Sequence map-based tools for determining variations in short tandem repeat regions

Abstract

The invention relates to a sequence map-based tool for determining variations in short tandem repeat regions. The present application relates to methods, apparatuses, systems, and computer program products for genotyping repetitive sequences, such as medically significant short tandem repetitive sequences. The method involves aligning reads to repetitive sequences represented by a sequence map, and genotyping the repetitive sequences using the aligned reads. The sequence diagrams are directed diagrams, each of which includes at least one self-loop representing a repetitive sub-sequence. The reads are paired end reads, and the two matches of each read pair are used to genotype the repetitive sequence. Some implementations may be used to determine degenerate codon repetitions. Some implementations are used for genotyping repetitive sequences each comprising two or more repetitive subsequences. Some implementations are useful for genotyping nucleic acid sequences, each comprising at least one repeat sequence and another genetic variation, such as an insertion, deletion or substitution.

Description

[0001] This application is a divisional application of an application with a filing date of March 6, 2020, an application number of 202080005551.0, and an invention name of “SEQUENCE MAP-BASED TOOL FOR IDENTIFYING CHANGES IN SHORT TANK REPEAT REGIONS”. Reference Merge

[0002] The PCT application form is filed concurrently with this specification and is incorporated herein by reference in its entirety and for all purposes. Background Art

[0003] Repeat expansions are a special class of microsatellite and minisatellite variants involving short tandem repeat (STR) polymorphisms. Repeat expansions are also known as dynamic mutations due to their instability when short tandem repeats expand beyond a certain size. Genetic diseases caused by unstable repeat expansions include fragile X syndrome (FXS), Huntington's disease, and amyotrophic lateral sclerosis (ALS).

[0004] Identifying repeat expansions is critical in the diagnosis and treatment of certain genetic diseases. However, it is difficult to determine the repeat sequence using short reads that do not completely traverse the repeat sequence. Therefore, it is desirable to develop methods to identify medically relevant repeat expansions using short reads. Summary of the invention

[0005] Specific implementations disclosed herein relate to methods, apparatus, systems, and computer program products for sequencing a locus that includes repetitive sequences, such as short tandem repeats that may be associated with a genetic disease. The method involves aligning reads with sequence graphs that each represent the locus, and using the aligned reads to genotype one or more repetitive sequences in the locus. Sequence graphs are directed graphs that each include at least one self-loop representing a repetitive sequence.

[0006] A first aspect of the present invention provides a computer-implemented method for genotyping a locus comprising a repetitive sequence. The method is implemented using a computer comprising one or more processors and a system memory. The method can be used to genotype one or more repetitive sequences, each repetitive sequence comprising one or more repetitive subsequences. The method comprises: (a) using one or more processors to collect sequence reads of a test sample from a database; (b) by the one or more processors, the sequence reads are aligned with the one or more repetitive sequences represented by each free sequence graph, wherein the sequence graph has a data structure of a directed graph, the directed graph having vertices representing nucleic acid sequences and directed edges connecting the vertices, and wherein the sequence graph comprises one or more self-loops, each self-loop representing a repetitive subsequence, each repetitive subsequence comprising a repetition of a repeating unit of one or more nucleotides; and (c) by the one or more processors, one or more genotypes of the one or more repetitive sequences are determined using the sequence reads aligned with the one or more repetitive sequences.

[0007] In some implementations, the repeat sequence of one or more repeat sequences includes a specific repeat unit, and the repeat specific unit includes at least one incompletely specified nucleotide. In some implementations, the specific repeat unit includes a degenerate codon.

[0008] In some implementations, the one or more self-loops include two or more self-loops representing two or more repeating subsequences.

[0009] In some implementations, the sequence diagram also includes two or more alternative paths for two or more alleles. In some implementations, the two or more alleles include insertions, deletions or substitutions. In some implementations, the substitutions include single nucleotide variations (SNVs) or single nucleotide polymorphisms (SNPs). In some implementations, the method also includes genotyping two or more alleles using sequence reads aligned with two or more alternative paths. In some implementations, genotyping two or more alleles includes providing the coverage of two or more alternative paths to a probability model to determine the probability of two or more alleles. In some implementations, the probability model simulates the probability of an allele as a function of allele coverage, the function being selected from a Poisson distribution, a negative binomial distribution, a binomial distribution, or a β-binomial distribution. In some implementations, the rate parameter of the Poisson distribution is estimated by the read length and average depth observed at the locus.

[0010] In some embodiments, the method further includes aligning the sequence reads to a reference genome to determine the genomic coordinates of the sequence reads before (b), and selecting a subset of the sequence reads as sequence reads to be aligned to one or more repetitive sequences, each of which is represented by a sequence graph. In some embodiments, the subset of sequence reads includes reads aligned to or near a region represented by the sequence graph. In some embodiments, the subset of sequence reads includes unaligned reads whose matches are mapped to or near a region represented by the sequence graph. In some embodiments, the subset of sequence reads includes reads aligned to one or more off-target regions, which are known hotspots for misaligned reads.

[0011] In some implementations, aligning the sequence reads to the sequence graph includes: finding kmer matches between the sequence reads and paths of the sequence graph; and extending the kmer matches to full alignments of nodes and edges of the sequence graph, the sequence graph including one or more self-loops.

[0012] In some implementations, aligning the sequence reads to the sequence graph includes shrinking the sequence graph by removing low confidence ends of the alignment.

[0013] In some implementations, aligning the sequence reads to the sequence graph includes merging alignments by: aligning subsequences of the reads to the sequence graph; and merging the alignments of the subsequences to form a complete alignment of the sequence reads.

[0014] In some implementations, the method further comprises generating a sequence map based on a locus specification comprising a locus structure of the locus.

[0015] In some embodiments, the sequence reads include paired-end reads, and operation (c) includes: (i) identifying anchor reads and anchor reads in the paired-end reads, wherein the anchor read is a read that is aligned to or near one or more repetitive sequences, and the anchor read is an unaligned read that is paired with the anchor read; and (ii) determining one or more genotypes of the one or more repetitive sequences using at least the anchor read.

[0016] In some implementations, operation (ii) includes determining one or more genotypes of one or more repetitive sequences using the anchor reads and the anchor reads. In some implementations, the anchor reads are aligned to about 5 kb of the repetitive sequence. In some implementations, the unaligned reads include reads that cannot be aligned with the sequence map or are poorly aligned.

[0017] In some implementations, the one or more repetitive sequences include short tandem repeat (STR) sequences. In some implementations, expansion of STRs is associated with fragile X syndrome, amyotrophic lateral sclerosis (ALS), Huntington's disease, Friedreich's ataxia, spinocerebellar ataxia, spinobulbar muscular atrophy, myotonic dystrophy, Machado-Joseph disease, or dentatorubalgebral pallidum Lewy body atrophy.

[0018] In some implementations, the method further comprises generating paired-end reads from the test sample using a sequencer.

[0019] In some embodiments, the method further comprises extracting a test sample from the individual.

[0020] In some embodiments, the test sample is a blood sample, a urine sample, a saliva sample, or a tissue sample.

[0021] In some implementations, the repeat unit comprises 1 to 50 nucleotides.

[0022] In some implementations, the read is shorter than at least one of the one or more repeat sequences.

[0023] Another aspect of the present disclosure provides a system for genotyping a locus including a repetitive sequence. In some specific implementations, the system includes: a system memory and one or more processors, the one or more processors being configured to: (a) collect sequence reads of a test sample from a database using the one or more processors; (b) align the sequence reads with the one or more repetitive sequences represented by each free sequence graph by the one or more processors, wherein the sequence graph has a data structure of a directed graph, the directed graph having vertices representing nucleic acid sequences and directed edges connecting the vertices, and wherein the sequence graph includes one or more self-loops, each of which represents a repetitive subsequence, each of which includes a repetition of a repetitive unit of one or more nucleotides; and (c) determine one or more genotypes of the one or more repetitive sequences by the one or more processors using the sequence reads aligned with the one or more repetitive sequences.

[0024] In some embodiments, the system further comprises a sequencer for sequencing the nucleic acid of the test sample.

[0025] In some implementations, one or more processors are configured to perform the various methods described herein.

[0026] Another aspect of the present disclosure provides a computer program product, including a non-transitory machine-readable medium storing program code, which, when executed by one or more processors of a computer system, causes the computer system to implement the above method for genotyping a locus comprising a repetitive sequence. The program code includes: (a) code for collecting sequence reads of a test sample from a database; (b) code for aligning the sequence reads with one or more repetitive sequences represented by a sequence graph, wherein the sequence graph has a data structure of a directed graph, the directed graph having vertices representing nucleic acid sequences and directed edges connecting the vertices, and wherein the sequence graph includes one or more self-loops, each of which represents a repetitive subsequence, each of which includes a repetition of a repetitive unit of one or more nucleotides; and (c) code for determining one or more genotypes of the one or more repetitive sequences using sequence reads aligned with the one or more repetitive sequences.

[0027] In some implementations, the program code includes code for performing the operations of the methods described herein.

[0028] This application provides the following: 1. A computer-implemented method comprising one or more processors and system memory, the method for genotyping one or more repetitive sequences, each repetitive sequence comprising one or more repetitive subsequences, the method comprising: (a) collecting sequence reads of a test sample from a database using the one or more processors; (b) aligning, by the one or more processors, the sequence reads with the one or more repetitive sequences represented by each free sequence graph, wherein the sequence graph has a data structure of a directed graph, the directed graph having vertices representing nucleic acid sequences and directed edges connecting the vertices, and wherein the sequence graph includes one or more self-loops, each self-loop representing a repetitive subsequence, and each repetitive subsequence including repetitions of repetitive units of one or more nucleotides; and (c) determining, by the one or more processors, one or more genotypes of the one or more repetitive sequences using the sequence reads aligned to the one or more repetitive sequences.

[0029] 2. The method according to item 1, wherein the repetitive sequence in the one or more repetitive sequences comprises a specific repetitive unit, wherein the specific repetitive unit comprises at least one incompletely specified nucleotide.

[0030] 3. A method according to item 2, wherein the specific repeating unit comprises a degenerate codon.

[0031] 4. A method according to any one of the preceding items, wherein the one or more self-loops include two or more self-loops representing two or more repeated subsequences.

[0032] 5. A method according to any one of the preceding items, wherein the sequence diagram also includes two or more alternative paths of two or more alleles.

[0033] 6. The method according to item 5, wherein the two or more alleles comprise insertions, deletions or substitutions.

[0034] 7. The method according to item 5, wherein the substitution comprises a single nucleotide variation (SNV) or a single nucleotide polymorphism (SNP).

[0035] 8. The method according to item 5 also includes genotyping the two or more alleles using sequence reads aligned with the two or more alternative pathways.

[0036] 9. A method according to item 8, wherein genotyping the two or more alleles includes providing the coverage of the two or more alternative pathways to a probability model to determine the probabilities of the two or more alleles.

[0037] 10. A method according to item 9, wherein the probability model simulates the probability of an allele as a function of the coverage of the allele, and the function is selected from Poisson distribution, negative binomial distribution, binomial distribution or β-binomial distribution.

[0038] 11. A method according to item 10, wherein the rate parameter of the Poisson distribution is estimated by the read length and average depth observed at the locus.

[0039] 12. The method according to any one of the preceding items further includes aligning the sequence reads with a reference genome before (b) to determine the genomic coordinates of the sequence reads, and selecting a subset of the sequence reads as the sequence reads to be aligned with the one or more repetitive sequences, each repetitive sequence represented by a sequence graph.

[0040] 13. A method according to item 12, wherein the subset of sequence reads includes reads that align with or are near a region represented by the sequence graph.

[0041] 14. A method according to item 12, wherein the subset of sequence reads includes unaligned reads, and the matches of the unaligned reads are mapped to the region represented by the sequence graph or near the region.

[0042] 15. A method according to item 12, wherein the subset of sequence reads includes reads aligned to one or more off-target regions, wherein the off-target regions are known hotspots for misaligned reads.

[0043] 16. The method according to any of the preceding items, wherein aligning the sequence reads with the sequence graph comprises: finding kmer matches between the sequence reads and paths of the sequence graph; and The kmer matches are extended to a complete alignment of nodes and edges of the sequence graph, the sequence graph including one or more self-loops.

[0044] 17. A method according to any of the preceding items, wherein aligning sequence reads to the sequence graph comprises shrinking the graph by removing low confidence ends of the alignment.

[0045] 18. The method according to any one of the preceding items, wherein aligning the sequence reads to the sequence graph comprises merging the alignments by: aligning a subsequence of the reads to a sequence graph; and The alignments of the subsequences are merged to form a complete alignment of the sequence reads.

[0046] 19. The method according to any of the preceding items further comprises generating the sequence map based on a locus specification including a locus structure of the locus.

[0047] 20. The method according to any one of the preceding items, wherein the sequence reads comprise paired-end reads, and operation (c) comprises: (i) identifying an anchor read and an anchor read in the paired-end reads, wherein the anchor read is a read aligned to or near the one or more repeat sequences, and the anchor read is an unaligned read paired with the anchor read; and (ii) determining the one or more genotypes of the one or more repetitive sequences using at least the anchor reads.

[0048] 21. The method according to item 20, wherein operation (ii) comprises determining the one or more genotypes of the one or more repeated sequences using the anchor reads and the anchor reads.

[0049] 22. A method according to item 20 or item 21, wherein the anchor read is aligned to a repetitive sequence of about 5 kb.

[0050] 23. A method according to any one of items 20 to 22, wherein the unaligned reads include reads that cannot be aligned or are poorly aligned to the sequence map.

[0051] 24. The method according to any one of the preceding items, wherein the one or more repetitive sequences comprise short tandem repeat (STR) sequences.

[0052] 25. The method according to item 24, wherein the amplification of the STR is associated with fragile X syndrome, amyotrophic lateral sclerosis (ALS), Huntington's disease, Friedreich's ataxia, spinocerebellar ataxia, spinal bulbar muscular atrophy, myotonic dystrophy, Machado-Joseph disease, or dentatorubral pallial Lewy body atrophy.

[0053] 26. The method according to any of the preceding items, further comprising generating paired-end reads from the test sample using a sequencer.

[0054] 27. The method according to any one of the preceding items, further comprising extracting the test sample from an individual.

[0055] 28. The method according to any one of the preceding items, wherein the test sample is a blood sample, a urine sample, a saliva sample or a tissue sample.

[0056] 29. The method according to any one of the preceding items, wherein the repeating unit comprises 1 to 50 nucleotides.

[0057] 30. The method according to any of the preceding items, wherein the read segment is shorter than at least one of the one or more repeated sequences.

[0058] 31. A system, comprising: system memory; and One or more processors configured to: (a) collecting sequence reads of test samples from a database; (b) aligning the sequence reads with the one or more repeated sequences represented by each free sequence graph, wherein the sequence graph has a data structure of a directed graph, the directed graph having vertices representing nucleic acid sequences and directed edges connecting the vertices, and wherein the sequence graph includes one or more self-loops, each self-loop represents a repeated subsequence, and each repeated subsequence includes a repetition of a repeating unit of one or more nucleotides; and (c) determining one or more genotypes of the one or more repetitive sequences using the sequence reads aligned to the one or more repetitive sequences.

[0059] 32. A computer program product comprising a non-transitory machine-readable medium storing program code, the program code, when executed by one or more processors of a computer system, causes the computer system to implement a method for genotyping a repetitive sequence in a test sample comprising nucleic acid, the program code comprising: (a) code for collecting sequence reads of test samples from a database; (b) code for aligning the sequence reads with the one or more repeated sequences represented by each free sequence graph, wherein the sequence graph has a data structure of a directed graph, the directed graph having vertices representing nucleic acid sequences and directed edges connecting the vertices, and wherein the sequence graph includes one or more self-loops, each self-loop representing a repeated subsequence, each repeated subsequence including a repetition of a repeating unit of one or more nucleotides; and (c) code for determining one or more genotypes of the one or more repetitive sequences using the sequence reads aligned to the one or more repetitive sequences.

[0060] Although the examples herein relate to humans and the language is primarily directed to humans, the concepts described herein are applicable to genomes from any plant or animal. These and other objects and features of the present disclosure will become more apparent from the following description and appended claims, or may be learned by the practice of the present disclosure as described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1A is a schematic diagram illustrating the difficulty in aligning sequence reads to repetitive sequences on a reference sequence.

[0062] Figure 1B is a diagram illustrating alignment of sequence reads using paired-end reads to overcome Figure 1A Schematic diagram of the difficulty shown.

[0063] Figure 1C A flow chart illustrating a process for genotyping a locus that includes repetitive sequences according to some implementations is shown.

[0064] Figure 1D A first sequence diagram representing a first locus is shown.

[0065] Figure 1E A second sequence diagram representing a second locus is shown.

[0066] Figure 1F A third sequence map representing a third genome is shown.

[0067] Figure 1GA schematic diagram of a process for determining the genotype of a variant at an HTT locus that includes two STR sequences is shown according to some implementations.

[0068] Figure 1H A schematic diagram of a process for determining the genotype of variants at a Lynch type I locus, including SNVs and STRs, according to some specific implementations is shown. Figure 1H The left panel shows a schematic diagram of the general process for targeted genotyping; the right panel shows the application of this process to genotyping variants at the locus associated with Lynch syndrome type I.

[0069] Figure 2 is a flow chart that provides a high-level description of an example method for determining the presence or absence of repetitive sequence expansion in a sample.

[0070] Figure 3 and Figure 4 is a flow chart illustrating an example of a method for detecting repeat expansion using paired-end reads.

[0071] Figure 5 is a flow chart of a method for determining repeat expansions using unaligned reads that are not associated with any repeat sequence of interest.

[0072] Figure 6 is a block diagram of a decentralized system for processing test samples.

[0073] Figure 7 The accuracy of genotyping CAG and CCG STRs in the HTT locus from simulated data using different methods is shown. DETAILED DESCRIPTION

[0074] The present disclosure relates to methods, apparatus, systems and computer program products for identifying repeat expansions of interest, such as expansions of medically significant repeat sequences. Examples of repeat expansions include, but are not limited to, expansions associated with genetic diseases, such as fragile X syndrome, ALS, Huntington's disease, Friedreich's ataxia, spinocerebellar ataxia, spinobulbar muscular atrophy, myotonic dystrophy, Machado-Joseph disease, and dentatorubral pallidum Lewy body atrophy.

[0075] Unless otherwise indicated, the practice of the methods and systems disclosed herein involves conventional techniques and apparatus commonly used in molecular biology, microbiology, protein purification, protein engineering, protein and DNA sequencing, and recombinant DNA fields, which are within the skill of the art. Such techniques and apparatus are known to those skilled in the art and are described in many texts and references (see, for example, Sambrook et al., "Molecular Cloning: A Laboratory Manual," 3rd ed. (Cold Spring Harbor),

[2001] ); and Ausubel et al., "Current Protocols in Molecular Biology"

[1987] ).

[0076] Numerical ranges include the numbers that define the ranges. Each maximum numerical limit given throughout this specification is intended to include each lower numerical limit, as if such lower numerical limits are explicitly written herein. Each minimum numerical limit given throughout this specification will include each higher numerical limit, as if such higher numerical limits are explicitly written herein. Each numerical range given throughout this specification will include each narrower numerical range that falls within such wider numerical ranges, as if such narrower numerical ranges are explicitly written herein.

[0077] The headings provided herein are not intended to limit the disclosure.

[0078] Unless otherwise defined herein, all technical and scientific terms used herein have the same meaning as those commonly understood by those of ordinary skill in the art to which the invention belongs. Various scientific dictionaries including the terms included herein are well known and available to those skilled in the art. Although any methods and materials similar or equivalent to the methods and materials described herein can also be used in the practice or testing of the embodiments disclosed herein, some methods and materials are described herein.

[0079] The terms defined immediately below are more fully described by reference to the specification as a whole.It is to be understood that this disclosure is not limited to the particular methodology, protocols, and reagents described, as these may vary depending on the context in which they are used by one skilled in the art.

[0080] definition As used herein, the singular terms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0081] Unless otherwise indicated, nucleic acids are written left to right in 5' to 3' orientation and amino acid sequences are written left to right in amino to carboxyl orientation, respectively.

[0082] The term "plurality" refers to more than one element. For example, the term as used herein refers to a plurality of nucleic acid molecules or sequence reads sufficient to identify significant differences in replicate amplification between a test sample and a control sample using the methods disclosed herein.

[0083] The term "repetitive sequence" refers to a nucleic acid sequence that includes repeated occurrences of shorter sequences. The shorter sequence is referred to as a "repeat unit" in this article. The repeated occurrence of a repeat unit is referred to as a "repeat" or "copy" of the repeat unit. In many cases, the position of the repeat sequence is associated with a gene encoding a protein. In other cases, the repeat sequence may be located in a non-coding region. The repeat unit may appear as a repeat sequence with or without interruptions between the repeat units. For example, in normal samples, the FMR1 gene often includes an AGG interruption in the CGG repeat, for example, (CGG) 10 + (AGG) + (CGG) 9. Samples lacking interruptions and having a few interrupted long repeat sequences are prone to repeated amplification of associated genes, which can lead to genetic diseases because repeated amplification is higher than a specific number. In various embodiments of the present disclosure, the number of repeats is counted as in-frame repeats, regardless of the interruption. The method for estimating in-frame repeats is further described below.

[0084] In various embodiments, the repeating unit includes 1 to 100 nucleotides. Many of the repeating units studied extensively are trinucleotide or hexanucleotide units. Some other repeating units that have been fully studied and are suitable for the embodiments disclosed herein include but are not limited to units of 4, 5, 6, 8, 12, 33 or 42 nucleotides. See, for example, Richards, Human Molecular Genetics, 2001, Vol. 10, No. 20, pp. 2187-2194. The application of the present invention is not limited to the specific number of the above-mentioned nucleotide bases, as long as they are relatively short compared to the repetitive sequences with multiple repetitions or copies of the repeating unit. For example, the repeating unit may include at least 3, 6, 8, 10, 15, 20, 30, 40, 50 nucleotides. Alternatively or in addition thereto, the repeating unit may include up to about 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 6 or 3 nucleotides.

[0085] Repeat sequences can be expanded under evolution, development and mutagenesis conditions to produce more copies of the same repeat unit. This is referred to in the art as "repeat amplification". Due to the unstable nature of repeat unit amplification, the process is also referred to as "dynamic mutation". Some repeat amplifications have been shown to be associated with hereditary diseases and pathological symptoms. Other repeat amplifications are not well understood or studied. The methods disclosed herein can be used to identify both previously known and new repeat amplifications. In some embodiments, the repeat sequence with repeat amplification is longer than about 100, 150, 300 or 500 base pairs (bp). In some embodiments, the repeat sequence with repeat amplification is longer than about 1000bp, 2000bp, 3000bp, 4000bp, 5000bp or 10000bp, etc.

[0086] In graph theory, vertices and edges are two basic units from which graphs are built. A vertex or node is one of the points that define a graph and can be connected by edges. In a schematic diagram of a graph, a vertex can be represented by a shape with a label, and an edge is represented by a line (undirected edge) or arrow (directed edge) extending from one vertex to another.

[0087] Two vertices connected by an edge are called the endpoints of the edge. A vertex x is said to be adjacent to another vertex y if the graph contains an edge (x, y).

[0088] An undirected graph consists of a set of vertices and a set of undirected edges (connecting unordered pairs of vertices), whereas a directed graph consists of a set of vertices and a set of directed edges (connecting ordered pairs of vertices).

[0089] In graph theory, each edge has two (or in a hypergraph, more) vertices attached to it (called its endpoints). Edges can be directed or undirected; undirected edges are also called lines, and directed edges are also called arcs or arrows.

[0090] A directed edge is an edge connecting an upstream vertex and a downstream vertex, where the upstream vertex appears before the directed edge and the downstream vertex appears after the directed edge.

[0091] An undirected edge is an edge connecting two vertices where either vertex can appear before the other in the graph path.

[0092] Ring, self-loop and single-node ring are used interchangeably herein. A ring has one node and an edge, wherein both ends of the edge are connected to the one node.

[0093] A cycle is a path consisting of two or more vertices, where the path of the cycle starts and ends at the same vertex. A simple cycle is a cycle that has no repeated vertices or edges other than the start and end vertices.

[0094] A cycle graph is a graph that includes at least one cycle.

[0095] An acyclic graph is a graph that does not contain any cycles or self-loops.

[0096] A directed acyclic graph (DAG) is a directed graph without any cycles or self-loops.

[0097] A graph path is a sequence of vertices and edges where both endpoints of an edge occur adjacent to the edge in the sequence. A graph path of a directed graph has upstream vertices that occur before a directed edge (or arc or arrow) and downstream vertices that occur after the directed edge.

[0098] The Poisson distribution is a discrete probability distribution that represents the probability of a given number of events occurring in a fixed time interval or space if those events occur at a known constant rate and are independent of the time since the last event.

[0099] The fully specified base symbols include G, A, T, C, representing guanine, adenine, thymine, and cytosine, respectively.

[0100] Incompletely specified nucleic acid designations include, inter alia, the following nucleic acid designations.

[0101] Purine (adenine or guanine): R Pyrimidine (thymine or cytosine): Y Adenine or thymine: W Guanine or cytosine: S Adenine or cytosine: M Guanine or thymine: K Adenine or thymine or cytosine: H Guanine or cytosine or thymine: B Guanine or Adenine or Cytosine: V Guanine or Adenine or Thymine: D Guanine or Adenine or Thymine or Cytosine: N The term "paired end read" refers to a read obtained from paired end sequencing, which obtains a read from each end of a nucleic acid fragment. Paired end sequencing involves fragmenting DNA into sequences called insert sequences. In some protocols (such as some protocols used by Illumina), reads from shorter insert sequences (e.g., about tens of bp to hundreds of bp) are referred to as short insert sequence paired end reads or simply paired end reads. In contrast, reads from longer insert sequences (e.g., about several thousand bp) are referred to as paired reads. In the present disclosure, both short insert sequence paired end reads and long insert sequence paired end reads can be used, and no distinction is made about the process for analyzing repeat amplification. Therefore, the term "paired end read" may refer to both short insert sequence paired end reads and long insert sequence paired reads, which are further described below. In some embodiments, paired end reads include reads of about 20bp to 1000bp. In some embodiments, paired end reads include reads of about 50bp to 500bp, about 80bp to 150bp, or about 100bp. It should be understood that the two reads in the paired ends need not be located at the extreme ends of the fragment being sequenced. Instead, one or both reads may be adjacent to the ends of the fragment. In addition, in the case of paired end reads, the methods exemplified herein can be performed using any of a variety of paired reads, regardless of whether the reads are derived from the ends of the fragment or other parts of the fragment.

[0102] As used herein, the term "alignment" and "aligning" refers to the process of comparing a read to a reference sequence to determine whether the reference sequence contains the read sequence. The alignment process attempts to determine whether the read can be mapped to the reference sequence, but does not always align the read to the reference sequence. If the reference sequence contains the read, the read can be mapped to the reference sequence, or in some embodiments, mapped to a specific position in the reference sequence. In some cases, the alignment simply tells whether the read is a member of a specific reference sequence (i.e., whether the read exists in the reference sequence). For example, the alignment of the read to the reference sequence of human chromosome 13 will tell whether the read exists in the reference sequence of chromosome 13. The tool that provides this information may be referred to as a set membership tester. In some cases, the alignment further indicates the position in the reference sequence to which the read is mapped. 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.

[0103] The reads of the comparison are one or more sequences that are identified as matching with a known reference sequence such as a reference genome in terms of their nucleic acid molecule order. The reads of the comparison and their determined positions on the reference sequence constitute sequence tags. The comparison can be performed manually, although it is usually implemented by a computer algorithm because it is impossible to compare the reads within a reasonable time period for implementing the method disclosed herein. An example of an algorithm from the comparison sequence is the efficient nucleotide data local alignment (ELAND) computer program distributed as a part of the Illumina Genomics analysis pipeline. Alternatively, a Bloom filter or a similar set membership tester can be used to compare the reads with the reference genome. See U.S. Patent Application No. 14 / 354,528 filed on April 25, 2014, which is incorporated herein by reference in its entirety. The matching of sequence reads in the comparison can be 100% sequence matching or less than 100% (e.g., non-complete matching).

[0104] As used herein, the term "mapping" refers to the assignment of a read sequence to a larger sequence, such as a reference genome, by alignment.

[0105] In some cases, one of the two paired end reads is aligned with a repetitive sequence of a reference sequence, while the other of the two paired end reads is not aligned with a repetitive sequence of the reference sequence. In such cases, the paired read aligned with the repetitive sequence of the reference sequence is referred to as an "anchor read". The paired end read that is not aligned with the repetitive sequence but paired with the anchor read is referred to as an anchor read. Therefore, the unaligned read can be anchored to the repetitive sequence and associated with the repetitive sequence. In some embodiments, the unaligned read includes both the read that cannot be aligned with the reference sequence and the read that is poorly aligned with the reference sequence. When the read is aligned with a reference sequence having multiple mismatched bases higher than a certain standard, the read is considered to be poorly aligned. For example, in various embodiments, when the read is aligned with at least about 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 mismatches, the read is considered to be poorly aligned. In some cases, a pair of two reads are aligned with the reference sequence. In such cases, in various implementations, both reads may be analyzed as "anchor reads."

[0106] The terms "polynucleotide", "nucleic acid" and "nucleic acid molecule" are used interchangeably and refer to a sequence of covalently linked nucleotides (i.e., ribonucleotides for RNA and deoxyribonucleotides for DNA) in which the 3' position of the pentose of one nucleotide is linked to the 5' position of the pentose of the next nucleotide via a phosphodiester group. Nucleotides include sequences of any form of nucleic acid, including but not limited to RNA and DNA molecules, such as free DNA (cfDNA) molecules. The term "polynucleotide" includes but is not limited to single-stranded polynucleotides and double-stranded polynucleotides.

[0107] The term "test sample" herein refers to a sample generally derived from a biological fluid, cell, tissue, organ or organism and including a nucleic acid or a mixture of nucleic acids, which has at least one nucleic acid sequence to be screened for copy number variation. In certain embodiments, the sample has at least one nucleic acid sequence, the copy number of which is suspected to have mutated. Such samples include, but are not limited to, sputum / oral fluid, amniotic fluid, blood, blood fractions or fine needle biopsy samples, urine, peritoneal fluid, pleural fluid, etc. Although samples are usually taken from human subjects (e.g., patients), the determination can be used for copy number variation (CNV) of any mammal, including but not limited to dogs, cats, horses, goats, sheep, cattle, pigs, etc. The sample can be used directly as obtained from a biological source, or after pretreatment to change the properties 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, filtration, precipitation, dilution, distillation, mixing, centrifugation, freezing, lyophilization, concentration, amplification, nucleic acid fragmentation, inactivation of interfering components, addition of reagents, lysis, 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.

[0108] The control sample can be a negative or positive control sample. A "negative control sample" or "unaffected sample" refers to a sample that includes a nucleic acid known or expected to have a repetitive sequence with a plurality of repetitions within the non-pathogenic range. A "positive control sample" or "affected sample" is known or expected to have a repetitive sequence with a plurality of repetitions within the pathogenic range. The repetitions of the repetitive sequence in the negative control sample have generally not been amplified beyond the normal range, while the repetitions of the repetitive sequence in the positive control sample have generally been amplified beyond the normal range. Therefore, the nucleic acid in the test sample can be compared to one or more control samples.

[0109] The term "sequence of interest" herein refers to a nucleic acid sequence that is associated with a difference in sequence expression between healthy individuals and diseased individuals. The sequence of interest can be a repetitive sequence on a chromosome that is amplified in a disease or genetic disorder. The sequence of interest can be a part of a chromosome, a gene, a coding sequence, or a non-coding sequence.

[0110] The term "next generation sequencing (NGS)" herein refers to sequencing methods that allow for massively parallel sequencing of clonally amplified molecules and single nucleic acid molecules. Non-limiting examples of NGS include sequencing by ligation and sequencing by synthesis using reversible dye terminators.

[0111] The term "parameter" herein refers to a numerical value that characterizes a physical property. Typically, a parameter characterizes a quantitative data set and / or a numerical relationship between a quantitative data set in a numerical manner. For example, the ratio (or a function of the ratio) between the number of sequence tags mapped to a chromosome and the chromosome length mapped by the tag is a parameter.

[0112] The term "criterion" herein refers to any number or quantity used as a cutoff value to characterize a sample (such as a test sample containing nucleic acid from an organism suspected of having a medical condition). The threshold value can be compared to the parameter value to determine whether the sample that produces this parameter value indicates that the organism has a medical condition. In certain embodiments, the threshold value is calculated using a control data set and used as a limit for diagnosing repeat expansion in an organism. In some specific implementations, if the result obtained by the method disclosed herein exceeds the threshold value, the subject can be diagnosed to have repeat expansion. The appropriate threshold value of the method described herein can be identified by analyzing the value calculated for a set of training samples or control samples. The threshold value can also be calculated by empirical parameters such as sequencing depth, read length, repeat sequence length, etc. Alternatively, it is known that the affected sample with repeat expansion can also be used to confirm that the selected threshold value can be used to distinguish between affected samples and unaffected samples in the test set. The choice of the threshold value depends on the confidence level that the user wishes to have to be classified. In some embodiments, the training set for identifying appropriate threshold values ​​includes at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 600, at least 700, at least 800, at least 900, at least 1000, at least 2000, at least 3000, at least 4000 or more qualified samples. It may be advantageous to use a larger qualified sample set to improve the diagnostic utility of threshold values.

[0113] The term "read" refers to a sequence read from a portion of a nucleic acid sample. Typically, although not necessarily, a read represents a short sequence of adjacent base pairs in a sample. The read can be symbolically represented by a base pair sequence (ATCG format) 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 mapped to a chromosome or genomic region or gene.

[0114] The term "genomic read" is used to refer to a read of any segment in the whole genome of an individual.

[0115] The term "site" refers to a unique position on a reference genome (i.e., chromosome ID, chromosome position and orientation). In some embodiments, a site can be the position of a residue, sequence tag, or fragment on a sequence.

[0116] As used herein, the term "reference genome" or "reference sequence" refers to any specific known genome 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. "Genome" refers to the complete genetic information of an organism or virus expressed as a nucleic acid sequence.

[0117] In various embodiments, the reference sequence is significantly larger than the reads to which it is aligned. 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, ... 5 times larger, or at least about 10 6 times larger, or at least about 10 7 times bigger.

[0118] In one example, the reference sequence is a sequence of a full-length human genome. Such a sequence may be referred to as a genome reference sequence. 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 a sequence may be referred to as a chromosome reference sequence. Other examples of reference sequences include genomes of other species, as well as chromosomes, subchromosome regions (such as chains) of any species, etc.

[0119] In some embodiments, the sequence length of the reference sequence for comparison can be about 1 to about 100 times the length of the read segment. In such embodiments, comparison and sequencing are considered to be targeted comparisons or sequencing, rather than full genome comparisons or sequencing. In these embodiments, the reference sequence generally includes genes and / or repetitive sequences of interest.

[0120] 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.

[0121] The term "clinically relevant sequence" herein refers to a nucleic acid sequence that is known or suspected to be associated or implicated in a genetic or disease condition. Determining whether a clinically relevant sequence is present can be used to determine or confirm a diagnosis of a medical condition, or provide a prognosis for the development of a disease.

[0122] When used in the context of nucleic acids or mixtures of nucleic acids, the term "derived" herein refers to the manner in which the nucleic acids are obtained from their source. For example, in one embodiment, a mixture of nucleic acids derived from two different genomes means that the nucleic acids (e.g., cfDNA) are naturally released by cells through naturally occurring processes (such as necrosis or apoptosis). In another embodiment, a mixture of nucleic acids derived from two different genomes means that the nucleic acids are extracted from two different types of cells from a subject.

[0123] When used in the context of obtaining a specific quantitative value, the term "based on" herein means calculating a specific quantitative value as an output using another quantity as an input.

[0124] The term "patient sample" herein refers to a biological sample obtained from a patient, i.e., a recipient of medical attention, care, or treatment. The patient sample can be any sample described herein. In certain embodiments, the patient sample is obtained by non-invasive surgery, such as a peripheral blood sample or a fecal sample. The methods described herein need not be limited to humans. Therefore, various veterinary applications are contemplated, in which case the patient sample can be a sample from a non-human mammal (e.g., a feline, a pig, a horse, a cow, etc.).

[0125] The term "biological fluid" herein refers to a liquid taken from a biological source, and includes, for example, blood, serum, plasma, sputum, lavage fluid, cerebrospinal fluid, urine, semen, sweat, tears, saliva, etc. As used herein, the terms "blood", "plasma" and "serum" explicitly encompass fractions or processed portions thereof. Similarly, in the case where the sample is taken from a biopsy, swab, smear, etc., the "sample" explicitly encompasses processed fractions or portions derived from a biopsy, swab, smear, etc.

[0126] As used herein, the term "corresponding to" sometimes refers to a nucleic acid sequence (e.g., a gene or chromosome) present in the genomes of different subjects that does not necessarily have the same sequence in all genomes, but is used to provide the identity of the sequence (e.g., a gene or chromosome) of interest rather than genetic information.

[0127] 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.

[0128] As used herein, the term "polynucleotide length" refers to the absolute number of nucleic acid monomer subunits (nucleotides) in a sequence or region of a reference genome. The term "chromosome length" refers to the known length of a chromosome given in base pairs, for example, the length provided in the NCBI36 / hg18 assembly of human chromosomes present on the World Wide Web at |genome|.|ucsc|.|edu / cgi-bin / hgTracks?hgsid=167155613&chromInfoPage=.

[0129] The term "subject" herein refers to human subjects as well as non-human subjects such as mammals, invertebrates, vertebrates, fungi, yeasts, bacteria, and viruses. Although the examples herein relate to humans and the language is primarily directed to humans, the concepts disclosed herein are applicable to genomes from any plant or animal and can be used in veterinary medicine, animal science, research laboratories, and other fields.

[0130] As used herein, the term "primer" refers to an isolated oligonucleotide that can serve as a starting point for synthesis when placed under conditions that induce the synthesis of extension products (e.g., conditions that include nucleotides, an inducing agent (such as a DNA polymerase) and a suitable temperature and pH). The primer can preferably be single-stranded to achieve maximum efficiency in amplification, but can alternatively be double-stranded. If double-stranded, the primer is first treated to separate its strands and then used to prepare extension products. The primer can be an oligodeoxyribonucleotide. The primer is long enough to initiate the synthesis of extension products in the presence of an inducing agent. The exact length of the primer will depend on many factors, including temperature, primer source, the purpose of the method, and parameters for primer design.

[0131] Introduction Short tandem repeats (STRs) are ubiquitous throughout the human genome. Although our understanding of STR biology is far from complete, emerging evidence suggests that STRs play important roles in fundamental cellular processes.

[0132] Repeat expansions are a class of special microsatellite and minisatellite variants involving STR polymorphisms. Repeat expansions are also called dynamic mutations due to the instability when short tandem repeat sequences expand beyond a certain size. STR expansions are the main cause of more than 20 serious neurological disorders, including amyotrophic lateral sclerosis, Friedreich's ataxia (FRDA), Huntington's disease (HD), and fragile X syndrome. Table 1 illustrates a small number of pathogenic repeat expansions that are different from the repeat sequences in normal samples. The columns in the table show the genes associated with the repeat sequence, the nucleic acid sequence of the repeat unit, the number of repeats of the repeat unit of the normal and pathogenic sequences, and the diseases associated with the repeat expansion.

[0133] Table 1 Examples of pathogenic repeat expansions

[0134] Genetic diseases involving repeat expansions are heterogeneous in many ways. The size of the repeat unit, the extent of expansion, the location relative to the affected gene, and the pathogenic mechanism can vary from disease to disease. For example, ALS involves a hexanucleotide repeat expansion of the nucleotides GGGGCC in the C9orf72 gene located on the short arm of open reading frame 72 of chromosome 9. In contrast, Fragile X syndrome is associated with an expansion of CGG trinucleotide repeats (triplets) affecting the Fragile X Mental Retardation 1 (FMR1) gene on chromosome X. The expansion of the CGG repeats can result in a failure to express the Fragile X Mental Retardation Protein (FMRP), which is required for normal neurodevelopment. Depending on the length of the CGG repeat sequence, the allele can be classified as normal (not affected by the syndrome), premutation (at risk for Fragile X-associated disorders), or full mutation (usually affected by the syndrome). According to various estimates, there are between 230 and 4000 CGG repeats in the mutated FMR1 gene, which causes Fragile X syndrome in affected patients, compared to 60 to 230 repeats in carriers prone to ataxia, and 5 to 54 repeats in unaffected individuals. Repeat expansions in the FMR1 gene cause autism, as approximately 5% of autistic individuals are found to have FMR1 repeat expansions. McLennan et al., "Fragile X Syndrome," Current Genomics, Vol. 12, No. 3, pp. 216-224, 2011. Definitive diagnosis of Fragile X syndrome involves genetic testing to determine the number of CGG repeats.

[0135] Various general characteristics of repeat expansion-related diseases have been identified in multiple studies. Repeat expansion or dynamic mutations are usually manifested as an increase in repeat number, where the mutation rate is related to the repeat number. Rare events such as the loss of repeat interruptions can lead to alleles with increased expansion potential, and such events are called founder events. There may be a relationship between the number of repeats in a repetitive sequence and the severity and / or onset of a disease caused by repeat expansion.

[0136] Therefore, identification and determination of repeat expansion are important for the diagnosis and treatment of various diseases. However, identifying repetitive sequences, especially using reads that do not completely pass through repetitive sequences, has various challenges. First, it is difficult to align repetitive sequences with reference sequences because there is no obvious one-to-one mapping between reads and reference genomes. In addition, even if the reads are aligned with the reference sequences, the reads are usually too short to fully cover the medically relevant repetitive sequences. For example, the reads can be about 100bp. In contrast, repeat expansion can span hundreds to thousands of base pairs. For example, in fragile X chromosome syndrome, the FMR1 gene can have far more than 1000 repeats, spanning more than 3000bp. Therefore, 100bp reads cannot map the full length of repeat expansion. In addition, assembling short reads into longer sequences may not overcome the problem of short reads and long repeats, because due to the fuzzy alignment of the repeats in one read with the repeats in another read, it is difficult to assemble short reads into longer sequences.

[0137] Alignment is the main cause of information loss due to incompleteness of the reference sequence, non-unique correspondence between reads and sites on the reference sequence, or significant deviations from the reference sequence. Systematic sequencing errors and other issues that affect read accuracy are secondary factors in the failure to detect repetitive sequences. In some experimental protocols, about 7% of reads were not aligned or had a MAPQ score of 0. Even as researchers work to improve sequencing technology and analytical tools, there will always be a significant amount of unaligned and poorly aligned reads. Specific implementations of the methods described herein rely on unaligned or poorly aligned reads to identify repeat expansions.

[0138] Methods for detecting repeat amplification using long reads have their own challenges. In next generation sequencing, currently available techniques for using longer reads are slower and more error-prone than techniques for using shorter reads. In addition, long reads are not feasible for some applications, such as sequencing cell-free DNA. Free DNA obtained in maternal blood can be used for prenatal genetic diagnosis. When using free DNA, free DNA exists as fragments that are generally shorter than 200. The specific implementation of the method described herein uses short reads to identify medically relevant repeat amplification.

[0139] In addition, conventional methods are not designed to handle complex loci with multiple repeats. Important examples of such loci include CAG repeats of HD flanked by CCG repeats, GAA repeats of FRDA flanked by adenosine homopolymers, and CAG repeats of spinocerebellar ataxia type 8 (SCA8) flanked by ACT repeats. An even more extreme example is the CCTG repeat in the CNBP gene, whose expansion leads to myotonic dystrophy type 2 (DM2). This repeat is adjacent to polymorphic TG and TCTG repeats (JELee and Cooper, 2009), making it particularly difficult to accurately align reads to this locus. Another type of complex repeat is the polyalanine repeat, which has been associated with at least nine diseases to date (Shoubridge and Gecz, 2012). The polyalanine repeat consists of repeats of the a-amino acid codons GCA, GCC, GCG, or GCT.

[0140] Variant clusters can affect alignment and genotyping accuracy (Lincoln et al., 2019). Variants adjacent to low-complexity polymorphic sequences can be additionally problematic, as methods used for variant discovery can output inconsistently represented clusters or false variant calls in such genomic regions. This is due in part to elevated error rates in sequencing data for such regions (Benjamini and Speed, 2012; Dolzhenko et al., 2017). An example is a single nucleotide variation (SNV) adjacent to an adenosine homopolymer in MSH2, which causes Lynch syndrome type I (Froggatt et al.; 1999).

[0141] The specific implementations disclosed herein can handle complex loci as described above. They use sequence graphs as a general and flexible model for each target locus.

[0142] In some specific implementations, the disclosed method solves the aforementioned challenges in identifying and determining repeat amplification by utilizing paired-end sequencing. Paired-end sequencing involves fragmenting DNA into sequences called insert sequences. In some protocols (such as some protocols used by Illumina), reads from shorter insert sequences (e.g., about tens of bp to hundreds of bp) are referred to as short insert sequence paired-end reads or simply paired-end reads. In contrast, reads from longer insert sequences (e.g., about several thousand bp) are referred to as paired reads. As described above, both short insert sequence paired-end reads and long insert sequence paired reads can be used in various specific implementations of the methods disclosed herein.

[0143] Figure 1A is a schematic diagram illustrating some of the difficulties in aligning sequence reads to repeat sequences on a reference sequence, particularly when aligning sequence reads obtained from a sample with long repeat sequences that are repeatedly expanded. Figure 1A At the bottom of the figure is a reference sequence 101, which has a relatively short repeat sequence 103 shown by the vertical hatching. In the middle of the figure is a hypothetical sequence 105 of a patient sample, which has a long repeat sequence 107 with repeat expansion, also shown by the vertical hatching. Sequence reads 109 and sequence reads 111 are shown at the positions of corresponding sites of the sample sequence 105 at the top of the figure. In some of these sequence reads (such as read 111), some base pairs are derived from the long repeat sequence 107, as also shown by the vertical hatching and highlighted by circles. Reads 111 with these repeats may be difficult to align with the reference sequence 101 because the repeats do not have a clear corresponding position on the reference sequence 101. Since these potentially unaligned reads cannot be clearly associated with the repeat sequence 103 in the reference sequence 101, it is difficult to obtain information about repeat sequences and repeat sequence expansion from these potentially unaligned reads 111. Furthermore, because these reads tend to be shorter than the long repeat sequence 107 with repeat expansion, they cannot directly provide definitive information about the identity or position of the repeat sequence 107. Additionally, the repeats in the reads 111 make them difficult to assemble due to their ambiguous corresponding positions on the reference sequence 101 and the ambiguous relationship between the reads 111. Reads that are partially from the long repeat sequence 107 in the sample (those reads shown as half-hatched lines and half-solid black) can be aligned by bases originating from outside the repeat sequence 107. If the read has few base pairs outside the repeat sequence 107, the alignment of the read may be poor or may not be aligned. Therefore, some of these reads with partial repeats can be analyzed as anchor reads, and other reads can be analyzed as anchor reads, as further described below.

[0144] Figure 1B is to show how paired-end reads can be used in some disclosed embodiments to overcome Figure 1A Schematic diagram of the difficulty shown. In paired-end sequencing, sequencing is performed from both ends of a nucleic acid fragment in a test sample. Figure 1B The bottom of FIG. 1 shows a reference sequence 101 and a sample sequence 105, as well as Figure 1A The reads shown are identical read 109 and read 111 . Figure 1BThe top of shows a fragment 125 derived from the test sample sequence 105 and a read 1 primer region 131 and a read 2 primer region 133 for obtaining two reads 135 and 137 of paired end reads. Fragment 125 is also referred to as an insert sequence for paired end reads. In some embodiments, the insert sequence can be amplified in the presence or absence of PCR. Some repetitive sequences, such as those including a large number of GC or GCC repeats, cannot be sequenced well using traditional methods including PCR amplification. For such sequences, amplification can be PCR-free. For other sequences, PCR can be used for amplification.

[0145] Figure 1B The insertion sequence 125 shown in corresponds to or originates from a segment of the sample sequence 105, which is flanked by two vertical arrows shown in the lower half of the figure. Specifically, the insertion sequence 125 has a repeating segment 127 corresponding to a portion of the long repeat 107 in the sample sequence 105. The length of the insertion sequence can be adjusted for various applications. In some embodiments, the insertion sequence may be slightly shorter than the repetitive sequence of interest or the repetitive sequence with repeated amplification. In other embodiments, the insertion sequence may have a length similar to the repetitive sequence or the repetitive sequence with repeated amplification. In other embodiments, the insertion sequence may even be longer than the repetitive sequence or the repetitive sequence with repeated amplification to a certain extent. In some embodiments further described below, such insertion sequences may be long insertion sequences for paired sequencing. Typically, the reads obtained from the insertion sequence are shorter than the repetitive sequence. Because the insertion sequence is longer than the read, the paired end read can better capture signals from the longer segments of the repetitive sequence in the sample than the single end read.

[0146] The illustrated insert sequence 125 has two read primer regions 131 and 133 at both ends of the insert sequence. In some embodiments, the read primer regions are intrinsic to the insert sequence. In other embodiments, the primer regions are introduced into the insert sequence by connection or extension. Shown at the left end of the insert sequence is the read 1 primer region 131, which allows the read 1 primer 132 to hybridize with the insert sequence 125. The extension of the read 1 primer 132 produces a first read or read 1 labeled 135. Shown at the right end of the insert sequence 125 is the read 2 primer region 133, which allows the read 2 primer 134 to hybridize with the insert sequence 125, thereby triggering the second read or read 2 labeled 137. In some embodiments, the insert sequence 125 may also include an index barcode region (not shown in the figure here), thereby providing a mechanism for identifying different samples during multiple sequencing. In some embodiments, paired end reads 135 and 137 can be obtained by sequencing the synthesis platform of Illumina. An example of a sequencing process implemented on such a platform is further described below in the Sequencing Methods section, which creates two paired-end reads and two index reads.

[0147] Then you can Figure 1B The paired end reads obtained as shown are aligned to a reference sequence 101 having a relatively short repeat sequence 103. Therefore, the relative position and orientation of a pair of reads are known. This allows unaligned or poorly aligned reads such as those shown in circle 111 to be indirectly associated with the relatively long repeat sequence 107 in the sample sequence 105 through the corresponding paired read 109 of the read, as shown in FIG. Figure 1B . In the illustrative example, the reads obtained from paired-end sequencing are about 100 bp and the insert sequence is about 500 bp. In this exemplary setting, the relative positions of the two paired-end reads are about 300 base pairs away from their 3' ends, and they have opposite orientations. The relationship between the read pairs allows better association of the reads with the repetitive regions. In some cases, the first read in a pair aligns with the non-repetitive sequence flanking the repetitive region on the reference sequence, and the second read in the pair does not align correctly with the reference sequence. See, e.g. Figure 1B A pair of reads 109a and 111a shown in the lower half of , wherein the left read 109a in the pair of reads is the first read and the right read 111a is the second read. Given that the two reads 109a and 111a are paired, although the second read 111a cannot be aligned with the reference sequence 101, the second read 111a can be associated with the repeat region 107 in the sample sequence 105. Knowing the distance and direction of the second read 111a relative to the first read 109a, the position of the second read 111a within the long repeat region 107 can be further determined. If there is a break between the repeats in the second read 111a, the position of the break relative to the reference sequence 101 can also be determined. In the present disclosure, a read such as the left read 109a that is aligned with a reference sequence is referred to as an anchor read. A read such as the right read 111a that is not aligned with a reference sequence but is paired with an anchor read is referred to as an anchor read. Therefore, unaligned sequences can be anchored to and associated with repeat amplifications. In this way, short reads can be used to detect long repeat amplifications. Although the challenge of detecting repeat amplifications generally increases with the increase in amplification length due to the increased difficulty of sequencing, the methods disclosed herein can detect signals from longer repeat amplification sequences higher than signals from shorter repeat amplification sequences. This is because as the repeat sequence or repeat amplification becomes longer, more reads will be anchored to the amplification region, more reads can fall completely into the repeat region, and more repetitions can occur per read.

[0148] In some embodiments, the methods disclosed herein involve analyzing the frequency distribution of the number of repeats found in anchor reads and anchor reads. In some embodiments, only anchor reads are analyzed. In other embodiments, both anchor reads and anchor reads are analyzed. The distribution of the test sample can be compared with an empirically or theoretically derived standard that separates unaffected samples from affected samples. In this way, it can be determined whether the test sample has the repeat expansion under consideration, and a clinically relevant determination can be made.

[0149] The methods and devices described herein can use next generation sequencing technology (NGS) that allows large-scale parallel sequencing. In certain embodiments, cloned amplified DNA templates or single DNA molecules are sequenced in a large-scale parallel manner in a flow cell (e.g., as described in Volkerding et al., Clin Chem, Vol. 55: 641-658

[2009] ; Metzker M Nature Rev, Vol. 11: 31-46

[2010] ). NGS sequencing technology includes, but is not limited to, pyrophosphate sequencing, sequencing by synthesis using reversible dye terminators, sequencing by oligonucleotide probe connection, and ion semiconductor sequencing. DNA from each sample can be sequenced separately (i.e., single sequencing), or DNA from multiple samples can be merged and sequenced as indexed genome molecules on a single sequencing run (i.e., multiple sequencing) to generate up to billions of DNA sequence reads. Examples of sequencing technologies that can be used to obtain sequence information according to the method of the present invention are further described below.

[0150] Various repeat amplification analyses using DNA samples involve comparing or mapping the sequence reads from a sequencer to a reference sequence. The reference sequence can be the sequence of the entire genome, the sequence of a chromosome, the sequence of a subchromosomal region, etc. From a computational perspective, repetitions cause ambiguity in the comparison, which in turn can produce deviations and errors even at the level of whole chromosome counts. In various embodiments, paired end reads connected to adjustable insert lengths can help eliminate the ambiguity of comparing repetitive sequences and detecting repeat amplification.

[0151] Genotyping variants at repetitive sequence loci using sequence maps Figure 1C A flow chart illustrating a process 140 for genotyping a locus comprising a repetitive sequence according to some implementations is shown. In some implementations, the locus is predefined in a variant catalog that includes a genomic location and a locus structure at the genomic location. Figure 1D , Figure 1E and Figure 1F Three different sequence diagrams are shown according to some implementations.

[0152] Figure 1GA schematic diagram of a process for determining the genotype of a variant at an HTT locus that includes two STR sequences is shown according to some implementations. Figure 1G Figure (a) in shows a portion of a variant catalog, which includes the locus and its structure as the locus specification. For example, ignoring the repeats, the sequence at the locus HTT is CAGCAACAGCGG (SEQ ID NO: 2); the sequence at the locus CNBP is CAGGCAGACA (SEQ ID NO: 3).

[0153] Figure 1H A schematic diagram of a process for determining the genotype of variants at a Lynch type I locus, including SNVs and STRs, according to some specific implementations is shown. Figure 1H Box 162 shows the general structure of a locus specification, and box 163 shows a specific example of a locus specification for Lynch type I (MSH2).

[0154] In the variant catalog, the locus structure is specified using a restricted subset of the regular expression syntax. For example, the repeat region linked to HD can be defined by the expression (CAG)*CAACAG(CGG)*, indicating that it has a variable number of CAG and CCG repeats separated by CAACAG interruptions, or SEQ ID NO: 2 (ignoring repeats); the region linked to the FRDA region corresponds to the expression (A)*(GAA)*; the region linked to SCA8 corresponds to (CTA)*(CTG)*; the DM2 repeat region consisting of three adjacent repeats is defined by (CAGG)*(CAGA)*(CA)* or SEQ ID NO: 3 (ignoring repeats); and the MSH2 SNV adjacent to the A homopolymer that causes Lynch syndrome type I corresponds to (A|T)(A)*.

[0155] In addition, conventional expressions are permitted to include multi-allelic or "degenerate" base symbols, which may be specified using the International Union of Pure and Applied Chemistry (IUPAC) symbols ("Nomenclature for Incompletely Specified Bases in Nucleic Acid Sequences, Recommendations 1984, International Joint Biochemical Nomenclature Committee (NC-IUB) 1986).

[0156] Incompletely specified bases corresponding to bases in degenerate codons are referred to herein as degenerate bases. Degenerate bases make it possible to represent certain classes of imperfect DNA repeats, in which, for example, different bases may appear in the same position. Using this notation, polyalanine repeats may be encoded by the expression (GCN)*, and polyglutamine repeats may be encoded by the expression (CAR)*.

[0157] In some implementations, the repetitive sequence included in the locus includes a short tandem repeat (STR) sequence. In some implementations, an extension of FTR is associated with fragile X syndrome, amyotrophic lateral sclerosis (ALS), Huntington's disease, Friedreich's ataxia, spinocerebellar ataxia, spinal bulbar muscular atrophy, myotonic dystrophy, Machado-Joseph disease, or dentatorubalgebral pallidum Lewy body atrophy.

[0158] Process 140 involves collecting nucleic acid sequence reads of a test sample from a database. See block 142. In some implementations, nucleic acid sequence reads have been initially aligned to a reference genome, but the process here re-aligns sequence reads to a locus of interest, as explained below. In an alternative implementation, reads may be aligned directly to a sequence map, without initial alignment to a reference genome.

[0159] Process 140 involves aligning the sequence reads with the sequence of a locus containing one or more repeat sequences. See box 144. The sequence of the locus is represented by data stored in a system memory having a data structure of a sequence graph. The sequence graph includes a directed graph having vertices representing nucleic acid sequences and directed edges connecting the vertices. The nucleic acid sequences in the vertices contain one or more nucleic acid bases. The sequence graph includes one or more self-loops. Each self-loop represents a repeat sequence of one or more repeat sequences. Each repeat sequence includes a repetition of a repeat unit of one or more nucleotides.

[0160] In some implementations, the sequence reads are initially aligned to a reference genome to determine the genomic coordinates of the reads before a subset of the initially aligned reads are aligned to one or more sequence graphs representing one or more sequences of interest. In some implementations, the initially aligned reads are aligned to the sequence graphs to determine repeat expansions at tens to thousands of regions (each region corresponding to a sequence graph). The total number of initially aligned reads that are re-aligned to the sequence graphs during each determination of an implementation can range from thousands to millions of reads.

[0161] In some implementations, the reads initially aligned to or near a sequence or locus of interest are selected as a subset of reads, which are then aligned to a repeat sequence, each repeat sequence represented by a sequence graph having one or more self-loops representing one or more repeat sequences. In various implementations, reads within about 10, 50, 100, 500, 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, 10,000, 50,000, 100,000 bases from a sequence or locus of interest are considered to be near a sequence or locus of interest. In some implementations, the reads within about 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000 or 10,000 bases from the locus of interest are near the locus of interest. Some original reads may be poorly initially aligned because, for example, they include repetitive sequences that are difficult to clearly align. In some implementations, the initial alignment is poor (e.g., as measured by alignment score) but the reads paired with the reads aligned near the locus of interest (in paired end read pairs) or the locus of interest are aligned with the sequence map. In some implementations, the initial alignment with the off-target region as a known hotspot is used for the reads of the misaligned reads and the sequence map alignment.

[0162] Figure 1D , Figure 1E and Figure 1F Three different sequence diagrams are shown according to some implementations. Figure 1DA first sequence graph 1100 representing a first locus including a repeating sequence having a trinucleotide repeating unit CAG is shown. The first sequence graph 1100 includes vertices 1102 and 1112 representing two flanking sequences, respectively. The first sequence graph also includes a vertex 1106 representing a repeating sequence including a trinucleotide repeating unit CAG. The first sequence graph includes a directed edge 1104 connecting vertex 1102 (flanking sequence) and vertex 1106 (CAG repeating sequence), the direction of which is from vertex 1102 to vertex 1106. The direction of the edge indicates the relative position of the two nucleic acid sequences. The first sequence graph also includes a directed edge 1104 connecting vertex 1102 (flanking sequence) and vertex 1106 (CAG repeating sequence), the direction of which is from vertex 1102 to vertex 1106. The first sequence graph also includes a directed edge 1110 connecting vertex 1106 (CAG repeating sequence) and vertex 1112 (flanking sequence), the direction of which is from vertex 1106 to vertex 1112. The first sequence graph also includes self-loops 1108, which represent that the repeat sequence includes a repeat unit CAG repeated one or more times (shown in vertex 1106). The path from the start vertex to the end vertex of the sequence graph represents the sequence of the locus, which may include nucleotides near the repeat sequence, such as flanking sequences.

[0163] Figure 1E A second sequence graph 1200 representing a second locus is shown. The second sequence graph 1200 includes vertices 1202 and 1224 representing two flanking sequences, respectively. The second sequence graph also includes vertices 1206 representing a repeating sequence including a trinucleotide repeating unit CAG and a vertex 1216 representing a repeating sequence including a trinucleotide repeating unit CCG, respectively. The second sequence graph also includes a vertex 1212 representing a non-repeating sequence CAACAG. The second sequence graph includes directed edges 1204, 1210, 1214, and 1220. These directed edges connect vertices 1202, 1206, 1212, 1216, and 1224 in a direction, as shown. The second sequence graph also includes a self-loop 1208, which indicates that the repeating sequence includes a repeating unit CAG repeated one or more times (shown in vertex 1206). The second sequence graph also includes a self-loop 1218, which indicates that the repeating sequence includes a repeating unit CCG repeated one or more times (shown in vertex 1216).

[0164] Figure 1FA third sequence diagram 1300 representing a third locus is shown. The third sequence diagram 1300 is similar to the second sequence diagram 1200, but includes two alternative paths representing two alleles CAC and CAT. These two alleles can be alleles of SNV or SNP. Directed edge 1310, vertex 1312, and directed edge 1314 represent the first allele of CAC. Directed edge 1316, vertex 1318, and directed edge 1320 represent the second allele of CAT. The third sequence diagram includes elements that are otherwise similar to those in the second sequence diagram, including vertices 1302, 1306, 1322, and 1328. The third sequence diagram also includes self-loops 1308 and 1324 indicating that the repeating sequences CAG repeat and CCG repeat. The third sequence diagram also includes directed edges 1304 and 1326.

[0165] In some implementations, sequence reads are aligned to a sequence graph using techniques described below.

[0166] 1. Kmer indexing is built on the entire graph so that given a kmer from a sequence, all graph nodes where such kmer starts or ends can be enumerated. In some cases, a kmer may start at one node and end at another node.

[0167] 2. For each graph hit, two subgraphs are extracted: one in the forward direction of the kmer and one in the reverse direction. The subgraphs are expanded repeat expansion until the remaining read length, and do not include any nodes that are farther away from the kmer hit than the remaining read length assuming the repeats were not expanded. This process is a breadth-first search and produces a data structure containing the following:

[0168] - Concatenation of all node sequences (including expanded repeats) in the subgraph - The index of the node, making it easy to get the node id from the offset in the sequence when backtracking on the Smith-Waterman algorithm - For each node start offset, the sequence of offsets of the node ends with incoming edges - An index for each node, making it easy to tell if a base is at the start of a node, and to enumerate all end offsets of predecessor nodes.

[0169] 3. Comparison -Support for affine gaps.

[0170] - Find the best scoring alignment of sequences given the above information and the penalty matrix.

[0171] Two different interfaces are available: - The best and second best alignment scores are reported.

[0172] - The entire array of best and second best alignment scores.

[0173] The alignment is a global alignment that penalizes gaps between the candidate kmer and the start of the aligned sequence. In some implementations, compile time parameters are adjusted.

[0174] The current algorithm for matrix filling can be used in two specific implementations: -A sequential ring with N*M complexity.

[0175] -Fixed-length compile-time parameter for sequential loops of fixed-size loops defaults to 16, and gcc automatically recognizes SSE or AVX vector instructions on the CPU and converts them into SSE or AVX vector instructions.

[0176] In some implementations, a specific repeat unit of a repeat sequence of one or more repeat sequences includes at least one incompletely specified nucleotide. In some implementations, a specific repeat unit includes a degenerate codon.

[0177] In some embodiments, one or more self-loops include two or more self-loops representing two or more repeating sequences. Figure 1E , Figure 1F and Figure 1G Figure (b) in .

[0178] In some implementations, the sequence diagram also includes two or more alternative paths for two or more alleles. See, e.g. Figure 1F Reference numerals 1312 and 1318. See also Figure 1H , reference numbers 165 and 167a of the Lynch type I locus (MSH2), wherein the upper path includes the vertex of the nucleic acid base A and the lower path includes the vertex of the nucleic acid base T.

[0179] In some embodiments, the two or more alleles include an insertion or deletion or a substitution. In some embodiments, the substitution includes a single nucleotide variation (SNV) or a single nucleotide polymorphism (SNP). See, e.g. Figure 1F Reference numbers 1312 and 1318.

[0180] In some implementations, aligning the sequence reads to the sequence graph includes: finding kmer matches between the sequence reads and a path of the sequence graph, and then extending the path to a complete alignment. In some implementations, the alignment includes extracting a subgraph around the path; expanding any loops in the subgraph to obtain a directed acyclic graph; and performing a Smith-Waterman alignment of the sequence reads relative to the directed acyclic graph.

[0181] In some implementations, aligning the sequence reads to the sequence graph includes shrinking the sequence graph by removing the low confidence ends of the alignment. After aligning the reads to the graph, the method searches for other similar alternative alignments. This is accomplished by realigning the original reads to a path through the graph that overlaps the path of the original alignment. This allows detection, for example, of whether one or both ends of the initial alignment have low confidence, indicating that the two ends can be aligned in different ways. Being able to detect high confidence and low confidence portions of an alignment allows one to accurately determine which genetic variants are supported by the reads.

[0182] In some implementations, aligning the sequence reads to the sequence graph includes merging alignments by: aligning subsequences of the reads to the sequence graph; and merging the alignments of the subsequences to form a complete alignment of the sequence reads.

[0183] In some implementations, the method further involves generating a sequence map based on a locus specification including a locus structure of the locus. In some implementations, the locus specification is defined in a variant catalog as described above.

[0184] See also Figure 1G Figures (b) to (d) in the figure are schematic diagrams of the alignment of the read segment with the sequence map of the HTT locus. Figure 1H Reference is schematically shown a locus analyzer 164 for performing alignment of reads to a sequence map, such as the locus Lynch type I (165).

[0185] Method 140 also involves determining one or more genotypes of one or more repetitive sequences using the sequence reads aligned to the sequence map. See block 140. See also Figure 1G Figure (e) in Figure 1 shows two STRs (CAG and CCG) identified at the HTT locus. The sequence on the left including the CAG repeat sequence is CAGCAGCAGCAGCAG (SEQ ID NO: 4). The sequence on the left including the CCG repeat sequence is CCGCCGCCGCCGCCG (SEQ ID NO: 5).

[0186] Figure 1H A variant genotyper module (168) for determining variants at a Lynch type I locus including SNVs having A / T alleles (169a) and A haplotype repeats (169b) is shown. Figure 1H Also shown is a variant analyzer module (166) for curating sequence alignment data and providing them to a variant genotyper (168), and a specific implementation of the variant analyzer for SNVs with A / T alleles (167a) and A monomer repeats (167b). The locus results from the genotyper are shown in Figure 1HThis is shown in box 170 , and specifically shown as the genotype for a SNV having an A / T allele ( 171a ) and an A monomer repeat ( 171b ).

[0187] In some implementations, the sequence graph includes two alternative paths of two alleles, and the method further involves using sequence reads aligned with the two or more alternative paths to genotype the two or more alleles. In some implementations, genotyping the two or more alleles involves providing the coverage of the two or more alternative paths to a probability model to determine the probability of the two or more alleles. In some implementations, the probability model simulates the probability of the allele as a function of the allele coverage, the function being selected from a Poisson distribution, a negative binomial distribution, a binomial distribution, or a beta-binomial distribution.

[0188] In some implementations, the probability function is a Poisson distribution and its rate parameter is estimated by the read length and average depth observed at the locus.

[0189] In a Poisson-based model, the probability of an allele is expressed as follows: P(Y=y) = (C y × e -C ) / y! •Y is the read coverage of the base • C is the average depth at the locus In some implementations, the average depth C is estimated as: C = LN / G • G is the length of the locus • L is the read length • N is the number of all read segments Graphics Tool Library In some implementations, the basic sequence graph functionality uses a graph tool library that implements the core graph abstractions (graphs themselves, graph paths, and graph comparisons), operations on them, and algorithms for comparing linear sequences to graphs.

[0190] In some implementations, the sequence graph consists of nodes and directed edges. The graph is allowed to contain self-loops (edges connecting a node to itself), but not other loops. The nodes contain sequences consisting of core bases and IUPAC degenerate base codes.

[0191] A graphics path is defined by the sequence of nodes that the path traverses, as well as the path's starting position at the first node and ending position at the last node. Positions are specified using a zero-based, half-open coordinate system. The library defines several operations on paths, including path extension and contraction, overlap checking, and path merging.

[0192] Graph alignment encodes how a linear query sequence (usually a sequenced read) is aligned with a graph. In some implementations, the graph alignment includes a graph path and a linear alignment sequence that defines the alignment of the query sequence with the nodes of the graph path. Using the corresponding operation on the path, the graph alignment can be contracted or merged with other graph alignments. Path contraction provides a mechanism for removing the low confidence end of the alignment, and alignment merging is used by the graph alignment algorithm to splice the complete alignment of the query sequence with the alignment of a subsequence (e.g., kmer). In some implementations, the alignment algorithm operates by finding the kmer match between the query sequence and the graph, and then extending the match to the complete alignment. In some implementations, the alignment includes extracting a subgraph (any loop in the expansion process) around the path corresponding to the kmer match. Then it performs the Smith-Waterman alignment for the resulting directed acyclic graph. In some implementations, the algorithm supports affine gap penalties and is written using a constant length loop to enable the compiler to generate SIMD code.

[0193] In some implementations, a graph path can be obtained using a search algorithm that involves extending or contracting the path by increasing or decreasing the number of repeats of repeat units represented by self-loops until the alignment meets a search criterion or converges (e.g., the alignment score is maximized).

[0194] In some implementations, a plurality of graph paths are generated from the sequence graph, each graph path representing a specific number of repetitions of a repeating unit represented by a self-loop. A query sequence is aligned to the plurality of graph paths, and then paths that meet an alignment criterion are selected for graph alignment.

[0195] Application Architecture Some implementations are designed as general tools for targeted variant genotyping ( Figure 1H ). During each run, the program attempts to genotype the set of variants described in the variant catalog file.

[0196] Variants that are immediately adjacent to each other are grouped into the same locus. Locus structures are specified using a restricted subset of the regular expression (RE) syntax. REs contain a sequence over an alphabet consisting of core base symbols and IUPAC degenerate base codes, and must contain one or more of the following expressions, possibly separated by sequence breaks: ( <sequence>)?、(<sequence a>|<sequence b>)、( <sequence> )*、( <sequence>)+. These expressions correspond to insertions / deletions, substitutions, sequence duplications 0 or more times, and sequence duplications at least once, respectively. In addition, the description of each locus includes a set of reference regions for the locus and reference coordinates for each constitutive variant.

[0197] The bulk of the work is orchestrated by objects of the LocusAanalyzer class, which synthesize a sequence graph representing the locus from the corresponding REs during initialization. After initialization, the locus analyzer processes the relevant reads by aligning them to the graph and then passing the resulting alignments to a VariantAnalyzer defined for each variant contained in the locus. The VariantAnalyzer extracts information associated with the genotyping and passes it to the genotyper that performs the actual genotyping. The results output by each genotyper are then used to create an output VCF file.

[0198] For example, the LocusAnalyzer, which processes loci with pathogenic variants associated with Lynch syndrome type I, uses the SNV Analyzer and STR Analyzer ( Figure 1H , right).

[0199] Indel Genotyping Instrument Some STRs may have small insertions or deletions (indels) nearby. Such indels are modeled as additional subgraphs in the flanking sequences of the STR. The number of reads mapped to each allele (or graph path) is modeled with a Poisson distribution whose rate parameter is estimated by the average depth and read length observed at the locus. Genotype likelihoods are calculated under a Bayesian framework.

[0200] Identifying repeat expansions Using the embodiments disclosed herein, various genetic conditions associated with repeat expansion can be determined with high efficiency, sensitivity and / or selectivity relative to conventional methods. Some embodiments of the present invention provide methods for identifying and determining medically relevant repeat expansions (such as CGG repeat expansions that cause mental retardation in fragile X chromosome syndrome) using sequence reads that do not completely pass through the repeat sequence. The length of short reads such as 100bp reads is not enough to be sequenced through many repeat expansions. However, when analyzed with the methods disclosed in the present invention, samples with repeat expansions show a statistically significant excess of reads containing a large number of repeat sequences. In addition, extremely large repeat expansions contain unaligned read pairs, in which two reads are completely or almost completely composed of repeat sequences. Use normal samples to identify background expectations.

[0201] Conventional ideas are that repeat amplification cannot be detected without a read spanning the entire repeat. Existing methods for detecting repeat amplification use targeted sequencing with long reads, and in some cases are unsuccessful because the reads are not long enough to span the repeat sequence. The results of some disclosed embodiments have partially obtained surprising results because they use normal (non-targeted) sequence data and a read length of only about 100bp, but bring very high sensitivity for detecting repeat amplification. The method described herein can use paired reads with an insertion length (i.e., two sequence reads and intervening sequences) shorter than the length of the entire repeat sequence to detect the number of repeat units in repeat amplification.

[0202] Turning to details of methods for determining the presence of repeat expansions according to some embodiments, Figure 2 A flow chart providing a high-level description of an embodiment for determining the presence or absence of repeat expansion of a repetitive sequence in a sample is shown. A repetitive sequence is a nucleic acid sequence that includes recurring short sequences called repeating units. Table 1 above provides examples of repeating units, the number of repeats of the repeating unit in the repeating sequence of normal and pathogenic sequences, genes associated with the repetitive sequence, and diseases associated with repeat expansion. Figure 2 The process 200 in starts with obtaining paired end reads of a test sample. See box 202. The paired end reads have been processed to be aligned with a reference sequence including a repetitive sequence of interest. In some cases, the alignment process is also referred to as a mapping process. The test sample includes nucleic acids and may be in the form of body fluids, tissues, etc., such as further described in the sample section below. The sequence reads have undergone an alignment process to be mapped to a reference sequence. Various alignment tools and algorithms can be used to attempt to align the reads with the reference sequence, as described elsewhere in this disclosure. Typically, in an alignment algorithm, some reads are successfully aligned with a reference sequence, while other reads may not be successfully aligned with the reference sequence or may not be aligned well with the reference sequence. The reads that are continuously aligned with the reference sequence are associated with sites on the reference sequence. Aligned reads and their associated sites are also referred to as sequence tags. As described above, some sequence reads that contain a large number of repetitions are often more difficult to align with a reference sequence. When a read is aligned with a reference sequence having multiple mismatched bases above a certain standard, it is considered that the read is poorly aligned. In various embodiments, a read is considered poorly aligned when it aligns with at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mismatches. In other embodiments, a read is considered poorly aligned when it aligns with at least about 5% mismatches. In other embodiments, a read is considered poorly aligned when it aligns with at least about 10%, 15%, or 20% mismatched bases.

[0203] like Figure 2 As shown, process 200 continues to identify anchor reads and anchor reads in paired end reads. See box 204. Anchor reads are reads in paired end reads that are aligned with or near the repetitive sequence of interest. For example, anchor reads can be aligned with a position on a reference sequence, and the sequence length of the reference sequence separated from the repetitive sequence is less than the sequence length of the inserted sequence. The separation length can be shorter. For example, anchor reads can be aligned with a position on a reference sequence, and the sequence length of the reference sequence separated from the repetitive sequence is less than the sequence length of the anchor read or less than the combined sequence length of the anchor read and the sequence connecting the anchor read to the anchor read (that is, the length of the inserted sequence minus the length of the anchor read). In some embodiments, the repetitive sequence of interest can be a repetitive sequence in the FMR1 gene, including the repetition of the repeating unit CGG. In a normal reference sequence, the repetitive sequence in the FMR1 gene includes about 6-32 repetitions of the repeating unit CGG. When repeated amplification exceeds 200 copies, repeated amplification tends to become pathogenic, thereby causing fragile X chromosome syndrome. In some embodiments, a read is considered aligned near a sequence of interest when it is aligned within 1000 bp of the repetitive sequence of interest. In other embodiments, this parameter can be adjusted, such as within about 100 bp, 200 bp, 300 bp, 400 bp, 500 bp, 600 bp, 700 bp, 800 bp, 900 bp, 1500 bp, 2000 bp, 3000 bp, 5000 bp, etc. In addition, the process also identifies anchor reads, which are reads that are paired with an anchor read but do not align well or cannot align to the reference sequence of the anchor read. Additional details of poorly aligned reads are described above.

[0204] Process 200 also involves determining whether repetitive expansion of a repetitive sequence is likely to be present in the test sample based at least in part on the identified anchor reads. See block 206. This determination step may involve various suitable analyses and calculations, as further described below. In some embodiments, the process uses the identified anchor reads and the anchor reads to determine whether repetitive expansion is likely to be present. In some embodiments, the number of repetitions in the identified anchor reads and the anchor reads is analyzed and compared to one or more criteria derived theoretically or derived from empirical data of affected control samples.

[0205] In various embodiments described herein, repeats are obtained as in-frame repeats, where two repeats of the same repeat unit fall into the same reading frame. A reading frame is a way of dividing a nucleotide sequence in a nucleic acid (DNA or RNA) molecule into a set of continuous, non-overlapping triplets. During translation, triplets encode amino acids and are referred to as codons. Thus, any particular sequence has three possible reading frames. In some embodiments, repeats are counted according to three different reading frames, and the largest count among the three counts is determined as the number of corresponding repeats of the read.

[0206] Examples of procedures involving additional operations and analysis are given in Figure 3 Shown in. Figure 3 A flow chart is shown, which shows a process 300 for detecting repeat amplification using paired end reads with a large number of repeats. Process 300 includes additional upstream actions for processing test samples. The process begins by sequencing a test sample including nucleic acids to obtain paired end reads. See box 302. In some embodiments, the test sample can be obtained and prepared in various ways, as further described in the sample section below. For example, the test sample can be a biological fluid (e.g., plasma) or any suitable sample as described below. The sample can be obtained using a non-invasive procedure such as a simple blood draw. In some embodiments, the test sample contains a mixture of nucleic acid molecules (e.g., cfDNA molecules). In some embodiments, the test sample is a maternal plasma sample containing a mixture of fetal and maternal cfDNA molecules.

[0207] Before sequencing, nucleic acid is extracted from the sample. Suitable extraction methods and devices are described elsewhere herein. In some implementations, the device processes DNA from multiple samples together to provide multiple libraries and sequence data. In some embodiments, the device 20 processes DNA from eight or more test samples in parallel. As described below, the sequencing system can process the extracted DNA to produce a library of encoded (e.g., barcoded) DNA fragments.

[0208] In some embodiments, the nucleic acids in the test sample can be further processed to prepare a sequencing library for multiple or single sequencing, as further described in the sequencing library preparation section below. After processing and preparing the sample, the nucleic acid can be sequenced by various methods. In some embodiments, various next generation sequencing platforms and protocols can be used, which are further described in the sequencing method section below.

[0209] Regardless of the specific sequencing platform and protocol, in block 302, at least a portion of the nucleic acids contained in the sample are sequenced to generate tens of thousands, hundreds of thousands, or millions of sequence reads, such as 100 bp reads. In some embodiments, the reads include paired end reads. In other embodiments, such as those described below with reference to Figure 5 Those described, in addition to paired end reads, can also use single-end long reads including more than hundreds, thousands or tens of thousands of bases to determine repeat sequences. In some embodiments, the sequence reads include about 20bp, about 25bp, about 30bp, about 35bp, about 36bp, about 40bp, about 45bp, about 50bp, about 55bp, about 60bp, about 65bp, about 70bp, about 75bp, about 80bp, about 85bp, about 90bp, about 95bp, about 100bp, about 110bp, about 120bp, about 130bp, about 140bp, about 150bp, about 200bp, about 250bp, about 300bp, about 350bp, about 400bp, about 450bp or about 500bp. It is expected that technological advances will enable single-end reads greater than 500bp, thereby enabling reads greater than about 1000bp when generating paired end reads.

[0210] Process 300 continues by aligning the paired end reads obtained from box 302 to a reference sequence that includes a repetitive sequence. See box 304. In some embodiments, the repetitive sequence is prone to expansion. In some embodiments, repeat expansion is known to be associated with a genetic disease. In other embodiments, repeat expansion of the repetitive sequence has not been previously studied to establish an association with a genetic disease. The methods disclosed herein allow for the detection of repetitive sequences and repeat expansions regardless of any associated pathology. In some embodiments, the reads are aligned to a reference genome, such as hg18. In other embodiments, the reads are aligned to a portion of a reference genome, such as a chromosome or chromosome segment. Reads that are uniquely mapped to a reference genome are referred to as sequence tags. In one embodiment, at least about 3×10 6 Qualified sequence tags, at least about 5×10 6 Qualified sequence tags, at least about 8×10 6 qualified sequence tags, at least about 10×10 6 Qualified sequence tags, at least about 15×10 6 Qualified sequence tags, at least about 20×10 6 qualified sequence tags, at least about 30×10 6 qualified sequence tags, at least about 40×10 6 qualified sequence tags, or at least about 50×10 6 Qualified sequence tags are obtained from reads that uniquely map to the reference genome.

[0211] In some embodiments, the process can filter the sequence reads before alignment. In some embodiments, read filtering is a quality filtering process enabled by a software program implemented in a sequencer to filter out erroneous and low-quality reads. For example, Illumina's Sequencing Control Software (SCS) and the common assessment of sequence and variant software programs filter out erroneous and low-quality reads by converting the raw image data generated by the sequencing reaction into intensity scores, base scores, quality score alignments, and other formats to provide biologically relevant information for downstream analysis.

[0212] In certain embodiments, the reads produced by the sequencing device are provided in electronic format.Comparison is realized using a computing device as described below.Each read is compared with a reference genome, which is usually very large (millions of base pairs) to identify the site where the read corresponds uniquely to the reference genome.In some embodiments, the alignment program allows limited mismatches between the read and the reference genome.In some cases, 1,2,3 or more base pairs in the read are allowed to mismatch with the corresponding base pairs in the reference genome, but still mapped.In some embodiments, when the read is compared with the reference sequence with no more than 1,2,3 or 4 bases, it is considered that the read is the read of the comparison.Correspondingly, the uncompared read is a read that cannot be compared or poorly compared.The poorly compared read is a read with more mismatches than the comparison read.In some embodiments, when the read is compared with the reference sequence with no more than 1%, 2%, 3%, 4%, 5% or 10% bases, it is considered that the read is the comparison read.

[0213] After aligning the paired-end reads to a reference sequence that includes the repetitive sequence of interest, process 300 proceeds to identify anchor reads and anchor reads in the paired-end reads. See box 306. As described above, anchor reads are paired-end reads that align to or near a repetitive sequence. In some embodiments, anchor reads are paired-end reads that align within 1 kb of a repetitive sequence. Anchor reads are paired with anchor reads, but they cannot align to the reference sequence or align poorly to the reference sequence, as described above.

[0214] Process 300 analyzes the number of repetitions of the repeating unit in the identified anchor read and / or anchor read to determine whether there is amplification of the repetitive sequence. More specifically, process 300 involves using the number of repetitions in the read to obtain the number of high count reads in the anchor read and / or anchor read. A high count read is a read whose number of repetitions exceeds a threshold. In some embodiments, a high count read is obtained only from an anchor read. In other embodiments, a high count read is obtained from both an anchor read and an anchor read. In some embodiments, if the number of repetitions is close to the maximum number of repetitions possible for a read, the read is considered to be a high count read. For example, if the read is 100bp and the repeating unit considered is 3bp, the maximum number of repetitions will be 33. In other words, the maximum value is calculated based on the length of the paired end read and the length of the repeating unit. Specifically, the maximum number of repetitions can be obtained by dividing the length of the read by the length of the repeating unit and rounding the number. In this example, various implementations may identify 100 bp reads with at least about 28, 29, 30, 31, 32, or 33 repeats as high count reads. The number of repeats for high count reads may be adjusted up or down based on empirical factors and considerations. In various embodiments, the threshold for high count reads is at least about 80%, 85%, 90%, or 95% of the maximum number of repeats.

[0215] Process 300 then determines whether there may be repeated amplification of the repetitive sequence based on the number of high count reads. See box 310. In some embodiments, the analysis compares the obtained high count reads with the judgment criteria, and if the criteria are exceeded, it is determined that repeated amplification may exist. In some embodiments, the judgment criteria are obtained from the distribution of high count reads of the control sample. For example, multiple control samples known to have or suspected of having normal repetitive sequences are analyzed, and the high count reads of the control samples are obtained in the same manner as described above. The distribution of high count reads of the control sample can be obtained, and the probability of an unaffected sample with a high count read greater than a specific value can be estimated. This probability allows sensitivity and selectivity to be determined given the judgment criteria set at the specific value. In some embodiments, the judgment criteria are set to a threshold value so that the probability of an unaffected sample with a high count read greater than the threshold value is less than 5%. In other words, the p value is less than 0.05. In these embodiments, as repeated amplification occurs, the repetitive sequence becomes longer, more reads may be completely derived from the repetitive sequence, and more high count reads of the sample can be obtained. In various alternative implementations, a more conservative decision criterion may be selected such that the probability of an unaffected sample having a read count higher than a threshold is less than about 1%, 0.1%, 0.01%, 0.001%, 0.0001%, etc. It should be understood that the decision criterion may be adjusted up or down based on various factors and the need to increase the sensitivity or selectivity of the assay.

[0216] In some embodiments, instead of or in addition to empirically obtaining a criterion for the number of high-count reads from a control sample, a criterion can be theoretically obtained for determining repeat expansion. Given multiple parameters, including the length of paired-end reads, the length of sequences with repeat expansion, and sequencing depth, the expected number of reads completely within the repeat can be calculated. For example, sequencing depth can be used to calculate the average spacing between reads in an aligned genome. If a single sample is sequenced to 30x depth, the total bases sequenced are equal to the size of the genome multiplied by the depth. For humans, this would be equivalent to approximately 3×10 9 ×30 = 9×10 10 If each read is 100 bp long, a total of 9 × 10 8 reads to achieve this depth. Since the genome is diploid, half of these reads sequence one chromosome / haplotype and the rest sequence the other chromosome / haplotype. Each haplotype has 4.5 × 10 8 reads, and dividing the total genome size by this number yields the average spacing between the start positions of each read, which is 3 × 10 every 6.7 bp. 9 / 4.5×10 8 =1 read.Can be based on the size of repetitive sequence in specific individual, use this number to estimate the number of reads that will be completely in this repetitive sequence.If total repetitive sequence size is 300bp, then any read that starts in the first 200bp of this repetitive sequence will be completely in repetitive sequence (based on 100bp read length, any read that starts in the last 100bp will be at least partially outside repetitive sequence).Due to expected reads will be compared every 6.7bp, therefore expected 200bp / (6.7bp / read)=30 reads are compared completely in repetitive sequence.Although there will be variability around this number, but this allows to estimate the total read that will be completely in repetitive sequence for any amplification size.The repetitive sequence length calculated according to this method and the corresponding expected read number that is compared completely in repetitive sequence are given in Table 2 of Example 1 below.

[0217] In some embodiments, the call criterion is calculated based on the distance between the first and last observation of a repeat sequence within a read, thereby allowing for mutations and sequencing errors in the repeat sequence.

[0218] In some embodiments, the process may also include diagnosing that the individual from whom the test sample was obtained has an elevated risk of a genetic disease such as fragile X syndrome, ALS, Huntington's disease, Friedreich's ataxia, spinocerebellar ataxia, spinobulbar muscular atrophy, myotonic dystrophy, Machado-Joseph disease, or dentatorubronuclear pallidum Lewy body atrophy. Such a diagnosis may be based on determining that repeat expansions may be present in the test sample, and based on genes and repeat sequences associated with repeat expansions. In other embodiments, when a genetic disease is unknown, some embodiments may detect abnormally high repeat counts to newly identify the genetic cause of the disease.

[0219] Figure 4 4 is a flowchart illustrating another process for detecting repeat amplification according to some embodiments. Process 400 uses the number of repeats in the paired end reads of the test sample instead of the high count reads to determine the presence of repeat amplification. Process 400 begins with sequencing the test sample including nucleic acid to obtain paired end reads. See box 402, which is equivalent to box 302 of process 300. Process 400 continues by aligning the paired end reads with a reference sequence including a repeat sequence. See box 404, which is equivalent to box 304 in process 300. The process continues by identifying anchor reads and anchor reads in the paired end reads, wherein the anchor reads are reads aligned with or near the repeat sequence, and the anchor reads are unaligned reads paired with the anchor reads. In some embodiments, unaligned reads include both reads that cannot be aligned with the reference sequence and reads that are poorly aligned with the reference sequence.

[0220] After identifying the anchor reads and anchor reads, process 400 obtains the number of repeats in the anchor reads and / or anchor reads from the test sample. See box 408. The process then obtains the distribution of the number of repeats of all anchor reads and / or anchor reads obtained from the test sample. In some embodiments, only the number of repeats from the anchor reads is analyzed. In other embodiments, the repetitions of both the anchor reads and the anchor reads are analyzed. The distribution of the number of repeats of the test sample is then compared with the distribution of one or more control samples. See box 410. In some embodiments, if the distribution of the test sample is statistically significantly different from the distribution of the control sample, the method determines that the repeat expansion of the repetitive sequence is present in the test sample. See box 412. Process 400 analyzes the number of repeats of reads including high counts and low counts, which is different from the process of analyzing only high counts, such as described above with respect to process 300.

[0221] In some embodiments, the comparison of the distribution of the test sample to the distribution of the control sample involves using a Mann-Whitney rank test to determine whether the two distributions are significantly different. In some embodiments, if the distribution of the test sample is more biased toward a higher number of repeats relative to the control sample, and the p-value of the Mann-Whitney rank test is less than about 0.0001 or 0.00001, the analysis determines that repeat expansion may be present in the test sample. The p-value can be adjusted as needed to improve the selectivity or sensitivity of the test.

[0222] The above is relative to Figures 2 to 4 The described process for detecting repeat expansions uses anchor reads, which are unaligned reads that are paired with reads that align to the repeat sequence of interest. Variations on these processes can include searching the unaligned reads for read pairs that consist almost entirely of a certain type of repeat sequence to discover new, previously unidentified repeat expansions that may be medically relevant. The method does not quantify the exact number of repeats, but effectively identifies extreme repeat expansions or outliers that should be flagged for further quantification. In combination with longer reads, the method is able to identify and quantify repeat sequences with a total length of up to 200 bp or more.

[0223] Figure 5 A flow chart of a process 500 for identifying repeat amplification using unaligned reads that are not associated with any repetitive sequence of interest is shown. The process 500 can detect repeat amplification using unaligned reads of the entire genome. The process begins by sequencing a test sample comprising nucleic acids to obtain paired end reads. See box 502. The process 500 proceeds by aligning paired end reads with a reference genome. See box 504. The process then identifies unaligned reads of the entire genome. Unaligned reads include paired end reads that cannot be aligned with a reference sequence or that align poorly with a reference sequence. See box 506. The process then analyzes the number of repeats of repeat units in the unaligned reads to determine whether repeat amplification may be present in the test sample. The analysis can be agnostic to any particular repetitive sequence. The analysis can be applied to a variety of possible repeat units, and the number of repeats of different repeat units from the test sample can be compared with the number of repeats of multiple control samples. The comparison techniques between the above-mentioned test sample and the control sample can be applied to the analysis. If the comparison shows that the test sample has an unusually high number of repeats, additional analysis may be performed to determine whether the test sample includes a repeat expansion of a particular repeat sequence of interest.

[0224] In some embodiments, additional analysis involves very long sequence reads, which may span long repeat sequences with medically relevant repeat expansions. The reads in this additional analysis are longer than paired end reads. In some embodiments, single molecule sequencing or synthetic long read sequencing is used to obtain long reads. In some embodiments, the relationship between repeat expansion and hereditary diseases is known in the art. However, in other embodiments, the relationship between repeat expansion and hereditary diseases does not need to be established in the art.

[0225] In some embodiments, the number of repeats of the repeat unit in the unaligned reads of analysis operation 510 is related to Figure 3 The analysis of the number of repetitions of the repeat unit in the unaligned reads of operation 308 is performed. The analysis includes obtaining the number of high count reads, wherein the high count reads are unaligned reads with a number of repetitions exceeding a threshold; and comparing the number of high count reads in the test sample with the judgment criteria. In some embodiments, the threshold value of the high count reads is at least about 80% of the maximum number of repetitions, which is calculated as the ratio of the length of the paired end read to the length of the repeat unit. In some embodiments, the high count reads also include reads that are paired with unaligned reads and have more repetitions than the threshold.

[0226] In some embodiments, prior to the additional analysis of operation 510, the process further involves (a) identifying paired end reads that are paired with unaligned reads and aligned to or near a repetitive sequence on a reference genome; and (b) providing the repetitive sequence as a specific repetitive sequence of interest in operation 510. Then, the additional analysis of the repetitive sequence of interest can be performed using the above combined Figures 2 to 4 Any of the methods described.

[0227] sample Samples for determining repeat expansion can include samples obtained from any cell, fluid, tissue, or organ comprising nucleic acid, in which repeat expansion of one or more repeat sequences of interest is to be determined. In some embodiments involving fetal diagnosis, it is advantageous to obtain free nucleic acid, such as cell-free DNA (cfDNA), from maternal bodily fluids. Free nucleic acids (including free DNA) can be obtained from biological samples, including but not limited to plasma, serum, and urine, by a variety of methods known in the art (see, e.g., Fan et al., Proc Natl Acad Sci, Vol. 105: 16266-16271

[2008] ; Koide et al., Prenatal Diagnosis, Vol. 25: 604-607

[2005] ; Chen et al., Nature Med., Vol. 2: 1033-1035

[1996] ; Lo et al., Lancet, Vol. 350: 485-487

[1997] ; Botezatu et al., Clin Chem, Vol. 46: 1078-1084, 2000; and Su et al., J Mol. Diagn., Vol. 6: 101-107

[2004] ).

[0228] In various embodiments, nucleic acids (e.g., DNA or RNA) present in a sample may be specifically or non-specifically enriched prior to use (e.g., prior to preparing a sequencing library). DNA is used as an example of nucleic acid in the following illustrative examples. Non-specific enrichment of sample DNA refers to whole genome amplification of sample genomic DNA fragments that can be used to increase sample DNA levels prior to preparing a cfDNA sequencing library. Methods for whole genome amplification are known in the art. Degenerate oligonucleotide primer PCR (DOP), primer extension PCR techniques (PEP), and multiple displacement amplification (MDA) are examples of whole genome amplification methods. In some embodiments, the sample is not enriched for DNA.

[0229] Samples comprising nucleic acids to which the methods described herein are applied generally include biological samples as described above ("test samples"). In some embodiments, nucleic acids to be screened for repeated amplification are purified or isolated by any of a variety of well-known methods.

[0230] Therefore, in certain embodiments, the sample includes or is substantially composed of purified or isolated polynucleotides, or it may include samples, such as tissue samples, biological fluid samples, cell samples, etc. Suitable biological fluid samples include, but are not limited to, blood, plasma, serum, sweat, tears, sputum, urine, sputum, ear discharge, lymph, saliva, cerebrospinal fluid, lavage fluid, bone marrow suspension, vaginal fluid, cervical lavage fluid, brain fluid, ascites, milk, respiratory tract, intestinal and urogenital tract secretions, amniotic fluid, milk and leukocyte separation samples. In some embodiments, the sample is a sample that is easily obtained by non-invasive surgery, such as blood, plasma, serum, sweat, tears, sputum, urine, sputum, ear fluid, saliva or feces. In certain embodiments, the sample is a peripheral blood sample or the plasma and / or serum fraction of a peripheral blood sample. In other embodiments, the biological sample is a swab or smear, a biopsy specimen or a cell culture. In another embodiment, the sample is a mixture of two or more biological samples, for example, the biological sample may include two or more of a biological fluid sample, a tissue sample, and a cell culture sample. As used herein, the terms "blood", "plasma", and "serum" explicitly encompass fractions or processed portions thereof. Similarly, in the case where the sample is taken from a biopsy, swab, smear, etc., "sample" explicitly encompasses processed fractions or portions derived from a biopsy, swab, smear, etc.

[0231] In certain embodiments, samples may be obtained from sources including, but not limited to, samples from different individuals, samples from different developmental stages of the same or different individuals, samples from different diseased individuals (e.g., individuals suspected of having a genetic disease), normal individuals, samples obtained at different stages of an individual's disease, samples obtained from individuals receiving different treatments for a disease, samples from individuals exposed to different environmental factors, samples from individuals susceptible to pathology, samples obtained from individuals exposed to infectious agents, and the like.

[0232] In an exemplary but non-limiting embodiment, the sample is a maternal sample obtained from a pregnant woman (e.g., a pregnant woman). In this case, the sample can be analyzed using the methods described herein to provide a prenatal diagnosis of a potential chromosomal abnormality of the fetus. The maternal sample can be a tissue sample, a biological fluid sample, or a cell sample. As a non-limiting example, biological fluids include blood, plasma, serum, sweat, tears, sputum, urine, sputum, ear discharge, lymph, saliva, cerebrospinal fluid, lavage fluid, bone marrow suspension, vaginal fluid, cervical lavage fluid, brain fluid, ascites, milk, secretions of the respiratory tract, intestinal tract, and urogenital tract, and leukocyte apheresis samples.

[0233] In certain embodiments, samples may also be obtained from tissues, cells or other sources containing polynucleotides cultured in vitro. Cultured samples may be obtained from sources including, but not limited to, cultures (e.g., tissues or cells) maintained in different culture media and conditions (e.g., pH, pressure or temperature), cultures (e.g., tissues or cells) maintained for different lengths of time, cultures (e.g., tissues or cells) treated with different factors or agents (e.g., candidate drugs or modulators), or cultures of different types of tissues and / or cells.

[0234] The method for separating nucleic acid from biological sources is well known, and will be different according to the nature of the source. Those skilled in the art can easily separate nucleic acid from the source required for the methods described herein. In some cases, it may be advantageous to fragment the nucleic acid molecules in the nucleic acid sample. Fragmentation can be random, or it can be specific, as for example achieved by digestion with restriction endonucleases. The method of random fragmentation is well known in the art, and includes for example restriction DNA enzyme digestion, alkali treatment and physical shearing.

[0235] Sequencing library preparation In various embodiments, sequencing can be performed on various sequencing platforms that require the preparation of sequencing libraries. Preparation generally involves fragmenting the DNA (sonication, nebulization, or shearing), followed by DNA repair and end polishing (blunt ends or A overhangs), and connection with platform-specific adapters. In one embodiment, the methods described herein can utilize next-generation sequencing technology (NGS), which allows for the individual sequencing of multiple samples in a single sequencing run with genomic molecules (i.e., single-plex sequencing) or with a combined sample containing indexed genomic molecules (e.g., multiplex sequencing). These methods can generate up to billions of DNA sequence reads. In various embodiments, the sequence of genomic nucleic acids and / or indexed genomic nucleic acids can be determined using, for example, the next-generation sequencing technology (NGS) described herein. In various embodiments, one or more processors as described herein can be used to perform analysis of a large amount of sequence data obtained using NGS.

[0236] In various embodiments, the use of such sequencing techniques does not include the preparation of sequencing libraries.

[0237] However, in certain embodiments, the sequencing methods contemplated herein include the preparation of sequencing libraries. In an exemplary method, sequencing library preparation involves randomly collecting DNA fragments (e.g., polynucleotides) modified by adapters to be sequenced. Polynucleotide sequencing libraries can be prepared from DNA or RNA (including equivalents or analogs of DNA or cDNA, such as DNA or cDNA of complementary or copy DNA produced by RNA templates) by the action of reverse transcriptase. Polynucleotides can be started in double-stranded form (e.g., dsDNA, such as genomic DNA fragments, cDNA, PCR amplification products, etc.), or in certain embodiments, polynucleotides can be started in single-stranded form (e.g., ssDNA, RNA, etc.) and have been converted into dsDNA form. For example, in certain embodiments, single-stranded mRNA molecules can be copied into double-stranded cDNAs suitable for preparing sequencing libraries. The precise sequence of the primary polynucleotide molecule is generally not important for the library preparation method, and can be known or unknown. In one embodiment, the polynucleotide molecule is a DNA molecule. More specifically, in certain embodiments, the polynucleotide molecule represents the entire genetic complement of an organism or substantially the entire genetic complement of an organism, and is a genomic DNA molecule (e.g., cellular DNA, free DNA (cfDNA), etc.), which typically includes intron sequences and exon sequences (coding sequences), as well as non-coding regulatory sequences such as promoter and enhancer sequences. In certain embodiments, the primary polynucleotide molecule includes a human genomic DNA molecule, such as a cfDNA molecule present in the peripheral blood of a pregnant subject.

[0238] The preparation of sequencing libraries for some NGS sequencing platforms is facilitated by using polynucleotides containing a specific range of fragment sizes. The preparation of such libraries typically involves fragmentation of large polynucleotides (e.g., cellular genomic DNA) to obtain polynucleotides in the desired size range to determine repeat expansion. The fragment or insert length is longer than the read length, and is typically longer than the sum of the lengths of two reads.

[0239] In some exemplary embodiments, the sample nucleic acid is obtained as genomic DNA, and the genomic DNA is fragmented into fragments of about 100 or more, about 200 or more, about 300 or more, about 400 or more, or about 500 or more base pairs, and the NGS method can be easily applied to these fragments. In some embodiments, paired end reads are obtained from an insert sequence of about 100-5000bp. In some embodiments, the insert sequence is about 100-1000bp long. These fragments are sometimes implemented as conventional short insert sequence paired end reads. In some embodiments, the insert sequence is about 1000-5000bp long. These fragments are sometimes implemented as long insert sequence paired reads as described above.

[0240] In some implementations, long insert sequences are designed to evaluate very long amplified repetitive sequences. In some implementations, paired reads can be applied to obtain reads spaced apart by thousands of base pairs. In these implementations, the insert sequence or fragment is in the range of hundreds to thousands of base pairs, with two biotin-linked adapters located at both ends of the insert sequence. The biotin-linked adapters then connect the two ends of the insert sequence to form a circularized molecule, which is then further fragmented. The sub-fragment including the biotin-linked adapter and the two ends of the original insert sequence is selected for sequencing on a platform designed to sequence shorter fragments.

[0241] Fragmentation can be achieved by any of a variety of methods known to those skilled in the art. For example, fragmentation can be achieved by mechanical methods, including, but not limited to, atomization, sonication, and water shearing. However, mechanical fragmentation typically cleaves the DNA backbone at the CO, PO, and CC bonds, thereby producing a heterogeneous mixture of blunt ends and 3'- and 5'-protruding ends with broken CO, PO, and / or CC bonds (see, e.g., Alnemri and Liwack, J Biol. Chem, Vol. 265: pp. 17323-17333

[1990] ; Richards and Boyer, J Mol Biol, Vol. 11: pp. 327-240

[1965] ), which may require repair because they may lack the 5'-phosphate required for subsequent enzymatic reactions (e.g., ligation of sequencing adapters required to prepare DNA for sequencing).

[0242] In contrast, cfDNA is typically present in fragments smaller than approximately 300 base pairs, and thus fragmentation is generally not necessary to generate sequencing libraries using cfDNA samples.

[0243] Typically, whether the polynucleotides are forcibly fragmented (e.g., in vitro fragmented) or naturally present as fragments, they are converted to blunt-ended DNA with a 5'-phosphate and a 3'-hydroxyl group. Standard protocols, e.g., protocols for sequencing using an Illumina platform such as described elsewhere herein, instruct the user to perform end repair on sample DNA, purify the end repair products prior to dA-tailing, and purify the dA-tailing products prior to the adapter ligation step of library preparation.

[0244] Various embodiments of the sequencing library preparation methods described herein do not require the performance of one or more steps typically required by standard protocols to obtain modified DNA products that can be sequenced by NGS. A simplified method (ABB method), a 1-step method, and a 2-step method are examples of methods for preparing sequencing libraries, which can be found in patent application 13 / 555,037 filed on July 20, 2012, which is incorporated herein by reference in its entirety.

[0245] Sequencing methods As described above, the prepared samples (eg, sequencing libraries) are sequenced as part of a procedure for identifying copy number variation. Any of a variety of sequencing technologies can be utilized.

[0246] Several sequencing technologies are commercially available, such as the sequencing-by-hybridization platform from Affymetrix (Sunnyvale, CA), the sequencing-by-synthesis platform from 454 Life Sciences (Bradford, CT), Illumina / Solexa (San Diego, CA), and Helicos Biosciences (Cambridge, MA), and the sequencing-by-ligation platform from Applied Biosystems (Foster City, CA), as described below. In addition to single-molecule sequencing using sequencing-by-synthesis from Helicos Biosciences, other single-molecule sequencing technologies include, but are not limited to, SMRT from Pacific Biosciences. ™ Technology, ION TORRENT ™ technologies, and nanopore sequencing, for example, developed by Oxford Nanopore Technologies.

[0247] Although the automatic Sanger method is considered to be a "first generation" technology, Sanger sequencing including automatic Sanger sequencing can also be used in the methods described herein. Other suitable sequencing methods include, but are not limited to, nucleic acid imaging techniques, such as atomic force microscopy (AFM) or transmission electron microscopy (TEM). Exemplary sequencing techniques are described in more detail below.

[0248] In some embodiments, the disclosed methods involve obtaining sequence information of nucleic acids in a test sample by massively parallel sequencing of millions of DNA fragments using Illumina's sequencing by synthesis and reversible terminator-based sequencing chemistry (e.g., as described in Bentley et al., Nature, Vol. 6: 53-59

[2009] ). The template DNA can be genomic DNA, such as cellular DNA or cfDNA. In some embodiments, genomic DNA from isolated cells is used as a template and is fragmented into a length of several hundred base pairs. In other embodiments, cfDNA is used as a template and does not need to be fragmented because cfDNA exists as short fragments. For example, fetal cfDNA circulates in the bloodstream in fragments of approximately 170 base pairs (bp) in length (Fan et al., Clin Chem, Vol. 56: 1279-1286

[2010] ), and there is no need to fragment this DNA prior to sequencing. Illumina's sequencing technology relies on ligating fragmented genomic DNA to a flat, optically transparent surface to which anchor oligonucleotides are bound. The template DNA is end-repaired to generate 5'-phosphorylated blunt ends and the fragments are sequenced using Klenow fragments. The polymerase activity of a fragment adds a single A base to the 3' end of the blunt-end phosphorylated DNA fragment. This addition prepares the DNA fragment for ligation to an oligonucleotide adapter that has an overhang of a single T base at its 3' end to improve ligation efficiency. The adapter oligonucleotide is complementary to the flow cell anchor oligonucleotide (not to be confused with the anchor / anchored read in the repeat amplification analysis). Under limiting dilution conditions, adapter-modified single-stranded template DNA is added to the flow cell and immobilized by hybridization to the anchor oligonucleotide. The ligated DNA fragments are extended and bridge-amplified to produce an ultra-high density sequencing flow cell with hundreds of millions of clusters, each containing approximately 1,000 copies of the same template. In one embodiment, the randomly fragmented genomic DNA is amplified using PCR before it is cluster amplified. Alternatively, a non-amplified genomic library preparation is used, and only cluster amplification is used to enrich the randomly fragmented genomic DNA (Kozarewa et al., Nature Methods, Vol. 6: pp. 291-295

[2009] ). The templates are sequenced using a robust four-color DNA sequencing-by-synthesis technique that employs reversible terminators with removable fluorescent dyes. High-sensitivity fluorescence detection is achieved using laser excitation and total internal reflection optics. Short sequence reads of approximately tens to hundreds of base pairs are aligned to a reference genome, and a specially developed data analysis pipeline software is used to identify unique mappings of short sequence reads to the reference genome. After the first read is completed, the template can be regenerated in situ to enable a second read from the opposite end of the fragment.Thus, single-end or paired-end sequencing of DNA fragments can be used.

[0249] Various embodiments of the present invention can use sequencing by synthesis that allows paired end sequencing. In some embodiments, Illumina's sequencing by synthesis involves clustering fragments. Clustering is the process in which each fragment molecule is amplified isothermally. In some embodiments, as in the examples described herein, the fragment has two different adapters connected to the two ends of the fragment, and the adapters allow the fragment to hybridize with two different oligonucleotides on the surface of the flow cell lane. The fragment also includes or is connected to two index sequences at both ends of the fragment, and the index sequences provide tags to identify different samples in multiple sequencing. In some sequencing platforms, the fragment to be sequenced is also referred to as an insertion sequence.

[0250] In some embodiments, the flow cell used for clustering in the Illumina platform is a slide with lanes. Each lane is a glass channel coated with a primer moss of two types of oligonucleotides. Hybridization is achieved by the first oligonucleotide of the two types of oligonucleotides on the surface. The oligonucleotide is complementary to the first adapter on one end of the fragment. The polymerase produces a complementary strand of the hybridized fragment. The double-stranded molecule is denatured, and the original template strand is washed away. The remaining strand is parallel to many other remaining strands and cloned and amplified by bridge application.

[0251] In bridge amplification, the second adapter region domain on the second end of the chain hybridizes with the second type of oligonucleotide on the surface of the flow cell. The polymerase produces a complementary strand, thereby forming a double-stranded bridge molecule. The double-stranded molecule denatures, resulting in two single-stranded molecules connected to the flow cell through two different oligonucleotides. The process is then repeated over and over, and is performed in millions of clusters at the same time, resulting in clonal amplification of all fragments. After bridge amplification, the reverse strand is cut and washed away, leaving only the forward strand. The 3' end is blocked to prevent unwanted priming.

[0252] After clustering, sequencing begins with extension of the first sequencing primer to generate the first read. In each cycle, fluorescently labeled nucleotides compete to be added to the growing chain. Only one fluorescently labeled nucleotide is incorporated based on the sequence of the template. After each nucleotide is added, the cluster is excited by a light source and emits a characteristic fluorescent signal. The number of cycles determines the length of the read. The emission wavelength and signal intensity determine the base call. All identical chains of a given cluster are read simultaneously. Hundreds of millions of clusters are sequenced in a massively parallel manner. At the completion of the first read, the read product is washed away.

[0253] In the next step of the protocol involving two index primers, an Index 1 primer is introduced and hybridized to the Index 1 region on the template. The Index region provides identification of the fragments, which can be used to demultiplex samples in a multiplex sequencing process. An Index 1 read is generated similarly to the first read. After the Index 1 read is completed, the read product is washed away and the 3' end of the strand is deprotected. The template strand is then folded and bound to the second oligonucleotide on the flow cell. The Index 2 sequence is read in the same manner as Index 1. The Index 2 read product is then washed away at the end of the step.

[0254] After reading both indexes, read 2 is initiated by using a polymerase to extend the second flow cell oligonucleotide to form a double-stranded bridge. The double-stranded DNA is denatured and the 3' end is blocked. The original forward strand is cut and washed away, leaving the reverse strand. Read 2 starts with the introduction of the read 2 sequencing primer. As with read 1, the sequencing steps are repeated until the desired length is achieved. The read 2 product is washed away. This entire process generates millions of reads, representing all fragments. Sequences from the pooled sample library are separated based on the unique index introduced during sample preparation. For each sample, reads of similar extensions of base calls are locally clustered. The forward and reverse reads are paired to produce contiguous sequences. These contiguous sequences are aligned to the reference genome for variant identification.

[0255] The above-mentioned sequencing by synthesis example involves paired-end reads, which are used in many embodiments of the disclosed methods. Paired-end sequencing involves 2 reads from both ends of a fragment. Paired-end reads are used to resolve ambiguous alignments. Paired-end sequencing allows the user to select the length of the insert sequence (or fragment to be sequenced) and sequence either end of the insert sequence, thereby generating high-quality, comparable sequence data. Because the distance between each paired read is known, the alignment algorithm can use this information to more accurately map reads on repetitive regions. This enables better read alignment, especially on difficult-to-sequence, repetitive regions in the genome. Paired-end sequencing can detect rearrangements, including insertions and deletions (indels) and inversions.

[0256] Paired end reads can use inserts of different lengths (i.e., different fragment sizes to be sequenced). As the default meaning in the present disclosure, paired end reads are used to refer to reads obtained from various insert sequence lengths. In some cases, in order to distinguish short insert sequence paired end reads from long insert sequence paired end reads, the latter are specifically referred to as paired reads. In some embodiments including mate pair reads, two biotin-linked adapters are first connected to the two ends of a relatively long insert sequence (e.g., several kb). The biotin-linked adapters are then connected to the two ends of the insert sequence to form a circularized molecule. Subfragments containing the biotin-linked adapters can then be obtained by further fragmenting the circularized molecule. Subfragments containing the two ends of the original fragment in reverse sequence order can then be sequenced by the same procedure as the above-mentioned short insert sequence paired end sequencing. More details of paired sequencing using the Illumina platform are shown in the online publication at the following website, which is incorporated by reference: res.illumina.com / documents / products / technotes / technote_nextera_matepair_data_processing.pdf

[0257] After DNA fragment sequencing, sequence reads of a predetermined length (e.g., 100 bp) are mapped to or compared with a known reference genome. The mapped or compared reads and their corresponding positions on the reference sequence are also referred to as tags. The analysis of many embodiments disclosed herein for determining repeated amplification utilizes poorly compared or unmatched reads and compared reads (tags). In one embodiment, the reference genome sequence is the NCBI36 / hg18 sequence, which is available on the World Wide Web at the following website: genome.ucsc.edu / cgi-bin / hgGateway?org=Human&db=hg18&hgsid=166260105). Alternatively, the reference genome sequence is GRCh37 / hg19, which is available on the World Wide Web at the following website: genome.ucsc.edu / cgi-bin / hgGateway. Other sources of public sequence information include GenBank, dbEST, dbSTS, EMBL (European Molecular Biology Laboratory) and DDBJ (DNA Database of Japan). A variety of computer algorithms can be used to align sequences, including, but not limited to, BLAST (Altschul et al., 1990), BLITZ (MPsrch) (Sturrock and Collins, 1993), FASTA (Person and Lipman, 1988), BOWTIE (Langmead et al., GenomeBiology, 10: R25.1-R25.10

[2009] ), or ELAND (Illumina, San Diego, CA, USA). In one embodiment, one end of a clonally amplified copy of a plasma cfDNA molecule is sequenced and processed by bioinformatics alignment analysis on an Illumina Genome Analyzer using the Efficient Large-Scale Alignment of Nucleotide Databases (ELAND) software.

[0258] In one exemplary but non-limiting embodiment, the methods described herein include obtaining sequence information of nucleic acids in a test sample using single molecule sequencing technology using Helicos True Single Molecule Sequencing (tSMS) technology (e.g., as described in Harris TD et al., Science, Vol. 320: pp. 106-109

[2008] ). In tSMS technology, a DNA sample is cut into chains of approximately 100 to 200 nucleotides, and a polyA sequence is added to the 3' end of each DNA chain. Each chain is labeled by adding fluorescently labeled adenosine nucleotides. The DNA chains are then hybridized to a flow cell that contains millions of oligonucleotide-T capture sites fixed to the surface of the flow cell. In certain embodiments, the density of the templates can be about 100 million templates / cm 2 The flow cell is then loaded into an instrument such as the HeliScope ™ In the sequencer, a laser illuminates the surface of the circulation cell, revealing the position of each template. A CCD camera can map the position of the template on the surface of the circulation cell. The template fluorescent label is then cut and washed away. The sequencing reaction begins by introducing a DNA polymerase and fluorescently labeled nucleotides. Oligo-T nucleic acids are used as primers. The polymerase incorporates the labeled nucleotides into the primer in a template-guided manner. The polymerase and unincorporated nucleotides are removed. The template that has been guided to incorporate the fluorescently labeled nucleotides is identified by imaging the surface of the circulation cell. After imaging, the cleavage step removes the fluorescent label, and the process is repeated with other fluorescently labeled nucleotides until the desired read length is reached. Sequence information is collected with each nucleotide addition step. Whole genome sequencing performed by single-molecule sequencing technology excludes or generally excludes PCR-based amplification in sequencing library preparation, and the method allows direct measurement of the sample rather than measuring copies of the sample.

[0259] In another exemplary but non-limiting embodiment, the methods described herein include obtaining sequence information of nucleic acids in a test sample using 454 sequencing (Roche) (e.g., as described in Margulies, M. et al., Nature, Vol. 437: pp. 376-380

[2005] ). 454 sequencing generally involves two steps. In the first step, the DNA is sheared into fragments of approximately 300 to 800 base pairs, and the fragments are blunt-ended. Oligonucleotide adapters are then attached to the ends of the fragments. The adapters act as primers for amplifying and sequencing the fragments. The fragments can be attached to DNA capture beads, such as streptavidin-coated beads, using, for example, adapter B containing a 5'-biotin tag. The fragments attached to the beads are PCR amplified within droplets of an oil-water emulsion. The result is multiple copies of clonally amplified DNA fragments on each bead. In the second step, the beads are captured in a well (e.g., a picoliter-sized well). Pyrophosphate sequencing is performed in parallel for each DNA fragment. The addition of one or more nucleotides generates a light signal that is recorded by a CCD camera in the sequencing instrument. The signal intensity is proportional to the number of nucleotides incorporated. Pyrosequencing utilizes the pyrophosphate (PPi) released when a nucleotide is added. PPi is converted to ATP by ATP sulfurylase in the presence of adenosine 5' phosphosulfate. Luciferase uses ATP to convert luciferin to oxyluciferin, and the reaction generates light that is measured and analyzed.

[0260] In another exemplary but non-limiting embodiment, the methods described herein include using SOLiD ™ Technology (from Applied Biosystems) to obtain sequence information of nucleic acids in test samples. ™ In sequencing by ligation, genomic DNA is sheared into fragments and adapters are attached to the 5' and 3' ends of the fragments to produce a fragment library. Alternatively, internal adapters can be introduced by attaching adapters to the 5' and 3' ends of the fragments, circularizing the fragments, digesting the circularized fragments to produce internal adapters, and attaching adapters to the 5' and 3' ends of the resulting fragments to produce a paired library. Next, a clonal bead population is prepared in a microreactor containing beads, primers, templates, and PCR components. After PCR, the template is denatured and the beads are enriched to separate the beads using an extended template. The template on the selected beads is subjected to a 3' modification that allows bonding to a slide. The sequence can be determined by sequential hybridization and connection of partially random oligonucleotides with bases (or base pairs) determined by the center identified by a specific fluorophore. After recording the color, the connected oligonucleotides are cut and removed, and the process is repeated.

[0261] In another exemplary but non-limiting embodiment, the methods described herein include using Pacific Biosciences' Single Molecule Real-Time (SMRT ™ ) sequencing technology obtains sequence information of nucleic acids in test samples. In SMRT sequencing, the continuous incorporation of dye-labeled nucleotides is imaged during DNA synthesis. A single DNA polymerase molecule is connected to the bottom surface of a single zero-mode wavelength detector (ZMW detector), which obtains sequence information while incorporating phosphorylated nucleotides into the growing primer chain. The ZMW detector contains a confinement structure that enables the observation of the incorporation of a single nucleotide by the DNA polymerase against a background of fluorescent nucleotides that diffuse rapidly outside the ZMW (e.g., in microseconds). It usually takes several milliseconds to incorporate a nucleotide into a growing chain. During this period, the fluorescent label is excited and produces a fluorescent signal, and the fluorescent tag is cleaved. The measurement of the corresponding fluorescence of the dye indicates which base is incorporated. This process is repeated to provide a sequence.

[0262] In another exemplary but non-limiting embodiment, the methods described herein include obtaining sequence information of nucleic acids in a test sample using nanopore sequencing (e.g., as described in Soni GV and Meller A, Clin Chem, Vol. 53: pp. 1996-2001

[2007] ). Nanopore sequencing DNA analysis technology is developed by many companies, including, for example, Oxford Nanopore Technologies (Oxford, United Kingdom), Sequenom, NABsys, etc. Nanopore sequencing is a single molecule sequencing technology, whereby a single molecule of DNA is directly sequenced as it passes through a nanopore. A nanopore is a hole with a diameter of typically about 1 nanometer. Immersing the nanopore in a conductive fluid and applying an electric potential (voltage) thereto causes a slight current due to ion conduction through the nanopore. The amount of current flowing is sensitive to the size and shape of the nanopore. When a DNA molecule passes through a nanopore, each nucleotide on the DNA molecule blocks the nanopore to varying degrees, thereby changing the magnitude of the current passing through the nanopore to varying degrees. Therefore, when a DNA molecule passes through a nanopore, this change in current provides a read of a DNA sequence.

[0263] In another exemplary but non-limiting embodiment, the methods described herein include obtaining sequence information of nucleic acids in a test sample using a chemically sensitive field effect transistor (chemFET) array (e.g., as described in U.S. Patent Application Publication No. 2009 / 0026082). In one example of this technology, a DNA molecule can be placed in a reaction chamber, and the template molecule can be hybridized with a sequencing primer bound to a polymerase. The incorporation of one or more triphosphates into a new nucleic acid chain at the 3' end of the sequencing primer can be recognized by a chemFET as a change in current. The array can have multiple chemFET sensors. In another example, a single nucleic acid can be attached to a bead, and the nucleic acid can be amplified on the bead, and each bead can be transferred to each reaction chamber on the chemFET array, wherein each chamber has a chemFET sensor, and the nucleic acid can be sequenced.

[0264] In another embodiment, the DNA sequencing technology is Ion Torrent single molecule sequencing, which pairs semiconductor technology with simple sequencing chemistry to convert chemically encoded information (A, C, G, T) directly into digital information (0, 1) on a semiconductor chip. In nature, when nucleotides are incorporated into DNA chains by polymerases, hydrogen ions are released as byproducts. IonTorrent uses a high-density array of micromechanical holes to perform this biochemical process in a massively parallel manner. Each hole holds a different DNA molecule. Below the hole is an ion-sensitive layer and below the ion sensor. When a nucleotide such as C is added to the DNA template and subsequently incorporated into the DNA chain, a hydrogen ion will be released. The charge from this ion will change the pH of the solution, which can be detected by the Ion Torrent's ion sensor. The sequencer - basically the world's smallest solid-state pH meter - called a base, goes directly from chemical information to digital information. The Ion Personal Genome Sequencer (PGM ™ ) The chip is sequentially filled with one nucleotide after another. If the next nucleotide that fills the chip does not match, no voltage change will be recorded and no base will be called. If there are two identical bases on the DNA strand, the voltage will double and the chip will record two identical bases as being called. Direct detection allows nucleotide incorporation to be recorded in seconds.

[0265] In another embodiment, the method of the present invention comprises using hybridization sequencing to obtain sequence information of nucleic acid in the test sample. Sequencing while hybridization comprises contacting a plurality of polynucleotide sequences with a plurality of polynucleotide probes, wherein each of the plurality of polynucleotide probes can be optionally connected to a substrate. The substrate can be a flat surface comprising an array of known nucleotide sequences. The hybridization pattern with the array can be used to determine the polynucleotide sequences present in the sample. In other embodiments, each probe is connected to beads, such as magnetic beads, etc. It is possible to determine the hybridization with the beads and to be used to identify a plurality of polynucleotide sequences in the sample.

[0266] In some embodiments of the methods described herein, the sequence reads are about 20 bp, about 25 bp, about 30 bp, about 35 bp, about 40 bp, about 45 bp, about 50 bp, about 55 bp, about 60 bp, about 65 bp, about 70 bp, about 75 bp, about 80 bp, about 85 bp, about 90 bp, about 95 bp, about 100 bp, about 110 bp, about 120 bp, about 130 bp, about 140 bp, about 150 bp, about 200 bp, about 250 bp, about 300 bp, about 350 bp, about 400 bp, about 450 bp, or about 500 bp in length. It is expected that technological advances will enable single-end reads greater than 500 bp and, when generating paired-end reads, reads greater than about 1000 bp. In some embodiments, paired end reads are used to determine repeat amplification, which includes sequence reads of about 20bp to 1000bp, about 50bp to 500bp, or 80bp to 150bp in length. In various embodiments, paired end reads are used to evaluate sequences with repeat amplification. The sequence with repeat amplification is longer than the read. In some embodiments, the sequence with repeat amplification is longer than about 100bp, 500bp, 1000bp, or 4000bp. The mapping of sequence reads is achieved by comparing the read sequence with the reference sequence to determine the chromosome source of the sequenced nucleic acid molecule, and no specific genetic sequence information is required. A small degree of mismatch (0-2 mismatches / read) can be allowed to explain the minor polymorphisms that may exist between the reference genome and the genome in the mixed sample. In some embodiments, the reads aligned with the reference sequence are used as anchor reads, and the reads that are paired with the anchor reads but cannot be aligned with the reference sequence or are poorly aligned are used as anchor reads. In some embodiments, poorly aligned reads can have a relatively large mismatch percentage per read, such as at least about 5%, at least about 10%, at least about 15%, or at least about 20% mismatch percentage per read.

[0267] Typically, multiple sequence tags (i.e., reads aligned to a reference sequence) are obtained for each sample. In some embodiments, at least about 3×10 6 sequence tags, at least about 5×10 6 sequence tags, at least about 8×10 6 sequence tags, at least about 10×10 6 sequence tags, at least about 15×10 6 sequence tags, at least about 20×10 6 sequence tags, at least about 30×10 6 sequence tags, at least about 40×10 6 sequence tags, or at least about 50×10 6 In some embodiments, the reads of each sample are mapped to all regions of the reference genome, thereby providing full genome reads. In other embodiments, the reads are mapped to sequences of interest, such as chromosomes, chromosome fragments, or repeated sequences of interest.

[0268] Devices and systems for determining repeat expansion The analysis of sequencing data and the diagnosis derived therefrom are usually performed using various computer-implemented algorithms and programs. Therefore, certain embodiments employ processes including data stored in or transmitted by one or more computer systems or other processing systems. The embodiments disclosed herein also include devices for performing these operations. The device may be specially constructed for the desired purpose, or it may be a general-purpose computer (or computer group) that is selectively activated or reconfigured by a computer program and / or data structure stored in the computer. In some embodiments, the processor group performs some or all of the operations in the analysis operation collaboratively (e.g., via a network or cloud computing) and / or in parallel. The processor or processor group used to perform the methods described herein may be of various types, including microcontrollers and microprocessors, such as programmable devices (e.g., CPLDs and FPGAs) and non-programmable devices (such as gate array ASICs or general-purpose microprocessors).

[0269] One embodiment provides a system for determining the genotype of a variant at a locus that includes a repetitive sequence, the system comprising a sequencer for receiving a nucleic acid sample and providing nucleic acid sequence information from the sample; a processor; and a machine-readable storage medium having instructions stored thereon, the instructions being used to be executed on the processor to genotype the variant in the following manner: (a) collecting nucleic acid sequence reads of a test sample from a database; (b) aligning the sequence reads with one or more repetitive sequences each represented by a sequence graph, wherein the sequence graph has a data structure of a directed graph having vertices representing nucleic acid sequences and directed edges connecting the vertices, and wherein the sequence graph includes one or more self-loops, each self-loop representing a repetitive subsequence, each repetitive subsequence including repetitions of a repeating unit of one or more nucleotides; and (c) determining one or more genotypes of the one or more repetitive sequences using the sequence reads aligned with the one or more repetitive sequences.

[0270] In some embodiments of any of the systems provided herein, the sequencer is configured to perform next generation sequencing (NGS). In some embodiments, the sequencer is configured to perform massively parallel sequencing using sequencing by synthesis with a reversible dye terminator. In other embodiments, the sequencer is configured to perform sequencing by ligation. In other embodiments, the sequencer is configured to perform single molecule sequencing.

[0271] In addition, certain embodiments relate to tangible and / or non-transitory computer-readable media or computer program products, which include program instructions and / or data (including data structures) for performing various computer-implemented operations. Examples of computer-readable media include, but are not limited to, semiconductor memory devices, magnetic media (such as disk drives), tapes, optical media (such as CDs), magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as read-only memory devices (ROMs) and random access memories (RAMs). Computer-readable media may be directly controlled by an end user, or the media may be indirectly controlled by an end user. Examples of directly controlled media include media located at a user facility and / or media that is not shared with other entities. Examples of indirectly controlled media include media that a user can indirectly access via an external network and / or via a service that provides shared resources (such as a "cloud"). Examples of program instructions include machine code such as generated by a compiler and files containing higher-level code that can be executed by a computer using an interpreter.

[0272] In various embodiments, the data or information used in the methods and devices disclosed in the present invention are provided in electronic format. Such data or information may include reads and labels derived from nucleic acid samples, reference sequences (including reference sequences that only provide or primarily provide polymorphisms), determinations such as repeat amplification determinations, consulting advice, diagnosis, etc. As used herein, data or other information provided in electronic format can be used for storage on a machine and transmission between machines. Conventionally, data in electronic format are provided digitally and can be stored as bits and / or bytes in various data structures, lists, databases, etc. Data can be embodied electronically, optically, etc.

[0273] One embodiment provides a computer program product for generating an output indicating whether there is repeated amplification in the test sample. The computer product may include instructions for executing any one or more of the above methods for determining repeated amplification. As explained, the computer product may include a non-transient and / or tangible computer-readable medium having a computer executable or compilable logic (e.g., instructions) recorded thereon to enable a processor to determine the repetition in the anchor read and the anchor read, and whether there is repeated amplification. In one example, the computer product includes a computer-readable medium having a computer executable or compilable logic (e.g., instructions) recorded thereon for enabling a processor to diagnose repeated amplification, the computer executable or compilable logic including: a receiving program for receiving sequencing data from at least a portion of a nucleic acid molecule undergoing an alignment with a repetitive sequence; a computer-assisted logic for analyzing repeated amplification from the received data; and an output program for generating an output indicating the presence, absence, or type of repeated amplification.

[0274] Sequence information from the sample under consideration can be mapped to a chromosome reference sequence to identify paired-end reads aligned or anchored to the repetitive sequence of interest and to identify repeat expansions of the repetitive sequence. In various embodiments, the reference sequence is stored in a database such as a relational database or an object database.

[0275] It should be understood that it is impractical, or even impossible in most cases, to perform the computational operations of the methods disclosed herein for unassisted humans. For example, without the assistance of a computing device, mapping a single 30 bp read from a sample to any one of the human chromosomes may require years of effort. Of course, the problem is complicated by the fact that reliable repeat amplification determinations typically require mapping thousands (e.g., at least about 10,000) or even millions of reads to one or more chromosomes.

[0276] In various implementations, the original sequence reads are aligned to one or more sequence graphs representing one or more sequences of interest. In various implementations, at least 10,000, 100,000, 500,000, 1,000,000, 5,000,000, or 10,000,000 reads are aligned to the one or more sequence graphs. In various implementations, the one or more sequence graphs include at least 1, 2, 5, 10, 50, 100, 500, 1000, 5,000, 10,000, or 50,000 sequence graphs.

[0277] In some implementations, before a subset of the initially aligned reads is aligned with one or more sequence graphs representing one or more sequences of interest, the original sequence reads are initially aligned with a reference genome to determine the genomic coordinates of the reads. In various implementations, at least 10,000, 100,000, 500,000, 1,000,000, 5,000,000, 10,000,000 or 100,000,000 reads are initially aligned with a reference genome. In some implementations, the initially aligned reads are re-aligned with the sequence graph to determine the repeat amplification at multiple regions (each region corresponding to the sequence graph). The total number of reads re-aligned with the sequence graph during each determination of the specific implementation can be in the range of thousands to millions of reads. In various implementations, at least 10,000,000 reads are re-aligned with each sequence graph. In various implementations, the one or more sequence diagrams include at least 1, 2, 5, 10, 50, 100, 500, 1000, 5,000, 10,000, or 50,000 sequence diagrams.

[0278] The methods disclosed herein can be performed using a system for determining the genotype of a variant at a locus comprising a repetitive sequence. The system may include: (a) a sequencer for receiving nucleic acid from a test sample, the sequencer providing nucleic acid sequence information from the sample; (b) a processor; and (c) one or more computer-readable storage media having instructions stored thereon for execution on the processor to genotype a variant at a locus comprising a repetitive sequence. In some embodiments, the method is indicated by a computer-readable medium having computer-readable instructions stored thereon for executing a method for identifying any repetitive amplification. Therefore, one embodiment provides a computer program product comprising a non-transitory machine-readable medium storing a program code, which, when executed by one or more processors of a computer system, causes the computer system to implement a method for identifying a repetitive amplification of a repetitive sequence in a test sample comprising nucleic acid, wherein the repetitive sequence comprises a repetition of a repeating unit of a nucleotide. The program code may include: (a) code for collecting sequence reads of test samples from a database; (b) code for aligning the sequence reads with one or more repeated sequences represented by each free sequence graph, wherein the sequence graph has a data structure of a directed graph, the directed graph having vertices representing nucleic acid sequences and directed edges connecting the vertices, and wherein the sequence graph includes one or more self-loops, each self-loop representing a repeated subsequence, each repeated subsequence including the repetition of a repeated unit of one or more nucleotides; and (c) code for determining one or more genotypes of the one or more repeated sequences using the sequence reads aligned with the one or more repeated sequences.

[0279] In some embodiments, the instructions may also include information related to the method of automatically recording the presence or absence of repeat and anchor reads and repeat amplification in the patient medical records of the human subject providing the test sample. The patient medical records can be maintained by, for example, a laboratory, a physician's office, a hospital, a health maintenance organization, an insurance company, or a personal medical record method can further involve prescribing, starting and / or changing treatment for obtaining the test sample from the human subject. This may involve performing one or more additional tests or analyses on additional samples obtained from the subject.

[0280] The method disclosed in the present invention can also be performed using a computer processing system, which is suitable for or configured to perform a method for identifying any repeated amplification. One embodiment provides a computer processing system that is suitable for or configured to perform the method as described herein. In one embodiment, the device includes a sequencing device, which is suitable for or configured to sequence at least a portion of the nucleic acid molecules in the sample to obtain the type of sequence information described elsewhere herein. The device may also include components for processing samples. Such components are described elsewhere herein.

[0281] Sequence or data can be directly or indirectly input into computer or be stored on computer readable medium.In one embodiment, computer system is directly connected to the sequencing equipment of reading and / or analyzing the nucleic acid sequence from sample.Sequence or other information from such instrument is provided via the interface in computer system.Alternatively, the sequence processed by system is provided by sequence storage source such as database or other storage library.Once available for processing device, memory device or mass storage device will buffer or store nucleic acid sequence at least temporarily.In addition, this memory device can store the label count of various chromosomes or genome etc.This memory can also store various routines and / or programs for analyzing presentation sequence or mapping data.Such program / routine can comprise the program for performing statistical analysis etc.

[0282] In one example, a user places a sample in a sequencing device. Data is collected and / or analyzed by a sequencing device connected to a computer. Software on the computer allows data collection and / or analysis. Data can be stored, displayed (via a monitor or other similar device) and / or sent to another location. The computer can be connected to the Internet, which is used to transfer data to a handheld device used by a remote user (e.g., a physician, scientist, or analyst). It should be understood that data can be stored and / or analyzed before transmission. In some embodiments, raw data is collected and sent to a remote user or device that will analyze and / or store the data. Transmission can occur via storage on a computer-readable medium, and the medium can be delivered to an end user (e.g., via mail). Remote users can be located in the same or different geographical locations, including but not limited to buildings, cities, states, countries, or continents.

[0283] In some embodiments, the method also includes collecting data (e.g., reads, markers, and / or reference chromosome sequences) about multiple polynucleotide sequences and sending the data to a computer or other computing system. For example, a computer may be connected to laboratory equipment, such as a sample collection device, a nucleotide amplification device, a nucleotide sequencing device, or a hybridization device. Then, the computer may collect applicable data collected by the laboratory equipment. The data may be stored on a computer at any step, for example, when collected in real time, before sending, during sending, or simultaneously with sending, or after sending. The data may be stored on a computer-readable medium that can be extracted from a computer. The collected or stored data may be, for example, transmitted from a computer to a remote location via a local area network or a wide area network (such as the Internet). At the remote location, various operations may be performed on the transmitted data, as described below.

[0284] In the systems, devices, and methods disclosed herein, the types of electronically formatted data that may be stored, transmitted, analyzed, and / or manipulated are as follows: Reads obtained by sequencing nucleic acids in a test sample Labels obtained by aligning reads to a reference genome or other reference sequence Reference genome or sequence Locus specifications indicating locus identity, position, and structure Read coverage Genotype of the variant Sequence diagram Graph Path Graphic comparison information Practical determination of repeat expansion Diagnosis (clinical condition associated with the diagnosis) Recommendations for further testing resulting from determination and / or diagnosis Treatment and / or monitoring plan derived from the determination and / or diagnosis These various types of data can be obtained, stored, analyzed, and / or manipulated at one or more locations using different devices. The range of processing options is wide. In the simplest case, all or most of this information is stored and used at the location where the test sample is processed (such as a doctor's office or other clinical environment). In the most complex case, the sample is obtained at one location, processed and optionally sequenced at different locations, the reads are aligned and a decision is made at one or more different locations, and a diagnosis, recommendation, and / or plan is prepared at yet another location (which may be the location where the sample was obtained).

[0285] In various embodiments, reads are generated with a sequencing device and then transmitted to a remote site where the reads are processed to generate repeat expansion determinations. At the remote location, for example, the reads are aligned to a reference sequence to generate anchor reads and anchor reads. In the processing operations, the following operations may be employed at different locations:

[0286] Sample collection Sample processing before sequencing Sequencing Analyze sequence data and derive repeat expansion calls diagnosis Reporting of diagnoses and / or findings to patients or healthcare providers Develop plans for further treatment, testing and / or monitoring Execute the plan consult Any one or more of these operations can be automated, as described elsewhere herein. Typically, the order-checking and analysis of sequence data and the determination of repeated amplification will be performed by calculation. Other operations can be performed manually or automatically.

[0287] Figure 6 One specific implementation of a decentralized system for generating a determination or diagnosis from a test sample is shown. Sample collection location 01 is used to obtain a test sample from a patient. The sample is then provided to processing and sequencing location 03, where the test sample can be processed and sequenced as described above. Location 03 includes a device for processing the sample and a device for sequencing the processed sample. As described elsewhere herein, the result of sequencing is a collection of reads, which are typically provided in an electronic format and provided to a network such as the Internet, which is accessed by Figure 6 Indicated by reference numeral 05 in FIG.

[0288] The sequence data is provided to a remote location 07 where analysis and decision generation is performed. The location may include one or more powerful computing devices, such as computers or processors. After the computing resources at location 07 have completed their analysis and generated a decision based on the received sequence information, the decision is relayed back to the network 05. In some implementations, not only a decision is generated at location 07, but also an associated diagnosis. The decision and / or diagnosis is then transmitted over the network and returned to the remote location 07. Figure 6 Sample collection location 01 is shown. As explained, this is just one of many variations on how to divide the various operations associated with generating a determination or diagnosis between various locations. A common variant involves providing sample collection, processing, and sequencing at a single location. Another variation includes providing processing and sequencing at the same location as analysis and determination generation.

[0289] experiment Example We applied the procedure to a simulated data set containing a wide range of CAG and CCG repeat sizes at the HTT locus. HTT or Huntingtin is the disease gene implicated in Huntington's disease (HD), a neurodegenerative disorder characterized by neonatal neuronal loss. This is thought to be caused by expanded, unstable trinucleotide repeats in the huntingtin gene that are translated as polyglutamine repeats in the protein product. A fairly wide range of trinucleotide repeats (9-35) has been identified in normal controls, and repeat numbers over 40 have been described as pathological.

[0290] As expected, when reads are aligned to a sequence map containing two replicates, the accuracy obtained using methods according to some embodiments is significantly higher than when the two replicates are analyzed independently. We observed similar improvements compared to other STR genotyping tools that are not designed to handle loci with multiple adjacent NTRs (see Figure 7 ).

[0291] To demonstrate that implementations can genotype degenerate DNA repeats, we analyzed polyalanine repeats in the PHOX2B gene in 150 healthy controls and one sample with a known pathogenic expansion. PHOX2B contains a 20-codon polyalanine repeat that can expand to cause congenital central hypoventilation syndrome. Consistent with what is known about this repeat (Amiel et al., 2003), all but a few controls were genotyped 20 / 20. These implementations accurately genotyped the only sample with an expansion of 20 / 27; the correctness of this genotype was confirmed by Sanger sequencing. These implementations also correctly detected a pathogenic SNV adjacent to an adenosine homopolymer in the MSH2 gene in three WGS replicates of samples obtained from SeraCare Life Sciences (see Supplementary Materials).

[0292] In summary, we have developed a novel approach that addresses the need for more accurate genotyping of complex loci. The approach can genotype polyalanine repeats and resolve difficult regions containing repeats and other repeats that are closely adjacent to small variants. We anticipate that the flexibility of the sequence graph framework now employed in the implementation will enable a variety of new variant calling applications.

[0293] Analysis of CAG and CCG STRs in the HTT locus We simulated short-read samples for each combination of CAG and CCG repeat genotypes using wgsim (Li, nd). We set the read length to 150, the distance between paired ends to 350, the standard deviation of paired-end distance to 50, the mutation rate and base error rate to 0.0010, and the indel ratio to 0. The number of mate pairs was set to produce 40x coverage of the locus. Reads were aligned to the GRCh37 reference with BWA-MEM 0.7.17-r1194-dirty (Li, 2013).

[0294] We used some implementations to analyze these data in two ways. First, we used the expression (CAG)*CAACAG(CCG)* to specify the structure of the HTT locus, ensuring that reads aligned to a sequence map that included both repeats. Then, we used some implementations to analyze each repeat independently. In this mode, reads aligned to a map representing CAG STRs and to a map representing CCG STRs, respectively. Finally, we analyzed both repeats independently using the latest versions of GangSTR and TredParse that do not use sequence maps to align reads to repeat regions ( Figure 7 ).

[0295] Figure 7 The accuracy of genotyping CAG and CCG STRs in the HTT locus for simulated data is shown. (a) The implementation is performed when the reads are aligned to a sequence map containing two replicates; (b) the implementation is performed when the replicates are analyzed independently; (c) GangSTR; (d) TredParse. For each simulated sample, we measured the maximum percentage deviation of the predicted STR length from the expected STR length. The blue dashed line corresponds to the genotype in the reference genome, and the red solid line represents the threshold for pathogen amplification. When genotyping two replicate sequences, the implementation accurately predicted the correct genotype for all samples. Joint genotyping provides a high advantage over genotyping the two replicates separately with any of the three tools, which show high relative error rates, especially when one of the replicates is significantly longer than the other.

[0296] Dataset The PCR-free WGS sequencing data of 150 unrelated controls used for the analysis of the PHOX2B polyalanine repeats was the Polaris Diversity Cohort. It consists of samples selected from the International Genome Sample Resources (1000 Genomes Project Consortium et al., 2015) (www.internationalgenome.org / ). WGS data are available from the European Genome-phenome Archive (EGA) (www.ebi.ac.uk / ega / home; PRJEB20654) and the NCBI Sequence Read Archive (SRA) (www.ncbi.nlm.nih.gov / sra; bioproject:387148). Description of the samples (github.com / Illumina / Polaris / wiki / HiSeqX-Diversity-Cohort).

[0297] Samples with 20 / 27 amplification in PHOX2B were obtained from Genetics Laboratories Molecular Genetics, Addenbrooke's Treatment Centre, Cambridge University.

[0298] SeraCare Life Sciences Sample Seraseq Hereditary Cancer DNA Mix v1 contains variants that are known to be both pathogenic and difficult to call. Variants have been synthetically added to the well-characterized cell line GM24385, with an expected variant frequency of 50% for all variants. One of these engineered mutations corresponds to a SNV in the MSH2 gene, which is directly adjacent to a long homopolymer A region.

[0299] Three replicates of the SeraCare sample were prepared with the Illumina TruSeq PCR Free Kit. Replicates were run on a single lane of the NovaSeq6000 using the XP workflow and sequenced with 2 × 151 reads. Replicates were analyzed using the ‘Sentieon DNASeq FASTQ to VCF’ and ‘Whole Genome Resequencing v8.0.0’ Basespace applications (https: / / basespace.illumina.com). Sentieon can be considered a proxy for the variant calling performance of the Broad’s BWA-GATK package, as Sentieon implements the same algorithm. None of the software solutions correctly called the MSH2 SNV. Fastq files and analysis results are provided via Basespace: https: / / basespace.illumina.com / s / HAQNxJyEtJLP.

[0300] The present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. The embodiments described should be considered in all respects as being merely illustrative and not restrictive. Therefore, the scope of the present disclosure is indicated by the appended claims rather than the foregoing description. All changes within the equivalent meaning and scope of the claims will be included within their scope.< / sequence> < / sequence> < / sequence>

Claims

1. A computer-implemented method comprising one or more processors and a system memory, the method being for characterizing one or more repetitive sequences, each repetitive sequence comprising one or more repetitive subsequences, the method comprising: (a) obtaining a sequence graph, wherein the sequence graph has a data structure of a graph, the graph having vertices representing nucleic acid sequences and directed edges connecting the vertices, and wherein the sequence graph includes one or more self-loops, each of which represents a repeated subsequence; as well as (b) aligning, by the one or more processors, sequence reads of the test sample with the one or more repetitive sequences each represented by the sequence graph.

2. The method of claim 1, wherein each repetitive subsequence comprises a repetition of a repeating unit of one or more nucleotides.

3. The method according to any one of claims 1 or 2, wherein the repeat sequence of the one or more repeat sequences comprises a specific repeat unit comprising at least one incompletely specified nucleotide. The method of claim 3 , wherein the specific repeat unit comprises a degenerate codon.

5. The method according to any one of claims 1-4, wherein the one or more self-loops include two or more self-loops representing two or more repeated subsequences.

6. The method according to any one of claims 1-5, wherein the sequence map further comprises two or more alternative paths of two or more alleles.

7. The method of claim 6, wherein the two or more alleles comprise an indel or a substitution.

8. The method of claim 6, wherein the substitution comprises a single nucleotide variation (SNV) or a single nucleotide polymorphism (SNP).

9. The method of claim 6, further comprising genotyping the two or more alleles using sequence reads aligned to the two or more pathways.

10. A computer-implemented method comprising one or more processors and system memory, the method for characterizing repeat expansions, the method comprising: (a) collecting sequence reads of a test sample using the one or more processors, wherein the sequence reads include paired-end reads; (b) aligning, by the one or more processors, the sequence reads to one or more repeat sequences represented by each free sequence graph; (c) identifying an anchor read and an anchor read in the paired-end reads, wherein the anchor read is a read aligned to or near a repeat sequence of the one or more repeated sequences, and wherein the anchor read is an unaligned read paired with the anchor read; and (d) determining a likelihood of repeat expansion in the test sample based at least in part on the identified anchor reads.

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