RU2799654C2 - Sequence graph-based tool for determining variation in short tandem repeat areas - Google Patents

Abstract

FIELD: bioinformatics.

SUBSTANCE: described embodiments relate to methods, devices, systems for genotyping repeat sequences, including short tandem repeats (STR), that are medically significant. The methods include aligning reads with a repeat sequence represented by a sequence graph and using the aligned reads to genotype the repeat sequence. A sequence graph is a directed graph that includes at least one simple cycle of its own, representing a subsequence of repetitions. In some embodiments, the reads are paired-end reads, and both conjugated reads of each pair of reads can be used to genotype repeat sequences. Some implementations can be used to determine the repeats of degenerate codons. Some implementation options can be used to genotyping repeat sequences, each of which includes two or more repeat subsequences. Some implementation options can be used to genotyping nucleotide sequences, each of which includes at least one repetitive subsequence and another genetic variant, such as an insertion, deletion or substitution.

EFFECT: invention expands the arsenal of means for genotyping.

31 cl, 7 dwg, 1 tbl, 1 ex

Description

INCLUDING BY LINK

[0001] The PCT Request Form is filed concurrently with this specification as part of this application. Each application for which benefit or priority is claimed in this application as specified in the concurrently filed PCT Request Form is hereby incorporated by reference in its entirety and for all purposes.

BACKGROUND OF THE INVENTION

[0002] Repeat expansions are a special class of microsatellite and minisatellite variants, including short tandem repeat (SRT) polymorphisms. Repeat expansions are also called dynamic mutations due to their instability when short tandem repeats expand beyond certain sizes. Genetic diseases caused by unstable repeat expansions include, but are not limited to, fragile X syndrome (FXS), Huntington's disease, and amyotrophic lateral sclerosis (ALS).

[0003] The detection of repeat expansions is important for the diagnosis and treatment of certain genetic diseases. However, it is difficult to determine repeat sequences with short reads that do not fully cover the repeat sequence. Thus, it is desirable to develop methods that use short reads in order to detect medically significant repeat expansions.

SUMMARY OF THE INVENTION

[0004] The described embodiments relate to methods, devices, systems, and computer software products for sequencing genomic loci, including repeat sequences, including short tandem repeat sequences, that may be associated with genetic disorders. Methods include aligning reads with sequence graphs each representing a genomic locus, and using aligned reads to genotype one or more repeat sequences at a genomic locus. Sequence graphs are directed graphs, each of which includes at least one simple cycle of its own, representing a sequence of repetitions.

[0005] In a first aspect of the description, methods are provided for computer-assisted genotyping of genomic loci, including repeat sequences. The methods are implemented using a computer including one or more processors and system memory. These methods can be used to genotype one or more repeat sequences, each containing one or more repeat subsequences. The methods include: (a) collecting test sample sequence reads from a database using one or more processors; (b) alignment with one or more processors, reading one or more sequences of repeats, each of which is represented by a sequence graph, and the sequence graph has a directed graph data structure, where the vertices represent nucleotide sequences, and directed edges connect the vertices, and the graph the sequence contains one or more own simple cycles, and each own simple cycle is a subsequence of repeats, with each subsequence of repeats containing repeats of a repeating unit of one or more nucleotides; and (c) one or more processors determining one or more genotypes of one or more repeat sequences using sequence reads aligned with the one or more repeat sequences.

[0006] In some embodiments, a repeat sequence of one or more repeat sequences contains a specific repeat unit containing at least one partially defined nucleotide. In some embodiments, a particular repeat unit contains degenerate codons.

[0007] In some embodiments, one or more native simple loops comprise two or more native simple loops representing two or more repeat subsequences.

[0008] In some embodiments, the sequence graph further comprises two or more alternative paths for two or more alleles. In some embodiments, two or more alleles contain a deletion or substitution. In some embodiments, the substitution includes a single nucleotide variant (SNR) or a single nucleotide polymorphism (SNP). In some embodiments, the method further includes genotyping two or more alleles using sequence reads aligned with two or more alternative pathways. In some embodiments, genotyping two or more alleles includes spanning two or more alternative paths to a probability model to determine the probabilities of two or more alleles. In some embodiments, the probability model models the probability of an allele as a function of allele coverage, with the function selected from a Poisson distribution, a negative binomial distribution, a binomial distribution, or a beta-binomial distribution. In some embodiments, the Poisson distribution rate parameter is estimated from the read length and average depth observed at the genomic locus.

[0009] In some embodiments, the method further includes aligning, to (b), a sequence read against a reference genome to determine genomic sequence read coordinates, and selecting a subset of the sequence read as the sequence read aligns with one or more repeat sequences, each of which represented by a sequence graph. In some embodiments, the subset of sequence reads includes reads aligned with or near the area represented by the sequence graph. In some embodiments, the subset of sequence reads includes unaligned reads whose conjugate reads map to or near the region represented by the sequence graph. In some embodiments, the subset of sequence reads comprises sequence reads aligned with one or more non-target regions that are known hotspots for misalignment reads.

[0010] In some embodiments, aligning a sequence read with a sequence graph includes: finding a match between the sequence read and the sequence graph path; and extending the kmer matching to complete alignment of nodes and edges of the sequence graph, including one or more of its own simple cycles.

[0011] In some embodiments, aligning a sequence read with a sequence graph includes reducing the graph by removing the ends of low confidence alignments.

[0012] In some embodiments, aligning a sequence read with a sequence graph includes combining alignments by: aligning read subsequences with a sequence graph; and combining subsequence alignments to complete the sequence read alignment.

[0013] In some embodiments, the method further includes generating a sequence graph based on the locus specification, including the structure of the genomic locus.

[0014] In some embodiments, sequence reads include paired end reads, and step (c) includes: (i) determining base and pinned reads in paired end reads, where base reads are aligned with one or more sequences repeats or with space next to them, and pinned reads are unaligned reads that are paired with pinned reads; and (ii) determining one or more genotypes for one or more repeat sequences using at least anchored reads.

[0015] In some embodiments, operation (ii) includes determining one or more genotypes for one or more repeat sequences using base reads as well as pinned reads. In some implementations, the base reads are aligned to within about 5 kb. repeat sequences. In some embodiments, misaligned reads include reads that cannot be aligned or are poorly aligned with the sequence graph.

[0016] In some embodiments, one or more repeat sequences include a short tandem repeat (SRT) sequence. In some embodiments, the expansion of CTP is associated with fragile X syndrome, amyotrophic lateral sclerosis (ALS), Huntington's disease, Friedreich's ataxia, spinal ataxia, spinobulbar muscular atrophy, myotonic dystrophy, Machado-Joseph disease, or dentato-rubro-pallido-Lewis atrophy .

[0017] In some embodiments, the method further includes using a sequencer to create paired end reads of the test sample.

[0018] In some embodiments, the method further includes withdrawing the test sample from the subject.

[0019] In some embodiments, the test sample is a sample of blood, urine, saliva, or tissue.

[0020] In some embodiments, the repeating unit includes from 1 to 50 nucleotides.

[0021] In some embodiments, the read is shorter than at least one of the one or more repetition sequences.

[0022] In another aspect of the description, systems are provided for genotyping genomic loci, including repeat sequences. In some embodiments, the system includes: system storage and one or more processors configured to perform the following: (a) collecting, using one or more processors, reads of a test sample sequence from a database; (b) alignment with one or more processors, reading one or more repeat sequences, each of which is represented by a sequence graph, wherein the sequence graph has a directed graph data structure, where vertices represent nucleotide sequences and directed edges connect vertices, and wherein the graph the sequence contains one or more own simple cycles, and each own simple cycle is a subsequence of repeats, with each subsequence of repeats containing repeats of a repeating unit of one or more nucleotides; and (c) one or more processors determining one or more genotypes of one or more repeat sequences using sequence reads aligned with the one or more repeat sequences.

[0023] In some embodiments, the system also includes a sequencer for sequencing the nucleic acids of the test sample.

[0024] In some embodiments, one or more processors are configured to operate in various ways described herein.

[0025] In another aspect of the description, a computer program product is provided, including a computer-readable medium for long-term storage of information containing program code, the execution of which by one or more processors of a computer system leads to the implementation by the computer system of the methods described above for genotyping genomic loci, including sequences repetitions. The program code includes (a) code for collecting test sample sequence reads from a database; (b) code for aligning sequence reads with one or more repeat sequences, each of which is represented on a sequence graph, wherein the sequence graph has a directed graph data structure, where vertices represent nucleotide sequences and directed edges connecting the vertices, and wherein the sequence graph contains one or more intrinsic simple cycles, each intrinsic simple cycle being a subsequence of repeats, each subsequence of repeats containing repeats of a repeating unit of one or more nucleotides; and (c) code for determining one or more genotypes of one or more repeat sequences using sequence reads aligned with the one or more repeat sequences.

[0026] In some implementations, the program code includes code for performing operations within the methods described herein.

[0027] Although the examples and terminology used herein refer to the human body, the concepts described herein are applicable to the genomes of any plant or animal. An understanding of these and other objects and features of the present description is facilitated by the following description and the appended claims; in addition, the necessary data can be obtained by the practical implementation of the contents of the description below.

BRIEF DESCRIPTION OF GRAPHICS

[0028] In FIG. 1A is a schematic diagram illustrating the difficulty of aligning a sequence with a repeat sequence on a reference sequence.

[0029] In FIG. 1B is a schematic diagram illustrating the alignment of sequence reads using paired end reads in accordance with certain implementations to overcome the difficulties illustrated in FIG. 1A.

[0030] In FIG. 1C is a flowchart illustrating the process of genotyping a genomic locus including a sequence of repeats, in accordance with some embodiments.

[0031] In FIG. 1D illustrates the first sequence graph representing the first genomic locus.

[0032] In FIG. 1E illustrates a second sequence graph representing the second genomic locus.

[0033] In FIG. 1F illustrates a third sequence graph representing a third genomic locus.

[0034] In FIG. 1G is a schematic diagram of a process for determining the genotypes of HTT locus variants comprising two TTP sequences, in accordance with some embodiments.

[0035] In FIG. 1H is a schematic diagram of a process for determining the genotypes of variants at the Lynch I locus, including NVG and CTP, in accordance with some embodiments. In the left panel of Fig. 1H is a schematic diagram of a general process for performing targeted genotyping; the right panel illustrates the application of this process within genotyping variants at the locus associated with Lynch I syndrome.

[0036] In FIG. 2 is a flowchart illustrating a high level image of an example method for determining the presence or absence of repeat sequence expansion in a sample.

[0037] In FIG. 3 and 4 are flow charts illustrating examples of methods for detecting repeat expansion using paired end reads.

[0038] In FIG. 5 is a flowchart of a method in which misaligned reads not associated with any repeat sequence of interest are used to determine repeat expansion.

[0039] In FIG. 6 shows a block diagram of the disperse system for processing the test sample.

[0040] In FIG. 7 illustrates the genotyping accuracy of CAG and CCG TTP at the HTT locus based on simulated data using various methods.

DETAILED DESCRIPTION

[0041] The description relates to methods, devices, systems, and computer program products for determining target repeat expansions, including expansions of repeat sequences, that are medically significant. Examples of repeat expansions include, without limitation, expansions associated with genetic disorders such as Fragile X syndrome, ABS, Huntington's disease, Friedreich's ataxia, spinal ataxia, spinobulbar muscular atrophy, myotonic dystrophy, Machado-Joseph's disease, and dentato-rubro- pallido-lewis atrophy.

[0042] Unless otherwise noted, the practice of the methods and systems described herein includes standard techniques and devices widely used in molecular biology, microbiology, protein purification, protein engineering, protein and DNA sequencing, and recombinant DNA, which are within the competence of specialists in this field. Such techniques and apparatus are known to those skilled in the art and are described in numerous reference publications (see, for example, Sambrook et al., “Molecular Cloning: A Laboratory Manual,” Third Edition (Cold Spring Harbor), [2001]); and Ausubel et al., “Current Protocols in Molecular Biology” [1987]).

[0043] Numeric ranges include numbers defining a range. It is intended that each maximum numeric limit occurring herein include every lesser numeric limit in the same manner as if such lesser numeric limit were explicitly stated herein. Each minimum numeric limit occurring herein will include each greater numeric limit in the same manner as if such greater numeric limit were expressly stated herein. Each numerical range occurring herein will include each narrower numerical range that falls within such broader numerical range, as if all such narrower numerical ranges were expressly stated herein.

[0044] The headings provided herein are not intended to be limiting in relation to the present description.

[0045] Unless otherwise indicated, all technical and scientific terms used in this document have the common meaning understood by any ordinary person skilled in the art. A variety of scientific terminology is well known and available to those skilled in the art, including the terminology used in this document. While any methods and materials similar or equivalent to those described herein find utility in the practice or testing of the embodiments described herein, some methods and materials have been described.

[0046] The terms defined below are more fully described with reference to the full version of the specification. It should be understood that the present description is not limited solely to specific methodology, protocols and reagents, as they may vary depending on the context in which they are used by specialists in this field.

Definitions

[0047] As used herein, the terms in the singular mean the same terms in the plural, unless the context requires otherwise.

[0048] Unless otherwise indicated, nucleic acids are written from left to right in the 5' to 3' orientation, and amino acid sequences are written from left to right in the amino to carboxy orientation, respectively.

[0049] The term "multiple" refers to more than one element. For example, the term is used herein to refer to a range of nucleic acid molecules or sequence reads sufficient to determine significant differences in repeat expansions in test samples and controls using the methods described herein.

[0050] The term "sequence of repeats" refers to a nucleotide sequence including repeated inclusions of a shorter sequence. In this document, a shorter sequence is referred to as a "repeating link". Repeated occurrences of the inclusion of repeating links are called "repetitions" or "copies" of the repeating link. In many contexts, the location of the repeat sequence is associated with the gene encoding the protein. In other situations, the sequence of repeats may be in the non-coding region. Repeating links can be formed in a sequence of repeats with or without breaks between repeating links. For example, in normal samples, the FMR1 gene typically includes an AGG break in CGG repeats, eg (CGG)10 + (AGG) + (CGG)9. Patterns with no break, as well as long repeat sequences with few breaks, tend to expand the associated gene repeats, which can lead to genetic diseases because the repeats expand beyond a certain number. In various embodiments of the present description, the number of repetitions is counted in the same way as the number of repetitions within the frame, regardless of the presence of gaps. Methods for evaluating repeats within a box are further described below.

[0051] In various embodiments, repeating units contain from 1 to 100 nucleotides. Many well-studied repeating units are trinucleotide or hexanucleotide units. Some other repeat units that have been well studied and are applicable to the embodiments described herein include, without limitation, units of 4, 5, 6, 8, 12, 33, or 42 nucleotides. See, for example, Richards (2001) Human Molecular Genetics, Vol. 10, no. 20 , 2187-2194. The scope of the present invention is not limited to the specific number of nucleotide bases described above, provided that they are relatively short compared to a repeat sequence having many repeats or copies of repeat units. For example, a repeating unit may contain at least 3, 6, 8, 10, 15, 20, 30, 40, 50 nucleotides. Alternatively or in addition, the repeat unit may be no more than about 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 6, or 3 nucleotides long.

[0052] The sequence of repeats can expand under conditions of development and mutagenesis to form more copies of the same repeat unit. This is referred to in the art as "repeat expansion". This process is also called "dynamic mutation" due to the unstable nature of the expansion of the repeat unit. Some repeat expansions have been shown to be associated with genetic diseases and pathological symptoms. Other repeat expansions are not well understood. The methods described herein can be used to detect both known and novel repeat expansions. In some embodiments, the repeat sequence having repeat expansion is longer than about 100, 150, 300, or 500 base pairs (bp). In some embodiments, the repeat sequence having repeat expansion is greater than about 1,000 bp, 2,000 bp, 3,000 bp, 4,000 bp. , 5000 b.p. or 10,000 b.p. etc.

[0053] In graph theory, a vertex and an edge are the two basic units from which graphs are built. A vertex or node is one of the points in a graph that can be connected by edges. In a graph diagram, a vertex can be represented as a shape with a label, and an edge can be represented by a line (non-directed edge) or an arrow (directed edge) passing from one vertex to another.

[0054] Two vertices connected by an edge are considered endpoints of the edge. A vertex x is considered adjacent to another vertex y if the graph contains an edge (x, y).

[0055] An undirected graph consists of a set of vertices and a set of undirected edges (connecting unordered pairs of vertices), and a directed graph consists of a set of vertices and a set of directed edges (connecting ordered pairs of vertices).

[0056] In graph theory, each edge has two vertices (there may be more on hypergraphs) to which it is attached and which are called its endpoints. Ribs may be directional or non-directional; Non-directional edges are also called lines, and directed edges are also called arcs or arrows.

[0057] A directed edge is an edge that connects a top and bottom vertex, with the top vertex before the directed edge and the bottom vertex after the directed edge.

[0058] An undirected edge is an edge connecting two vertices, where either vertex can be in front of the other on a path in the graph.

[0059] As used herein, the terms "loop", "native simple loop", and "one-node loop" are used interchangeably. The cycle has one node and an edge, both ends of which are connected to one node.

[0060] A cycle is a path containing two or more vertices, and the path of the cycle begins and ends with the same vertex. A simple loop is a loop that has no repeating vertices or edges other than the start and end vertices.

[0061] A cyclic graph is a graph that contains at least one cycle.

[0062] An acyclic graph is a graph that does not contain cycles or its own simple cycles.

[0063] A directed acyclic graph (DAG) is a directed graph without any cycles or its own simple cycles.

[0064] A path in a graph is a sequence of vertices and edges, in which both endpoints of an edge are adjacent to an edge in the sequence. A path in a directed graph has an upper vertex that is before the directed edge (or arc/arrow) and a lower vertex that is after the directed edge.

[0065] The Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring within a fixed interval of time or space, if those events occur at a known constant rate and regardless of the time since the last event.

[0066] Fully defined base symbols include G, A, T, C for guanine, adenine, thymine, and cytosine, respectively.

[0067] The list of partially defined nucleic acids includes, among others, the following components:

[0068] Purine (adenine or guanine): R

[0069] Pyrimidine (thymine or cytosine): Y

[0070] Adenine or thymine. W

[0071] Guanine or cytosine: S

[0072] Adenine or cytosine: M

[0073] Guanine or thymine: K

[0074] Adenine, thymine or cytosine: H

[0075] Guanine, cytosine or thymine: B

[0076] Guanine, adenine or cytosine: V

[0077] Guanine, adenine or thymine. D

[0078] Guanine, adenine, thymine or cytosine: N

[0079] The term "paired end reads" refers to reads obtained by paired end sequencing, which results in one read from each end of the nucleic acid fragment. Paired end sequencing involves fragmenting DNA to form sequences called inserts. In some protocols, including those used by Illumina, reads from shorter inserts (eg, tens to hundreds of bp) are referred to as paired short insert end reads or paired end reads. In contrast, reads from longer insertions (eg, on the order of several thousand bp) are called conjugated pair reads. In the present description, paired end readings with short insertions and conjugated pair readings with long insertions may be used; however, they are not differentiated with respect to the analysis of repeat expansions. Therefore, the term "paired end reads" can refer to both paired end reads with short insertions and mated pair reads with long insertions, which are further described herein below. In some embodiments, paired end reads include reads from about 20 bp. up to 1,000 b.p. In some embodiments, paired end reads include reads from about 50 bp. up to 500 bp, from about 80 bp up to 150 bp or about 100 b.p. It should be understood that the two reads at the paired end need not be located at the extreme end of the fragment being sequenced. Instead, one or both readings may be near the end of the fragment. Moreover, the methods exemplified herein in the context of paired end reads can be performed with any of a plurality of paired reads, regardless of whether the reads are from the end or another portion of the fragment.

[0080] As used herein, the term "alignment" refers to the process of comparing a read with a reference sequence and determining whether the reference sequence contains a read sequence. The alignment process attempts to determine if the read can be matched to the reference sequence, but does not always result in a read aligned to the reference sequence. If the reference sequence contains a read, then the read may be matched to the reference sequence or, in some embodiments, to a specific location in the reference sequence. In some cases, the alignment simply indicates whether the read is a member of a particular reference sequence (ie, whether the read is present or absent in the reference sequence). For example, the alignment of a read with respect to the reference sequence for human chromosome 13 would indicate whether the read is present in the reference sequence for chromosome 13. The tool that provides this information may be referred to as a set testing module. In some cases, the alignment further indicates the location in the reference sequence against which the read is matched. For example, if the reference sequence is a human genome-wide sequence, then the alignment may indicate the presence of a read on chromosome 13 and may further indicate that the read is on a particular strand and/or site on chromosome 13.

[0081] Aligned reads are one or more sequences that are determined to match according to the order of their nucleic acid molecules with a known reference sequence, such as a reference genome. The aligned read and its defined location on the reference sequence constitute the sequence label. Alignment can be performed manually, although it is usually implemented using a computer algorithm, since it is not possible to align reads in a reasonable period of time to implement the methods described herein. One example of a sequence alignment algorithm is the Efficient Local Alignment of Nucleotide Data (ELAND) computer program distributed as part of the Illumina genomic analysis workflow. In an alternative implementation, a Bloom filter or similar set membership test module can be used to align reads with reference genomes. See US Patent Application No. 14/354,528, filed April 25, 2014, which is incorporated herein by reference in its entirety. The sequence read agreement during the alignment process may be 100% or less than 100% sequence match (ie, non-perfect match).

[0082] As used herein, the term "mapping" means assigning a sequence of reads to a larger sequence, such as a reference genome, by alignment.

[0083] In some cases, the reading of one end of the two paired end reads is aligned with the repetition sequence of the reference sequence, and the reading of the other end of the two paired end reads is not aligned. In such cases, the paired read that is aligned with the repeat sequence of the reference sequence is referred to as the "base read". A paired end read that is not aligned with the repetition sequence but connected to the base read is called a pinned read. In this way, an unaligned read can be anchored and associated with a sequence of repetitions. In some embodiments, misaligned reads include both reads that cannot be aligned with the reference sequence and reads that are poorly aligned with the reference sequence. If a read is aligned with the reference sequence and the level of mismatches is above a certain criterion, the read is considered to be poorly aligned. For example, in various embodiments, a read is considered poorly aligned if at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mismatches were noted during its alignment. In some cases, both reads of a pair are aligned with the reference sequence. In such cases, both reads can be analyzed as "base reads" in various implementations.

[0084] The terms "polynucleotide", "nucleic acid", and "nucleic acid molecule" are used interchangeably and refer to a covalently linked sequence of nucleotides (e.g., ribonucleotides for RNA and deoxyribonucleotides for DNA) in which the 3' position of the pentose of one nucleotide is linked by a phosphodiester group with position 5' of the pentose of the next nucleotide. nucleotides include sequences of any form of nucleic acid, including, without limitation, RNA and DNA molecules, such as free-cellular DNA (scDNA). The term "polynucleotide" includes, without limitation, single and double stranded polynucleotides.

[0085] The term "test sample" as used herein refers to a sample, typically obtained from a biological fluid, cell, tissue, organ, or organism, containing a nucleic acid or mixture of nucleic acids containing at least one nucleotide sequence that must be checked for variation in the number of copies. In certain embodiments, the sample has at least one nucleotide sequence, the copy number of which was presumably subject to change. Such samples include, without limitation, sputum/oral fluid, amniotic fluid, blood, blood fraction, or fine needle biopsy samples, as well as urine, peritoneal fluid, pleural fluid, and the like. Although a sample is often taken from a human subject (e.g., a patient), assays can be used for copy number variation (CNV) in samples taken from any mammal, including, but not limited to, dogs, cats, horses, goats, sheep, cattle , pigs, etc. The sample can be used directly after receiving from a biological source or after pre-treatment to change the nature of the sample. For example, such pre-treatment may include obtaining plasma from blood, diluting viscous liquids, etc. Pretreatment methods may also include, without limitation, filtration, precipitation, dilution, distillation, mixing, centrifugation, freezing, lyophilization, concentration, amplification, nucleic acid fragmentation, inactivation of interfering components, addition of reagents, lysing, and the like. Where such pretreatment methods are used on a sample, such pretreatment methods typically involve nucleic acid(s) remaining in the test sample, sometimes at a concentration proportional to their concentration in the untreated test sample (e.g. , in a sample that is not subjected to such type(s) of pre-treatment). Such "processed" samples are still considered biological "test" samples with respect to the methods described herein.

[0086] The control sample can be either negative or positive. The term "negative control sample" or "unaffected sample" refers to a sample containing nucleic acids that are known or expected to have a repeat sequence with a repeat number in the range that is not pathogenic. It is known that a "positive control sample" or "affected sample" has a sequence of repeats with a number of repeats in the range that is pathogenic. Repeats in the repeat sequence in the negative control typically did not expand beyond the normal range, while repeats in the repeat sequence in the positive control typically expanded beyond the normal range. Thus, the nucleic acids in the test sample can be compared to one or more control samples.

[0087] The term "sequence of interest" as used herein refers to a nucleotide sequence associated with differences in sequence representations between healthy and diseased patients. The sequence of interest may be a sequence of repeats on a chromosome that expands with a disease, including a hereditary disease. The sequence of interest may be part of a chromosome, a gene, a coding sequence or a non-coding sequence.

[0088] As used herein, the term "next generation sequencing (NGS)" refers to sequencing methods that allow for mass parallel sequencing of clonally amplified molecules and single nucleic acid molecules. Non-limiting examples of NGS include sequencing by synthesis using reversible dye terminators, as well as sequencing by ligation.

[0089] The term "parameter" as used herein refers to a numerical value characterizing a physical property. Often, a parameter is used to characterize a numerical data set and/or a numerical relationship between quantitative data sets. For example, the parameter is the ratio (or ratio function) between the number of sequence labels mapped to a chromosome and the length of the chromosome to which the labels are mapped.

[0090] The term "recognition criterion" as used herein refers to any number or quantity that is used as a cutoff to characterize a sample, such as a test sample containing a nucleic acid taken from a subject suspected of having a medical condition. The threshold value can be compared with a parameter value to determine whether a sample producing such a parameter value indicates a medical condition in the subject. In certain embodiments, a threshold value is calculated using a set of control data and used as a limit when performing diagnostics of repeat expansion in an organism. In some embodiments, if a threshold value has been exceeded by the results obtained by the methods described herein, the subject may be diagnosed with repeat expansion. Within the methods described herein, the appropriate threshold values can be determined by analyzing the values calculated from the training set of samples or control samples. Thresholds can also be calculated from empirical parameters such as sequencing depth, read length, repeat sequence length, etc. Alternatively, affected samples definitely having repeat expansion can also be used to confirm that the selected thresholds can be used to distinguish unaffected samples within the test sequence. The choice of threshold depends on the level of confidence that the user wishes to make for the classification. In some embodiments, the training set used to determine the appropriate thresholds contains at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, 2,000, 3,000, 4,000 or more qualified samples. The use of large sets of qualified samples to increase the diagnostic threshold value may be preferable.

[0091] The term "read" refers to reading the sequence of a portion of a nucleic acid sample. Typically, a read is a short sequence of linked nucleotide base pairs in a sample. The read may symbolically represent the sequence of nucleotide base pairs (in the form of ATCG) of the sample fragment. It may be stored in a storage device and processed as necessary to determine if it matches the reference sequence or other criteria. The reading can be obtained directly from the sequencing machine or indirectly from stored sequence information related to the 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, such as that can be aligned and aligned to a chromosome, genomic region, or genome .

[0092] The term "reading the genome" is used to refer to the reading of any segments of the entire genome of the subject.

[0093] The term "site" refers to a unique position (i.e., chromosome identifier, chromosome position, and orientation) in a reference genome. In some embodiments, a site may be a residue, a sequence label, or a segment position on a sequence.

[0094] As used herein, the term "reference genome" or "reference sequence" refers to any specific known genomic sequence, partial or complete, of any organism or virus that can be used to refer to certain sequences obtained from a subject. For example, the reference genome used for human subjects as well as many other organisms can be found at the National Center for Biotechnology Information at ncbi.nlm.nih.gov. The term "genome" refers to the complete genetic information about an organism or virus, expressed in nucleotide sequences.

[0095] In various implementations, the reference sequence is significantly larger than the reads that align with it. For example, it may be at least about 100 times greater, or at least about 1,000 times greater, or at least about 10,000 times greater, or at least about 10 ⁵ times greater, or at least at least about 10 ⁶ times more, or at least about 10 ⁷ times more.

[0096] In one example, the base sequence is the sequence of the full length human genome. Such sequences may be referred to as reference genomic sequences. In another example, the base sequence is limited to a specific human chromosome, such as chromosome 13. In some embodiments, the reference Y chromosome is the Y chromosomal sequence from the hg19 version of the human genome. Such sequences may be referred to as reference chromosome sequences. Other examples of reference sequences include the genomes of other species, as well as chromosomes, subchromosomal regions (such as chromatids) of any species.

[0097] In some embodiments, the reference sequence required to perform the alignment may be about 1 to about 100 times the length of the read. In such embodiments, the alignment and sequencing is considered to be a target alignment or sequencing rather than a whole genome alignment or sequencing. In these embodiments, the reference sequence typically includes the gene and/or repeat sequence of interest.

[0098] In various embodiments, the reference sequence is a consensus sequence or other combination obtained from multiple individuals. However, in some applications, the reference sequence may be selected from a particular individual.

[0099] The term "clinically significant sequence" as used herein means a nucleotide sequence that is definitely or suspected to be associated with a disease, including hereditary. Determining the absence or presence of a clinically significant sequence may be useful in determining or confirming the diagnosis of a medical condition, or in predicting the development of a disease.

[00100] The term "derived" as used herein in the context of a nucleic acid or mixture of nucleic acids means that the nucleic acid(s) is(are) derived from the source(s) from which they originate. For example, in one embodiment, a mixture of nucleic acids derived from two different genomes means that the nucleic acids, eg scDNA, were naturally released by the cells through natural processes such as necrosis or apoptosis. In another implementation, the term "a mixture of nucleic acids derived from two different genomes" means that the nucleic acids were extracted from two different types of patient cells.

[00101] The term "based" when used in the context of obtaining a specific quantitative value in this document refers to the use of a different amount as an input to calculate a specific quantitative value as an output.

[00102] As used herein, the term "patient sample" refers to a biological sample taken from a patient, i. a person receiving medical care or treatment. The patient sample may be any of the samples described herein. In some embodiments, a patient sample is collected using non-invasive procedures, such a sample may be, for example, a sample of peripheral blood or feces. The methods described herein may be applied to more than just humans. Thus, a variety of veterinary applications are contemplated, in which case the patient sample may be a sample taken from a non-human mammal (eg, feline, porcine, equine, bovine, etc.).

[00103] The term "biological fluid" as used herein refers to fluid taken from a biological source and includes, for example, blood, serous exudate, plasma, sputum, lavage, cerebrospinal fluid, urine, semen, sweat, lacrimal fluid, saliva, and etc. As used herein, the terms "blood", "plasma", and "serous exudate" refer to the fractions or processed portions thereof. Similarly, in the case where the sample is obtained by performing a biopsy, swab, swab, etc., the term "sample" means the processed fraction or portion obtained by performing a biopsy, swab, swab, etc.

[00104] The term "corresponding" as used herein sometimes refers to a nucleotide sequence, such as a gene or chromosome, that is present in the genome of different subjects and that does not necessarily have the same sequence in all genomes, but serves to define and not to providing genetic information about a sequence of interest, such as a gene or chromosome.

[00105] The term "chromosome" as used herein refers to the hereditary gene carrier of a living cell, which consists of chromatin chains containing DNA and protein components (in particular, histones). This document uses the standard internationally recognized numbering system for the individual chromosomes of the human genome.

[00106] The term "polynucleotide length" as used herein refers to the absolute number of monomeric subunits (nucleotides) of a nucleic acid in a sequence or region of a reference genome. The term "chromosome length" refers to the known length of a chromosome given by nucleotide base pairs, such as that given in the assembly NCBI36/hg18 of the human chromosome, the data of which can be found on the Internet at the following address: |genome|.|ucsc|.|edu/cgi- bin/hgTracks?hgsid=167155613&chromInfoPage=

[00107] The terms "subject" and "patient" as used herein refer to a human subject as well as a non-human subject such as a mammal, invertebrate, vertebrate, fungus, yeast, bacterium, and virus. Although the examples and terminology used herein refer to the human body, the concepts described herein are applicable to the genomes of any plant or animal and can be applied in the fields of veterinary medicine, animal science, laboratory research, and the like.

[00108] As used herein, the term “primer” refers to an isolated oligonucleotide that is capable of acting as a synthesis initiation point when placed under conditions that are inductive to the synthesis of an extension product (e.g., such conditions imply the presence of nucleotides, an inducing agent, such as DNA polymerase, suitable temperature and pH). The primer may preferably be single stranded for maximum amplification efficiency, but may also be double stranded. In case the primer is double-stranded, it is first processed to separate helices before it is used to obtain extension products. The primer may be an oligodeoxyribonucleotide. The primer is long enough to initiate the process of synthesis of extension products in the presence of an induction agent. The exact primer lengths depend on many factors, including temperature, primer sources, and the methods and parameters used to create the primers.

5.2. Introduction

[00109] Short tandem repeats (SRTs) are ubiquitous throughout the human genome. Although the biological component of CFT is not yet fully understood, new evidence suggests that CFT play an important role in basic cellular processes.

[00110] Repeat expansions are a special class of microsatellite and minisatellite variants, including CTP polymorphisms. Repeat expansions are also called dynamic mutations due to their instability when short tandem repeats expand beyond a certain size. CFT expansions are the underlying cause of more than 20 severe neurological disorders, including amyotrophic lateral sclerosis, Friedreich's ataxia (FRDA), Huntington's disease (HD), and fragile X syndrome. Table 1 shows a small number of pathogenic repeat expansions other than repeat sequences in normal samples. The columns illustrate genes associated with repeat sequences, nucleotide sequences of repeat units, the number of repeats of repeat units in normal and pathogenic sequences, and diseases associated with repeat expansions.

Table 1. Examples of pathogenic repeat expansions

Gene Repeat Normal pathogenic Disease FMR1 CGG 6-60 200-900 Fragile X chromosome AR CAG 9-36 38-62 Spinobulbar muscular atrophy GHTT CAG 11-34 40-121 Huntington's disease FXN GAA 6-32 200-1700 Friedreich's ataxia ATXN1 CAG 6-39 40-82 Spinal ataxia ATXN10 ATTCT 10-20 500-4500 Spinal ataxia ATXN2 CAG 15-24 32-200 Spinal ataxia ATXN3 CAG 13-36 61-84 Spinal ataxia ATXN7 CAG 4-35 37-306 Spinal ataxia C9ofr72 GGGGCC < 30 100's ABS

[00111] Genetic diseases associated with repeat expansions are heterogeneous in many respects. the size of the repeat unit, degree of expansion, location relative to the affected gene, and pathogenic mechanism may differ depending on the disease. For example, ABS involves the expansion of GGGGCC nucleotide hexanucleotide repeats 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 the expansion of the CGG trinucleotide repeat (triplet repeat) affecting gene 1 ( FMR1 ) on the X chromosome, which is the cause of mental retardation in fragile X syndrome. Expansion of CGG repeats can lead to an inability to express the FMRP protein, the absence of which leads to mental retardation in fragile X syndrome and the presence of which is essential for normal neurodevelopment. Depending on the length of the repeat, the CGG allele can be classified as normal (not affected by the syndrome), premutational (at risk for developing X fragility disorders), or fully mutated (usually affected by the syndrome). According to various estimates, there are 230 to 4,000 CGG repeats in mutated FMR1 genes that cause fragile X syndrome in patients, compared with 60 to 230 repeats in ataxia-prone carriers, and 5 to 54 repeats in healthy controls. subjects. Expansion of the FMR1 gene repeats is a cause of autism, since it has been found that in about 5% of autistic patients, the expansion of the FMR1 gene repeats is the cause of autism. McLennan, et al. (2011), Fragile × Syndrome, Current Genomics 12(3): 216-224. Definitive diagnosis of fragile X syndrome involves performing genetic testing to determine the number of CGG repeats.

[00112] Many studies have identified various common properties of diseases associated with the expansion of repeats. Repeat expansion or dynamic mutation usually manifests itself as an increase in the number of repeats, the rate of such mutation being related to the number of repeats. Rare events, such as loss of repeat gap, can lead to an increase in the likelihood of allele expansion; such events are called adverse events. There may be a relationship between the number of repeats in a sequence of repeats and the severity of disease caused by the expansion of repeats and/or its development.

[00113] Thus, the detection of repeat expansions plays an important role in the diagnosis and treatment of various diseases. However, the determination of repeat sequences, especially using reads that do not fully cover the repeat sequence, presents a number of problems. First, it is difficult to align the repeats with the reference sequence because there is no clear one-to-one mapping between the read and the reference genome. Furthermore, even if a read is aligned with a reference sequence, the reads are often too short to fully cover the medically significant repeat sequence. For example, reads may be about 100 bp in length. By comparison, repeat expansion can span hundreds to thousands of base pairs. For example, in Fragile X Syndrome, the FMR1 gene can have more than 1,000 repeats spanning more than 3,000 bp. Thus, a 100 bp read. cannot match the full length of the repeat expansion. In addition, assembling short reads into a longer sequence does not overcome the problem of short reads and long repeats, since the assembly of short reads into a longer sequence is difficult due to the ambiguity in the alignment of repeats within one read with repeats in another read.

[00114] Alignment is the primary cause of loss of information, either 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. Sequencing biases and other problems affecting read accuracy are a secondary cause of failures in detecting repeat sequences. In some experimental protocols, about 7% of reads are misaligned or have a MAPQ score of 0. Even if researchers work to improve sequencing technology and analysis tools, there will always be a significant number of misaligned and poorly aligned reads. The implementations of the repeat expansion detection methods described herein are based on the detection of misaligned or poorly aligned reads.

[00115] Methods that use long reads to detect repeat expansion have certain drawbacks. In the context of next generation sequencing, currently available technologies that use longer reads are slower and more error prone than those that use shorter reads. Moreover, in some applications it is not possible to perform long reads, such as free-cell DNA sequencing. Free cell DNA obtained from maternal blood can be used for prenatal genetic diagnosis. Free-cell DNA exists in fragments that are typically shorter than 200 when using free-cell DNA. Embodiments of the methods described herein use short reads to determine medically significant repeat expansions.

[00116] Moreover, traditional methods are not designed to handle complex loci containing many repeats. Important examples of such loci are the CAG repeat causing HD flanked by the CCG repeat, the GAA repeat causing FRDA flanked by adenosine homopolymer, and the CAG repeat causing spinal cerebellar ataxia type 8 (SCA8) flanked by the ACT repeat. An even more striking example is the CCTG repeat in the CNBP gene, whose expansions cause myotonic dystrophy type 2 (DM2). This repeat is contiguous with the TG and TCTG polymorphic repeats (JE Lee and Cooper 2009), making it particularly difficult to accurately align reads with this locus. Another type of complex repeat is the polyalanine repeat, currently associated with at least nine disorders (Shoubridge and Gecz 2012). Polyalanine repeats consist of repeats of the a-amino acid codons GCA, GCC, GCG, or GCT.

[00117] Variant clusters can affect the alignment and accuracy of genotyping (Lincoln et al. 2019). Variants adjacent to low complexity polymorphic sequences may present additional problems because variant detection methods may result in clusters of misrepresented or false variant recognitions in such genomic regions. This is partly due to the increased error rate in these areas of sequencing data (Benjamini and Speed 2012; Dolzhenko et al., 2017). One example is the single nucleotide variant (SNR) adjacent to the adenosine homopolymer in the MSH2 gene that causes Lynch syndrome I (Frogatt et al. 1999).

[00118] Within the embodiments described herein, processing of complex loci may be performed as described above. They use a sequence graph as a general flexible model for each target locus.

[00119] In some embodiments, the described methods solve the aforementioned problems in detecting and recognizing repeat expansion by using paired end-to-end sequencing. Paired end sequencing involves fragmenting DNA to form sequences called inserts. In some protocols, including those used by Illumina, reads from shorter inserts (eg, tens to hundreds of bp) are referred to as paired short insert end reads or paired end reads. In contrast, reads from longer insertions (eg, on the order of several thousand bp) are called conjugated pair reads. As noted above, in various embodiments of the methods described herein, both paired end reads with short insertions and mated pair reads with long insertions can be used.

[00120] FIG. 1A is a schematic illustration showing certain difficulties in aligning sequence reads with a repeat sequence on a reference sequence, especially when aligning sequence reads derived from a pattern of long repeat sequence having repeat expansion. At the bottom of Fig. 1A illustrates a reference sequence 101 with a relatively short repeat sequence 103 depicted in vertical dashed lines. Illustrated in the middle of the figure is a hypothetical sequence 105 of a patient sample having a long repeat sequence 107 with repeat expansion, which is also depicted by vertical dashed lines. At the top of the figure are sequence reads 109 and 111, illustrated at the locations of the respective sites of the sample 105 sequence. around. Reads 111 having these repeats are potentially difficult to align with reference sequence 101 because such repeats do not have clear locations on reference sequence 101. repeat sequences and expansion of the repeat sequence from these potentially misaligned reads 111 is difficult to implement. In addition, since these reads are typically shorter than the long repeat sequence 107 having repeat expansion, they cannot provide comprehensive information about the type or location of the repeat sequence 107. In addition, repetitions in reads 111 make them difficult to assemble from - due to the ambiguity of their locations on the reference sequence 101 and the ambiguity of the relationships between 111 reads. Reads that partially belong to the sequence of long repeats 107 in the sample, half shaded and half shaded in black in the illustration, can be aligned with bases that do not belong to the repeat sequence 107. If reads contain too few base pairs outside of the 107 repeat sequence, the reads may align poorly or not align at all. Thus, some of these partial repeat reads may be analyzed as base reads, while others may be analyzed as pinned reads, as described further below.

[00121] In FIG. 1B is a schematic diagram illustrating how paired end reads can be used in some of the embodiments described to overcome the difficulties illustrated in FIG. 1A. In paired end sequencing, sequencing occurs at both ends of the nucleic acid fragments in the test sample. At the bottom of Fig. 1B shows the reference sequence 101 and the sample sequence 105, as well as reads 109 and 111 equivalent to those illustrated in FIG. 1A. From above in Fig. 1B illustrates fragment 125 derived from test sample sequence 105 and read 1 primer region 131 as well as read 2 primer region 133 to produce two reads 135 and 137 paired end reads. Fragment 125 is also mentioned as an insert for paired end readings. In some embodiments, inserts can be amplified with or without PCR. Some repeat sequences, such as those containing a large number of GC or GCC repeats, cannot be sequenced with sufficient quality by traditional methods involving PCR amplification. Amplification of such sequences can be carried out without the use of PCR. Amplification of other sequences can be performed using PCR

[00122] The insert 125 shown in FIG. 1B is derived from or corresponds to the sequence region of sample 105 flanked by the two vertical arrows depicted in the lower half of the figure. In particular, the insert 125 includes a repeating section 127 corresponding to a portion of the long repeat 107 in the pattern sequence 105. The length of the inserts can be adjusted depending on the application. In some embodiments, inserts may be somewhat shorter than the repeat sequence under investigation or the repeat sequence having repeat expansion. In other embodiments, inserts may be similar in length to a repeat sequence or a repeat expansion sequence. In other embodiments, inserts may even be somewhat longer than a repeat sequence or a repeat expansion sequence. In some embodiments, such inserts may be long inserts to perform conjugated pair sequencing in some embodiments, described further below. As a rule, reads obtained from inserts are shorter than a sequence of repetitions. Because inserts are longer than reads, paired end reads can better capture signals over a longer span of repeat sequences in a sample than single end reads.

[00123] Insert 125 shown has two read primer regions 131 and 133 at the two ends of the insert. In some embodiments, the read primer regions are native to the insert. In other embodiments, the primer regions are introduced into the insert by ligation or extension. The left end of the insert shows a read 1 primer region 131 which allows read 1 primer 132 to hybridize with insert 125. The completion of read 1 primer 132 generates a first read or read 1, labeled 135. The right end of insert 125 shows a read primer 133 region. 2 that allows read 2 primer 134 to hybridize with insert 125, initiating a second read, or read 2, labeled 137. In some embodiments, insert 125 may also include barcoded index regions (not illustrated in the figure) that allow determination of various samples in the process of multichannel sequencing. In some embodiments, 135 and 137 paired end reads can be obtained by sequencing Illumina using synthesis platforms. An example of a sequencing process implemented on such a platform is further described below in the "Sequencing Methods" section, during which two paired end reads and two index reads are generated.

[00124] Then, the paired end reads obtained by the method depicted in FIG. 1B can be aligned with a reference sequence 101 having a relatively short repeat sequence 103. Thus, the relative location and direction of a pair of reads is known. This allows an unaligned or poorly aligned read, such as that indicated in circle 111, to be indirectly associated with a sequence of relatively long repeats 107 in pattern sequence 105, via a corresponding paired read 109, as indicated at the bottom of FIG. 1b. In an illustrative example, paired end sequencing reads are about 100 bp and inserts are about 500 bp. In this exemplary configuration, the relative locations of the two paired end reads are about 300 bp from their 3' ends and are in opposite directions. The ratio between pairs of reads allows better association of reads with repeat areas. In some cases, the first read in a pair aligns with a repeat-free sequence flanking the repeat region on the reference sequence, and the second read in the pair does not align properly with the pattern. See, for example, the reading pair 109a and 111a shown in the bottom half of FIG. 1B, where the first reading is the left reading 109a and the second reading is the right reading 111a. Given the pairing of two reads 109a and 111a, the second read 111a can be associated with the repeat region 107 in pattern sequence 105, even though the second read 111a cannot be aligned with the reference sequence 101. Knowing the distance and direction of the second read 111a relative to the first read 109a, the location of the second read 111a within the long repeat region 107 can be further determined. In the event that a break exists between repeats in the second read 111a, the location of the break relative to the reference sequence 101 can also be determined. which is aligned with the reference is called the base reading. A read, such as a 111a right read, that is not aligned with the reference sequence, but is paired with the base read, is referred to as a sticky read. Thus, the misaligned sequence may be associated with repeat expansion. Thus, short reads can be used to detect long repeat expansions. Although the problem of detecting repeat expansions generally increases with expansion length due to the complexity of performing sequencing; the methods described herein allow detection of higher signal from longer repeat expansion sequences than from shorter repeat expansion sequences. This is because as the length of the repeat sequence or repeat expansion increases, more reads will be anchored in the expansion area, and more reads will have a chance of falling into the repeat region, and more repeats may occur with each read.

[00125] In some embodiments, the described methods include analyzing the frequency distribution of the number of repeats found within baseline and anchored reads. In some embodiments, only pinned reads are parsed. In other implementations, both base and sticky reads are analyzed. The test sample distribution can be compared to a criterion derived empirically or theoretically and used to separate unaffected samples from affected samples. In this way, it can be determined whether there is an expansion of repeats in question in the test sample and, accordingly, a recognition can be performed.

[00126] The methods and apparatus described herein may utilize next generation sequencing (NGS) technology that allows for massively parallel sequencing. In certain embodiments, clonally amplified DNA templates or single DNA molecules are sequenced inside a flow cell by mass parallel sequencing (e.g., as described in Volkerding et al. Clin Chem 55:641-658 [2009]; Metzker M Nature Rev 11:31- 46 [2010]). NGS sequencing technologies include, but are not limited to, pyrosequencing, sequencing by synthesis using reversible dye terminators, sequencing by ligation of oligonucleotide probes, and ion semiconductor sequencing. DNA from individual samples can be sequenced individually (i.e., perform single-channel sequencing) or DNA from multiple samples can be combined and sequenced as indexed genomic molecules (i.e., perform multi-channel sequencing) in a single sequencing run to generate up to several hundred million reads of DNA sequences. Examples of sequencing technologies that can be used to obtain sequence information within the present method are further described below.

[00127] Various repeat expansion assays using DNA samples involve aligning or matching sequencer sequence reads to a reference sequence. The reference sequence may be a whole genome sequence, a chromosome sequence, a subchromosomal region sequence, and the like. From a computational point of view, repetitions create ambiguity in alignment, which in turn can lead to errors at the whole chromosome count level. In a variety of implementations, paired end reads, in combination with an adjustable insert length, can help disambiguate repeat sequence alignment and detect repeat expansion.

Genotyping Variants at the Repeat Sequence Locus Using the Sequence Graph

[00128] In Fig. 1C is a flowchart illustrating a process 140 for genotyping a genomic locus containing a repeat sequence, in accordance with some embodiments. In some embodiments, the genetic locus is predetermined in a variant catalog containing the genomic positions and the structure of the loci at the genomic positions. On FIG. 1D, 1E and 1F illustrate three different sequence graphs in accordance with some embodiments.

[00129] In Fig. 1G illustrates a schematic diagram of a process for determining the genotypes of HTT locus variants containing two TTP sequences, in accordance with some embodiments. On panel (a) of Fig. 1G shows part of a variant catalog containing genomic loci and loci specifications, in particular their structure. For example, without counting repeats, the sequence at the HTT locus is CAGCAACAGCGG (Seq. No: 2); the sequence at the CNBP locus is CAGGCAGACA (Seq. No: 3).

[00130] In Fig. 1H illustrates a schematic diagram of a process for determining the genotypes of variants at the Lynch I locus containing NVG and CTP, in accordance with some embodiments. On FIG. 1H, box 162 illustrates the general structure of locus specifications, box 163 shows a specific example of a Lynch I (MSH2) locus specification.

[00131] In the variant directory, the locus structure is specified using a limited subset of the regular expression syntax. For example, the area of repetitions associated with HD, can be determined by the expression (CAG)* CAACAG(CGG)* or Seq. no: 2 (excluding repeats), which means that it contains variable numbers of CAG and CCG repeats separated by a CAACAG break; the area associated with the FRDA area corresponds to the expression (A)*(GAA)*; the region associated with SCA8 matches the expression (CTA)*(CTG)*; the area of repetitions DM2, consisting of three adjacent repetitions, is determined by the expression (CAGG)*(CAGA)*(CA)* or Seq. No.: 3 (excluding repetitions); ONV MSH2 is adjacent to the homopolymer that causes Lynch syndrome I, and corresponds to the expression (A|T)(A)*.

[00132] In addition, regular expressions may contain multi-allelic or "degenerate" basic characters, which can be indicated using the designation of the "International Union of Pure and Applied Chemistry" (IUPAC) ("List of partially defined bases in nucleotide sequences. Recommendations", 1984 Nomenclatural Committee of the International Union of Biochemists (NC IBU) "1986).

[00133] Partially defined bases corresponding to bases in degenerate codons are referred to herein as degenerate bases. Degenerate bases allow certain classes of imperfect DNA repeats to be represented, within which, for example, different bases can occur in the same position. Using this notation, polyalanine repeats can be encoded by (GCN)* and polyglutamine repeats can be encoded by (CAR)*.

[00134] In some embodiments, the repeat sequence included in the genomic locus includes a short tandem repeat (SRT) sequence. In some embodiments, FTR expansion is associated with fragile X syndrome, amyotrophic lateral sclerosis (ALS), Huntington's disease, Friedreich's ataxia, spinal ataxia, spinobulbar muscular atrophy, myotonic dystrophy, Machado-Joseph disease, or dentato-rubro-pallido-Lewis atrophy .

[00135] Process 140 includes collecting reads of nucleic acid sequences of the test sample from a database. See box 142. In some embodiments, the nucleotide sequence reads are initially aligned with the reference genome, but in this case, the process realigns the sequence reads with the genomic locus of interest, as described below. In alternative implementations, the reads can be directly aligned to the sequence graph without first being aligned to the reference genome.

[00136] Process 140 aligns sequence reads with a genomic locus sequence including one or more repeat sequences. See block 144. The genomic locus sequence is represented by data stored in system memory, which also stores the sequence graph data structure. the sequence graph includes a directed graph where vertices represent nucleotide sequences and directed edges connect the vertices. the nucleotide sequence represented by the top includes one or more nucleotide bases. The sequence graph includes one or more simple loops of its own. Each own simple cycle is a sequence of repetitions of one or more sequences of repetitions. Each repeat sequence includes repeats of a repeating unit of one or more nucleotides.

[00137] In some embodiments, sequence reads are initially aligned with a reference genome to determine the genomic coordinates of the reads before a subset of the initially aligned reads are aligned with one or more sequence graphs representing one or more sequences of interest. In some embodiments, the initially aligned reads are aligned with sequence graphs to define repeat expansions ranging from several tens to several thousand regions (each region corresponds to a sequence graph). The total number of initially aligned reads that are realigned to the sequence graphs during each application of the implementation options may range from thousands to many millions of reads.

[00138] In some embodiments, reads initially aligned with a sequence or locus of interest are selected as a subset of reads, then the subset is aligned with repeat sequences, each of which is represented in a sequence graph, the sequence graph having one or more simple cycles of its own, and these own simple loops represent one or more repetition sequences. In various embodiments, a reading within about 10, 50, 100, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000 , 100,000 bases of the sequence or locus under study, is considered to be close to the sequence or locus under study. In some embodiments, a read within about 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000 bases of the locus of interest is adjacent to the locus of interest. Some of the raw reads may initially be ill-aligned because they include repeat sequences that are difficult to align in an unambiguous manner. In some embodiments, reads that are initially poorly aligned (for example, as measured using an alignment score) but are paired with a read that is aligned with or near the locus of interest (in a read pair with paired ends) are aligned with the sequence graph. . In some implementations, reads initially aligned with non-target areas, which are known hotspots for misalignment reads, are aligned with the sequence graph.

[00139] In Fig. 1D, 1E and 1F illustrate three different sequence graphs in accordance with some embodiments. On FIG. 1D illustrates a first sequence graph 1100 representing the first genomic locus containing a repeat sequence having a CAG trinucleotide repeat unit. The first sequence graph 1100 includes vertices 1102 and 1112, respectively representing two flanking sequences. The first sequence graph also includes apex 1106, which is a repeat sequence containing a CAG trinucleotide repeat unit. The first sequence graph includes a directed edge 1104 connecting node 1102 (flanking sequence) and node 1106 (CAG repeat sequence) in the direction from node 1102 to node 1106. The direction of the edge indicates the relative position of the two nucleotide sequences. The first sequence graph also includes a directed edge 1104 connecting node 1102 (flanking sequence) and node 1106 (CAG repeat sequence) in the direction from node 1102 to node 1106. The first sequence graph also includes a directed edge 1110 connecting node 1106 ( CAG repeat sequence) and node 1112 (flanking sequence) in the direction from node 1106 to node 1112. The first sequence graph also includes its own simple cycle 1108, which is a repeat sequence containing a repeating CAG link (illustrated by node 1106) that repeats one or more times. The path from the start node to the end node of the sequence graph is the sequence of the genomic locus, which may contain nucleotides that are close to the sequence of repeats, such as flanking sequences.

[00140] In Fig. 1E illustrates a second sequence graph 1200 representing the second genomic locus. The second sequence graph 1200 includes vertices 1202 and 1224, respectively representing two flanking sequences. The second sequence graph also includes a vertex 1206 and a vertex 1216 representing a repeat sequence containing. a CAG trinucleotide repeat unit, and a repeat sequence containing a CCG trinucleotide repeat unit, respectively. The second sequence graph also includes a vertex 1212 representing the CAACAG sequence with no repeats. 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 directed manner, as shown in the illustration. The second sequence graph also includes its own simple loop 1208, which is a repeat sequence containing a repeating CAG link (illustrated by node 1206) that repeats one or more times. The second sequence graph also includes its own simple loop 1218, which is a repeat sequence containing a repeating CCG link (illustrated by node 1216) that repeats one or more times.

[00141] In FIG. 1F illustrates a third sequence graph 1300 representing the third genomic locus. The third sequence graph 1300 is similar to the second sequence graph 1200, but includes two alternative pathways representing two alleles of CAC and CAT. The two alleles may be SNV or SNP alleles. Directed edge 1310, peak 1312 and directed edge 1314 represent the first CAC allele. Directed edge 1316, peak 1318 and directed edge 1320 represent the second CAT allele. The third sequence graph includes elements otherwise similar to those of the second sequence graph, including vertices 1302, 1306, 1322, and 1328. It also includes its own simple loops 1308 and 1324 pointing to CAG and CCG repeat sequences. It further includes directional ribs 1304 and 1326.

[00142] In some embodiments, sequence reads are aligned with the sequence graph using the techniques described below.

[00143] 1. The index of kmer is built on the basis of the entire graph in such a way that, if there is a value of kmer from the sequence, it is possible to recalculate all the nodes of the graph where such a kmer begins or ends. In some cases, a kmer may start on one node and end on another node.

[00144] 2. For each point of the graph, two subgraphs are registered: one in the forward direction of the mer and the other in the reverse direction. On the subgraphs, the expansion of repeats is “unrolled” to the remaining length of the read, while there are no nodes on them located farther from the hit point of the kmers compared to the rest of the read length, and it is assumed that the expansion of repeats does not occur. The procedure is a breadth-first search method and is used to generate a data structure containing the following elements:

[00145] - Sequence of all node sequences (including extended repeats) on a subgraph

[00146] - A node index that makes it easy to derive a node ID from an offset in a sequence when performing a Smith-Waterman backtracking search.

[00147] - Sequence of node end offsets with incoming edges for each initial node offset

[00148] - An index for each node that makes it easy to determine whether the base is at the beginning of the node or not at the beginning of the node, as well as counting all end offsets of previous nodes.

[00149] 3. Alignment serves to:

[00150] - Maintaining affine gaps.

[00151] - Finding the best sequence alignment(s) given the above information and the penalty matrix.

[00152] Two different interfaces are available:

[00153] - Data on the best alignment and the second best result of the alignment evaluation are given.

[00154] - The entire array of best alignments, plus the second best result of the alignment score.

[00155] Alignments are general alignments that define a gap penalty between a potential kmer and the start of an aligned sequence. In some implementations, compile-time options are tuned.

[00156] The matrix filling algorithm used is available in two implementations:

[00157] - Sequential loops with N*M complexity.

[00158] - Sequential loops of fixed length loops with a default fixed length compile time of 16 that gcc automatically recognizes and converts to SSE or AVX vector instructions on the CPU.

[00159] In some embodiments, a repeat unit of one or more repeat sequences contains at least one partially defined nucleotide. In some embodiments, a particular repeat unit contains degenerate codons.

[00160] In some embodiments, one or more native simple loops comprise two or more native simple loops representing two or more repetition sequences. See, for example, FIG. 1E, Fig. 1F and FIG. 1G, panel (b).

[00161] In some embodiments, the sequence graph further comprises two or more alternative paths for two or more alleles. See, for example, FIG. 1F, reference numbers 1312 and 1318. See also FIG. 1H, reference numerals 165 and 167a indicating the Lynch I (MSH2) locus, where the upper path includes the vertex for the A nucleic acid base and the lower path includes the vertex for the T nucleic acid base.

[00162] In some embodiments, two or more alleles contain a deletion or substitution. In some embodiments, the substitution includes a single nucleotide variant (SNR) or a single nucleotide polymorphism (SNP). See, for example, FIG. 1F, reference numbers 1312 and 1318.

[00163] In some embodiments, aligning a sequence read with a sequence graph includes: finding a match between the sequence read and the path of the sequence graph and then expanding that path to a full alignment. In some implementations, alignment includes allocating a subgraph along a path; "unfolding" cycles on a subgraph to obtain a directed acyclic graph; and performing a Smith-Waterman alignment with respect to reading the sequence against a directed acyclic graph.

[00164] In some embodiments, aligning a sequence read with a sequence graph includes reducing the graph by removing the ends of low confidence alignments. After the read is aligned with the graph, the method searches for other similar alternative alignments. This is done by re-aligning the original read with the paths of the graph that overlaps the path of the original alignment. This allows you to determine whether one or both ends of the original alignment has a low level of confidence; this low level of confidence indicates that they could be aligned in a different way. The ability to detect parts of an alignment with high and low levels of confidence allows you to accurately determine which genetic variants are supported within a read.

[00165] In some embodiments, aligning a sequence read with a sequence graph includes combining alignments by: aligning read subsequences with a sequence graph; and combining subsequence alignments to complete the sequence read alignment.

[00166] In some embodiments, the process also generates a sequence graph based on the locus specification, including the structure of the genomic locus. In some embodiments, the locus specification is defined in the variant catalog as explained above.

[00167] See also panels (b)-(d) of FIG. 1G for schematic illustrations of read alignment with the sequence graph of the HTT locus. On FIG. 1H schematically depicts 164 loci parsers for performing read alignment with a sequence graph, including rel. locus Lynch I (165).

[00168] Method 140 further includes determining one or more genotypes of one or more repeat sequences using sequence reads aligned with a sequence graph. See block 140. See also panel (e) of FIG. 1G shows the definition of two CTPs (CAG and CCG) at the HTT locus. The sequence on the left, including the CAG repeats, is CAGCAGCAGCAGCAG (Seq. No: 4). The sequence on the left, including the CCG repeats, is CCGCCGCCGCCGCCG (Seq. No: 5).

[00169] In FIG. 1H depicts the Variant Genotyper (168) software module for detecting variants at the Lynch I locus, including NVGs with A/T alleles (169a) and repeat monomer A (169b). On FIG. 1H also shows the Variant Analyzer software modules (166) for managing sequence alignment data and passing it to the Variant Genotyper software (168), as well as how the Variant Analyzer software can be implemented to work with NVGs with A/T alleles (167a) and repeat monomer A (167b). Results Rel. locus obtained using the Genotyper software are illustrated in FIG. 1H, box 170, also shows the OHV genotype with alleles A/T (171a) and repeating monomer A (171b).

[00170] In some embodiments, the sequence graph includes two alternative paths for two alleles, and the method further includes genotyping two or more alleles using sequence reads aligned with the two or more alternative paths. In some embodiments, genotyping two or more alleles includes spanning two or more alternative paths to a probability model to determine the probabilities of two or more alleles. In some embodiments, the probability model models the probability of an allele as a function of allele coverage, with the function selected from a Poisson distribution, a negative binomial distribution, a binomial distribution, or a beta-binomial distribution.

[00171] In some embodiments, the probability function is a Poisson distribution and its rate parameter is estimated from read length and average depth observed at a genomic locus.

[00172] In the Poisson model, the probability of an allele is expressed as follows:

[00173] P(Y=y) = (C ^y × e ^-C )/y!

[00174] • y is the coverage of base reads

[00175] • C is the average depth observed at the genomic locus

[00176] In some embodiments, the average depth C is determined as follows.

[00177] C=LN/G

[00178] • G is the length of the genomic locus

[00179] • L is the read length

[00180] • N is the total number of reads

Graph Tools Library

[00181] In some embodiments, when working with sequence graphs, the GraphTools library is used. The library contains key diagrams of graphs (graphs themselves, graph paths and graph alignments), operations with them, and algorithms for aligning linear sequences with graphs.

[00182] In some embodiments, the sequence graph consists of nodes and directed edges. Graphs can include their own simple cycles (edges that connect a node to itself), but not other cycles. The nodes contain sequences consisting of base bases and degenerate base codes defined by IUPAC.

[00183] A path in a graph is defined by the sequence of nodes through which the path passes, as well as the starting point of the path at the first node and the end point at the last node. Positions are indicated using a "semi-open" zero-based coordinate system. The library provides notations for many operations on paths, including extending and shortening paths, overlap checks, and merge paths.

[00184] Graph alignments contain codes for how linear query sequences (typically read sequences) are aligned with graphs. In some implementations, a graph alignment comprises a graph path and a sequence of linear alignments that define the alignment of the requested sequence with the nodes of the graph path. With appropriate path operations, graph alignments can be shortened or combined with other graph alignments. Path reduction provides a mechanism to eliminate the ends of low-confidence alignments, while alignment joining is used by graph alignment algorithms to combine the full alignment of the requested sequence with subsequence (eg, kmer) alignments. In some implementations, the alignment algorithm works by looking for a measure match between the requested sequence and the graph, and then expanding that match to a full alignment. In some implementations, the alignment includes extracting a subgraph along a path that matches the meter match (with "unrolling" the loops in the process). Then a Smith-Waterman alignment is performed with respect to the resulting directed acyclic graph. In some implementations, the algorithm supports defining affine gap penalties and writes using constant length loops to generate SIMD code by compilers.

[00185] In some implementations, a path in a graph can be obtained using a search algorithm that includes expanding or shortening the path by increasing or decreasing the number of repetitions of a repeating link represented by its own simple loop until the alignment reaches a search criterion or convergence (for example, in the case when the alignment score reaches the maximum value).

[00186] In some embodiments, based on the graph, sequences generate a set of paths in the graph, with each path in the graph representing a specific number of repetitions of a repeating link represented by its own simple cycle. The requested sequence is aligned with a plurality of paths in the graph, and then a path corresponding to the alignment criterion is selected to perform graphical alignment.

Application method structure

[00187] Some embodiments are designed as a general way to perform targeted genotyping of variants (FIG. 1H). During each cycle, the program attempts to genotype a set of variants,

[00188] options described in the catalog file. Variants located in close proximity to each other are grouped into the same locus. The locus structure is specified using a limited subset of the regular expression (RE) syntax. REs contain alphabetical sequences of basic base symbols and degenerate base codes defined by IUPAC; they must contain one or more of the following expressions (<sequence>)?, (<sequence a>|<sequence b>), (<sequence>)*, (<sequence>)+, optionally separated by sequence breaks. These expressions correspond to insertions/deletions, substitutions, repetition of the sequence 0 or more times, and repetition of the sequence at least once, respectively. In addition, the description of each locus contains a set of base regions for that locus, as well as the base coordinates of each constituent variant.

[00189] The bulk of the work is ordered by objects of the LocusAnalyzer class, which synthesizes a sequence graph representing the locus of the corresponding RE during initialization. After initialization, the loci analyzer processes the corresponding reads by aligning them with the graph and then passing the resulting alignments to the VariantAnalyzer software, which is designed to work with all variants contained in the locus. The Variant Analyzer software collects information relevant to the related variant genotyping process and passes it on to the Genotyper software responsible for performing the genotyping process. The results generated by each unit of Genotyper software are then used to create an output file in VCF format.

[00190] For example, the LocusAnalyzer responsible for processing the locus with a pathogenic variant associated with Lynch I syndrome uses an NVG analyzer and a CTP analyzer (FIG. S1, right panel).

5.8. Using the Genotyper software to work with deletions

[00191] Some CFTs may have a small insertion or deletion nearby. Such deletions are modeled as additional subgraphs of flanking CTP sequences. The number of reads associated with each allele (or path in a graph) is modeled with a Poisson distribution whose rate parameter is estimated from the average read depth and length observed at the locus. The probability of the genotype is calculated according to the Bayesian scheme.

5.9. Identification of repeat expansions

[00192] Using the embodiments described herein, various hereditary diseases related to repeat expansion can be determined with high efficiency, sensitivity, and/or selectivity compared to conventional methods. In some embodiments, the present invention provides methods for detecting and recognizing medically significant repeat expansions, such as CGG repeat expansion causing mental retardation in fragile X syndrome, using sequence reads that do not fully capture the repeat sequence. Short reads, such as 100 bp reads, are not long enough to perform sequencing of multiple repeat expansions. However, when analyzed by the described methods, repeat expansion samples show a statistically significant excess of reads containing a large number of repeat sequences. In addition, extremely large repeat expansions contain misaligned pairs of reads, where both reads consist entirely or almost entirely of a sequence of repeats. Normal samples are used to determine background expectations.

[00193] It is a commonly held belief that repeat expansion cannot be detected without applying a read covering the entire repeat. Existing approaches to detecting repeat expansions use targeted sequencing with long reads, and in some cases such reads are not successful due to insufficient length, as a result of which they cannot cover the sequence of repeats. The results of some of the described embodiments were unexpected, in part because they use normal (non-target) sequence data and a read length of about 100 bp, but they provide very high sensitivity in detecting repeat expansions. The methods described herein detect the number of repeat units in repeat expansion using paired reads having an insertion length (ie, two sequence reads and an intermediate sequence) that is shorter than the length of the entire repeat sequence.

[00194] With regard to the details of methods for determining the presence of repeat expansion, in accordance with some embodiments, FIG. 2 is a high-level block diagram of embodiments for determining the presence or absence of repeat expansion in a sequence of repeats in a sample. A repeat sequence is a nucleotide sequence containing repeating short sequences called repeat units. The above Table 1 sets out examples of repeating units, the number of repetitions of repeating units in repeating sequences rel. normal and pathogenic sequences, genes associated with repeat sequences, and diseases associated with repeat expansion. The process 200 illustrated in FIG. 2 begin by obtaining paired end reads of the test sample. See block 202. Paired end reads are processed to align with a reference sequence containing the repeat sequence of interest. In some contexts, the alignment process is also referred to as the matching process. The test sample contains nucleic acid and may be in the form of a biological fluid, tissue, etc., as further described in the "Sample" section below. The sequence reads were subjected to an alignment process to match against a reference sequence. Various alignment tools and algorithms can be used to perform read alignment with the reference sequence, as described elsewhere in this specification. Typically, when performing alignment algorithms, some reads align successfully with the reference sequence, while other reads may not align as well or may align poorly with the reference sequence. Reads that are sequentially aligned with the reference sequence are associated with sites on the reference sequence. Aligned reads and their associated sites are also called sequence marks. As explained above, aligning some reads of high repeat sequences with a reference sequence is more difficult. If a read is aligned with the reference sequence and the level of mismatches is above a certain criterion, the read is considered to be poorly aligned. In various embodiments, reads are considered poorly aligned if they are aligned with at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mismatches. In other implementations, reads are considered poorly aligned if they are aligned with at least about 5% mismatches. In other embodiments, a read is considered poorly aligned if it is aligned with at least about 10%, 15%, or 20% of mismatches.

[00195] As shown in FIG. 2, process 200 describes the definition of base and anchor reads in paired end reads. See block 204. Base reads are read between paired end reads that are aligned with or adjacent to the target repeat sequence. For example, the base read may be aligned to a location on a reference sequence that is separated from the repeat sequence by a sequence length that is less than the length of the insert sequence. The separation length may be shorter. For example, a base read may be aligned to a location on a reference sequence that is separated from the repeat sequence by a sequence length that is less than the length of the base read sequence, or less than the total length of the base read sequence and the sequence that connects the base read to the anchored read (insert length minus the length of the anchored read). reading). In some embodiments, the repeat sequence under investigation may be the sequence of repeats in the FMR1 gene, including repeats of the CGG repeat unit. In a normal reference sequence, the repeat sequence in the FMR1 gene includes about 6 to 32 repeats of the CGG repeat unit. Since the repeats expand to over 200 copies, there is a possibility of pathogenic repeat expansion, which causes fragile X syndrome. In some embodiments, a read is considered aligned near the sequence of interest when it is aligned within 1,000 bp. from the studied sequence of repetitions. In other embodiments, this parameter may be adjusted, for example, within about 100 bp, 200 bp, 300 bp, 400 bp, 500 bp. bp, 600 bp, 700 bp, 800 bp, 900 bp, 1500 bp, 2000 bp n.s., 3,000 b.s., 5,000 b.s. and so on. In addition, the process also defines sticky reads, which are reads that are paired with base reads but are poorly aligned or cannot be aligned with their reference sequence. See above for more information on misaligned reads.

[00196] The process 200 further includes determining the probability that repeat sequence expansion is present in the test sample, based on the determined anchored reads, at least in part. See block 206. This step of the determination process may include a variety of suitable analysis and calculation methods, as further described below. In some embodiments, the process uses certain baseline reads as well as anchored reads to determine the likelihood of repeat expansion. In some embodiments, the number of repeats in certain baseline and anchored reads is analyzed and compared to one or more criteria derived theoretically or derived from empirical data from affected controls.

[00197] In the various embodiments described herein, repeats are generated as in-frame repeats, with two repeats of the same repeat unit falling within the same reading frame. A reading frame is a method of dividing a sequence of nucleotides in a nucleic acid molecule (DNA or RNA) into a set of consecutive non-overlapping triplets. During conversion, triplets code for amino acids, and such triplets are called codons. Thus, any sequence has three possible reading frames. In some embodiments, repeats are counted according to three different reading frames, and the one count of the three that shows the highest value is considered the number of corresponding repeats in the read.

[00198] An example of a process including performing additional steps and assays is illustrated in FIG. 3. In FIG. 3 is a flow diagram illustrating the process 300 required to detect repeat expansion using paired end reads having a high repeat count. Method 300 includes additional steps for processing a test sample. The process begins with sequencing of the test sample containing nucleic acids to obtain paired end reads. See block 302. In some embodiments, the test sample may be obtained and prepared in a variety of ways, as further described below in the "Samples" section. For example, the test sample may be a biological fluid, such as plasma, or any suitable sample, as described below. The sample may be obtained using a non-invasive procedure, such as a routine blood draw. In some embodiments, the test sample contains a mixture of nucleic acid molecules, such as scDNA molecules. In some embodiments, the test sample is a maternal plasma sample containing a mixture of fetal and maternal DNA molecules.

[00199] Prior to sequencing, nucleic acids are extracted from the sample. Suitable extraction processes and apparatus are described elsewhere in this document. In some embodiments, the device performs co-processing of DNA from multiple samples to multiplex libraries and sequence data. In some embodiments, the device 20 processes the DNA of eight or more test samples in parallel. As described below, the extracted DNA can be processed by the sequencing system to produce a library of encoded (eg, barcoded) DNA fragments.

[00200] In some embodiments, the nucleic acids in the test sample can be further processed to prepare libraries for single-channel or multi-channel sequencing, as further described below in the "Library Preparation for Sequencing" section. After sample processing and preparation, nucleic acid sequencing can be performed in a variety of ways. In some embodiments, a variety of next generation sequencing platforms and protocols may be used, which are further described in the "Sequencing Methods" section below.

[00201] Regardless of the specific sequencing platform and protocol, at 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, including those described below with reference to FIG. 5, in addition to paired end reads, single end long reads spanning over hundreds of thousands or tens of thousands of bases can be used to determine the repeat sequence. In some embodiments, the sequence reads are about 20 bp, about 25 bp, about 30 bp, about 35 bp, about 36 bp ., about 40 bp, about 45 bp, about 50 bp, about 55 bp, about 60 bp, about 65 bp .n.o., 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, about 140 bp, about 150 bp, about 200 bp, about 250 bp, about 300 bp, about 350 bp, about 400 bp about., about 450 b.p. or about 500 b.p. When generating paired end reads, technological advances are expected to enable single end reads greater than 500 bp. and reads over 1,000 b.p.

[00202] Process 300 describes how to align the results of paired end reads obtained from block 302 with a reference sequence including a sequence of repeats. See block 304. In some embodiments, the repeat sequence tends to expand. In some embodiments, repeat expansion is known to be associated with a genetic disease. In other embodiments, repeat expansion of the repeat sequence has not previously been studied to establish a link with a genetic disease. The methods described herein allow detection of repeat sequence and repeat expansion independent of any associated pathology. In some embodiments, reads are aligned with a reference genome; example: hg18. In other embodiments, the reads are aligned with a portion of the reference genome, such as a chromosome or segment of a chromosome. Reads that are uniquely mapped to the reference genome are called sequence marks. In one embodiment, at least about 3 x 10 ⁶ qualified sequence marks, at least about 5 x 10 ⁶ qualified sequence marks, at least about 8 x 10 ⁶ qualified sequence marks, at least about 10 x 10 ⁶ qualified marks sequence, at least about 15 x 10 ⁶ qualified sequence marks, at least about 20 x 10 ⁶ qualified sequence marks, at least about 30 x 10 ⁶ qualified sequence marks, at least about 40 x 10 ⁶ qualified sequence marks, or at least about 50 x 10 ⁶ qualified sequence marks are derived from reads that uniquely match the reference genome.

[00203] In some implementations, the process may filter sequence reads before alignment. In some embodiments, read filtering is a quality filtering process performed by the sequencer software to filter out erroneous and poor quality reads. For example, Illumina's Sequencing Control Software (SCS), as well as the Consensus Assessment of Sequence and Variation, are used to cull misread and low-quality reads by converting raw image data generated from sequencing reactions into intensity values, base-calculation processes , quality assessments and additional formats for collecting biologically relevant information for the further analysis stage.

[00204] In certain embodiments, reads obtained with a sequencing machine are in electronic format. The alignment process is carried out using a computing device, as described below. Individual reads are compared to a reference genome, which is often extensive (millions of base pairs), to determine points where the reads uniquely match the reference genome. In some embodiments, alignments allow limited mismatch between reads and the reference genome. In some cases, a mismatch of 1, 2, 3, or more of the corresponding nucleotide base pairs in the reference genome is allowed, and the match is still performed. In some embodiments, reads are considered aligned reads when the reads are aligned with a reference sequence containing no more than 1, 2, 3, or 4 nucleotide base pairs. Accordingly, misaligned reads are reads that cannot be aligned or are poorly aligned. Poorly aligned reads are reads with more mismatches than aligned reads. In some embodiments, reads are considered aligned reads when the reads are aligned with a reference sequence containing no more than 1%, 2%, 3%, 4%, 5%, or 10% of base pairs.

[00205] After aligning the paired end reads with the reference sequence containing the repeat sequence of interest, a process 300 performs determination of base and pinned reads between paired end reads. See block 306. As mentioned above, base reads are paired end reads aligned with or near the repeat sequence. In some embodiments, base reads are paired end reads that are aligned within 1 kb. Repeat sequences. Pinned reads are paired with base reads, but they cannot be aligned or are poorly aligned with the reference sequence, as described above.

[00206] As part of the process 300, the number of repetitions of repeating units in certain base and/or anchored reads is analyzed to determine the presence or absence of expansion of the repeat sequence. More specifically, the process 300 includes applying the number of repeats in reads to obtain high volume read numbers in base and/or sticky reads. High volume reads are reads with more repeats than the threshold. In some embodiments, bulk reads are obtained from pinned reads only. In other implementations, high volume reads are obtained from both base and pinned reads. If, in some implementations, the number of repetitions approaches the maximum possible number of repetitions for a read, the read is considered a large read. For example, if the read is 100 bp and the repeat unit in question is 3 bp, the maximum repeat count would be 33. In other words, the maximum value is calculated based on the length of the paired end reads and the length of the repeat unit. In particular, the maximum number of repetitions can be obtained by dividing the read length by the length of the repeating link and rounding the resulting value down. In this example, various implementations may define 100 bp reads having at least about 28, 29, 30, 31, 32, or 33 repeats in high volume reads. The number of repetitions in high volume reads can be adjusted up or down based on empirical factors. In various embodiments, the threshold for high volume reads is at least about 80%, 85%, 90%, or 95% of the maximum number of repeats.

[00207] Process 300 then determines the possible presence of repeat expansion in the repeat sequence based on the number of large volume reads. See block 310. In some embodiments, the analysis compares the resulting high volume reads against a recognition criterion and determines whether repeat expansion is likely to be present when the criterion values are exceeded. In some embodiments, the recognition criterion is derived from the distribution of large volume control sample reads. For example, a plurality of controls having or suspected to have a normal repeat sequence are analyzed and high volume reads are obtained for the controls in the same manner as described above. You can perform the distribution of reads of a large volume rel. control samples and estimate the probability of an unaffected sample with large reads exceeding a specific value. This probability makes it possible to determine the sensitivity and selectivity, taking into account the recognition criterion established with respect to this particular value. In some embodiments, the recognition criterion is set to a threshold value, and thus the probability of an unaffected sample with high volume reads exceeding the threshold value is less than 5%. In other words, the p-value is less than 0.05. In these embodiments, as the repeats expand, the repeat sequence becomes longer, and it becomes possible to have more reads exclusively within frames in the repeat sequence, and it also becomes possible to obtain more sample reads. In a variety of alternative implementations, a lower recognition criterion can be chosen such that the probability of an unaffected sample with large reads exceeding the threshold is less than about 1%, 0.1%, 0.01%, 0.001%, 0 .0001% etc. It should be understood that the recognition criterion can be adjusted up or down depending on a variety of factors and the need to increase the sensitivity or selectivity of the test.

[00208] In some embodiments, instead of using an empirical variant of obtaining a recognition criterion from the number of reads of a large volume from control samples, or in addition to it, a recognition criterion can theoretically be obtained to determine the expansion of repeats. It is possible to calculate the expected number of reads that are completely within the repeat range given a number of parameters including the length of paired end reads, the length of the sequence having repeat expansion, and the sequencing depth. For example, sequencing depth can be used to calculate the average distance between reads in an aligned genome. In case a single sample was sequenced at 30x depth, the total number of bases sequenced equals the product of genome size times depth. In the case of applying the present method to a human, this amount will be about 3×10 ⁹ 30=9×10 ¹⁰ . If each read is 100 bp long, then only 9 × 10 ⁸ reads are required to reach this depth. Since the genome is diploid, half of these reads are sequencing results from one chromosome/haplotype, and the remaining reads are sequencing results from another chromosome/haplotype. For each haplotype, 4.5 × 10 ⁸ reads are carried out, and when dividing the total genome size by this number, the average distance between the initial positions of each reading is obtained, i.e. on average 3 × 10 ^{9 /} 4.5 × 10 ⁸ = 1 reading for every 6.7 b.p. You can use this number to estimate the number of reads that will fall entirely within a repeat sequence based on the size of a given repeat sequence in a particular subject. In case the total size of the repeat sequence is 300 bp, any read starting within the first 200 bp. of a given repeat sequence will be entirely within the repeat sequence (reads starting within the last 100 bp will be at least partially outside the repeat sequence based on 100 bp read lengths). Since the read is expected to level off every 6.7 bp, it is expected that 200 bp / (6.7 bp per read) = 30 reads will fully align within the repeat sequence. Although this number may vary, it gives an estimate of the total number of reads that will be completely within the repeat sequence at any expansion size.

[00209] In some embodiments, a recognition criterion is calculated based on the distance between the first and last observation of a repeat sequence within reads, thus allowing for mutations in the repeat sequence and sequencing errors.

[00210] In some embodiments, the method may further include diagnosing the susceptibility of the subject from which the test sample is obtained to an increased risk of developing genetic diseases, such as fragile X syndrome, ABS, Huntington's disease, Friedreich's ataxia, spinocerebellar ataxia, spinobulbar muscular atrophy, myotonic dystrophy, Machado-Joseph disease, dentato-rubro-pallido-Lewis atrophy, etc. Such a diagnosis can be based on determining the probability of the presence of repeat expansion in the test sample, as well as on the sequence of repeats and genes associated with repeat expansion. In other embodiments, in cases where the genetic disease is unknown, some embodiments may be used to detect an abnormally high number of repeats in order to identify the genetic preconditions for the disease.

[00211] In Fig. 4 is a block diagram illustrating another method for detecting repeat expansion, in accordance with some embodiments. To determine the presence of repeat expansion in process 400, instead of large volume reads, the number of repeats in paired end reads of the test sample is used. Process 400 begins by sequencing the test sample containing the nucleic acid to obtain paired end reads. See block 402 equivalent to block 302 of process 300. Process 400 continues by aligning paired end reads with a reference sequence containing a sequence of repeats. See block 404 equivalent to block 304 of process 300. The process continues by determining base values and base reads in paired end reads, where base reads are aligned with or near the repeat sequence and sticky reads are unaligned reads that are conjugated with basic readings. In some embodiments, misaligned reads include both reads that cannot be aligned and reads that are poorly aligned with the reference sequence.

[00212] Once baseline and anchored reads are determined, process 400 obtains the number of repeats in baseline and/or anchored reads of the test sample. See block 408. The process then obtains a distribution of the number of repetitions in the base and/or anchored reads of the test sample. In some embodiments, only the number of repetitions obtained within pinned reads is analyzed. In other implementations, repetitions of both pinned reads and base reads are analyzed. Then the distribution of the number of repetitions of the test sample is compared with the distribution of the number of repetitions of one or more control samples. See block 410. In some embodiments, the process determines the presence of repeat expansion of a repeat sequence in a test sample if the distribution of the test sample is statistically significantly different from the distribution of control samples. See block 412. Process 400 analyzes the number of repetitions for reads, including high volume reads and low volume reads, which is different from the process that analyzes only high volume reads, as described above with respect to process 300.

[00213] In some embodiments, comparing the distribution of a test sample with that of control samples includes using a Mann-Whitney rank test to determine whether the two distributions are significantly different. In some embodiments, the assay performs a determination of the likely presence of repeat expansion in a test sample if the distribution of the test sample is more skewed toward more repeats than controls and the p-value of the Mann-Whitney rank test is less than about 0.0001 or 0.00001. If necessary, you can adjust the p-value to improve the selectivity or sensitivity of the test.

[00214] As part of the repeat expansion detection processes described above with respect to FIG. 2-4, pinned reads are used, which are unaligned reads paired with reads aligned to the repeat sequence under study. Variations in these processes may include searching the misaligned reads for pairs of reads that consist almost entirely of a sequence of repeats of some type to discover new previously undetermined repeat expansions that may be medically significant. This method does not quantify the exact number of repeats, but is effective for determining extreme repeat expansions or artifacts that must be flagged for further quantification. Combined with longer reads, this method can both detect and quantify repeats up to 200 bp. or more within the total length.

[00215] In Fig. 5 illustrates a flowchart of a process 500 in which misaligned reads not associated with any repeat sequence of interest are used to determine repeat expansion. As part of process 500, unaligned whole genome reads can be used to detect repeat expansion. The process begins with sequencing of the test sample containing nucleic acids to obtain paired end reads. See block 502. Process 500 continues by aligning the paired end reads with the reference genome. See block 504. The process then performs a determination of whole genome misaligned reads. Misaligned reads include paired end reads that cannot be aligned or are poorly aligned with the reference sequence. See block 506. The process then analyzes the number of repeats of the repeat unit in the misaligned reads to determine the likelihood of repeat expansion in the test sample. Such analysis may be independent of any particular repeat sequence. Such analysis can be applied to a variety of repeat units, and the number of repeats in the various repeat units of the test sample can be compared with the number of repeat units in several control samples. In this assay, the methods of comparing the test sample with the control samples described above can be applied. If, as a result of the comparison, it was found that the test sample has an abnormally large number of repetitions of a repeating unit, additional analysis can be performed to determine the presence of expansion of repeats of a particular test sequence of repeats in the test sample. See block 510.

[00216] In some embodiments, the additional analysis includes reading very long sequences, which can potentially span long repeat sequences having repeat expansions that are medically significant. The reads performed in this additional analysis are longer than the paired end reads. In some embodiments, single molecule sequencing or synthetic sequencing using long reads is used to obtain long reads. In some embodiments, the association between repeat expansion and a genetic disease is known in the art. However, in other embodiments, the relationship between repeat expansion and the genetic disease may not be known in the art.

[00217] In some implementations, the analysis of the number of repetitions of the repeating element in the misaligned reads of operation 510 includes performing a large amount of analysis comparable to the analysis performed within operation 308 (see Fig. 3). The analysis includes obtaining the number of bulk reads, wherein the bulk reads are unaligned reads with more repetitions than a threshold value; and comparing the number of reads of the large volume of reads in the test sample with the recognition criterion. In some embodiments, the threshold for high volume reads is at least about 80% of the maximum number of repeats, with the maximum value calculated as the ratio of the length of the paired end reads to the length of the repeat link. In some embodiments, high volume reads also include reads that are associated with misaligned reads and have more repetitions than the threshold.

[00218] In some embodiments, prior to further analysis of step 510, the process further includes (a) determining paired end reads conjugated with misaligned reads and aligned with a repeat sequence on or near the reference genome; and (b) providing the repeat sequence of interest as the specific repeat sequence under study for operation 510. Then, any of the methods in accordance with FIG. 2-4 described above.

Samples

[00219] Samples used to determine repeat expansion may include samples taken from any cell, fluid, tissue or organ, including nucleic acids, in which it is necessary to determine the expansion of repeats in one or more repeat sequences of interest. In some embodiments related to fetal diagnosis, it is preferable to obtain free-cell nucleic acids, such as free-cell DNA (scDNA), from maternal body fluid. Free-cell nucleic acids, including free-cell DNA, can be obtained by a variety of methods known in the art from biological samples, including, without limitation, plasma, serous exudate, and urine (see, for example, Fan et al., Proc Natl Acad Sci 105:16266-16271 [2008] Koide et al., Prenatal Diagnosis 25:604-607 [2005] Chen et al., Nature Med. 2: 1033-1035 [1996] Lo et al., Lancet 350: 485-487 [1997]; Botezatu et al., Clin Chem. 46: 1078-1084, 2000; and Su et al., J Mol. Diagn. 6: 101-107 [2004]).

[00220] In various embodiments, nucleic acids (eg, DNA or RNA) present in a sample may be enriched in a specific or non-specific manner prior to use (eg, prior to preparing a library for sequencing). In the illustrative examples below, DNA is used as an example of nucleic acids. Non-specific enrichment of a DNA sample means genome-wide amplification of fragments of the sample's genomic DNA that can be used to increase the level of the DNA sample prior to preparation of the scDNA sequencing library. Whole genome amplification methods are known to those skilled in the art. Examples of whole genome amplification methods are degenerate oligonucleotide primed PCR (DOP), primer extension PCR (PEP), and multiple displacement amplification (MDA). In some embodiments, the sample may be unenriched DNA.

[00221] A sample containing nucleic acids to which the methods described herein are applied typically includes a biological sample ("test sample") as described above. In some embodiments, nucleic acids to be tested for repeat expansion are purified or isolated by any of the well known methods.

[00222] Accordingly, in certain embodiments, the sample contains or essentially consists of a purified or isolated polynucleotide, or may contain samples, including a tissue sample, a biological fluid sample, a cellular sample, and the like. Suitable biological fluid samples include , without limitation, samples of blood, plasma, serous exudate, sweat, lacrimal fluid, sputum, urine, ear fluid, lymph, saliva, cerebrospinal fluid, lavage, bone marrow suspension, vaginal fluid, transcervical lavage, brain fluid, ascitic fluid, respiratory, intestinal and genitourinary tract secretions, amniotic fluid, milk and leukophoresis specimens. In some embodiments, the sample is a sample readily obtained by non-invasive procedures, such as blood, plasma, serous exudate, sweat, lacrimal fluid, sputum, urine, ear fluid, saliva, or feces. In some embodiments, the sample is a peripheral blood sample or a plasma fraction and/or serous exudate of a peripheral blood sample. In other embodiments, the biological sample is a biopsy, swab, swab, or cell culture material. In another embodiment, the sample is a mixture of two or more biological samples; for example, a biological sample may include two or more samples of a biological fluid, tissue, or cell culture. As used herein, the terms "blood", "plasma", and "serous exudate" refer to the fractions or processed portions thereof. Similarly, in the case where the sample is obtained by performing a biopsy, swab, swab, etc., the term "sample" means the processed fraction or portion obtained by performing a biopsy, swab, swab, etc.

[00223] In some embodiments, samples may be obtained from sources, including, without limitation, samples obtained from different subjects, samples from the same or different subjects at different developmental stages, samples from different diseased subjects (e.g., subjects with suspected genetic disease), healthy subjects, samples obtained at different stages of the patient's disease, samples obtained from a patient subjected to various treatments for the disease, samples obtained from patients exposed to various environmental factors, samples obtained from patients, predisposed to the manifestation of pathologies, samples obtained from patients exposed to an infectious agent of the disease, etc.

[00224] In one exemplary non-limiting embodiment, the sample is a sample obtained from a pregnant woman. In this case, the sample can be analyzed using the methods described herein to perform prenatal diagnosis of potential fetal chromosomal abnormalities. The sample obtained from the mother's body may be a tissue sample, a biological fluid sample, or a cell sample. The term "biological fluid" includes, without limitation, blood, plasma, serous exudate, sweat, lacrimal fluid, sputum, urine, ear fluid, lymph, saliva, cerebrospinal fluid, lavage, bone marrow suspension, vaginal fluid, transcervical lavage, fluid brain, ascitic fluid, secretions of the respiratory, intestinal and genitourinary tract, amniotic fluid, milk and blood leukocytes.

[00225] In some embodiments, samples can also be obtained from tissues, cells, or other polynucleotide-containing sources cultured under artificial conditions. Cultured specimens may be obtained from sources including, but not limited to, different cultures (e.g., tissues or cells) maintained under different media and conditions (e.g., pH, pressure, and temperature), cultures (e.g., tissues or cells) stored for different time periods, cultures (eg tissues or cells) treated with different factors or reagents (eg potential drugs or modulators), or cultures belonging to different tissue and/or cell types.

[00226] Methods for isolating nucleic acids from biological sources are well understood and may vary depending on the properties of the particular source. Specialists in this field can perform the selection of nucleic acids from the source as needed in the framework of the method described in this document. In some cases, fragmentation of nucleic acid molecules in a nucleic acid sample may be preferable. Fragmentation can be performed in a random order, as well as in a specific order, for example, when digested with a restriction endonuclease. Methods for random fragmentation performed at random are well known in the art and include, for example, limited deoxyribonuclease digestion, alkaline treatment, and physical hydrodynamic fragmentation.

Preparing Libraries for Sequencing

[00227] In various embodiments, sequencing can be performed on a variety of sequencing platforms requiring the preparation of a sequencing library. Preparation typically includes DNA fragmentation (sonication, pulverization, or hydrodynamic fragmentation) followed by DNA repair and end processing (blunt end or overhanging A end), as well as platform-specific adapter ligation. In one embodiment, the methods described herein may use next generation sequencing (NGS) technologies that allow multiple samples to be sequenced individually as genomic molecules (i.e., single-channel sequencing) or as pooled samples containing indexed genomic molecules (for example, multichannel sequencing) within a single sequencing run. These methods can generate up to several hundred million reads of DNA sequences. In various embodiments, genomic nucleic acid and/or indexed genomic nucleic acid sequences can be determined, for example, using the next generation sequencing (NGS) technologies described herein. In various embodiments, analysis of a large amount of sequence data obtained using NGS can be performed using one or more processors, as described herein.

[00228] In various implementations, the use of such sequencing technologies does not include the preparation of libraries for sequencing.

[00229] However, in certain embodiments, the sequencing methods provided herein include preparing libraries for sequencing. In one exemplary approach, preparing libraries for sequencing includes generating a random set of adapter-modified DNA fragments (eg, polynucleotides) ready for sequencing. Libraries for polynucleotide sequencing can be obtained from DNA or RNA, including equivalents, analogues of DNA or cDNA, such as DNA or cDNA that are complementary, or from copy DNA obtained from an RNA template by reverse transcriptase. Polynucleotides may be formed in double-stranded form (e.g., dsDNA such as genomic DNA fragments, cDNA, PCR amplification products, etc.) or in certain embodiments, polynucleotides may be formed in single-stranded form (e.g., ssDNA, RNA, and etc.), and converted into the form of dsDNA. By way of example, in certain embodiments, single-stranded mRNA molecules can be copied into double-stranded cDNA suitable for use in preparing a library for sequencing. The exact sequence of the primary polynucleotide molecules is generally not material for use in the library preparation process, and may or may not be known. In one embodiment, the polynucleotide molecules are DNA molecules. More specifically, in certain embodiments, the polynucleotide molecules represent the entire genetic complement of an organism, or substantially the entire genetic complement of an organism, and are genomic DNA molecules (e.g., cellular DNA, free-cellular DNA (scDNA), etc.) that, typically include both an intron sequence and an exon sequence (coding sequence), as well as non-coding regulatory sequences such as promoter and enhancer sequences. In some embodiments, the primary polynucleotide molecules comprise human genomic DNA molecules, for example, scDNA molecules present in the peripheral blood of a pregnant patient.

[00230] Obtaining libraries for sequencing for some NGS sequencing platforms is facilitated by the use of polynucleotides containing a certain range of fragment sizes. The preparation of such libraries typically involves the fragmentation of large polynucleotides (eg, cellular genomic DNA) to obtain polynucleotides in the desired size range to determine repeat expansion.

[00231] The length of a fragment or insert is greater than the length of a read, and typically greater than the sum of the lengths of two reads.

[00232] In some exemplary embodiments, a sample of nucleic acid(s) is obtained as genomic DNA, which is(are) fragmented into fragments containing about 100, 200, 300, 400, 500 or more nucleotide base pairs and to which apply NGS methods. In some embodiments, paired end reads are obtained from inserts of about 100-5,000 bp in length. In some embodiments, inserts are about 100-1000 bp in length. Sometimes they are implemented as ordinary paired end readings with short inserts. In some embodiments, inserts are about 1,000-5,000 bp in length. Sometimes they are implemented as conjugate pair reads with long insertions, as described above.

[00233] In some implementations, long inserts may be used to evaluate very long extended repeat sequences. In some embodiments, conjugated pair reads may be used to obtain reads separated by thousands of nucleotide base pairs. In these embodiments, inserts or fragments range from hundreds to thousands of nucleotide base pairs with two biotin compound adapters at the two ends of the insert. The biotin junction adapters then connect the two ends of the insert to form a round molecule, which is then further fragmented. For sequencing on a platform capable of sequencing shorter fragments, a subfragment was selected that includes biotin compound adapters and the two ends of the original insert.

[00234] Fragmentation can be performed by any of a number of methods known to those skilled in the art. For example, fragmentation can be performed by mechanical means, including, but not limited to, pulverization, sonication, and hydrodynamic fragmentation. However, mechanical fragmentation typically cleaves the DNA backbone at the CO, PO, and CC bonds to form a heterogeneous mixture of blunt and 3' and 5' overhangs with broken CO, PO, and CC bonds (see, e.g., Alnemri and Liwack, J Biol Chem 265:17323-17333 [1990]; Richards and Boyer, J Mol Biol 11:327-240 [1965]), which may need to be reduced because they may lack the necessary 5'-phosphate for subsequent enzymatic reactions , for example, to ligate sequencing adapters needed to obtain DNA for sequencing.

[00235] In contrast, scDNA typically exists as fragments of less than about 300 bp, and hence fragmentation is generally not required to generate a sequencing library using scDNA samples.

[00236] In general, whether polynucleotides have been artificially fragmented (e.g., fragmented under artificial conditions), or they exist as fragments in nature, they are converted to blunt-ended DNA containing 5'-phosphates and 3' -hydroxyl. Standard protocols, such as sequencing protocols using, among others, the Illumina platform, as described elsewhere in this document, provide instructions for repairing the end of a DNA sample, purifying end-repair products before joining the dA end, and purifying products attached to the dA end. end of dA prior to the library preparation steps using adapter ligation.

[00237] The various implementations of the methods for preparing libraries for sequencing described herein eliminate the need for one or more steps that are typically required under standard protocols to obtain a modified DNA product that can be sequenced by NGS. Abbreviated method (abbr. method), 1-step method and 2-step method are examples of methods for preparing a library for sequencing, which can be found in patent application 13/555,037, filed July 20, 2012, the contents of which are fully incorporated herein. document by reference.

5.12. Sequencing methods

[00238] As noted above, the resulting samples (eg, sequencing libraries) are sequenced as part of the copy number variation(s) technique. Can use any of a variety of sequencing technologies.

[00239] Sequencing devices and technologies are on the market, such as the hybridization sequencing platform manufactured by Affymetrix Inc. (Sunnyvale, CA, USA), as well as sequencing-by-synthesis platforms from 454 Life Sciences (Bradford, CT, USA), Illumina/Solexa (San Diego, CA, USA), and Helicos Biosciences (Cambridge, MA, USA) and a ligation sequencing platform from Applied Biosystems (Foster City, CA, USA) as described below. In addition to single molecule sequencing, which is performed using sequencing by synthesis by Helicos Biosciences, other single molecule sequencing technologies include, without limitation, Pacific Biosciences' SMRT™ technology, ION TORRENT ^™ technology, and nanopore sequencing developed by, for example, Oxford Nanopore. technologies.

[00240] Although automated Sanger is considered a "first generation" technology, Sanger sequencing, including automated Sanger sequencing, can also be used within the scope of the methods described herein. Additional sequencing methods include, without limitation, nucleic acid imaging technologies such as atomic force microscopy (AFM) or transmission electron microscopy (TEM). Illustrative examples of sequencing technologies are described in more detail below.

[00241] In some embodiments, the described methods include obtaining information about the sequence of nucleic acids in the test sample by mass parallel sequencing of millions of DNA fragments using sequencing by synthesis according to the Illumina method and reversible chemical analysis of the terminator-based sequencing method (for example, as described in the publication Bentley et al., Nature 6:53-59 [2009]). The template DNA may be genomic DNA, such as cellular DNA or scDNA. In some embodiments, genomic DNA from isolated cells is used as a template, which is then fragmented to form lengths of several hundred base pairs. In other embodiments, the scDNA is used as a template and fragmentation is not required because the scDNA exists in short fragments. For example, fetal scDNA circulates in the circulation as fragments of about 170 base pairs (bp) in length (Fan et al., Clin Chem 56:1279-1286 [2010]), and DNA fragmentation prior to sequencing is not required. Illumina's sequencing technology is based on anchoring fragmented genomic DNA to a transparent, flat surface containing oligonucleotide anchors. The template DNA is terminated to produce 5'-phosphorylated blunt ends, and the polymerase action of the Klenow fragment is used to add one base to the 3' end of the blunt phosphorylated DNA fragments. As part of this addition, DNA fragments are prepared for ligation using oligonucleotide adapters that have an overhanging end of a single T base at the 3' end to improve ligation efficiency. The adapter oligonucleotides are complementary to the base oligonucleotides of the flow cell (not to be confused with the base/pinned reads in the repeat expansion assay). Under limiting and diluting conditions, adapter-modified single-stranded template DNA was added to a flow cell and immobilized by hybridization with base oligonucleotides. Extend and bridge amplify the attached DNA fragments to create an ultra-high-density sequencing flow cell with hundreds of millions of clusters, each containing about 1,000 copies of the same template. In one embodiment, amplification of randomly fragmented genomic DNA using PCR is performed before it is subjected to cluster amplification. Alternatively, a non-amplified genomic library preparation method is used and the randomly fragmented genomic DNA is enriched using cluster amplification alone (Kozarewa et al., Nature Methods 6:291-295 [2009]). The templates are sequenced using deep four-color DNA sequencing by synthesis, which uses reversible terminators with removable fluorescent dyes. Highly sensitive fluorescence detection is performed by laser excitation and the use of a complete set of internal optical reflectors. Short sequence reads of about tens to several hundred base pairs are aligned with the reference genome, and a unique mapping of the short sequence reads to the reference genome is determined using specially designed data analysis software. After the first read is completed, the matrices can be restored directly in place to allow a second read to be performed from the opposite end of the fragments. Thus, single or pairwise end-to-end sequencing of DNA fragments can be used.

[00242] In various embodiments of the present disclosure, sequencing by synthesis providing paired end sequencing can be used. In some embodiments, the Illumina sequencing-by-synthesis platform has a fragment clustering feature. Clustering is a process in which each fragment molecule is isothermally amplified. In some embodiments of the example described herein, the fragment has two different adapters attached to the two ends of the fragment; such adapters allow the fragment to hybridize to two different oligonucleotides on the surface of the flow cell strip. The fragment further includes two index sequences at the two ends of the fragment or has a connection with them, and the index sequences allow the use of labels to identify different samples in multichannel sequencing. In some sequencing platforms, the fragment to be sequenced is also referred to as an insert.

[00243] In some embodiments, the clustering flow cell used on the Illumina platform is a streaked slide. Each lane is a glass channel coated with two types of oligonucleotides. Hybridization is performed using the first of two types of oligonucleotides that are on the surface. This oligonucleotide is complementary to the first adapter at one end of the fragment. The polymerase creates a complementary strand of the hybridized fragment. The double-stranded molecule is denatured and the parent template strand is washed out. The remaining strand is clonally amplified by the application of bridges in parallel with the rest of the strands.

[00244] When performing bridge amplification, the second region of the adapter at the second end of the strand hybridizes to type 2 oligonucleotides on the surface of the flow cell. The polymerase creates a complementary chain, forming a double-stranded bridging molecule. This double-stranded molecule is denatured, resulting in two single-stranded molecules attached to the flow cell by two different oligonucleotides. The process then repeats a number of times; this process is carried out simultaneously with the participation of millions of clusters, which leads to clonal amplification of all fragments. After the bridge amplification is performed, the reverse strands are cleaved and washed out, leaving only the forward strands. The 3' ends are blocked to prevent unwanted priming.

[00245] After clustering, the sequencing process begins by extending the first sequencing primer, resulting in a first read. Within each cycle, fluorescently labeled nucleotides tend to be added to the growing chain. Based on the sequence of the matrix, only one of them is embedded. After adding each nucleotide, the cluster is excited with a light source, and a characteristic fluorescent signal is emitted. The number of cycles determines the length of the read. The emission wavelength and signal intensity determine the base calling process. For a given cluster, all identical nets are read at the same time. Hundreds of millions of clusters are sequenced by massively parallel sequencing. After completion of the first reading, the reading product is washed away.

[00246] At the next stage of work in accordance with the protocols using two index primers, index primer 1 is introduced into the index region 1 of the template and hybridized with it. Index regions allow you to define fragments that can be used to demultiplex samples as part of a multichannel sequencing process. Index read 1 is generated similarly to the first read. After index 1 reading is complete, the read product is washed out and the 3' end of the chain is also deprotected. The template strand is then folded and bound to a second oligonucleotide on the flow cell. The index 2 sequence is read in the same manner as index 1. Then, at the end of the step, the index 2 read product is washed out.

[00247] After reading two readings, reading 2 begins with the use of polymerases to perform expansion of the oligonucleotides of the second flow cell, resulting in the formation of a double-stranded bridge. This double-stranded DNA is denatured and the 3' end blocked. The original forward strand is cleaved off and washed out, leaving only the reverse strand. Read 2 is started by introducing the read 2 sequencing primer. As with read 1, the sequencing steps are repeated until the desired length is reached. Read product 2 washes out. As part of this process, you can get millions of reads representing all the fragments. Sequences from pooled sample libraries are separated based on unique indexes assigned during sample preparation. With the participation of each sample, local clustering of readings of similar segments of base recognition results is carried out. The forward and backward reads are paired, resulting in a connected sequence. These linking sequences are aligned with a reference genome to determine variants.

[00248] An example of sequencing by synthesis involves the use of paired end reads, which are used in many embodiments of the described methods. Paired end sequencing produces 2 reads from both ends of the fragment. To eliminate ambiguity in alignments, paired end reads are used. When performing paired end sequencing, users can select the length of the insert (or fragment to be sequenced) and sequence both ends of the insert, resulting in the generation of high quality aligned sequence data. Since the distance between all paired reads is known, alignment algorithms can use this information to more accurately match repeating regions. This allows for better alignment of reads, especially in repetitive regions of the genome that are difficult to sequence. Paired end sequencing can detect rearrangements, including insertions, deletions, and inversions.

[00249] For paired end reads, inserts of different lengths (i.e., different fragment size for sequencing) can be used. Paired end reads are used as a starting point in this specification to refer to reads obtained from different insertion lengths. In some cases, to distinguish paired end reads with short insertions from paired end reads with long insertions, the latter, in particular, are called conjugate pair reads. In some embodiments involving conjugated pair reads, two biotin junction adapters are first attached to the two ends of a relatively long insert (eg, several kb in length). The biotin compound adapters then link the two ends of the insert to form a round molecule. A subfragment that combines biotin compound adapters can be obtained by additional fragmentation of the round molecule. Then, a subfragment including the two ends of the original fragment in opposite sequence order can be sequenced using the same procedure as rel. described above paired end sequencing with a short insert. For more information about performing conjugated pair sequencing using the Illumina platform, see the online post at the following URL: res.illumina.com/documents/products/technotes/technote_nextera_matepair_data_processing.pdf. This publication is also incorporated herein by reference in its entirety.

[00250] After sequencing of DNA fragments, reads of sequences of a given length, for example, 100 bp, are compared or aligned with a known reference genome. Matched or aligned reads and their respective locations on the reference sequence are also called tags. In the assays of many of the embodiments described herein, reads that are either poorly aligned or cannot be aligned in principle, and aligned reads (labels) are used to determine repeat expansion. In one embodiment, the reference genomic sequence is the NCBI36/hg18 sequence, which can be found on the Internet at the following address: genome.ucsc.edu/cgibin/hgGateway?org=Human&db=hg18&hgsid=166260105). In an alternative implementation, the reference genomic sequence is the GRCh37/hg19 sequence, which can be found on the Internet at the following address: genome.ucsc.edu/cgi-bin/hgGateway. Other sources of publicly available sequence information include the Genetic Database, dbEST, dbSTS, EMBL (European Molecular Biology Laboratory), and DDBJ (DNA Database of Japan). Sequence alignment can be performed using a number of computer algorithms including, but not limited to, BLAST (Altschul et al., 1990), BLITZ (MPsrch) (Sturrock & Collins, 1993), FASTA (Person & Lipman, 1988), BOWTIE (Langmead et al., Genome Biology 10:R25.1-R25.10 [2009]) or ELAND (Illumina, Inc., San Diego, CA, USA). In one embodiment, one end of the clonally expanded copies of plasma scDNA molecules is sequenced and processed by performing bioinformatic alignment analysis for the Illumina Genome Analyzer using the Efficient Large-Scale Alignment of Nucleotide Databases (ELAND) software.

[00251] In one illustrative, non-limiting embodiment, the methods described herein include obtaining information about the nucleic acid sequence in the test sample using Helicos True Single Molecule Sequencing (tSMS) technology (for example, described in the publication Harris TD et al., Science 320:106-109 [2008]). According to the tSMS technique, a DNA sample is cut into chains of about 100 to 200 nucleotides in length, and a polyA sequence is added to the 3' end of each DNA strand. Each strand is labeled by adding fluorescently labeled adenosine. The DNA strands are then hybridized to a flow cell containing millions of T-nucleotide capture sites immobilized on the surface of the flow cell. In certain embodiments, the matrices may have a density of about 100 million matrices/cm ² . The flow cell is loaded into an instrument, such as a HeliScopeTM sequencer, and a laser is illuminated on the surface of the flow cell, revealing the position of each array. A CCD camera can be used to match the position of the arrays on the surface of the flow cell. The matrix fluorescent label is then cleaved off and washed out. The sequencing reaction begins with the introduction of DNA polymerase and a fluorescently labeled nucleotide. T-oligonucleotide acid serves as a primer. The polymerase inserts the labeled nucleotides into the primer under the control of a template. The polymerase and non-integrated nucleotides are removed. Arrays into which a fluorescently labeled nucleotide is directionally inserted are recognized by visualization of the surface of the flow cell. After imaging is complete, the fluorescent label is removed as part of the cleavage step and the process is repeated with other fluorescently labeled nucleotides until the desired read length is reached. Sequence information is collected at each step of adding nucleotides. Whole genome sequencing using single molecule sequencing technologies eliminates the need for PCR amplification in preparing libraries for sequencing, and the methods also allow direct measurement of a sample instead of measuring copies of that sample.

[00252] In another illustrative, non-limiting embodiment, the methods described herein include obtaining information about the nucleic acid sequence in the test sample using 454 sequencing (Roche) (for example, as described in Margulies, M et al Nature 437:376-380 [2005]). 454 sequencing typically involves two steps. At the first stage, DNA is subjected to hydrodynamic fragmentation to separate it into fragments having about 300-800 base pairs each and blunt ends. The oligonucleotide adapters are then ligated to the ends of the fragments. Adapters serve as primers for performing amplification and sequencing of fragments. The fragments can be attached to DNA capture microspheres, eg, streptavidin-coated microspheres, using, for example, Adapter B, which contains a 5'-biotin tag. The fragments attached to the microspheres are amplified by PCR in drops of an oil-water emulsion. The result is multiple copies of clonally amplified DNA fragments on each microsphere. In the second step, the microspheres are captured in wells (eg, picoliter). Pyrosequencing of each DNA fragment is carried out in a parallel manner. By adding one or more nucleotides, a light signal is generated, which is recorded by a CCD camera in a sequencing machine. The signal strength is proportional to the number of inserted nucleotides. When performing pyrosequencing, pyrophosphate (PPi) is used, which is released when nucleotides are added. PPi is converted to ATP by ATP sulfurylase in the presence of adenosine 5'-phosphosulfate. Luciferase interacts with ATP to convert luciferin to oxyluciferin, and as a result of this reaction, light is generated, which is then measured and analyzed.

[00253] In another illustrative non-limiting embodiment, the methods described herein include obtaining nucleic acid sequence information in a test sample using SOLiD™ technology (Applied Biosystems). In SOLiD™ ligation sequencing, genomic DNA is hydrodynamically fragmented to separate it into fragments, and adapters are attached to the 5' and 3' ends of the fragments to generate a library of fragments. In an alternative implementation, internal adapters can be introduced by ligating the adapters to the 5' and 3' ends of the fragments, rounding the fragments, splitting the round fragment to form an internal adapter, and attaching adapters to the 5' and 3' ends of the resulting fragments to generate a library of conjugated pairs. Then, populations of clonal microspheres are prepared in microreactors containing microspheres, primers, template, and PCR components. After PCR, the templates are denatured and the microspheres are enriched to separate extended array microspheres. The matrices on the selected microspheres are subjected to a 3' modification, as a result of which bonds are established with the glass slide. Sequencing is performed by sequential hybridization and ligation of partially random oligonucleotides to a specific central base (or base pair) defined by a particular fluorophore. After color registration, the ligated oligonucleotide is digested and removed, then the process is repeated.

[00254] In another exemplary, non-limiting embodiment, the methods described herein include obtaining nucleic acid sequence information in a test sample using real-time sequencing technology (SMRT™) from Pacific Biosciences. SMRT sequencing visualizes continuous incorporation of dye-labeled nucleotides during DNA synthesis. Single molecules of DNA polymerase are attached to the bottom surface of separate zero-mode wavelength detectors (ZMW detectors) which acquire sequence information during the insertion of phospho-linked nucleotides into the growing primer strand. The ZMW detector contains a confining structure that allows one nucleotide insertion to be observed by DNA polymerase against a backdrop of fluorescent nucleotides that rapidly diffuse in and out of the ZMW (within microseconds). Inclusion of a nucleotide into a growing chain usually takes a few milliseconds. During this period of time, the fluorescent label is excited and produces a fluorescent signal, after which the fluorescent label is cleaved. Measuring the corresponding dye fluorescence indicates which base has been incorporated. The process is repeated to obtain a sequence.

[00255] In another exemplary, non-limiting embodiment, the methods described herein include obtaining nucleic acid sequence information in a test sample using nanopore sequencing (e.g., as described in Soni GV and Meller A Clin Chem 53: 1996-2001 [2007]). Nanopore sequencing DNA analysis techniques have been developed by a number of companies including, for example, Oxford Nanopore Technologies (Oxford, UK), Sequenom, NABsys, and the like. Nanopore sequencing is a single molecule sequencing technology in which a single DNA molecule is sequenced directly as it passes through a nanopore. A nanopore is a small hole with a diameter, usually on the order of 1 nanometer. Immersing a nanopore in an electrically conductive fluid medium and connecting a potential (voltage) source to it results in the appearance of a small electric current due to the conduction of ions through the nanopores. The strength of the flowing current depends on the size and shape of the nanopore. When a DNA molecule passes through a nanopore, each nucleotide of the DNA molecule blocks the nanopore to a different extent, changing the amount of current passing through the nanopore to a different extent. Thus, such a change in current during the passage of a DNA molecule through a nanopore makes it possible to obtain a reading of the DNA sequence.

[00256] In another exemplary, non-limiting embodiment, the methods described herein include obtaining nucleic acid sequence information in a test sample using a chemically sensitive field-controlled transistor (chemFET) (e.g., as described in US Patent Application Publication No. 2009/0026082). In one embodiment of this technique, DNA molecules can be placed in reaction chambers and template molecules can be hybridized to a polymerase-linked sequencing primer. The insertion of one or more triphosphates into a new nucleotide strand at the 3' end of a sequencing primer can be recognized by chemFET by a change in current. The array may include a plurality of chemFET sensors. In another example, individual nucleic acids can be attached to microspheres, nucleic acid amplification can also be performed on the microsphere, and individual microspheres can be transferred to separate reaction chambers on a chemFET array, each chamber equipped with a chemFET sensor; in addition, nucleic acid sequencing can be performed.

[00257] In another embodiment, the DNA sequencing technology is single molecule sequencing using Ion Torrent technology, which combines semiconductor technology with the chemical analysis of a conventional sequencing method to directly convert chemically encoded information (A, C, G, T) into digital information (0 , 1) using a semiconductor chip. In nature, when a nucleotide is inserted into a DNA strand by a polymerase, a hydrogen ion by-product is released. The Ion Torrent technology uses a high-density array of micromachined wells to perform this biochemical process through mass parallel sequencing. Each well contains a single DNA molecule. Below the wells is an ion-sensing layer, and below that is an ion sensor. When a nucleotide, such as C, is added to the DNA template and then inserted into the DNA strand, a hydrogen ion is released. The charge on this ion will change the pH value of the solution, which in turn is recorded by the Ion Torrent ion sensor. The sequencer, the world's smallest solid-state pH meter, is used for base recognition by converting chemical analysis data into digital data. The Ion Personal Genome Machine (PGM™) sequencer then sequentially fills the chip with nucleotides one by one. In case of mismatch of the next nucleotide filling the chip. Voltage changes will not register and grounds will not be recognized. If there are two identical bases on the DNA strand, the voltage is doubled and the chip registers two identical bases. The direct detection method makes it possible to register the inclusion of nucleotides within a few seconds.

[00258] In another embodiment, the present method includes obtaining information about the sequence of nucleic acids in the test sample using sequencing by hybridization. Sequencing by hybridization comprises bringing a plurality of polynucleotide sequences into contact with a plurality of polynucleotide probes, each of the plurality of polynucleotide probes may optionally be attached to a substrate. The substrate may be a flat surface containing an array of known nucleotide sequences. To determine the polynucleotide sequences present in the sample, you can use the pattern to perform hybridization with the array. In other embodiments, each probe is attached to a microsphere, such as a magnetic microsphere or the like. Microbead hybridization can be used to determine multiple polynucleotide sequences in a sample.

[00259] In some embodiments of the methods described herein, 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, 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 b.p. When creating paired end reads, technological advances are expected to enable single end reads greater than 500 bp. and reads over 1,000 b.p. In some embodiments, paired end reads are used to determine repeat expansion, which contain reads of sequences from about 20 bp in length. up to 1,000 bp, from about 50 bp up to 500 bp or from 80 b.p. up to 150 bp In various embodiments, paired end reads are used to evaluate a sequence having repeat expansion. A sequence with repeat expansion is longer than reads. In some embodiments, the sequence having repeat expansion is longer than about 100 bp, 500 bp, 1,000 bp. or 4,000 b.p. Sequence read matching is performed by comparing the read sequence with that of a reference sample to determine the chromosomal origin of the sequenced nucleic acid molecule, in which case no specific genetic sequence information is required. A small degree of mismatch (0-2 mismatches per read) is allowed, taking into account minor polymorphisms that may exist between the reference genome and the genomes in the mixed sample. In some implementations, reads that are aligned with the reference sequence are used as base reads, and those reads that are paired with the base reads but fail to align or are poorly aligned with the reference sequence are used as sticky reads. In some embodiments, poorly aligned reads may have a relatively high number of mismatches per read, such as at least about 5%, 10%, 15%, or 20% mismatches per read.

[00260] Typically, a plurality of sequence marks (i.e., reads aligned with a reference sequence) are obtained for each sample. In some embodiments, at least about 3 x 10 ⁶ sequence marks, at least about 5 x 10 ⁶ sequence marks, at least about 8 x 10 ⁶ sequence marks, at least about 10 x 10 ⁶ sequence marks, at least at least about 15 x 10 ⁶ sequence marks, at least about 20 x 10 ⁶ sequence marks, at least about 30 x 10 ⁶ sequence marks, at least about 40 x 10 ⁶ sequence marks, or at least about 50 x 10 ⁶ sequence tags, including, for example, 100 bp. Obtained by matching reads against a reference genome for each sample. In some embodiments, all sequence reads are mapped to all regions of the reference genome, allowing genome-wide reads to be performed. In other embodiments, the reads are matched to a sequence of interest, such as a chromosome, segment of a chromosome, or sequence of repeats of interest.

5.13. Device and systems for determining the expansion of repeats

[00261] The analysis of sequencing data and diagnostics obtained from them, as a rule, is performed using various computer algorithms and programs. Thus, in some embodiments, processes are employed that include the use of data stored or transmitted by one or more computer systems or other processing systems. The embodiments described in this document also apply to the device used to perform these operations. This device may be specially designed to achieve the desired purpose, and such a device may be a general purpose computer (or group of computers), specially configured by a computer program and/or data structure stored in the computer. In some embodiments, a group of processors is responsible for performing some or all of these analytics in a collaborative (eg, networked or cloud computing) and/or parallel fashion. The processor or group of processors used to implement the methods described herein can be of various types, including microcontrollers and microprocessors, such as programmable devices (for example, FPGAs and FPGAs), and non-programmable devices, such as specialized gate array ICs or general purpose microprocessors. destination.

[00262] In one embodiment, a system is provided for determining the genotypes of variants at genomic loci, including repeat sequences, the system also includes a sequencer for receiving nucleic acid samples and providing information about the nucleotide sequence of such samples; CPU; and a computer-readable storage medium containing instructions executable on said processor and used to genotype variants using the following methods: (a) collecting test sample nucleic acid sequence reads from a database; (b) aligning the sequence reads with one or more repeat sequences, each of which is represented on a sequence graph, wherein the sequence graph has a directed graph data structure, where the vertices represent nucleotide sequences and directed edges connecting the vertices, and the sequence graph contains one or more of its own simple cycles, with each own simple cycle being a subsequence of repeats , and each subsequence of repeats contains repeats of the repeating link of one or more nucleotides; and (c) determining one or more genotypes of one or more repeat sequences using sequence reads aligned with one or more repeat sequences.

[00263] 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 mass parallel sequencing using sequencing by synthesis using reversible dye terminators. In other embodiments, the sequencer is configured to perform sequencing by ligation. In other embodiments, the sequencer is configured to perform single molecule sequencing.

[00264] In addition, certain embodiments relate to tangible media and/or computer-readable media designed for long-term storage of information, or computer program products that include program instructions and/or data (including data structures) for performing various operations with using a computer. Examples of computer-readable media include, but are not limited to, solid-state storage devices, magnetic media such as disk drives, magnetic tape, optical media such as disks, magneto-optical media, and hardware devices specifically provided for storing and executing program instructions such as permanent storage devices (ROM) and random access memory (RAM). Computer-readable media may be directly or indirectly controlled by the end user. Examples of directly managed media include media located at a user's site and/or media from which data is not publicly available. Examples of indirectly managed media include media indirectly available to a user through an external network and/or a public service, such as a "cloud service". Examples of program instructions include both machine code, including that generated by a compiler, and files containing higher-level code that can be executed by a computer using an interpreter.

[00265] In various embodiments, the data used in the described methods, as well as used in the device, is in electronic format. Such data may include reads and labels derived from a nucleic acid sample, reference sequences (including reference sequences that exclusively or predominantly result in polymorphisms), recognitions such as repeat expansion recognitions, recommendations, contraindications, diagnoses, and the like. In accordance with the content of this document, data in electronic format can be stored in the device and transferred between devices. Typically, data in electronic format is provided in digital form and may be stored as a set of bits and/or bytes in various data structures, lists, databases, and so on. Data can be realized in electronic, optical and other form.

[00266] In one embodiment, a computer program product is provided for generating output data indicative of the presence or absence of repeat expansion in a test sample. The computer product may include instructions for implementing any one or more of the methods for determining repeat expansion described above. As explained above, a computer product may include a tangible computer-readable medium and/or a non-volatile storage medium containing recorded executable or compiled program code (e.g., instructions) that allows the processor to perform determination of sticky reads and repetitions in sticky reads, and also the presence or absence of repeat expansion. In one example, the computer product includes a computer-readable medium containing written executable or compileable program code (eg, instructions) to enable the processor to perform repeat expansion determination and comprising: a procedure for obtaining sequencing data from at least a portion of nucleic acid molecules aligned with a repeat sequence; a machine algorithm for performing a repeat expansion analysis based on said output data; and a procedure for generating output data indicative of the presence, absence, and type of said repeat expansion.

[00267] Sequence information in a sample of interest can be compared to reference chromosomal sequences to determine paired end reads aligned with or anchored to the repeat sequence of interest, and to determine repeat expansion of the repeat sequence. In various embodiments, the reference sequences are stored in a database, such as a relational or object-oriented database.

[00268] It should be understood that in most cases, performing computational operations within the framework of the methods described herein without assistance is impractical or even impossible. For example, to match one read from a 30 bp sample. with any of the human chromosomes, it may take several years if a computing device is not used. Of course, the problem is exacerbated by the fact that qualitative recognition of repeat expansion requires matching thousands (eg, at least about 10,000) or even millions of reads with one or more chromosomes.

[00269] In various embodiments, raw sequence reads are aligned with one or more sequence graphs representing one or more sequences of interest. In various embodiments, at least 10,000, 100,000, 500,000, 1,000,000, 5,000,000, or 10,000,000 reads are aligned with one or more sequence graphs. In various embodiments, the one or more sequence graphs include at least 1, 2, 5, 10, 50, 100, 500, 1,000, 5,000, 10,000, or 50,000 sequence graphs.

[00270] In some embodiments, raw sequence reads are initially aligned with a reference genome to determine the genomic coordinates of the reads before a subset of the originally aligned reads are aligned with one or more sequence graphs representing one or more sequences of interest. In various embodiments, 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 the reference genome. In some embodiments, the originally aligned reads are realigned with sequence graphs to determine repeat expansions in multiple regions (each region corresponds to a sequence graph). The total number of reads that realign with the sequence graphs during each application of the implementation options may range from thousands to many millions of reads. In various implementations, 10,000,000 reads are realigned with each sequence graph. In various embodiments, the one or more sequence graphs include at least 1, 2, 5, 10, 50, 100, 500, 1,000, 5,000, 10,000, or 50,000 sequence graphs.

[00271] The methods described herein can be implemented using a system for determining the genotypes of variants at a genomic locus that includes a sequence of repeats. The system may include: (a) a sequencer for receiving nucleic acids from a test sample, providing information about the nucleotide sequence of the sample; (b) a processor; and (c) one or more computer-readable storage media containing instructions executable on said processor and used to genotype variants at genomic loci, including repeat sequences. In some embodiments, instructions for implementing the methods may be obtained from a computer-readable medium that stores computer-readable instructions for performing the repeat expansion determination. Thus, in one embodiment, a computer program product is provided that contains a computer-readable medium for long-term storage of information, on which a program code is stored, which, when executed by one or more processors of a computer system, leads to the implementation of a method for determining the expansion of repetitions of a sequence of repetitions in a test sample, including nucleic acids, where the sequence of repeats includes repeats of a repeating link of nucleotides. The program code may include: (a) code for collecting test sample sequence reads from a database; (b) code for aligning sequence reads with one or more repeat sequences, each of which is represented on a sequence graph, wherein the sequence graph has a directed graph data structure, where vertices represent nucleotide sequences and directed edges connecting the vertices, and wherein the sequence graph contains one or more intrinsic simple cycles, each intrinsic simple cycle being a subsequence of repeats, each subsequence of repeats containing repeats of a repeating unit of one or more nucleotides; and (c) code for determining one or more genotypes of one or more repeat sequences using sequence reads aligned with the one or more repeat sequences.

[00272] In some embodiments, the instructions may further include automatically recording information related to the method, such as relating to repeats and pinned reads, and the presence or absence of indications of repeat expansion in the medical records of the human subject from whom the test sample. The patient's medical records may be maintained and stored, for example, in a laboratory, doctor's office, hospital, health care facility, insurance company, or the method of using a personal medical record may additionally include the appointment, initiation and / or change of treatment of the patient from whom the test sample is taken . This process may include performing one or more additional tests or analyzes of additional samples taken from the subject.

[00273] The described methods can also be performed using a computer processing system specially adapted to perform repeat expansion determinations. In one embodiment, an implementation of a computer processing system specifically configured to perform a sequence of steps in a method described herein is provided. In one embodiment, the apparatus includes a sequencing device specifically configured to sequence at least a portion of the nucleic acid molecules in a sample to obtain sequence type information described elsewhere herein. The apparatus may also include components for processing the sample. Such components are described elsewhere in this document.

[00274] The sequence or other data may be entered into a computer or stored on a computer-readable medium in a direct or indirect manner. In one embodiment, the computer system is directly connected to a sequencing device that reads and/or analyzes nucleic acid sequences obtained from samples. The acquisition of sequences or other data using such devices is accomplished by interacting with a computer system interface. In an alternative implementation, the sequences processed by the system are obtained from a storage location for the sequences, such as a database or other storage. Once the processing device is accessed, the storage device or mass storage device is used to buffer or store (including temporary) nucleic acid sequences. In addition, a number of marks for different chromosomes or genomes, or the like, may be stored in the storage device. The storage device may also store various subroutines and/or programs for parsing the sequence representation or associated data. Such programs/routines may include programs for performing statistical analysis, etc.

[00275] In one example, a user places a sample into a sequencing machine. Data collection and/or analysis is performed using a sequencing machine connected to a computer. The software installed on the computer makes it possible to collect and/or analyze data. Data may be stored, displayed (using a monitor or other similar device), and/or sent to other locations. The computer may be connected to the Internet used to transfer data to a mobile device used by a remote user (eg, a doctor, scientist, or laboratory assistant). It should be understood that data may be stored and/or analyzed prior to such transmission. In some embodiments, the raw data is collected and remotely sent to the user or to a data analysis and/or storage device. The transmission of data may be accomplished by storing it on a computer-readable medium, such medium being sent to the end user (eg, by mail). The remote user may be in the same or a different geographic location, including, without limitation, a building, city, state, country, or continent.

[00276] In some embodiments, the methods also collect multiple polynucleotide sequence data (eg, reads, tags, and/or reference chromosomal sequences) and send the data to a computer or other computing system. For example, the computer may be connected to laboratory equipment such as a sample collection apparatus, a nucleotide amplification apparatus, a nucleotide sequencing apparatus, or a hybridization apparatus. The computer can then collect the relevant data collected using the laboratory equipment. The data may be stored on the computer at any stage, such as during real-time collection, before it is sent, during or after it is sent. The data may be stored on a computer-readable medium, which may also be retrieved from a computer. The collected or stored data may be transmitted from a computer to a remote location, such as via a local area network or a wide area network such as the Internet. At a remote location, various operations may be performed on the transmitted data, as described below.

[00277] Described below are the types of electronically formatted data that can be stored, transmitted, parsed, and/or processed in systems, device, and implementation of the methods described herein.

Reads obtained by sequencing nucleic acids in the test sample

Labels obtained by aligning reads with a reference genome or other reference sequence or sequences

Reference genome or sequence

Locus specification, which indicates the type of locus, its location and structure

Read coverage

Variant genotype

Sequence graph

Count Ways

Graph alignment information

Actual Recognitions of Repeat Expansion

Diagnoses (clinical condition associated with relevant recognitions)

Recommendations for performing additional tests based on recognitions and/or diagnostics

Treatment and/or monitoring plans derived from recognitions and/or diagnosis

[00278] These types of data may be received, stored, analyzed and/or processed in one or more locations using dedicated devices. There are many options for their processing. In one instance, all or most of this information is stored and used at the site of study sample processing, such as a doctor's office or other clinical setting. Otherwise, a sample is obtained at one location, processed and optionally sequenced at another location, reads are aligned and recognized at one or more other locations, and diagnoses, recommendations, and/or plans are prepared at yet another location (which may be the sampling location). sample).

[00279] In various embodiments, reads are generated by a sequencing machine and then transmitted to a remote site where they are processed to recognize the presence of repeat expansion. For example, at a given remote location, reads are aligned with a reference sequence to create base and sticky reads. Processing operations that may be applied in various locations include the following:

Collection of samples

Sample processing prior to sequencing

Sequencing

Analysis of sequence data and recognition of the presence of repeat expansion

Diagnostics

Communicating information about the diagnosis and/or recognition result to the patient or healthcare worker developing a plan for further treatment, testing and/or monitoring Implementing the plan

Consulting

[00280] These operations can be automated in accordance with the contents of other sections of this document. Typically, sequencing, analysis of sequence data, and recognition of the presence of repeat expansion are performed by calculation. Other operations can be performed manually or automatically.

[00281] In Fig. 6 illustrates one implementation of a dispersion system for performing recognition or diagnosis based on a test sample. Sample collection point 01 is used to obtain a test sample from a patient. The samples are then transferred to processing and sequencing site 03 where test samples are processed and sequenced as described above. Location 03 contains the sample processing device as well as the processed sample sequencing device. The result of sequencing, as described elsewhere in this document, is a set of reads that are typically presented in electronic format and transmitted to a network such as the Internet; such a set is designated 05 in FIG. 6.

[00282] The sequence data is transmitted to a remote location 07 where analysis and recognition are performed. A given location may contain one or more powerful computing devices, such as computers or processors. After the analysis is completed and recognition is performed based on the received sequence information at location 07, the results of this recognition are returned to the network 05. In some embodiments, not only recognition results, but also diagnosis information is generated at location 07. The recognition and/or diagnosis results are then transmitted over the network and returned to the sample collection point 01 as shown in FIG. 6. As explained above, this is just one of many options for how the various operations associated with generating recognition or diagnostic results can be shared between different locations. One common implementation option involves the collection, processing and sequencing of samples in one place. Another option involves performing processing and sequencing at the same location where the analysis was performed and generation of recognition results.

EXPERIMENTAL

Examples

[00283] The program was applied to a simulated dataset containing a wide range of CAG and CCG repeat sizes at the HTT locus. HTT or huntingtin is a disease gene associated with Huntington's disease (HD), a neurodegenerative disorder characterized by loss of striatal neurons. This is believed to be caused by an increased, unstable trinucleotide repeat in a gene called huntingtin, which is translated as a polyglutamine repeat in the protein produced. In normal control samples, a fairly wide range of trinucleotide repeats (9-35) was determined, and the number of repeats exceeding 40 was called pathological.

[00284] As expected, the accuracy achieved using the methods of some embodiments was significantly higher in cases where reads aligned with a sequence graph containing both repeats compared to cases where both repeats were analyzed independently. A similar improvement was observed over the other CTP genotyping process (FIG. 7).

[00285] To demonstrate the possibility of DNA repeat genotype degeneration within implementation methods, we analyzed the polyalanine repeat in the PHOX2B gene in 150 controls from healthy subjects and one sample containing a known pathogen type expansion. PHOX2B contains a 20-codon polyalanine repeat that can expand, causing congenital central hypoventilation syndrome. According to the well-known repeat description (Amiel et al. 2003), all but a few controls were genotyped in a 20/20 ratio. Within the implementation options performed accurate genotyping of a single sample with expansion in the ratio of 20/27; This genotype was confirmed by Sanger sequencing. Also within the embodiments, pathogenic NVD adjacent to the adenosine homopolymer in the MSH2 gene was correctly identified in three whole genome sequencing replicates of a sample obtained from SeraCare Life Sciences (see Supplementary Materials).

[00286] Thus, we have developed a new method that satisfies the need for more accurate genotyping of complex loci. This method allows the genotyping of polyalanine repeats and the separation of complex regions containing repeats in close proximity to small variants and other repeats. It is expected that the flexibility of the sequence graph structure used in this document will allow the implementation of many new ways of recognizing variants.

Analysis of CAG and CCG CFT at the HTT locus

[00287] For each combination of CAG and CCG repeat genotypes, a short read pattern was modeled using WGSIM (Li, n/o). We set the read length value to 150, the distance between the ends of the pairing to 350, the value of the standard deviation of the distance between the ends of the pairing to 50, the mutation rate and the basic error rate to 0.0010, and the proportion of deletions to 0. The number of pairs was set as follows. way to get 40-fold coverage of the locus. The reads were aligned with the GRCh37 benchmark with BWA-MEM 0.7.17-r1194-dirty. (Li 2013).

[00288] We analyzed this data in two ways using some implementation options. First, we indicated the structure of the HTT locus using (CAG)*CAACAG(CCG)* expression, ensuring read alignment with a sequence graph containing both repeats. We then used some implementations to perform an independent analysis of each iteration. In this mode, the reads were aligned with the graph representing the CAG QFT, and they were individually aligned with the graph representing the CCG QFT. Finally, we analyzed both repeats independently using the latest versions of GangSTR and TredParse, which do not use sequence graphs to align reads with the repeat region (Fig. 7).

[00289] In Fig. 7 illustrates the genotyping accuracy of CAG and CCG TTPs at the HTT locus based on simulated data. (a) Characteristics of an implementation in which reads are aligned with a sequence graph containing both repeats; (b) characteristics of an implementation in which the repeats are analyzed independently of each other; (c) GangSTR characteristics; (d) characteristics of TredParse. We measured the maximum percentage deviation of the predicted CHF lengths from the expected CHF lengths for each simulated sample. The dotted blue lines indicate the genotype in the reference genome, and the solid red lines indicate the threshold value of pathogenic expansions. Within this embodiment, the correct genotypes of all samples are accurately predicted when both samples are genotyped together. Joint genotyping has a great advantage over separate genotyping of both repeats using any of the three tools, which is characterized by a high relative error rate, especially in cases where one of the repeats is significantly longer than the other.

Datasets

[00290] Non-PCR whole genome sequencing data relating to 150 unrelated controls used to analyze the PHOX2B polyalanine repeat represent the Polaris Diversity group. This group consists of samples selected from the International Genome Sample Resource (1000 Genomes Project Consortium et al. 2015) (www|.|internationalgenome|.|org/). Whole genome sequencing data are available from the European Genome-Phenome Archive (EGA; www|.|ebi|.|ac|.|uk/ega/home; PRJEB20654) and from the Sequence Read Archive (RAR) of the National Center for Biotechnology Information (CBR; www |.|ncbi|.|nlm|.|nih|.|gov/sra; bioproject:387148). Description of samples (github|.|com/Illumina/Polaris/wiki/HiSeqX-Diversity-Cohort).

[00291] A sample having a 20/27 expansion of PHOX2B was provided by Genetics Laboratories Molecular Genetics, Addenbrook Medical Research Center, Connecticut, and the University of Cambridge.

[00292] A sample called Seraseq Inherited Cancer DNA Mix v1 provided by SeraCare Life Sciences contains distinctly pathogenic variants that are difficult to recognize. These variants were synthetically added to the well-studied GM24385 cell line; the expected frequencies of such variants are 50%. One of these artificial mutations corresponds to the OHV in the MSH2 gene, which is directly adjacent to the region of the long homopolymer A.

[00293] The preparation of three replicates of the SeraCare sample was performed using the Illumina TruSeq PCR Free kit. Replicates were run on a single NovaSeq6000 lane using the XP workflow followed by sequencing at 2 × 151 read lengths. Replicates were analyzed using Sentieon DNASeq FASTQ to VCF software and Whole Genome Resequencing v8.0.0 software on the Basespace cloud platform (https http://basespace.illumina.com). Sentieon can be viewed as a measure of the performance of variant recognition using Broad's BWA-GATK software package, which implements the same algorithms. None of the software units allowed for the correct determination of the NVG MSH2. Fastq files and analysis results are provided in the following section of the Basespace cloud platform: https://basespace.illumina.com/s/HAQNxJyEtJLP

[00294] the Content of the present description can be implemented in other forms, provided that its essential characteristics are preserved. The described embodiments are to be considered as illustrative and non-limiting implementations only. Thus, the scope of this document is determined by the relevant appended clauses and not by the above description. All changes consistent with the value and range of item equivalence should be included in their scope.

Claims (45)

1. A method implemented using a computer equipped with one or more processors and system memory for genotyping one or more repeat sequences, each of which contains one or more repeat subsequences, including: (a) generating and collecting test sample sequence reads from a database using one or more processors and a sequencer; (b) alignment with one or more processors, reading one or more sequences of repeats, each of which is represented by a sequence graph, and the sequence graph has a directed graph data structure, where the vertices represent nucleotide sequences, and directed edges connect the vertices, and the graph the sequence contains one or more own simple cycles, and each own simple cycle is a subsequence of repeats, with each subsequence of repeats containing repeats of a repeating unit of one or more nucleotides; (c) one or more processors determining one or more genotypes of one or more repeat sequences using sequence reads aligned with the one or more repeat sequences. 2. The method of claim 1, wherein the repeat sequence of one or more repeat sequences comprises a specific repeat unit containing at least one partially defined nucleotide. 3. The method of claim 2, wherein the particular repeat unit contains degenerate codons. 4. A method according to any one of the preceding claims, wherein the one or more eigensimple loops comprise two or more eigensimple loops representing two or more repeating subsequences. 5. The method according to any one of the preceding claims, wherein the sequence graph further comprises two or more alternative paths for two or more alleles. 6. The method of claim 5, wherein two or more alleles contain a deletion or substitution. 7. The method of claim 5, wherein the replacement comprises a single nucleotide variant (SNP) or a single nucleotide polymorphism (SNP). 8. The method of claim 5, further comprising genotyping two or more alleles using sequence reads aligned with two or more alternative pathways. 9. The method of claim 8 wherein genotyping the two or more alleles includes spanning two or more alternative paths to a probability model to determine the probabilities of the two or more alleles. 10. The method of claim 9, wherein the probability model models 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 beta-binomial distribution. 11. The method of claim 10, wherein the Poisson distribution rate parameter is estimated from read length and mean depth observed at the genomic locus. 12. The method of any one of the preceding claims, further comprising aligning to (b) a sequence read against a reference genome to determine genomic sequence read coordinates, and selecting a subset of the sequence read as the sequence read aligns with one or more repeat sequences, each of which represented by a sequence graph. 13. The method of claim 12, wherein the subset of sequence reads comprises reads aligned with or near a region represented by the sequence graph. 14. The method of claim 12, wherein the subset of sequence reads comprises unaligned reads whose conjugate reads map to or near the area represented by the sequence graph. 15. The method of claim 12, wherein the subset of sequence reads comprises sequence reads aligned with one or more non-target regions that are known hotspots for misalignment reads. 16. A method according to any one of the preceding claims, wherein aligning the sequence read with the sequence graph comprises: search for a correspondence between the reading of the sequence and the path of the graph of the sequence; And extending the correspondence of the measures to complete alignment of the nodes and edges of the sequence graph, including one or more of its own simple cycles. 17. The method of any one of the preceding claims, wherein aligning the sequence read with the sequence graph includes reducing the graph by removing the ends of the low confidence alignments. 18. A method according to any one of the preceding claims, wherein aligning a sequence read with a sequence graph comprises combining alignments by: aligning read subsequences with the sequence graph; And combining subsequence alignments to obtain a full read sequence alignment. 19. The method of any one of the preceding claims, further comprising generating a sequence graph based on the locus specification containing the structure of the genomic locus. 20. The method of any one of the preceding claims, wherein the sequence reads comprise paired end reads and step (c) includes: (i) defining base and pinned reads in paired end reads, where the base reads are aligned with or with space adjacent to one or more repeat sequences, and the pinned reads are unaligned reads that are paired with the pinned reads; And (ii) determining one or more genotypes for one or more repeat sequences using at least anchored reads. 21. The method of claim 20, wherein step (ii) includes determining one or more genotypes of one or more repeat sequences using baseline reads as well as anchored reads. 22. The method of claim 20 or 21, wherein the base reads are aligned to within about 5 kb. repeat sequences. 23. The method according to any one of paragraphs. 20-22, in which misaligned reads contain reads that cannot be aligned or are poorly aligned with the sequence graph. 24. The method of any one of the preceding claims, wherein the one or more repeat sequences comprise a short tandem repeat (SRT) sequence. 25. The method according to claim 24, in which the expansion of CTP is associated with fragile X syndrome, amyotrophic lateral sclerosis (ABS), Huntington's disease, Friedreich's ataxia, spinal ataxia, spinobulbar muscular atrophy, myotonic dystrophy, Machado-Joseph's disease or dentato- Rubro-Pallido-Lewis atrophy. 26. The method of any one of the preceding claims, further comprising using a sequencer to generate paired end reads of the test sample. 27. A method according to any one of the preceding claims, further comprising removing the test sample from the body of the subject. 28. The method according to any one of the preceding claims, wherein the test sample is a blood sample, a urine sample, a saliva sample, or a tissue sample. 29. The method according to any one of the preceding claims, wherein the repeating unit contains from 1 to 50 nucleotides. 30. A method according to any one of the preceding claims, wherein the reads are shorter than at least one of the repeat sequences. 31. A system for genotyping one or more repeat sequences, each containing one or more repeat subsequences, comprising: system memory; And one or more processors configured to: (a) generating using a sequencer and collecting reads of the test sample sequence from a database; (b) aligning sequence reads with one or more repeat sequences, each of which is represented on a sequence graph, wherein the sequence graph has a directed graph data structure, where vertices represent nucleotide sequences and directed edges connecting the vertices, and wherein the sequence graph contains one or more than its own simple cycles, and each own simple cycle is a subsequence of repeats, and each subsequence of repeats contains repeats of a repeating unit of one or more nucleotides; (c) determining one or more genotypes of one or more repeat sequences using sequence reads aligned with one or more repeat sequences.

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