Chromosome variation detection method and device, electronic equipment and readable storage medium

Abstract

The disclosure provides a chromosome variation detection method and device, an electronic device and a computer readable storage medium. The chromosome variation detection method comprises: obtaining a gene sequencing sequence of a sample to be detected; aligning the gene sequencing sequence with a target region of a reference sequence of a reference genome to obtain alignment information; and sorting the gene sequencing sequence aligned to the reference sequence to obtain a sorted sequencing sequence; performing chromosome large fragment variation detection on the sorted sequencing sequence according to the alignment information to obtain a candidate variation type of each candidate variation interval of the sorted sequencing sequence; and for at least one candidate variation type, detecting each candidate variation interval under the candidate variation type according to a preset rule corresponding to the candidate variation type to obtain a variation result of each candidate variation interval.

Description

Technical Field

[0001] This disclosure relates to the field of bioinformatics processing, specifically to a method and apparatus for detecting chromosome variations, an electronic device, and a computer-readable storage medium. Background Technology

[0002] Large chromosomal aberrations refer to variations in chromosome length such as deletions, duplications, and inversions exceeding 50 bp. For example, Down syndrome, the most common chromosomal disorder detected in newborn screening, is caused by aneuploidy on chromosome 21, while Cri-du-chat syndrome is caused by a deletion on a short arm of chromosome 5. These conditions can lead to growth retardation and severe intellectual disability in newborns. Furthermore, numerous domestic and international studies have shown a link between large chromosomal aberrations and cancer development, such as the BRCA1 and BRCA2 genes, which are associated with breast cancer susceptibility. Summary of the Invention

[0003] This disclosure provides a method and apparatus for detecting chromosome variations, an electronic device, and a computer-readable storage medium.

[0004] Firstly, this disclosure provides a method for detecting chromosome variations, including:

[0005] Obtain the gene sequencing sequence of the sample to be tested;

[0006] The gene sequencing sequence is aligned with the target region of the reference sequence of the reference genome to obtain alignment information; and the gene sequencing sequences aligned to the reference sequence are sorted to obtain the sorted sequencing sequence.

[0007] Based on the alignment information, large chromosomal fragment variation detection is performed on the sorted sequencing sequences to obtain the candidate variation type for each candidate variation interval of the sorted sequencing sequences;

[0008] For at least one candidate mutation type, each candidate mutation interval under the candidate mutation type is detected according to the preset rule corresponding to the candidate mutation type, and the mutation result of each candidate mutation interval is obtained.

[0009] In some embodiments, the alignment information includes: the insert fragment length of the sequencing read pair;

[0010] Based on the alignment information, the sorted sequencing sequences are subjected to large-fragment chromosomal variation detection, specifically including:

[0011] Determine whether the length of the inserted fragment of the sequencing read pair exceeds a preset length range. If so, the interval covered by the sequencing read pair in the target region is taken as the variation interval.

[0012] Based on the comparison information, the candidate mutation type for each mutation interval is determined.

[0013] In some embodiments, the alignment information further includes: information for characterizing whether the left-end sequencing read and the right-end sequencing read in the sequencing read pair are located on the same chromosome; and the alignment direction of the left-end sequencing read and the alignment direction of the right-end sequencing read;

[0014] Based on the comparison information, the candidate mutation type for each mutation interval is determined, specifically including:

[0015] Obtain the peak and variance of the insert length for all sequencing fragment pairs;

[0016] When the left-end and right-end sequencing reads in the sequencing read pair are located on the same chromosome, the candidate variant type of the variant interval corresponding to the sequencing read pair is determined based on the alignment direction of the left-end sequencing read, the alignment direction of the right-end sequencing read, the insertion length of the sequencing fragment pair, the peak value, and the variance.

[0017] In some embodiments, the candidate variant type of the variant interval corresponding to the sequencing read pair is determined based on the alignment direction of the left-end sequencing read and the alignment direction of the right-end sequencing read, the insert length of the sequencing fragment pair, the peak value, and the variance. Specifically, this includes:

[0018] When the alignment direction of the left-end sequencing read is forward and the alignment direction of the right-end sequencing read is reverse, and Insert_d > Insert_p + N*δ, the candidate variant type of the variant interval corresponding to the sequencing read pair is determined to be a candidate deletion.

[0019] When the alignment direction of the left-end sequencing read is reversed, the alignment direction of the right-end sequencing read is forward, and Insert_d < Insert_p - N*δ, the candidate variant type of the variant interval corresponding to the sequencing read pair is determined to be a candidate repeat.

[0020] When the alignment direction of the left-end sequencing read and the alignment direction of the right-end sequencing read are both forward or both reverse, the candidate variant type of the variant interval corresponding to the sequencing read pair is determined to be a candidate inversion.

[0021] Where Insert_d is the insertion length of the sequencing fragment pair, Insert_p is the peak value, δ is the variance, and N is an integer, 5≤N≤10.

[0022] In some embodiments, the candidate variant type of at least one candidate variant interval is: candidate missing;

[0023] The preset rule corresponding to the candidate missing is:

[0024] Obtain the sequencing depth of the sorted sequencing sequence at the i-th and j-th sites in the candidate variant interval, and the average sequencing depth in the candidate variant interval;

[0025] Determine whether formula (1) is true. If it is true, then determine that the candidate mutation interval has a missing mutation:

[0026] depth_i / depth_avg < para1, and depth_j / depth_avg < para1. Formula (1)

[0027] Wherein, depth_i is the sequencing depth of the sequenced sequence at the i-th site in the candidate variant interval, and depth_j is the sequencing depth of the sequenced sequence at the j-th site in the candidate variant interval; para1 is between 0 and 1 / 3.

[0028] In some embodiments, the candidate variation type of at least one candidate variation interval is: candidate repetition;

[0029] The preset rule corresponding to the candidate duplicate is:

[0030] Obtain the sequencing depth of the sorted sequencing sequence at the m-th and n-th sites in the candidate variant interval, and the average sequencing depth in the candidate variant interval;

[0031] Determine whether formula (2) is true. If it is true, then determine that the candidate variation interval has a candidate duplication:

[0032] depth_m / depth_avg > para2, and depth_n / depth_avg > para2. Formula (2)

[0033] Wherein, depth_m is the sequencing depth of the sequenced sequence at the m-th site in the candidate variant interval, and depth_n is the sequencing depth of the sequenced sequence at the n-th site in the candidate variant interval; para2 is between 1.5 and 10.

[0034] In some embodiments, obtaining the gene sequencing sequence of the sample to be tested includes:

[0035] Obtain the initial gene sequencing sequence of the sample to be tested;

[0036] The initial gene sequencing sequence is filtered to obtain the gene sequencing sequence.

[0037] In some embodiments, filtering the initial gene sequencing sequence specifically includes:

[0038] Based on preset base quality thresholds and length thresholds, the initial gene sequencing sequence is screened to obtain intermediate sequencing sequences;

[0039] Remove the adapter sequence from the intermediate sequencing sequence to obtain the gene sequencing sequence.

[0040] Secondly, this disclosure provides a chromosome variation detection device, comprising:

[0041] The sequence acquisition module is configured to acquire the gene sequencing sequence of the sample to be tested;

[0042] The alignment module is configured to align the gene sequencing sequence with the target region of the reference sequence of the reference genome to obtain alignment information; and to sort the gene sequencing sequences aligned to the reference sequence to obtain sorted sequencing sequences.

[0043] The first detection module is configured to perform large-fragment chromosome variation detection on the sorted sequencing sequence based on the alignment information, and obtain the candidate variation type for each candidate variation interval of the sorted sequencing sequence.

[0044] The second detection module is configured to, for at least one candidate mutation type, detect each candidate mutation interval under the candidate mutation type according to a preset rule corresponding to the candidate mutation type, and obtain the mutation result of each candidate mutation interval.

[0045] Thirdly, this disclosure provides an electronic device, including a memory and a processor, wherein a computer program is stored on the memory, and the computer program, when executed by the processor, implements the above-described method.

[0046] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-described method. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0048] Figure 1 This is a schematic diagram of a chromosome variation detection method provided in some embodiments of this disclosure.

[0049] Figure 2 This is a schematic diagram of a chromosome variation detection method provided in some other embodiments of this disclosure.

[0050] Figure 3 This is a base quality map of the filtered gene sequencing sequences.

[0051] Figure 4 This is a distribution of the insertion fragment lengths for three samples provided in one example of this disclosure.

[0052] Figure 5 This is a schematic diagram of a large segment deletion variant of chromosome provided in one example of this disclosure.

[0053] Figure 6 This is a schematic diagram of a large segment of chromosomal duplication variation provided as an example of this disclosure.

[0054] Figure 7 This is a schematic diagram of a chromosome variation detection device provided in some embodiments of this disclosure.

[0055] Figure 8 This is a schematic diagram of an electronic device provided in some embodiments of the present disclosure.

[0056] Figure 9 This is a schematic diagram of a computer-readable storage medium provided in some embodiments of this disclosure. Detailed Implementation

[0057] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0058] Some definitions of terms related to the specific embodiments of this disclosure:

[0059] High-throughput sequencing, also known as next-generation sequencing (NGS), is characterized by its ability to sequence hundreds of thousands to millions of DNA molecules simultaneously and its generally shorter read lengths. Sequencing refers to analyzing the base sequence of a specific DNA fragment, specifically the arrangement of adenine (A), thymine (T), cytosine (C), and guanine (G). The advent of rapid DNA sequencing methods has greatly advanced research and discovery in biology and medicine.

[0060] A read is a sequence obtained after high-throughput sequencing, containing sequencing base information and quality value information.

[0061] The human genome includes coding DNA, non-coding DNA, and mitochondrial DNA.

[0062] Sequence alignment refers to arranging two or more sequences together to indicate their similarities.

[0063] SR: Split-read.

[0064] Existing methods for detecting large chromosomal variants include copy number abnormality (CNV-seq), whole-genome sequencing (WGS), targeted gene sequencing (TGS), and fluorescence in situ hybridization (FISH). The advantages and disadvantages of each method are shown in Table 1. FISH suffers from low throughput and reliance on the experience of the testing personnel, and typically involves limited sample sizes, making it difficult to detect multiple large chromosomal variants using low-throughput methods. While WGS offers high throughput, it suffers from high sequencing costs, large data volumes, and long subsequent data processing times. TGS, on the other hand, sequences only the target region of interest in the genome, and due to its high throughput and high sequencing depth, it is gradually becoming the mainstream detection method in areas such as genetic disease diagnosis and tumor diagnosis. Therefore, it is necessary to establish analytical methods and workflows for identifying large chromosomal variants using targeted sequencing.

[0065] Table 1

[0066]

[0067] Currently, among the methods for identifying large chromosomal variants based on targeted sequencing, further improvements are needed to enhance the detection accuracy of large chromosomal variants.

[0068] Figure 1 This diagram illustrates a chromosome variation detection method provided in some embodiments of this disclosure. This method is used to detect large segments of chromosome variation. Figure 1 As shown, methods for detecting chromosome variations include:

[0069] S10. Obtain the gene sequencing sequence of the sample to be tested.

[0070] The sample to be tested can be a DNA sample from a patient with a chromosomal disorder, such as the patient's plasma or serum. The gene sequence of the sample can be obtained using a third-generation sequencer.

[0071] S20. Align the gene sequencing sequence with the target region of the reference sequence of the reference genome to obtain alignment information; and sort the gene sequencing sequences aligned to the reference sequence according to the alignment information to obtain the sorted sequencing sequence.

[0072] The reference genome is, for example, the hg19 genome. After alignment, the obtained alignment information may include: the regions covered by the left and right sequencing reads in the sequencing read pairs that can be aligned to the reference sequence on the reference genome; the insert length of the sequencing reads; whether the left and right sequencing reads are located on the same chromosome; the alignment direction information of the left and right sequencing reads, etc.

[0073] The sorting of gene sequencing sequences aligned to the reference sequence can specifically include: sorting the sequencing reads aligned to the reference sequence according to the chromosome number to which each sequencing read (i.e., each left-end sequencing read and each right-end sequencing read) is aligned. For example, if the sequencing read numbered 1 is aligned to chromosome 5 and the reading read numbered 2 is aligned to chromosome 4, then in the sorted sequencing sequence, the sequencing read numbered 1 will be located after the sequencing read numbered 2.

[0074] S30. Based on the alignment information, perform large-fragment chromosome variation detection on the sorted sequencing sequences to obtain the candidate variation type for each candidate variation interval of the sorted sequencing sequences.

[0075] The candidate variant interval is the interval covered by the entire sequencing reads that have the candidate variant. For example, if the left-end sequencing read covers the interval from the 1st to the 20th site on the reference genome, and the right-end sequencing read covers the interval from the 50th to the 80th site on the reference genome, then the candidate variant interval corresponding to this sequencing read is the 1st to the 80th site.

[0076] S40. For at least one candidate mutation type, each candidate mutation interval under the candidate mutation type is detected according to the preset rule corresponding to the candidate mutation type, and the mutation result of each candidate mutation interval is obtained.

[0077] Candidate variant types can include candidate deletions, candidate duplications, etc. Different candidate variant types may correspond to different preset rules.

[0078] In this embodiment, the sequenced sequences are subjected to large-fragment chromosomal variation detection to obtain candidate variation types for each candidate variation interval. Each candidate variation type can be considered as a candidate variation set, which includes candidate variation intervals with that candidate variation type. Subsequently, each candidate variation interval in at least one candidate variation set is further detected, i.e., the candidate variation sets within the candidate variation sets are filtered and screened, thereby filtering out false positives and improving detection accuracy.

[0079] Figure 2 This is a schematic diagram of a chromosome variation detection method provided in some other embodiments of this disclosure, such as... Figure 2 As shown, methods for detecting chromosome variations include:

[0080] S10. Obtain the gene sequencing sequence of the sample to be tested.

[0081] In some embodiments, step S10 may specifically include:

[0082] S11. Obtain the initial gene sequencing sequence of the sample to be tested. The initial gene sequencing sequence is a paired-end sequencing sequence, which includes multiple sequencing reads, each of which includes a left-end sequencing read and a right-end sequencing read.

[0083] S12. Filter the initial gene sequencing sequences to obtain gene sequencing data.

[0084] Optionally, step S12 may specifically include: screening the initial gene sequencing sequence according to preset base quality thresholds and length thresholds to obtain intermediate sequencing sequences; removing adapter sequences from the intermediate sequencing sequences to obtain the gene sequencing sequence.

[0085] For example, reads with base quality less than a first preset threshold, reads accounting for more than a second preset threshold in the initial gene sequencing sequence, and reads with length less than a third preset threshold are filtered to obtain intermediate sequencing sequences. When removing adapter sequences from the intermediate sequencing sequences, adapter sequences on the sequencing platform can be used as reference adapter sequences. When the overlap length between the left and right ends of a read in the intermediate sequencing sequence and the reference adapter sequence is greater than or equal to a certain preset length (e.g., 3 bp), that read is considered an adapter sequence.

[0086] By filtering the initial gene sequencing sequences, low-quality sequencing data can be removed, thus providing accurate data for subsequent testing.

[0087] Optionally, after filtering the initial gene sequencing sequences, data statistics can be performed on the initial gene sequencing sequences and the filtered gene sequencing sequences to generate a data quality report, which facilitates the adjustment of the parameters used in the filtering process. The data quality report may include the number of reads, the number of bases, and the proportion of data with quality values ​​of Q20 and Q30.

[0088] Table 2 presents statistical information on the data before and after filtering in one example provided in this disclosure. P1 represents the left-end sequencing read, and P2 represents the right-end sequencing read; that is, sample 1-P1 represents the left-end sequencing read in sample 1, and sample 1-P2 represents the right-end sequencing read in sample 1. `bases` represents the number of bases, and `%bases` represents the percentage of bases remaining after filtering relative to the number of bases before filtering; `%reads` represents the percentage of read pairs remaining after filtering relative to the number of read pairs before filtering.

[0089] Table 2

[0090]

[0091]

[0092] During filtering, the reference adapter sequence was 'AGATCGGAAGAGC', and on average, 3.3% of reads per sample contained this adapter sequence. Figure 3 This is a base quality graph of the filtered gene sequencing sequences. The horizontal axis represents position, and the vertical axis represents the phreed score. Figure 3 It can be seen that high-quality data is obtained after filtration in step S12, with all base quality values ​​above 30.

[0093] S20. Align the gene sequencing sequence with the target region of the reference sequence of the reference genome to obtain alignment information; and sort the gene sequencing sequences aligned to the reference sequence to obtain the sorted sequencing sequence.

[0094] This process involves using a pre-defined alignment algorithm to align gene sequencing sequences to the target region of a reference sequence. After obtaining the alignment information, the gene sequencing sequences aligned to the reference sequence can be sorted based on the coordinates of each base in the reference sequence, resulting in sorted sequencing sequences.

[0095] Alignment information may include: the length of the inserted fragment of the sequencing reads; information used to characterize whether the left and right sequencing reads in the sequencing reads are located on the same chromosome; the alignment direction of the left and right sequencing reads; and the region covered by the sequencing reads in the target region.

[0096] In some embodiments, after sorting the gene sequencing sequences aligned to the reference sequence, the sequencing depth of each site in the target region, the average sequencing depth in the target region, the coverage, and the target capture efficiency of the sorted sequencing sequences can be calculated, and this information can be used to monitor the capture results in the target region.

[0097] The sequencing depth at a given site refers to the number of reads covering that site. The average sequencing depth in the target region refers to the average sequencing depth at each site within the target region.

[0098] Figure 4 Here is a distribution of the insertion fragment lengths for three samples provided in one example of this disclosure, as shown in the image. Figure 4 As shown, the horizontal axis represents the insert size, and the left vertical axis represents the count. The distribution of the count is as follows: Figure 4 As shown in the bar chart, the right vertical axis represents the proportion of reads whose length is greater than the length of the inserted segment, and their distribution is as follows: Figure 4 The dotted lines in the table indicate the sequence. In Sample 1, most reads had an insert length of 152; in Sample 2, most reads had an insert length of 154; and in Sample 3, most reads had an insert length of 145. The capture efficiency and coverage statistics for the target region are shown in Table 3. In Table 3, capture_efficiency represents the capture efficiency of the target region, coverage 1X represents the coverage at a sequencing depth of 1, and coverage 40X represents the coverage at a sequencing depth of 40.

[0099] Table 3

[0100]

[0101] S30. Based on the alignment information, perform large-fragment chromosome variation detection on the sorted sequencing sequences to obtain the candidate variation type for each candidate variation interval of the sorted sequencing sequences.

[0102] Specifically, step S30 may include:

[0103] S31. Determine whether the length of the inserted fragment of the sequencing reads exceeds the preset length range. If so, take the interval covered by the sequencing reads in the target region as the variant interval.

[0104] The sequencing reads cover a continuous interval within the target region. The left-hand sequencing read covers the interval from site 1 to site 20 within the target region, and the right-hand sequencing read covers the interval from site 30 to site 50 within the target region. Therefore, the sequencing reads cover the interval from site 1 to site 50 within the target region.

[0105] S32. Based on the comparison information, determine the candidate variant types for each variant interval.

[0106] Optionally, step S32 may include:

[0107] S32a, Obtain the peak Insert_p and variance δ of the insert fragment length for all sequencing reads.

[0108] The peak value Insert_p is the maximum length of the inserted fragment among all sequencing reads.

[0109] S32b. When the left-end sequencing read and the right-end sequencing read in the sequencing reads are located on the same chromosome, the candidate variant type of the variant interval corresponding to the sequencing reads is determined based on the alignment direction of the left-end sequencing read, the alignment direction of the right-end sequencing read, the insertion length of the sequencing fragment pair, the above-mentioned peak value Insert_p and variance δ.

[0110] The alignment direction of the sequencing reads can be forward or reverse. For example, if the sequencing reads cover the interval from the 1st to the 50th site, the alignment direction is forward; if the sequencing reads cover the interval from the 50th site to the 1st site, the alignment direction is reverse.

[0111] In step S32b, when the left-end sequencing read and the right-end sequencing read in the sequencing reads are located on the same chromosome, if the alignment direction of the left-end sequencing read is forward and the alignment direction of the right-end sequencing read is reverse, and Insert_d > Insert_p + N*δ, then the candidate variant type of the variant interval corresponding to the sequencing reads is determined to be a candidate deletion.

[0112] When the left-hand and right-hand sequencing reads are located on the same chromosome, if the alignment direction of the left-hand sequencing read is reversed and the alignment direction of the right-hand sequencing read is forward, and Insert_d < Insert_p - N*δ, then the candidate variant type of the variant interval corresponding to the sequencing read is determined to be a candidate repeat. Here, N is an integer, 5 ≤ N ≤ 10.

[0113] When the left-end and right-end sequencing reads are located on the same chromosome, if the alignment direction of the left-end sequencing read and the alignment direction of the right-end sequencing read are both forward or both reverse, then the candidate variant type of the variant interval corresponding to the sequencing read is determined to be a candidate inversion.

[0114] S40. For at least one candidate mutation type, each candidate mutation interval under the candidate mutation type is detected according to the preset rule corresponding to the candidate mutation type, and the mutation result of each candidate mutation interval is obtained.

[0115] By grouping candidate mutation intervals corresponding to the same candidate mutation type into a candidate mutation set, step S40 is equivalent to filtering the candidate mutation intervals in at least one candidate mutation set. Optionally, the candidate mutation intervals in the candidate mutation set can be filtered using the readdepth (RD) method.

[0116] Optionally, the preset rules corresponding to the candidate deletion variants include S41a to S42a:

[0117] S41a, Obtain the sequencing depth of the sorted sequencing sequence at the i-th and j-th sites in the candidate variant interval, and the average sequencing depth in the candidate variant interval.

[0118] S42a. Determine whether formula (1) is true. If it is true, then determine that the candidate mutation interval has a missing mutation:

[0119] depth_i / depth_avg < para1, and depth_j / depth_avg < para1. Formula (1)

[0120] Where depth_i is the sequencing depth of the sequenced sequence at the i-th site in the candidate variant region, and depth_j is the sequencing depth of the sequenced sequence at the j-th site in the candidate variant region. |ij|>d1, where d1 can be set according to actual needs, d1≥50, for example, d1 can be 50, 60, 80, 100, 150, etc. para1 is a parameter that controls the strictness of screening candidate lost variants, for example, para1 is between 0 and 1 / 3.

[0121] In other words, for a set of candidate missing variants, we determine whether each candidate variant interval in the set satisfies formula (1). If it does, it means that the candidate variant interval that satisfies formula (1) has indeed experienced a missing variant.

[0122] Figure 5 This is a schematic diagram illustrating a large chromosomal deletion variant provided in one example of this disclosure. Figure 5 In the diagram, R1 represents the left-hand sequencing read, and R2 represents the right-hand sequencing read. The black triangles indicate the target regions of the genome. For example... Figure 5 As shown, the sequencing depth of the target region within the elliptical frame is relatively small, indicating that the sequencing sequence of the sample to be tested is missing a fragment of region a, that is, the sample to be tested has undergone deletion mutation.

[0123] Optionally, the preset rules corresponding to the candidate duplication variants include S41b to S42b:

[0124] S41b, Obtain the sequencing depth of the sorted sequencing sequence at the m-th and n-th sites in the candidate variant interval, and the average sequencing depth in the candidate variant interval.

[0125] S42b determines whether formula (2) is true. If it is true, then the candidate variation interval is determined to have candidate duplication.

[0126] depth_m / depth_avg > para2, and depth_n / depth_avg > para2. Formula (2)

[0127] Where depth_m is the sequencing depth of the sequenced sequence at the m-th site in the candidate variant region, and depth_n is the sequencing depth of the sequenced sequence at the n-th site in the candidate variant region; |mn|>d2, and d2 can be set according to actual needs, d2≥50, for example, d2 can be 50, 60, 80, 100, 150, etc. para2 is a parameter that controls the strictness of screening candidate duplicate variants, which can be adjusted according to specific needs, for example, para2 can be between 1.5 and 10.

[0128] In other words, for a candidate set of repeated mutations, we determine whether each candidate mutation interval in the set satisfies formula (2). If it does, it means that the candidate mutation interval that satisfies formula (2) has indeed undergone repeated mutation.

[0129] Figure 6 This is a schematic diagram illustrating a large segment of chromosomal duplication variation provided in one example of this disclosure. Figure 6 In the diagram, R1 represents the left-hand sequencing read, R2 represents the right-hand sequencing read, and the black triangles indicate the target regions of the genome. For example... Figure 6 As shown, the sequencing depth of the sequencing sequence in the target region within the elliptical frame is relatively large, indicating that the sample to be tested has undergone duplication mutations.

[0130] The chromosome variation detection method provided in this disclosure is a targeted sequencing method. Compared with the WGS method, the targeted sequencing method has greater advantages in sequencing cost and analysis time. Moreover, compared with methods such as RNA-seq that only identify chromosome non-integer ploidy variations for a few specific diseases, the targeted detection results of the method disclosed in this disclosure are more comprehensive (including large chromosome segment duplications, deletions, inversions, etc.). In addition, the method provided in this disclosure obtains a candidate variation set based on the alignment direction of SR, left-end sequencing reads, and right-end sequencing reads, as well as the determination of sequencing depth, and further filters the variation intervals in the candidate variation set, thereby filtering out false positive variations and improving detection accuracy.

[0131] Figure 7 This is a schematic diagram of a chromosome variation detection device provided in some embodiments of this disclosure, which is used to perform the methods described above. Figure 7 As shown, the chromosome variation detection device includes: a sequence acquisition module 10, an alignment module 20, a first detection module 30, and a second detection module 40.

[0132] The sequence acquisition module 10 is configured to acquire the gene sequencing sequence of the sample to be tested.

[0133] The alignment module 20 is configured to align the gene sequencing sequence with the target region of the reference sequence of the reference genome to obtain alignment information; and to sort the gene sequencing sequences aligned to the reference sequence to obtain sorted sequencing sequences.

[0134] The first detection module 30 is configured to perform large-fragment chromosome variation detection on the sorted sequencing sequence based on the alignment information, and obtain the candidate variation type for each candidate variation interval of the sorted sequencing sequence.

[0135] The second detection module 40 is configured to, for at least one candidate mutation type, detect each candidate mutation interval under the candidate mutation type according to a preset rule corresponding to the candidate mutation type, and obtain the mutation result of each candidate mutation interval.

[0136] Figure 8 This is a schematic diagram of an electronic device provided in some embodiments of the present disclosure, such as... Figure 8 As shown, the electronic device 100 includes a memory 101 and a processor 102. The memory 101 stores a computer program, which, when executed by the processor 102, implements the aforementioned method, for example, implementing... Figures 1 to 2 Steps S10 to S40 in the process.

[0137] Electronic device 100 may be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Electronic device 100 may include, but is not limited to, a processor 102 and a memory 101. Those skilled in the art will understand that... Figure 8 This is merely an example of electronic device 100 and does not constitute a limitation on electronic device 100. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 100 may also include input / output devices, network access devices, buses, etc.

[0138] Processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processor 102 may be a microprocessor or any conventional processor.

[0139] The memory 101 can be an internal storage unit of the electronic device 100, such as a hard disk or RAM of the electronic device 100. The memory 101 can also be an external storage device of the electronic device 100, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 100. Furthermore, the memory 101 can include both internal and external storage units of the electronic device 100. The memory 101 is used to store the computer program and other programs and data required by the terminal device. The memory 101 can also be used to temporarily store data that has been output or will be output.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0141] Figure 9 This is a schematic diagram of a computer-readable storage medium provided in some embodiments of this disclosure, such as... Figure 9 As shown, a computer-readable storage medium 200 stores a computer program 201, which, when executed by a processor, implements the above-described method, for example, implementing... Figures 1 to 2 Steps S10 to S40 are described above. The computer-readable storage medium 200 includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0142] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.

Claims

1. A method for detecting chromosome variations, characterized in that, include: Obtain the gene sequencing sequence of the sample to be tested; The gene sequencing sequence is compared with the target region of the reference sequence of the reference genome to obtain the alignment information; The gene sequencing sequences aligned to the reference sequence are sorted to obtain the sorted sequencing sequences; the alignment information includes: the insertion fragment length of the sequencing read pair; Based on the alignment information, large chromosomal fragment variation detection is performed on the sorted sequencing sequences to obtain the candidate variation type for each candidate variation interval of the sorted sequencing sequences; For at least one candidate mutation type, each candidate mutation interval under the candidate mutation type is detected according to the preset rule corresponding to the candidate mutation type, and the mutation result of each candidate mutation interval is obtained. Based on the alignment information, the sorted sequencing sequences are subjected to large-fragment chromosomal variation detection, specifically including: Determine whether the length of the inserted fragment of the sequencing read pair exceeds a preset length range. If so, the interval covered by the sequencing read pair in the target region is taken as the variation interval. Based on the comparison information, determine the candidate mutation type for each mutation interval; The alignment information further includes: information used to characterize whether the left-end sequencing read and the right-end sequencing read in the sequencing read pair are located on the same chromosome; and the alignment direction of the left-end sequencing read and the alignment direction of the right-end sequencing read; Based on the comparison information, the candidate mutation type for each mutation interval is determined, specifically including: Obtain the peak and variance of the insert length for all sequencing fragment pairs; When the left-end and right-end sequencing reads in the sequencing read pair are located on the same chromosome: When the alignment direction of the left-end sequencing read is forward and the alignment direction of the right-end sequencing read is reverse, and Insert_d > Insert_p + N*δ, the candidate variant type of the variant interval corresponding to the sequencing read pair is determined to be a candidate deletion. When the alignment direction of the left-end sequencing read is reversed, the alignment direction of the right-end sequencing read is forward, and Insert_d < Insert_p - N*δ, the candidate variant type of the variant interval corresponding to the sequencing read pair is determined to be a candidate repeat. When the alignment direction of the left-end sequencing read and the alignment direction of the right-end sequencing read are both forward or both reverse, the candidate variant type of the variant interval corresponding to the sequencing read pair is determined to be a candidate inversion. Where Insert_d is the insertion length of the sequencing fragment pair, Insert_p is the peak value, δ is the variance, and N is an integer, 5≤N≤10.

2. The method according to claim 1, characterized in that, The candidate variant type for at least one candidate variant interval is: candidate missing; The preset rule corresponding to the candidate missing is: Obtain the sequencing depth of the sorted sequencing sequence at the i-th and j-th sites in the candidate variant interval, and the average sequencing depth depth_avg in the candidate variant interval; Determine whether formula (1) is true. If it is true, then determine that the candidate mutation interval has a missing mutation: depth_i / depth_avg < para1, and depth_j / depth_avg < para1. Formula (1) Wherein, depth_i is the sequencing depth of the sequenced sequence at the i-th site in the candidate variant interval, and depth_j is the sequencing depth of the sequenced sequence at the j-th site in the candidate variant interval; para1 is between 0 and 1 / 3.

3. The method according to claim 1, characterized in that, The candidate variation type of at least one candidate variation interval is: candidate duplication; The preset rule corresponding to the candidate duplicate is: Obtain the sequencing depth of the sorted sequencing sequence at the m-th and n-th sites in the candidate variant interval, and the average sequencing depth depth_avg in the candidate variant interval; Determine whether formula (2) is true. If it is true, then determine that the candidate variation interval has a candidate duplication: depth_m / depth_avg > para2, and depth_n / depth_avg > para2. Formula (2) Wherein, depth_m is the sequencing depth of the sequenced sequence at the m-th site in the candidate variant interval, and depth_n is the sequencing depth of the sequenced sequence at the n-th site in the candidate variant interval; para2 is between 1.5 and 10.

4. The method according to claim 1, characterized in that, Obtaining the gene sequencing sequence of the sample to be tested includes: Obtain the initial gene sequencing sequence of the sample to be tested; The initial gene sequencing sequence is filtered to obtain the gene sequencing sequence.

5. The method according to claim 1, characterized in that, Filtering the initial gene sequencing sequences specifically includes: Based on preset base quality thresholds and length thresholds, the initial gene sequencing sequence is screened to obtain intermediate sequencing sequences; Remove the adapter sequence from the intermediate sequencing sequence to obtain the gene sequencing sequence.

6. A chromosome variation detection device, characterized in that, include: The sequence acquisition module is configured to acquire the gene sequencing sequence of the sample to be tested; The alignment module is configured to align the gene sequencing sequence with the target region of the reference sequence of the reference genome to obtain alignment information; and to sort the gene sequencing sequences aligned to the reference sequence to obtain sorted sequencing sequences; the alignment information includes: the insertion fragment length of the sequencing read pair; The first detection module is configured to perform large-fragment chromosome variation detection on the sorted sequencing sequence based on the alignment information, and obtain the candidate variation type for each candidate variation interval of the sorted sequencing sequence. The second detection module is configured to, for at least one candidate mutation type, detect each candidate mutation interval under the candidate mutation type according to a preset rule corresponding to the candidate mutation type, and obtain the mutation result of each candidate mutation interval; The first detection module is configured to determine whether the length of the inserted fragment of the sequencing read pair exceeds a preset length range; if so, the interval covered by the sequencing read pair in the target region is taken as a variant interval; and determine the candidate variant type of each variant interval based on the alignment information; wherein the alignment information further includes: information for characterizing whether the left-end sequencing read and the right-end sequencing read in the sequencing read pair are located on the same chromosome; and the alignment direction of the left-end sequencing read and the alignment direction of the right-end sequencing read; Based on the comparison information, the candidate mutation type for each mutation interval is determined, specifically including: Obtain the peak and variance of the insert length for all sequencing fragment pairs; When the left-end and right-end sequencing reads in the sequencing read pair are located on the same chromosome: When the alignment direction of the left-end sequencing read is forward and the alignment direction of the right-end sequencing read is reverse, and Insert_d > Insert_p + N*δ, the candidate variant type of the variant interval corresponding to the sequencing read pair is determined to be a candidate deletion. When the alignment direction of the left-end sequencing read is reversed, the alignment direction of the right-end sequencing read is forward, and Insert_d < Insert_p - N*δ, the candidate variant type of the variant interval corresponding to the sequencing read pair is determined to be a candidate repeat. When the alignment direction of the left-end sequencing read and the alignment direction of the right-end sequencing read are both forward or both reverse, the candidate variant type of the variant interval corresponding to the sequencing read pair is determined to be a candidate inversion. Where Insert_d is the insertion length of the sequencing fragment pair, Insert_p is the peak value, δ is the variance, and N is an integer, 5≤N≤10.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it implements the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 5.

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