference sequence of chromosome 13. In some cases, the alignment further indicates the position to which the read or tag is mapped in the reference sequence. For example, if the reference sequence is a human whole genome sequence, the alignment may indicate that the read is present on chromosome 13, and may also indicate that the read is present on a specific chain and / or site of chromosome 13. A "site" can be a unique position on a polynucleotide sequence or a reference genome (i.e., chromosome ID, chromosome position and orientation). In some embodiments, the site can provide the position of a residue, sequence tag or segment on a sequence.

[0051] The reads or tags of the alignment are one or more sequences that are identified as matching known sequences from a reference genome in terms of their nucleic acid molecule order. The alignment can be performed manually, although it is usually implemented by a computer algorithm because it is not possible to align the reads within a reasonable time period for implementing the methods disclosed herein. The matching of sequence reads in the alignment can be 100% sequence matching or less than 100% (non-complete matching).

[0052] The alignment can be performed by modifications and / or combinations of methods such as Burrows-Wheeler Aligner (BWA), iSAAC, BarraCUDA, BFAST, BLASTN, BLAT, Bowtie, CASHX, Cloudburst, CUDA-EC, CUSHAW, CUSHAW2, CUSHAW2-GPU, drFAST, ELAND, ERNE, GNUMAP, GEM, GensearchNGS, GMAP and GSNAP, Geneious Assembler, LAST, MAQ, mrFAST and mrsFAST, MOM, MOSAIK, MPscan, Novoaligh and NovoalignCS, NextGENe, Omixon, PALMapper, Partek, PASS, PerM, PRIMEX, QPalma, RazerS, REAL, cREAL, RMAP, rNA, RT Investigator, Segemehl, SeqMap, Shrec, SHRiMP, SLIDER, SOAP, SOAP2, SOAP3 and SOAP3-dp, SOCS, SSAHA and SSAHA2, Stampy, SToRM, Subread and Subjunc, Taipan, UGENE, VelociMapper, XpressAlign and ZOOM.

[0053] As used herein, the term "mapping" refers to the specific assignment of sequence reads to a larger sequence, such as a reference genome, by alignment.

[0054] "Genetic variation" or "genetic change" refers to a specific genotype present in certain individuals, and generally genetic variation is present in a statistically significant subgroup of individuals. The presence or absence of genetic variance can be determined using the methods or devices described herein. In certain embodiments, the presence or absence of one or more genetic variations is determined according to the results provided by the methods and devices described herein. In some embodiments, genetic variation is a chromosomal abnormality (e.g., aneuploidy), a partial chromosomal abnormality, or mosaicism, each of which is described in more detail herein. Non-limiting examples of genetic variation include one or more deletions (e.g., microdeletions), repetitions (e.g., microrepetitions), insertions, mutations, polymorphisms (e.g., single nucleotide polymorphisms), fusions, repetitive sequences (e.g., short tandem repeats), differential methylation sites, differential methylation forms, etc., and combinations thereof. Insertions, repetitive sequences, deletions, repetitions, mutations, or polymorphisms can be of any length, and in some embodiments, the length is from about 1 base or base pair (bp) to about 250 megabases (Mb). In some embodiments, the insertion, repetitive sequence, deletion, duplication, mutation, or polymorphism is from about 1 base or base pair (bp) to about 1,000 kilobases (kb) in length (e.g., about 10 bp, 50 bp, 100 bp, 500 bp, 1 kb, 5 kb, 10 kb, 50 kb, 100 kb, 500 kb, or 1000 kb in length).

[0055] Genetic variation is sometimes a deletion. In certain embodiments, a deletion is a mutation (e.g., a genetic aberration) in which a portion of a chromosome or a DNA sequence is lost. Deletion is generally a loss of genetic material. Any number of nucleotides may be deleted. Deletion may include deletion of one or more whole chromosomes, chromosome fragments, alleles, genes, introns, exons, any noncoding region, any coding region, their fragments or their combinations. Deletion may include microdeletion. Deletion may include deletion of a single base.

[0056] Genetic variation is gene duplication sometimes.In certain embodiments, repetition is the mutation (for example, genetic aberration) that part of chromosome or DNA sequence is copied and inserted back into genome.In certain embodiments, gene duplication (that is, repetition) is any repetition of DNA region.In some embodiments, repetition is the nucleic acid sequence repeated (usually in series) in genome or chromosome.In some embodiments, repetition may include the following copies: one or more full chromosomes, chromosome fragments, alleles, genes, introns, exons, any non-coding region, any coding region, their fragments or their combinations.Repetition may include micro-repetition.Repetition sometimes includes one or more copies of replicated nucleic acid.Repetition is sometimes characterized as a gene region repeated once or multiple times (for example, repeated 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 times).In some cases, the scope of repetition can be a small region (several thousand base pairs) to a full chromosome.Repetition is often caused by the error of homologous recombination or by retrotransposon events.Repetition is associated with certain types of proliferative diseases.Repetition can be characterized using genomic microarray or comparative genomic hybridization (CGH).

[0057] Genetic variation is sometimes insertion. Insertion is sometimes adding one or more nucleotide base pairs to a nucleic acid sequence. Insertion is sometimes micro-insertion. In certain embodiments, insertion includes adding a chromosome fragment to a genome, a chromosome, or a fragment thereof. In certain embodiments, insertion includes adding an allele, a gene, an intron, an exon, any non-coding region, any coding region, a fragment thereof, or a combination thereof to a genome or a fragment thereof. In certain embodiments, insertion includes adding (i.e., inserting) a nucleic acid of unknown origin to a genome, a chromosome, or a fragment thereof. In certain embodiments, insertion includes adding (i.e., inserting) a single base.

[0058] Genetic variation sometimes includes copy number variation, that is, the copy number variation of the nucleic acid sequence present in the experimental sample compared with the copy number of the nucleic acid sequence present in the reference sample. In certain embodiments, the nucleic acid sequence is 1kb or larger. In some cases, the nucleic acid sequence is a full chromosome or a significant portion thereof." copy number variant" can refer to the nucleic acid sequence found to have copy number differences by comparing the expected level of the nucleic acid sequence concerned with the nucleic acid sequence concerned in the experimental sample. For example, the level of the nucleic acid sequence concerned in the test sample is compared with the nucleic acid sequence level present in the qualified sample. Copy number variant / variation can include deletion (including microdeletion), insertion (including microinsertion), repetition, multiplication and translocation. CNV covers chromosome aneuploidy and partial aneuploidy.

[0059] Embodiments of methods and systems for detecting RHCE*CE-D(2)-CE gene conversion events

[0060] Figure 2is a block diagram schematically illustrating an exemplary method 200 for detecting a RHCE*CE-D(2)-CE gene conversion event in a nucleic acid sample. In some embodiments, the method 200 is implemented on a computer. The method 200 may be embodied in a set of executable program instructions stored on a computer-readable medium (such as one or more disk drives) of a computing system. For example, Figure 3A and Figure 3B The server device 3102 shown and described in more detail below can execute a set of executable program instructions to implement the method 200. When the method 200 is initiated, the executable program instructions can be loaded into a memory such as a RAM and executed by one or more processors of the server device 3102. Figure 3B The illustrated server device 3102 describes the method 200, but the description is merely illustrative and is not intended to be limiting. In some embodiments, the method 200 or portions thereof may be performed serially or in parallel by multiple computing systems.

[0061] like Figure 2 As shown, a method 200 for detecting an RHCE*CE-D(2)-CE gene conversion event in a nucleic acid sample may begin at block 201, where a sequence read aligned to an RHD gene or an RHCE gene is received. For example, the sequence read aligned to an RHD gene or an RHCE gene may be mapped to a reference sequence to determine the alignment to the RHD gene or the RHCE gene. Next, the method 200 may proceed to block 202, where the combined copy number of the RHD gene and the RHCE gene in the nucleic acid sample is estimated. The method 200 may then proceed to block 203, where the copy number of the RHD-specific base and the RHCE-specific base at each of a plurality of predetermined differentiation sites of the RHD gene and the RHCE gene is estimated. Next, the method 200 may proceed to block 204, where the probability of RHCE*CE-D(2)-CE gene conversion in the nucleic acid sample is calculated based on the estimated copy number of the RHD gene and the RHCE gene and the estimated copy number of each RHD-specific and RHCE-specific base at each of a plurality of predetermined differentiation sites.

[0062] Receive sequence reads aligned to the RHD gene or RHCE gene

[0063] In some embodiments, the methods and systems disclosed herein include the step of receiving a plurality of sequence reads aligned to a RHD gene or to a RHCE gene.In some embodiments, the sequence reads are generated from a sample obtained from a subject.

[0064] Sequence reads can be generated by techniques such as sequencing by synthesis, sequencing by binding, or sequencing by ligation. Sequence reads can be generated using instruments such as MINISEQ, MISEQ, NEXTSEQ, HISEQ, and NOVASEQ sequencing instruments from Illumina (San Diego, California). Sequence reads can have a length of, for example, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1250, 1500, 1750, 2000 or more base pairs (bp). For example, the length of each sequence read is about 100 base pairs to about 1000 base pairs. The sequence read may include paired end sequence reads. The sequence read may include single end sequence reads. The sequence read may be generated by whole genome sequencing (WGS). The WGS may be clinical WGS (cWGS). The sample may include cells, cell-free DNA, cell-free fetal DNA, amniotic fluid, a blood sample, a biopsy sample, or a combination thereof.

[0065] In some embodiments, sequence reads are obtained by aligning the reads with the RHD or RHCE regions of a reference sequence. In some embodiments, sequence reads are obtained by aligning a first plurality of sequence reads generated from a sample with a reference genome sequence to obtain a second plurality of sequence reads aligned with RHD genes or RHCE genes in the reference genome sequence. In some embodiments, the computing system stores the first plurality of sequence reads in a memory. The computing system may load the first plurality of sequence reads into the memory. The sequence reads may be aligned with the RHD gene or RHCE gene in the reference sequence, wherein the alignment quality score is 0 or higher. The sequence reads may be aligned with the RHD gene or RHCE gene in the reference sequence with an alignment quality score of about 0 (e.g., when the sequence is aligned with a region that is highly homologous to genes and gene paralogs).

[0066] In some embodiments, the sequence reads are obtained from a file containing sequencing information. In some embodiments, the file is on a computer storage medium (e.g., a computer hard drive, such as a spinning disk drive or a solid state drive). In some embodiments, the file is stored in the format of a BAM, SAM, CRAM, or VCF file. In some embodiments, the sequence reads cover the breakpoint region of the RHCE*CE-D(2)-CE gene conversion event.

[0067] Estimation of combined copy number

[0068] In some embodiments, estimating the combined copy number of the RHD gene and the RHCE gene comprises counting sequence reads aligned with the RHD gene or the RHCE gene. In some embodiments, the combined copy number between the RHD gene and the RHCE gene is estimated by counting the total number of reads aligned with the RHD or RHCE in the reference genome sequence. In some embodiments, counting the total number of reads aligned with the RHD or RHCE in the reference genome sequence includes counting sequence reads that can be mapped to the RHD gene or the RHCE gene with equal confidence (resulting in 0 mapping quality). In some embodiments, the sequence reads are aligned with the regions in both RHD and RHCE with a mapping quality of 0, because due to the high homology between the regions of the RHD gene and the RHCE gene, the sequences between the two regions are the same. In some embodiments, by counting sequence reads with low mapping quality (including a mapping quality of 0), the combined copy number of the RHD gene and the RHCE gene can be estimated, despite high sequence homology.

[0069] In some embodiments, estimating the combined copy number comprises normalizing the counts of sequence reads aligned to the RHD gene or the RHCE gene and applying a Gaussian mixture model. In some embodiments, the Gaussian mixture model includes multiple Gaussians, each representing a different integer copy number, taking into account the normalized number of sequence reads aligned to the RHD gene or the RHCE gene (e.g., normalized and / or corrected sequence reads). For example, the read counts can be normalized by the length of the region and for a set of 3000 genomic regions of 2000bp that are expected to be consistently diploid in the population. In some embodiments, the Gaussian mixture model is then used to infer the most likely copy number of the RHD+RHCE genes based on the observed normalized depth signal.

[0070] The total copy number may be, for example, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more copies. The Gaussian mixture model may include a one-dimensional Gaussian mixture model. The multiple Gaussian functions of the Gaussian mixture model may represent integer copy numbers, such as 0 to 5, 0 to 6, 0 to 7, 0 to 8, 0 to 9, 0 to 10, 0 to 11, 0 to 12, 0 to 13, 0 to 14 or 0 to 15. For example, the multiple Gaussian functions of the Gaussian mixture model may represent integer copy numbers from 0 to 10. The average value of each Gaussian function in the multiple Gaussian functions may be the integer copy number represented by the Gaussian function. The average value of each Gaussian function in the multiple Gaussian functions may be the integer copy number represented by the Gaussian function (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or higher copy numbers). The standard deviation of the Gaussian function can be or be about, for example, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 or higher. The plurality of Gaussian functions of the Gaussian mixture model can include, for example, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more Gaussian functions. For example, the plurality of Gaussian functions of the Gaussian mixture model can include 5 Gaussian functions.

[0071] To estimate the combined copy number of the RHD gene and the RHCE gene, the computing system can use a Gaussian mixture model and a predetermined posterior probability threshold to determine the total copy number of the RHD gene and the RHCE gene, taking into account the normalized number of sequence reads aligned to the RHD gene or the RHCE gene. The predetermined posterior probability threshold can be, for example, 0.7, 0.75, 0.8, 0.85, 0.95 or greater.

[0072] Estimation of gene-specific base copy number at a predetermined differentiation locus

[0073] In some embodiments, the methods and systems disclosed herein include the step of estimating the copy number of the RHD-specific base and the RHCE-specific base at each of a plurality of predetermined differentiation sites of the RHD gene and the RHCE gene.

[0074] In some embodiments, sequence information (such as basecall) is evaluated at one or more predetermined differentiation sites. As used herein, a "predetermined differentiation site" refers to a site in a nucleic acid sequence that differs between the RHD gene and the RHCE gene sequences. For example, a predetermined differentiation site can be fixed in a population, and a base difference between the RHD gene and the RHCE gene is observed in at least 90%, at least 95%, at least 98%, or at least 99% of the population. In some embodiments, the RHCE*CE-D(2)-CE gene conversion results in a first breakpoint, and the plurality of predetermined differentiation sites include at least two predetermined differentiation sites flanking the first breakpoint. In some embodiments, the plurality of predetermined differentiation sites may include sites corresponding to positions selected from chr1:25405587, chr1:25405596, chr1:25409676, or chr1:25409958 of the reference genome hg38 (e.g., available from GenBank Assembly Accession No. GCA 000001405.15).

[0075] In some embodiments, the proportion of reads supporting expected RHD-specific bases and RHCE-specific bases is evaluated at each predetermined differentiation site in a plurality of predetermined sites. For example, sequence reads including RHD-specific bases at a predetermined differentiation site in a plurality of predetermined differentiation sites can be counted, and sequence reads including RHCE-specific bases at a predetermined differentiation site can be counted. The counts can be normalized using the methods described with reference to estimating the combined copy number of RHD genes and RHCE genes.

[0076] A computing system (e.g., server device 3102) can determine a normalized number of sequence reads that contain RHD-specific or RHCE-specific bases at a given predetermined differentiation site. To determine a normalized number of sequence reads that contain RHD-specific or RHCE-specific bases, the computing system can determine a normalized number of sequence reads that contain RHD-specific or RHCE-specific bases at a predetermined differentiation site using (1a) the depth of sequence reads that align to the predetermined differentiation site and contain RHD-specific or RHCE-specific bases, (1b) the length of the predetermined differentiation site, (2a) the depth of sequence reads that align to regions of the RHD gene or RHCE gene that do not include the predetermined differentiation site, and / or (2b) the length of each region of the RHD gene or RHCE gene that does not include the predetermined differentiation site.

[0077] Estimation of the probability of the RHCE*CE-D(2)-CE gene conversion event

[0078] In some embodiments, the methods and systems disclosed herein include the following steps: calculating the probability of RHCE*CE-D(2)-CE gene conversion in a nucleic acid sample based on the estimated combined copy number of the RHD gene and the RHCE gene and the estimated copy number of the RHD-specific base and the RHCE-specific base at each of a plurality of predetermined differentiation sites.

[0079] In some embodiments, the gene-specific copy number (e.g., the copy number of each of the RHD gene and the RHCE gene) is estimated for each of the plurality of predetermined differentiation sites. The gene-specific copy number can be based on the proportion of sequence reads containing RHD-specific or RHCE-specific bases at the predetermined differentiation site. In some embodiments, the method comprises multiplying the proportion of sequence reads supporting RHD-specific bases or RHCE-specific bases at each predetermined differentiation site by the estimated combined copy number, thereby estimating the gene-specific copy number at each predetermined differentiation site. The gene-specific copy number can be, for example, 0, 1, 2, 3, 4, or more. The gene-specific copy number can be an integer.

[0080] In some embodiments, the method includes detecting changes in gene-specific copy number (e.g., changes in the proportion of reads supporting RHD-specific or RHCE-specific bases) in consecutive predetermined differentiation sites, and estimating the probability of a RHCE*CE-D(2)-CE gene conversion event. For example, if a portion of an RHD gene or an RHCE gene has been replaced by a corresponding region from another gene, this will result in an increase or decrease in the proportion of reads supporting RHD-specific bases and RHCE-specific bases at the predetermined differentiation site.

[0081] In some embodiments, to determine the likelihood of RHCE*CE-D(2)-CE gene conversion, for one or more pairs of consecutive predetermined differentiation sites among a plurality of predetermined differentiation sites, the computing system can determine the copy number of the RHCE-specific base at the consecutive predetermined differentiation sites, taking into account the following factors: (1) the number of sequence reads aligned with RHD genes or RHCE genes that each contain two or more RHCE-specific bases at the consecutive predetermined differentiation sites, (2) the number of sequence reads aligned with RHD genes or RHCE genes that each contain an RHCE-specific base and an RHD-specific base, or that each contain an RHD-specific base and an RHCE-specific base at the consecutive predetermined differentiation sites, and / or (3) the number of sequence reads aligned with RHD genes or RHCE genes that each contain an RHCE base at the consecutive predetermined differentiation sites.

[0082] Identification of sequence reads covering the breakpoint

[0083] In some embodiments, the methods and systems disclosed herein include the step of identifying one or more sequence reads that cover a first breakpoint and include RHD-specific bases at a first predetermined differentiation site flanking the first breakpoint and RHCE-specific bases at a second predetermined differentiation site flanking the first breakpoint. Thus, the method may include identifying one or more sequence reads in a plurality of sequence reads aligned to an RHD gene or an RHCE gene, the one or more sequence reads covering one of the two breakpoints of the RHCE*CE-D(2)-CE gene conversion and including at least two predetermined differentiation sites, one on either side of the breakpoint, having an RHD-specific base at a first predetermined differentiation site flanking the breakpoint, and having an RHCE-specific base at a second predetermined site flanking the breakpoint.

[0084] In some embodiments, the RHCE*CE-D(2)-CE gene conversion results in a second breakpoint, and the plurality of predetermined differentiation sites include at least two predetermined differentiation sites flanking the second breakpoint. In some embodiments, the method further comprises identifying one or more sequence reads that encompass the second breakpoint and include an RHD-specific base at a first predetermined differentiation site flanking the second breakpoint and an RHCE-specific base at a second predetermined differentiation site flanking the second breakpoint. Thus, in some embodiments, the method comprises, for each of the two breakpoints of the RHCE*CE-D(2)-CE gene conversion, identifying one or more sequence reads that encompass each breakpoint and include an RHD-specific base at a first predetermined differentiation site flanking the breakpoint and an RHCE-specific base at a second predetermined differentiation site flanking the breakpoint.

[0085] In some embodiments, the predetermined differentiation sites flanking the breakpoint are selected from sites corresponding to positions selected from chr1:25405587, chr1:25405596, chr1:25409676, or chr1:25409958 of the reference genome hg38.

[0086] Construction of candidate haplotypes

[0087] In some embodiments, the methods and systems disclosed herein further comprise the step of constructing one or more candidate haplotypes. In some embodiments, the one or more candidate haplotypes cover the breakpoint region of the RHCE*CE-D(2)-CE gene conversion.

[0088] In some embodiments, constructing one or more candidate haplotypes comprises phasing the predetermined differentiation site using sequence reads aligned to the RHD gene or the RHCE gene. In some embodiments, phasing the predetermined differentiation site comprises constructing one or more candidate haplotypes based on all sequenced bases at a first predetermined differentiation site, and extending the one or more candidate haplotypes to a second predetermined differentiation site by aligning sequence reads of the RHD gene or the RHCE gene.

[0089] For example, a candidate haplotype may be formed by all the sequenced bases at a first predetermined differentiation site. For example, if two bases based on basecalls from sequencing reads covering the first predetermined differentiation site are possible at the first predetermined differentiation site, two candidate haplotypes may be formed. In some embodiments, the haplotype is then extended to the next predetermined differentiation site by considering all sequencing reads that can be uniquely assigned to a single candidate haplotype. In some embodiments, if these sequencing reads only support a single base at the next differentiation site for a given candidate haplotype, the haplotype is extended with the base. In some embodiments, when a candidate haplotype can extend two possible bases at a second predetermined differentiation site, both possible extended haplotypes are included in the set of candidate haplotypes, causing the set to grow by 1. In some embodiments, a subsequent extension step is performed at a third predetermined differentiation site, and these steps may be repeated until all sites have been processed. In some embodiments, the method generates a set of candidate haplotypes based on bases observed at multiple predetermined differentiation sites.

[0090] In some embodiments, the computing system uses sequence reads aligned to the RHD gene or RHCE gene comprising a plurality of predetermined differentiation sites to construct one or more candidate haplotypes derived from the RHD gene or the RHCE gene in a region of the RHCE gene comprising a plurality of predetermined differentiation sites. For example, the sequence reads can be aligned to a reference sequence such that the sequence reads overlap with the predetermined differentiation sites. The sequence reads can be aligned to a region of the RHD gene or a corresponding region of the RHCE gene comprising a plurality of predetermined differentiation sites, wherein the alignment quality score is 0 or higher.

[0091] In some embodiments, one or more candidate haplotypes include a wild-type RHD haplotype, a wild-type RHCE haplotype, and / or a RHCE*CE-D(2)-CE haplotype. The RHCE*CE-D(2)-CE haplotype may include an RHD base and an RHCE base. The RHCE*CE-D(2)-CE haplotype may be a recombinant variant. The RHCE*CE-D(2)-CE haplotype may include an RHCE variant haplotype. The haplotype may include a reciprocal recombinant variant. The haplotype may include a non-reciprocal recombinant variant or a gene conversion variant. The reference sequence may include a reference genome sequence.

[0092] To phase one or more haplotypes derived from the RHD gene or the RHCE gene, the computing system may use sequence reads aligned to the RHD or RHCE region comprising the plurality of predetermined differentiation sites to analyze connectivity information between predetermined differentiation sites in the plurality of predetermined differentiation sites. To phase one or more haplotypes derived from the RHD gene or the RHCE gene, the computing system may use sequence reads aligned to two or more of the plurality of predetermined differentiation sites to phase one or more haplotypes derived from the RHD gene or the RHCE gene.

[0093] In some embodiments, the first and second predetermined differentiation sites can flank the RHCE*CE-D(2)-CE gene conversion breakpoint. In some embodiments, the predetermined differentiation sites flanking the breakpoint are selected from sites corresponding to positions selected from chr1:25405587, chr1:25405596, chr1:25409676, or chr1:25409958 of the reference genome hg38.

[0094] For example, the boundaries of the RHCE*CE-D(2)-CE gene conversion event can be confirmed by phasing the predetermined differentiation sites using sequencing reads of the RHD gene or the RHCE gene mapped to each breakpoint region. In some embodiments, the method further comprises confirming the RHCE*CE-D(2)-CE gene conversion by identifying sequencing reads or sequencing read pairs that encompass the RHCE*CE-D(2)-CE breakpoint and contain RHD-specific bases and RHCE-specific bases at consecutive predetermined differentiation sites.

[0095] Identification of predetermined differentiation sites

[0096] Disclosed herein are methods and systems for identifying a plurality of predetermined differentiation sites. In some embodiments, the method includes identifying a single base difference between the sequences of the RHD gene and the RHCE gene in a reference sequence. For example, the reference sequence of the RHD gene can be compared to the reference sequence of the RHCE gene by aligning the sequences to each other and recording all sites with a single base difference between the two gene sequences. The locations of these differentiation sites in the RHD gene and the RHCE gene can then be stored in an electronic memory. For example, a file including a list of single base differences can be created.

[0097] In some embodiments, the method includes selecting a single base difference that is fixed in the population as a differentiation site. For example, the method may include, for multiple nucleic acid samples (such as multiple nucleic acid samples from a population of individuals), receiving multiple sequence reads aligned with the RHD gene and the RHCE gene. In some embodiments, the multiple nucleic acid samples are derived from individuals in the population, such as more than 100, more than 500, more than 1,000, more than 5,000 or more than 10,000 individuals. In some embodiments, the population is a diverse population, such as a genetically diverse population including individuals from multiple races, such as to account for differences in population type and increase the likelihood that the single base difference does not include differences due to population type. The method may also include, for each of the multiple nucleic acid samples, estimating the gene-specific copy number of the RHD gene and the copy number of the RHCE gene. The method may also include selecting a subset of nucleic acid samples from the multiple nucleic acid samples, wherein the nucleic acid sample subset includes nucleic acid samples estimated to be diploid of the RHD gene and diploid of the RHCE gene (e.g., using only data from samples estimated not to contain RHCE*CE-D(2)-CE gene conversion). The method may also include selecting a single base difference having a copy number consistent with a diploid of the RHD gene and the RHCE gene in a subset of nucleic acid samples of at least 90%, at least 95%, at least 97%, at least 98% or at least 99% of the nucleic acid samples.

[0098] The method may also include creating a file listing the positions of the selected single base differences, thereby generating a file including a plurality of predetermined differentiation sites. In some embodiments, the file is on a computer storage medium (e.g., a computer hard drive, such as a rotating disk drive or a solid-state drive). In some embodiments, the file is stored in the format of a BAM, SAM, CRAM, or VCF file. The file may include information about the predetermined differentiation site, such as the name of the chromosome where the predetermined differentiation site is located, a 1-based inclusive start position in the RHCE, an expected base sequence of the RHCE read mapped to the start position in the RHCE, an inclusive start position based on 1 in the RHD, an expected base sequence of the RHD read mapped to the start position in the RHD, an RHCE region corresponding to the RHD start position, a unique name of the predetermined differentiation site, and / or the direction of the predetermined differentiation site given by the direction of the gene.

[0099] Variant Calling

[0100] In some embodiments, the methods and systems disclosed herein further comprise the step of performing variant calling at a predetermined differentiation site in a plurality of predetermined differentiation sites. In some embodiments, variant calling is performed at each predetermined differentiation site in a gene that undergoes gene conversion (i.e., an RHCE gene), wherein the alternative allele is a base observed in the source of the gene conversion event (i.e., an RHD gene). In some embodiments, heterozygous or homozygous variant calling is performed based on the gene-specific copy number observed at each predetermined differentiation site within the gene conversion event region.

[0101] In some embodiments, variant calls are made for RHCE*CE-D(2)-CE gene conversions. In some embodiments, the variant calls include homozygous or heterozygous variant calls, including at separate predetermined differentiation sites and / or for RHCE*CE-D(2)-CE gene conversions.

[0102] In some embodiments, the methods and systems disclosed herein also include the step of creating a file comprising variant calls. In some embodiments, the file is on a computer storage medium (e.g., a computer hard drive, such as a rotating disk drive or a solid state drive). In some embodiments, the file is stored in the format of a BAM, SAM, CRAM, or VCF file. In some embodiments, the file is a VCF file.

[0103] Explain the opposite directions of the RHD and RHCE genes

[0104] In some embodiments, such as Figure 1B and Figure 1C As depicted in the diagram of , the RHD gene and the RHCE gene are paralogous genes in opposite orientations within the genome. Thus, in some embodiments, the methods and systems account for the opposite orientations of the RHD gene and the RHCE gene. In some embodiments, when counting or identifying sequence reads that include RHD-specific bases or RHCE-specific bases at a predetermined differentiation site, the opposite orientations of the RHD gene and the RHCE gene are accounted for.

[0105] For example, in Figure 1B In the embodiment of , the predetermined differentiation site is shown, which has the RHD-specific base "C" (cytosine) and the RHCE-specific base "A" (adenine). Figure 1C As shown in the embodiment of the present invention, the sequence reads aligned to the RHD gene include C at the predetermined differentiation site. If the gene conversion from RHD to RHCE occurs at the predetermined differentiation site, the sequence reads aligned to the RHCE gene will be expected to include "G" (base pair complementarity of guanine and cytosine) at the predetermined site due to the opposite orientation of the RHD gene and the RHCE gene, as shown in FIG. Figure 1C shown.

[0106] Therefore, in some embodiments, estimating the copy number of RHD-specific bases and RHCE-specific bases at each of multiple predetermined differentiation sites of the RHD gene and the RHCE gene includes counting sequence reads including the RHD-specific base or its complementary sequence at a predetermined differentiation site among the multiple predetermined differentiation sites, and counting sequence reads including the RHCE-specific base or its complementary sequence at the predetermined differentiation site.

[0107] Implementation of sequencing system

[0108] Figure 3A A diagram of an environment in which an RHCE*CE-D(2)-CE detection system may operate according to one or more implementations is shown. The following paragraphs describe the RHCE*CE-D(2)-CE detection system with respect to illustrative drawings depicting exemplary implementations and embodiments. For example, Figure 3A A schematic diagram of a computing system 3000 in which an RHCE*CE-D(2)-CE detection system 3106 operates according to one or more implementations is shown. As illustrated, the computing system 3000 includes one or more server devices 3102 connected to user client devices 3108, local devices 3118, and sequencing devices 3114 via a network 3112. The network 3112 may include any suitable network over which computing devices may communicate.

[0109] like Figure 3A As shown in , computing system 3000 includes server device 3102. In various specific implementations, server device 3102 can generate, receive, analyze, store and transmit electronic data, such as data of nucleic acid polymers for nucleic acid base calling or sequencing. In some specific implementations, server device 3102 receives various data from sequencing device 3114, such as data from sample genome and / or sequence reads. Server device 3102 can also communicate with user client device 3108. Specifically, server device 3102 can send sequence reads, direct nucleic acid base calling, nucleic acid base calling and / or sequencing metric data to user client device 3108.

[0110] As shown, the server device 3102 includes a sequencing application 3110. Typically, the sequencing application 3110 analyzes data (e.g., call data) received from the sequencing device 3114 or elsewhere to determine the nucleobase sequence of the nucleic acid polymer. For example, the sequencing application 3110 can receive raw data from the sequencing device 3114 and determine the nucleobase sequence of the sample genome or nucleic acid fragment. In some specific implementations, the sequencing application 3110 determines the sequence of nucleobases in DNA and / or RNA fragments or oligonucleotides.

[0111] As also shown, the sequencing application 3110 includes a RHCE*CE-D(2)-CE detection system 3106. As described below, the RHCE*CE-D(2)-CE detection system 3106 can detect RHCE*CE-D(2)-CE gene conversion events in a nucleic acid sample. For example, in some embodiments, the RHCE*CE-D(2)-CE detection system 3106 receives sequence reads obtained from a nucleic acid sample. The RHCE*CE-D(2)-CE detection system 3106 further estimates the combined copy number of the RHD gene and the RHCE gene in the nucleic acid sample. The RHCE*CE-D(2)-CE detection system 3106 further estimates the copy number of the RHD-specific base and the RHCE-specific base at each of a plurality of predetermined differentiation sites of the RHD gene and the RHCE gene. Based on the estimated combined copy number of the RHD gene and the RHCE gene and the estimated copy number of the RHD-specific base and the RHCE-specific base at each of multiple predetermined differentiation sites, the RHCE*CE-D(2)-CE detection system 3106 can calculate the probability of RHCE*CE-D(2)-CE gene conversion in the nucleic acid sample.

[0112] Furthermore, while the RHCE*CE-D(2)-CE detection system 3106 is described as being implemented on the server device 3102 as part of the sequencing application 3110, in some implementations, the RHCE*CE-D(2)-CE detection system 3106 is implemented (e.g., completely or partially located) by the user client device 3108, the sequencing device 3114, and / or the local device 3118. As mentioned, in still other implementations, the RHCE*CE-D(2)-CE detection system 3106 is implemented by one or more other components of the computing system 3000 (e.g., the sequencing device 3114). Specifically, the RHCE*CE-D(2)-CE detection system 3106 can be implemented across the server device 3102, the network 3112, the user client device 3108, the local device 3118, and the sequencing device 3114 in a variety of different ways.

[0113] like Figure 3AAs further shown in , the computing system 3000 includes a user client device 3108. In various specific implementations, the user client device 3108 can generate, store, receive and send digital data. Specifically, the user client device 3108 can receive data from the sequencing device 3114. As further shown, the user client device 3108 includes a sequencing application 3110. The sequencing application 3110 can be a network application or a native application (e.g., a mobile application, a desktop application, or a network application) stored and executed on the user client device 3108. The sequencing application 3110 receives data from the sequencing application 3110 and / or the RHCE*CE-D(2)-CE detection system 3106. For example, the user client device 3108 can receive a variant call file and / or an alignment file from the sequencing application 3110.

[0114] The sequencing application 3110 may include instructions that, when executed, cause the user client device 3108 to receive data from the RHCE*CE-D(2)-CE detection system 3106 and present the data from the sequencing device 3114 and / or the server device 3102. In addition, the sequencing application 3110 may instruct the user client device 3108 to display data of variant calls (e.g., nucleobase calls) or an indication of the calculated probability of a RHCE*CE-D(2)-CE gene conversion event. In practice, the user client device 3108 may display the nucleobase call results and / or an indication of the predicted RHCE*CE-D(2)-CE gene conversion for the genomic sample.

[0115] like Figure 3A , computing system 3000 includes sequencing equipment 3114. In various specific implementations, sequencing equipment 3114 sequences genome samples or other nucleic acid polymers. For example, sequencing equipment 3114 analyzes nucleic acid fragments or oligonucleotides extracted from genome samples to directly or indirectly generate data on sequencing equipment 3114. More specifically, sequencing equipment 3114 receives and analyzes the nucleic acid sequence extracted from genome samples in nucleotide sample slides (e.g., circulation pool). In one or more specific implementations, sequencing equipment 3114 utilizes SBS to sequence genome samples or other nucleic acid polymers. As a supplement or alternative to communicating across network 3112, in some specific implementations, sequencing equipment 3114 bypasses network 3112 and directly communicates with user client device 3108.

[0116] like Figure 3AAs further described in , in some implementations, server device 3102 comprises a distributed server collection, wherein server device 3102 comprises a number of server devices distributed across network 3112 and located in the same or different physical locations. For example, server device 3102 can be implemented in whole or in part on a local device 3118. For illustration, local device 3118 can implement sequencing application 3110 and / or RHCE*CE-D(2)-CE detection system 3106. In addition, server device 3102 and / or local device 3118 can include a content server, an application server, a communication server, a network hosting server, or another type of server.

[0117] Figure 3A The user client device 3108 shown in the figure may include various types of client devices. For example, in some specific implementations, the user client device 3108 includes a non-mobile device, such as a desktop computer or a server, or other types of client devices. In various specific implementations, the user client device 3108 includes a mobile device, such as a laptop, a tablet computer, a mobile phone, or a smart phone.

[0118] although Figure 3A Components of computing system 3000 are shown communicating via network 3112, but in some implementations, components of computing system 3000 may also communicate directly with each other, bypassing network 3112. For example, in some implementations, user client device 3108 communicates directly with sequencing device 3114. Additionally, in some implementations, user client device 3108 communicates directly with RHCE*CE-D(2)-CE detection system 3106 and / or server device 3102. In some implementations, user client device 3108 communicates directly with local device 3118. Additionally, RHCE*CE-D(2)-CE detection system 3106 may access one or more databases hosted on or accessed by server device 3102 or elsewhere in computing system 3000.

[0119] Figure 3B Can be combined Figure 3A 00106] Block diagram of an exemplary server device 3102 for use with the illustrative sequencing system 3000 of FIG. 100107] Server device 3102 can be configured to detect RHCE*CE-D(2)-CE gene conversion in a nucleic acid sample. Figure 3B The general architecture of the depicted server device 3102 includes an arrangement of computer hardware and software components. The server device 3102 may include Figure 3BThose more (or less) elements shown. However, in order to provide the disclosure that can be realized, it is not necessary to show all these general conventional elements. As shown, server device 3102 includes processing unit 310, network interface 320, computer readable medium driver 330, input / output device interface 340, display 350 and input device 360, all of which can communicate with each other through communication bus. Network interface 320 can provide connection with one or more networks or computing systems. Therefore, processing unit 310 can receive information and instructions from other computing systems or services via network. Processing unit 310 can also communicate with memory 370 via input / output device interface 340, and also provide output information for optional display 350. Input / output device interface 340 can also accept input from optional input device 360 ​​(such as keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game board, accelerometer, gyroscope or other input device).

[0120] Memory 370 may contain computer program instructions (grouped into modules or components in some embodiments) that processing unit 310 executes to implement one or more embodiments. Memory 370 typically includes RAM, ROM, and / or other persistent, secondary, or non-transitory computer-readable media. Memory 370 may store an operating system 372 that provides computer program instructions for use by processing unit 310 in general management and operation of computing device 3102. Memory 370 may store a reference genome 373, for example, for use by sequencing application 3110. Memory 370 may also include computer program instructions and other information for implementing various aspects of the present disclosure.

[0121] For example, in one embodiment, memory 370 includes a sequencing application 3110, which may include a RHCE*CE-D(2)-CE detection system 3106. The RHCE*CE-D(2)-CE detection system 3106 can perform the methods disclosed herein. In addition, memory 370 may include or communicate with a data storage 390 and / or one or more other data storages that store one or more inputs, one or more outputs, and / or one or more results (including intermediate results) of detecting a RHCE*CE-D(2)-CE gene conversion in a nucleic acid sample of the present disclosure, such sequencing reads, determined candidate haplotypes, and determined variant calls (e.g., detection of a RHCE*CE-D(2)-CE gene conversion).

[0122] In some embodiments, the system and method disclosed in the present invention may be related to a method for transferring or distributing certain sequence data analysis features and sequence data storage information to a cloud computing environment or a cloud-based network. User interactions with sequencing data, genomic data, or other types of biological data can be mediated via a central hub that stores and controls access to various interactions with data. In some embodiments, a cloud computing environment can also provide sharing of medical protocols, analytical methods, libraries, sequence data, and distributed processing for sequencing, analysis, and reporting. In some embodiments, a cloud computing environment helps users modify or annotate sequence data. In some embodiments, the system and method can be implemented in a computer browser, on demand, or online.

[0123] In some embodiments, software written to perform the methods as described herein is stored in some form of computer readable media, such as a memory, CD-ROM, DVD-ROM, memory stick, flash drive, hard drive, SSD hard drive, server, mainframe storage system, etc.

[0124] In some embodiments, the method can be written in any programming language in various suitable programming languages, such as compiled languages ​​such as C, C#, C++, Fortran and Java. Other programming languages ​​can be scripting languages, such as Perl, MatLab, SAS, SPSS, Python, Ruby, Pascal, Delphi, R and PHP. In some embodiments, the method is written in C, C#, C++, Fortran, Java, Perl, R, Java or Python. In some embodiments, the method can be a stand-alone application with data input and data display modules. Alternatively, the method can be a computer software product and can include such a class, wherein the distributed object includes an application containing a computing method as described herein.

[0125] In some embodiments, the method can be incorporated into existing data analysis software (such as data analysis software found on a sequencing instrument). Software including the method of computer implementation as described herein is directly installed on a computer system, or is indirectly maintained on a computer readable medium and loaded onto a computer system as needed. In addition, the method can be located on a computer remote from the location where the data is generated, such as software found on a server or the like maintained in another location relative to the location where the data is generated (such as provided by a third-party service provider).

[0126] The measuring instrument, desktop computer, laptop computer or server may include a processor operatively communicating with an accessible memory, and the accessible memory includes instructions for realizing the system and method. In some embodiments, a desktop computer or laptop computer operatively communicates with one or more computer-readable storage media or devices and / or output devices. The measuring instrument, desktop computer and laptop computer can operate under many different computer-based operating languages, such as the operating languages ​​used by the computer system based on Apple or the computer system based on PC. The measuring instrument, desktop computer and / or laptop computer and / or server system can also provide a computer interface for creating or modifying experimental definitions and / or conditions, viewing data results and monitoring experimental processes. In some embodiments, the output device can be a graphical user interface, such as a computer monitor or computer screen, a printer, a handheld device such as a personal digital assistant (i.e., PDA, Blackberry, iPhone), a tablet computer (e.g., iPAD), a hard disk drive, a server, a memory stick, a flash drive, etc.

[0127] Computer readable storage device or medium can be any device such as server, mainframe, supercomputer, tape system, etc. In some embodiments, storage device can be located at the site of the position of measuring instrument, such as adjacent to or next to measuring instrument. For example, relative to measuring instrument, storage device can be located in the same room, in the same building, in adjacent buildings, on the same floor in a building, on different floors in a building, etc. In some embodiments, storage device can be located outside the measuring instrument site or away from the measuring instrument. For example, relative to measuring instrument, storage device can be located in different parts of a city, different cities, different states, different countries, etc. In the embodiment where storage device is located away from the measuring instrument, communication between one or more of measuring instrument and desktop computer, laptop computer or server is usually connected by the Internet (wirelessly or by access point using network cable). In some embodiments, storage device can be maintained and managed by individuals or entities directly associated with measuring instrument, while in other embodiments, storage device can be maintained and managed by third parties, usually at the location of individuals or entities associated with measuring instrument at the far end. In the embodiment as described herein, output device can be any device for visualizing data.

[0128] The measuring instrument, desktop computer, laptop computer and / or server system itself can be used to store and / or retrieve a computer-implemented software program including a computer code for executing and implementing the computing method as described herein, data used in implementing the computing method, etc. One or more of the measuring instrument, desktop computer, laptop computer and / or server may include one or more computer-readable storage media, which are used to store and / or retrieve a computer-implemented software program including a computer code for executing and implementing the computing method as described herein, data used in implementing the computing method, etc. The computer-readable storage medium may include, but is not limited to, one or more of a hard drive, an SSD hard drive, a CD-ROM drive, a DVD-ROM drive, a floppy disk, a tape, a flash memory stick or card, etc. In addition, a network including the Internet can be a computer-readable storage medium. In some embodiments, a computer-readable storage medium refers to a computing resource storage device that can be accessed by a computer network through a corporate network provided by the Internet or a service provider, rather than, for example, a local desktop computer or laptop computer at a location away from the measuring instrument.

[0129] In some embodiments, computer-readable storage media for storing and / or retrieving computer-implemented software programs containing computer code for performing and implementing computational methods as described herein, data for use in implementing computational methods, etc. are operated and maintained by a service provider that operatively communicates with the assay instrument, desktop computer, laptop computer, and / or server system via an Internet connection or a network connection.

[0130] In some embodiments, the hardware platform for providing a computing environment includes a processor (i.e., a CPU), wherein processor time and memory layout such as a random access memory (i.e., RAM) are system considerations. For example, a smaller computer system provides a cheap, fast processor and a large memory and storage capacity. In some embodiments, a graphics processing unit (GPU) can be used. In some embodiments, the hardware platform for performing a computing method as described herein includes one or more computer systems with one or more processors. In some embodiments, smaller computers are clustered together to create a supercomputer network.

[0131] In some embodiments, the computing methods as described herein are performed on a collection of interconnected or internally connected computer systems (i.e., grid technology) that can run various operating systems in a coordinated manner. For example, the CONDOR framework (University of Wisconsin-Madison) and the systems available through United Devices are examples of coordinating multiple independent computer systems for the purpose of processing large amounts of data. These systems can provide a Perl interface to submit, monitor and manage large sequence analysis jobs on a cluster through serial or parallel configurations.

[0132] Example

[0133] Some aspects of the embodiments discussed above are further disclosed in detail in the following examples, which are not intended to limit the scope of the present disclosure in any way. It will be appreciated by those skilled in the art that many other embodiments also fall within the scope of the present disclosure, as described herein above and in the claims.

[0134] Example 1

[0135] The reference genome sequences of the RHD gene and the RHCE gene were aligned to each other, and all sites with a single base difference between the two gene sequences were selected. The positions of these divergent sites in the RHD gene and the RHCE gene were stored.

[0136] Used in a project called the 1000 Genomes Project Sequencing profiles nucleic acid samples from different population groups of approximately 3200 individuals. Short sequence reads from nucleotide samples are used to determine whether each single base difference between RHD and RHCE is fixed in the population. To this end, a subset of samples with an estimated combined copy number of 4 for RHD+RHCE is selected to limit to those samples without copy number changes. If a significant portion (10% or more) of the differential sites between RHD and RHCE have a read ratio that supports RHD-specific bases (RHD alleles) and RHCE-specific bases (RHCE alleles) that is inconsistent with the hypothesis that the sample has two copies of each gene (diploid hypothesis), another set of samples is filtered out. This step excludes samples where the diploid hypothesis is not true for either gene, or those samples with large gene conversion events.

[0137] Using a subset of filtered samples, each site with a difference between the RHD gene and the RHCE gene was filtered based on the consistency of the site having a proportion of reads supporting the RHD allele or the RHCE allele, and the read proportions were consistent with the two copies of each gene in the selected sample set. If at least 98% of the population samples had similar proportions of reads supporting the RHD allele and the RHCE allele, the site was selected as a "fixed differentiation site". If these proportions were not met, the site was excluded from the list of fixed differentiation sites. 793 differentiation sites were determined from the RHCE and RHD genes, which were single base pair differences in the homologous regions of RHCE and RHD that were found fixed in the population (occurring in more than 98% of the population).

[0138] Example 2

[0139] Sequence reads aligned to either RHCE or RHD genes from the HG002 reference genome were taken as input. The combined copy number of both RHCE and RHD genes was estimated from the read depth of reads aligned in the RH gene regions, normalized by the read depth of 3000 normalizing regions.

[0140] A file containing two pairs of differentiation sites flanking the RHCE*CE-D(2)-CE breakpoint site in the population and a file containing potential haplotypes of the RHCE*CE-D(2)-CE variants were provided as input. Two breakpoints were identified for the RHCE*CE-D(2)-CE gene conversion event, with their corresponding differentiation sites located at positions chr1:25405587 and chr1:25405596 (hg38) for the first breakpoint and at positions chr1:25409676 and chr1:25409958 (hg38) for the second breakpoint.

[0141] Candidate haplotypes are formed by a series of extension steps that use all reads that overlap with the predetermined differentiation site between the gene and its paralogous genes, as well as the total number of haplotypes obtained from the combined copy number of the RHCE and RHD genes. A set of candidate haplotypes is formed from all possible bases at the first predetermined differentiation site. The haplotype is then extended at the next differentiation site by considering all reads that can be uniquely assigned to a single candidate haplotype. If these reads only support a single base at the next differentiation site for a given candidate haplotype, the haplotype is extended with that base. When a candidate haplotype can be extended by two bases at the next differentiation site, both possible extended haplotypes are included in the set of candidate haplotypes, causing the set to grow by 1. Subsequent extension steps are performed at adjacent predetermined differentiation sites until all predetermined differentiation sites are processed.

[0142] To detect the RHCE*CE-D(2)-CE recombinant variant, a haplotype supporting the recombinant variant at the first breakpoint and a haplotype supporting the recombinant variant near the second breakpoint were identified. Because both identified candidate haplotypes supported the recombinant variant at the breakpoints, the RHCE*CE-D(2)-CE recombinant variant was detected.

[0143] After detecting the RHCE*CE-D(2)-CE gene conversion, the copy number of the predetermined differentiation site included in the gene conversion region is estimated based on the number of reads containing the RHCE-specific base at the predetermined differentiation site. If the predetermined differentiation site has an estimated RHCE copy number of 0, a homozygous variant is called for the predetermined differentiation site. If the predetermined differentiation site has an estimated RHCE copy number of 1, a heterozygous variant is called for the predetermined differentiation site. A file in VCF format including the variant calls is saved.

[0144] VCF formatted files including variant calls generated by the method described in this example as well as variant calls from other general variant calling methods were compared to a "true VCF" file that included variant calls that were assumed to be most representative of the HG002 sample. The "true VCF" file was also compared to a variant call file generated using a variant calling method that is not specific for the RHCE*CE-D(2)-CE gene transformation. Figure 4 As shown, a specific implementation of the system and method for detecting RHCE*CE-D(2)-CE gene conversion reduced 66 false negative variant calls, meaning that 66 additional SNPs were accurately called variants.

[0145] Other considerations

[0146] The embodiments described herein are exemplary. These embodiments may be modified, rearranged, alternatively processed, etc., and these are still encompassed within the teachings set forth herein. One or more of the steps, processes, or methods described herein may be performed by one or more processing devices and / or digital devices that are appropriately programmed.

[0147] Various exemplary imaging or data processing techniques described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of the two. To illustrate this interchangeability of hardware and software, various exemplary components, blocks, modules, and steps have been described above in general terms with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. The described functionality can be implemented in different ways for each specific application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0148] The various exemplary detection systems described in conjunction with the embodiments disclosed herein may be implemented or executed by a machine designed to perform the functions described herein, such as a processor configured with specific instructions, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof. The processor may be a microprocessor, but in an alternative, the processor may be a controller, a microcontroller or a state machine, a combination thereof, or the like. The processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. For example, the systems described herein may be implemented using discrete memory chips, a portion of a memory in a microprocessor, flash memory, EPROM, or other types of memory.

[0149] The elements of the methods, processes or algorithms described in conjunction with the embodiments disclosed herein can be directly implemented in hardware, in software modules executed by a processor, or in a combination of the two. The software module can reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of computer-readable storage medium known in the art. An exemplary storage medium can be coupled to a processor so that the processor can read information from the storage medium and write information thereto. In an alternative, the storage medium can be integral with the processor. The processor and the storage medium can reside in an ASIC. The software module can include computer executable instructions that cause a hardware processor to execute computer executable instructions.

[0150] Unless otherwise specifically stated or understood in the context of use, conditional language used herein such as "can", "might", "may", "for example", etc. is generally intended to convey that certain embodiments include, while other embodiments do not include certain features, elements and / or states. Therefore, such conditional language is generally not intended to imply that features, elements and / or states are in any way necessary for one or more embodiments, nor is it intended to imply that one or more embodiments necessarily include logic for determining whether these features, elements and / or states are included in any particular embodiment or to be performed in any particular embodiment with or without author input or prompting. The terms "include", "comprising", "having", "involving", etc. are synonyms, used inclusively in an open manner, and do not exclude additional elements, features, actions, operations, and the like. Moreover, the term "or" is used in its inclusive sense (rather than in its exclusive sense), so that when used, for example, to connect a list of elements, the term "or" means one, some, or all of the elements in the list.

[0151] Unless specifically stated otherwise or otherwise understood in the context in which it is generally used to refer to an item, term, etc., disjunctive language such as the phrase "at least one of X, Y, or Z" can be X, Y, or Z, or any combination thereof (e.g., X, Y, and / or Z). Thus, such disjunctive language is generally not intended to, and should not, imply that certain embodiments require that at least one of X, at least one of Y, or at least one of Z each be present.

[0152] The terms "about" or "approximately" and the like are synonymous and are used to indicate that the value modified by the term has an understanding range associated therewith, where the range may be ±20%, ±15%, ±10%, ±5%, or ±1%. The term "substantially" is used to indicate that a result (e.g., a measured value) is close to a target value, where "close" may mean, for example, that the result is within 80% of the value, within 90% of the value, within 95% of the value, or within 99% of the value.

[0153] Unless expressly stated otherwise, articles such as "a" or "an" should generally be interpreted as including one or more of the described items. Thus, phrases such as "a device configured to" or "a device for" are intended to include one or more of the recited devices. Such one or more of the described devices may also be collectively configured to perform the stated statements. For example, "a processor for performing statements A, B, and C" may include a first processor configured to perform statement A and working in conjunction with a second processor configured to perform statement B and statement C.

[0154] Although the above detailed description has shown, described and pointed out the novel features applied to the exemplary embodiments, it should be understood that various omissions, substitutions and changes in the form and details of the illustrated devices or algorithms can be made without departing from the essence of the present disclosure. As will be appreciated, certain embodiments described herein can be embodied in a form that does not provide all of the features and benefits set forth herein, as some features can be used or practiced separately from other features. All changes that come within the meaning and range of equivalency of the claims are to be included within their scope.

[0155] It should be understood that all combinations of the aforementioned concepts (assuming such concepts are not mutually contradictory) are contemplated as being part of the inventive subject matter disclosed herein. Specifically, all combinations of the claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein.

[0156] The scope of the present disclosure is not intended to be limited by the specific disclosure of the examples in this section or elsewhere in this specification, and may be limited by the claims presented in this section or elsewhere in this specification or presented in the future. The language of the claims should be interpreted broadly based on the language employed in the claims, and not limited to the examples described in this specification or during the prosecution of the application, which examples should be understood to be non-exclusive.

Claims

1. A computer-implemented method for detecting a RHCE*CE-D(2)-CE gene conversion event in a nucleic acid sample, the method comprising: receiving sequence reads aligned to the RHD gene or the RHCE gene; estimating the combined copy number of the RHD gene and the RHCE gene in the nucleic acid sample; estimating the copy number of the RHD-specific base and the RHCE-specific base at each of a plurality of predetermined differentiation sites of the RHD gene and the RHCE gene; as well as Based on the estimated combined copy number of the RHD gene and the RHCE gene and the estimated copy number of the RHD-specific base and the RHCE-specific base at each of the multiple predetermined differentiation sites, the probability of RHCE*CE-D(2)-CE gene conversion in the nucleic acid sample is calculated.

2. The method of claim 1, wherein the RHCE*CE-D(2)-CE gene conversion produces a first breakpoint, and wherein the plurality of predetermined differentiation sites includes at least two predetermined differentiation sites flanking the first breakpoint.

3. The method of claim 2, wherein the method further comprises identifying one or more sequence reads that encompass the first breakpoint and include RHD-specific bases at a first predetermined differentiation site flanking the first breakpoint and RHCE-specific bases at a second predetermined differentiation site flanking the first breakpoint.

4. The method of claim 3, wherein the RHCE*CE-D(2)-CE gene conversion produces a second breakpoint, wherein the plurality of predetermined differentiation sites include at least two predetermined differentiation sites flanking the second breakpoint, and wherein the method further comprises identifying one or more sequence reads that encompass the second breakpoint and include RHD-specific bases at a first predetermined differentiation site flanking the second breakpoint and RHCE-specific bases at a second predetermined differentiation site flanking the second breakpoint.

5. A method according to claim 1, wherein estimating the copy number of RHD-specific bases and RHCE-specific bases at each of multiple predetermined differentiation sites of the RHD gene and the RHCE gene includes counting sequence reads of RHD-specific bases at a predetermined differentiation site among the multiple predetermined differentiation sites, and counting sequence reads of RHCE-specific bases at the predetermined differentiation site.

6. A method according to claim 5, wherein calculating the probability of RHCE*CE-D(2)-CE gene conversion includes estimating the gene-specific copy number at each of the multiple predetermined differentiation sites based on the proportion of sequence reads containing RHD-specific or RHCE-specific bases at the predetermined differentiation site multiplied by the estimated combined copy number of the RHD gene and the RHCE gene.

7. The method of claim 6, wherein calculating the probability of RHCE*CE-D(2)-CE gene conversion comprises detecting changes in the gene-specific copy number in consecutive predetermined differentiation sites.

8. The method of claim 1 or claim 2, wherein estimating the combined copy number of the RHD gene and the RHCE gene comprises counting sequence reads aligned to the RHD gene or the RHCE gene.

9. The method of claim 8, wherein estimating the combined copy number comprises normalizing the counts of the sequence reads aligned to the RHD gene or the RHCE gene and applying a Gaussian mixture model.

10. The method according to any one of the preceding claims, wherein the method accounts for the opposite orientation of the RHD gene and the RHCE gene.

11. The method according to any one of the preceding claims, wherein the plurality of predetermined differentiation sites are identified by a method comprising the steps of: identifying single base differences between the RHD gene and the RHCE gene sequences in a reference sequence, and Single base differences that are fixed in the population are selected as differentiation sites.

12. The method according to claim 11, wherein selecting a single base difference fixed in a population as a differentiation site comprises: for a plurality of nucleic acid samples, receiving a plurality of sequence reads aligned to the RHD gene and the RHCE gene, for each of the plurality of nucleic acid samples, estimating a gene-specific copy number of the RHD gene and a copy number of the RHCE gene, selecting a subset of nucleic acid samples from the plurality of nucleic acid samples, wherein the subset of nucleic acid samples comprises nucleic acid samples estimated to be diploid of the RHD gene and diploid of the RHCE gene, and A single base difference having a copy number consistent with a diploid of the RHD gene and the RHCE gene is selected in at least 90% of the nucleic acid samples in the subset of nucleic acid samples.

13. The method according to any one of the preceding claims, wherein the method further comprises constructing one or more candidate haplotypes.

14. The method of claim 13, wherein the one or more candidate haplotypes cover the breakpoint region of the RHCE*CE-D(2)-CE gene conversion.

15. The method of claim 13, wherein constructing one or more candidate haplotypes comprises phasing the predetermined differentiation site using sequence reads aligned to the RHD gene or the RHCE gene.

16. The method of claim 15, wherein phasing the predetermined differentiation site comprises: constructing one or more candidate haplotypes based on all sequenced bases at the first predetermined differentiation site, and The one or more candidate haplotypes are extended to a second predetermined differentiation site by aligning sequence reads of the RHD gene or the RHCE gene.

17. The method of claim 16, wherein the first and second predetermined differentiation sites flank the RHCE*CE-D(2)-CE gene conversion breakpoint.

18. The method of any one of the preceding claims, further comprising performing variant calling at a predetermined differentiation site among the plurality of predetermined differentiation sites.

19. The method of any of the preceding claims, further comprising performing variant calling of the RHCE*CE-D(2)-CE gene conversion.

20. The method of claim 18 or claim 19, wherein the variant calls comprise homozygous or heterozygous variant calls.

21. The method of any preceding claim, further comprising creating a file comprising variant calls.

22. A method according to any of the preceding claims, wherein the predetermined differentiation site comprises a site corresponding to a position selected from chr1:25405587, chr1:25405596, chr1:25409676 or chr1:25409958 of the reference genome hg38.

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