Method, system and equipment for detecting internal tandem repetition and storage medium

Abstract

The invention discloses a method, a system and equipment for detecting internal tandem repeat and a storage medium, and the key points of the technical scheme are as follows: obtaining a first reference sequence according to at least one target exon sequence corresponding to a protooncogene, and obtaining a second reference sequence according to at least one target intron sequence corresponding to the protooncogene; comparing the sequencing data of the to-be-detected sample to the second reference sequence to obtain a first comparison result, extracting an uncompared sequence from the sequencing data according to the first comparison result, and comparing the uncompared sequence to the first reference sequence to obtain a second comparison result; and determining a first detection result according to the second comparison result and a first reference sequence, and performing false positive filtering on the first detection result to obtain a second detection result. According to the invention, false positive can be reduced so as to ensure the accuracy and reliability of subsequent analysis.

Description

Technical Field

[0001] This invention belongs to the field of gene sequencing technology, specifically relating to a method, system, device, and storage medium for detecting internal tandem repeats. Background Technology

[0002] Internal tandem duplication (ITD) is usually caused by repetitive sequences within a gene. ITD is a common mutation type, such as a common mutation type in FMS-like tyrosine kinase 3 (FLT3).

[0003] In the process of detecting internal tandem repeats, sequencing data is usually obtained by sequencing the sample. However, when using sequencing platforms, most sequencing platforms produce short reads, which leads to false positives for INDEL and false negatives for ITD. In addition, in the "wet experiment" process of transcriptome sequencing, incomplete digestion of gDNA can cause gDNA contamination, resulting in the sequencing data containing some genomic DNA, which can cause false positives for ITD. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device, and storage medium for detecting internal tandem repeats, reducing false positives and false negatives to ensure the accuracy and reliability of subsequent analyses, while avoiding missed detections.

[0005] The first aspect of the present invention provides a method for detecting internal tandem repeats, comprising:

[0006] The first reference sequence is obtained based on at least one target exon sequence corresponding to the proto-oncogene, and the second reference sequence is obtained based on at least one intron sequence corresponding to the proto-oncogene.

[0007] The sequencing data of the sample to be tested is aligned to the second reference sequence to obtain the first alignment result. Based on the first alignment result, the unaligned sequence is extracted from the sequencing data and aligned to the first reference sequence to obtain the second alignment result.

[0008] The first detection result is determined based on the second comparison result and the first reference sequence. False positives are filtered out from the first detection result to obtain the second detection result.

[0009] In some implementations, determining the first detection result based on the second alignment result and the first reference sequence includes:

[0010] The target soft shearing sequence is determined based on the second alignment result;

[0011] A first candidate breakpoint is determined based on the target soft shearing sequence, and a first candidate repeating sequence is determined based on the first candidate breakpoint, the second alignment result, and the first reference sequence.

[0012] A first local reference sequence is constructed based on the first candidate repeating sequence, the first candidate breakpoint, and the first reference sequence. A second candidate breakpoint is selected from the first candidate breakpoints based on the second alignment result and the first local reference sequence.

[0013] A local alignment algorithm is used to calculate the allele frequency of each second candidate repeat sequence at the second candidate breakpoint, wherein the second candidate repeat sequence is the first candidate repeat sequence corresponding to the second candidate breakpoint; the second candidate repeat sequences are filtered to obtain internal tandem repeat sequences, and the internal tandem repeat sequences are used as the first detection result.

[0014] In some implementations, determining the first candidate repeating sequence based on the first candidate breakpoint, the second alignment result, and the first reference sequence includes:

[0015] Determine the search direction based on the first candidate breakpoint;

[0016] From the first candidate breakpoint along the search direction, search for a soft shear sequence from the second alignment results within a window of a preset length;

[0017] The searched soft-splitting sequence is matched with the first reference sequence for similarity, and the successfully matched soft-splitting sequence is used as the first candidate repeat sequence.

[0018] In some implementations, constructing a first local reference sequence based on the first candidate repeating sequence, the first candidate breakpoint, and the first reference sequence includes:

[0019] The correct alignment sequence is determined from the first reference sequence based on the second comparison result;

[0020] The splicing point of the correctly aligned sequence is determined based on the first candidate breakpoint;

[0021] The first candidate repeating sequence and the correctly aligned sequence are spliced ​​together according to the splicing point to obtain the first local reference sequence.

[0022] In some implementations, selecting a second candidate breakpoint from the first candidate breakpoints based on the second alignment result and the first local reference sequence includes:

[0023] Based on the second comparison result and the first local reference sequence, the number of alignment sequences in the first local reference sequence that are aligned with the first candidate breakpoint is calculated.

[0024] Compare the number of alignment sequences of the first candidate breakpoint with the number threshold, and select the first candidate breakpoint whose number of alignment sequences is not less than the number threshold as the second candidate breakpoint.

[0025] In some implementations, the step of using a local alignment algorithm on the second candidate repeat sequence to calculate the allele frequency of the second candidate breakpoint includes:

[0026] Extract all reads within the genomic region corresponding to the second candidate repeat sequence as candidate reads;

[0027] Each candidate read segment is compared with the first reference sequence to obtain a first comparison score;

[0028] Each candidate read segment is compared with the first local reference sequence to obtain a second comparison score;

[0029] Calculate the score difference between each second alignment score and the corresponding first alignment score, and determine whether each candidate read segment is a supporting sequence based on each score difference;

[0030] The number of supporting sequences corresponding to the second candidate breakpoint is counted to obtain the number of target sequences. The allele frequency of the second candidate breakpoint is calculated based on the number of target sequences and the sequencing depth.

[0031] In some implementations, filtering for false positives in the first detection result to obtain the second detection result includes:

[0032] The first detection result is filtered for false positives based on a preset blacklist database to obtain the filtered detection result.

[0033] The filtered test results are then filtered based on the batch frequency and total sample frequency of historical test samples to obtain a second test result.

[0034] A second aspect of the present invention provides a system for detecting internal series repetitions, comprising:

[0035] The reference sequence generation module is used to obtain a first reference sequence based on at least one exon sequence corresponding to the proto-oncogene and a second reference sequence based on at least one intron sequence corresponding to the proto-oncogene.

[0036] The data alignment module is used to align the sequencing data of the sample to be tested to a second reference sequence to obtain a first alignment result, extract unaligned sequences from the sequencing data based on the first alignment result, and align the unaligned sequences to the first reference sequence to obtain a second alignment result.

[0037] The result detection module is used to determine a first detection result based on the second alignment result and the first reference sequence, filter false positives from the first detection result, and obtain a second detection result.

[0038] A third aspect of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0039] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described above.

[0040] The technical solution provided by this invention has the following advantages and effects: by aligning sequencing data to a second reference sequence and then extracting unaligned sequences from the sequencing data that are not aligned to the second reference sequence, the residual DNA sequences in the transcriptome data can be removed, that is, the false soft splicing signals caused by intron sequences are removed, greatly reducing false positives and ensuring the accuracy and reliability of subsequent analysis; by filtering the first detection results for false positives, the false positives in the detection results are further reduced. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the method for detecting internal series repetitions provided by the present invention;

[0042] Figure 2 This is a structural block diagram of the detection system for internal series repetition provided by the present invention;

[0043] Figure 3 This is an internal structural diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0044] To facilitate understanding of the present invention, specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0045] Unless otherwise specified or defined, the terms "first," "second," etc., used in this document are for distinguishing names only and do not represent a specific number or order.

[0046] Unless otherwise stated or defined, the term “and / or” as used herein includes any and all combinations of one or more of the related listed items.

[0047] It should be noted that in this article, "fixed to" or "connected to" can mean directly fixed to or connected to a component, or indirectly fixed to or connected to a component.

[0048] like Figure 1As shown, this embodiment provides a method for detecting internal tandem repeats, including the following steps S1~S3:

[0049] Step S1: Obtain the first reference sequence based on at least one target exon sequence corresponding to the proto-oncogene, and obtain the second reference sequence based on at least one intron sequence corresponding to the proto-oncogene.

[0050] In practical applications, extracting at least one target exon sequence from the human reference genome based on a proto-oncogene involves using the exon sequence of the tandem repeat region within the proto-oncogene as the target exon sequence. If the proto-oncogene corresponds to only one target exon sequence, that target exon sequence is the first reference sequence. If the proto-oncogene corresponds to multiple target exon sequences, these multiple target exon sequences are spliced ​​together to obtain the first reference sequence. For example, if the proto-oncogene is FLT3, the corresponding target exon sequences are exons 14 and 15, and exons 14 and 15 are spliced ​​together to obtain the first reference sequence.

[0051] Based on the transcript number of the proto-oncogene, extract at least one target intron sequence corresponding to the proto-oncogene. If the proto-oncogene corresponds to only one target intron sequence, then that target intron sequence is the second reference sequence. If the proto-oncogene corresponds to multiple target intron sequences, then the multiple target intron sequences are spliced ​​together to obtain the second reference sequence. For example, if the proto-oncogene is FLT3, then the corresponding target intron sequences are intron sequences 14 and 15, and intron sequences 14 and 15 are spliced ​​together to obtain the second reference sequence.

[0052] Step S2: Align the sequencing data of the sample to be tested to the second reference sequence to obtain the first alignment result. Based on the first alignment result, extract the unaligned sequence from the sequencing data and align the unaligned sequence to the first reference sequence to obtain the second alignment result.

[0053] In practical applications, the test sample is total RNA extracted from peripheral blood or bone marrow aspirate. The total RNA is specifically transcribed into cDNA, which is then amplified by PCR (Polymerase Chain Reaction), purified using magnetic beads, and followed by cDNA library construction, purification, and sequencing. Sequencing data is obtained by quality control of the sequencing data. By aligning the sequencing data to a second reference sequence and then extracting unaligned sequences from the sequencing data, residual DNA sequences in the transcriptome data can be removed. This eliminates false soft-splitting signals caused by intron sequences, significantly reducing false positives and ensuring the accuracy and reliability of subsequent analyses. The unaligned sequences are converted into FASTQ files, and then the unaligned sequences converted into FASTQ files are aligned to the first reference sequence. PCR repetitive sequences are marked and removed. Then, local realignment is performed on potential variable regions, which can be soft-splitting sequencing reads. The quality score of the sequencing data after local realignment is then corrected to eliminate systematic bias in the original quality score, resulting in a BAM file, which is the second alignment result.

[0054] Step S3: Determine the first detection result based on the second comparison result and the first reference sequence, filter the first detection result for false positives, and obtain the second detection result.

[0055] Further, determining the first detection result based on the second alignment result and the first reference sequence includes:

[0056] The target soft shearing sequence is determined based on the second alignment result;

[0057] A first candidate breakpoint is determined based on the target soft shearing sequence, and a first candidate repeating sequence is determined based on the first candidate breakpoint, the second alignment result, and the first reference sequence.

[0058] A first local reference sequence is constructed based on the first candidate repeating sequence, the first candidate breakpoint, and the first reference sequence. A second candidate breakpoint is selected from the first candidate breakpoints based on the second alignment result and the first local reference sequence.

[0059] A local alignment algorithm is used on each second candidate repeat sequence to calculate the allele frequency of each second candidate repeat sequence at the second candidate breakpoint, wherein the second candidate repeat sequence is the first candidate repeat sequence corresponding to the second candidate breakpoint; the second candidate repeat sequence is filtered to obtain the filtered sequence, and the internal tandem repeat sequence is obtained, and the internal tandem repeat sequence is used as the first detection result.

[0060] In practical applications, a portion of the sequencing sequence containing internal tandem repeats can align to the first reference sequence. However, due to "repeated insertions," another portion of the sequencing sequence may not align completely to the first reference sequence. When the repeat fragment is long or the insertion position is complex, the alignment algorithm will "cut" part of the sequence, resulting in soft cutting of internal tandem repeat reads onto the first reference sequence. The sequencing sequence exhibiting soft cutting is then identified as the soft-cut sequence. The algorithm scans the proto-oncogene region of the second alignment result for soft-cut sequences, identifying those with a soft-cut length greater than or equal to a length threshold (e.g., 10 bp) as the target soft-cut sequence. After obtaining the target soft-cut sequence, the location where the soft cutting occurred is marked as the first candidate breakpoint.

[0061] Further, determining the first candidate repeating sequence based on the first candidate breakpoint, the second alignment result, and the first reference sequence includes:

[0062] Determine the search direction based on the first candidate breakpoint;

[0063] From the first candidate breakpoint along the search direction, search for a soft shear sequence from the second alignment results within a window of a preset length;

[0064] The searched soft-splitting sequence is matched with the first reference sequence for similarity, and the successfully matched soft-splitting sequence is used as the first candidate repeat sequence.

[0065] In practical applications, if the first candidate breakpoint is at the 5' end of the target soft-splitting sequence (i.e., the 3' end sequence of the target soft-splitting sequence is correctly aligned and soft splicing occurs at the 5' end), the search direction is downstream. Starting from the first candidate breakpoint, the search proceeds downstream within a window of a preset length (e.g., 1000 bp). During the search, a dynamic programming algorithm is used for similarity matching. This similarity matching setting allows for mismatches, indels, or ITDs (internal tandem duplications) introduced during amplification, which may have slight differences from their original template. If a highly similar soft-splitting sequence is detected in the first reference sequence, this soft-splitting sequence is considered a potential ITD and recorded as the first candidate repeat sequence. Similarly, if the first candidate breakpoint is at the 3' end of the target soft-splitting sequence (i.e., the 5' end sequence of the target soft-splitting sequence is correctly aligned and soft splicing occurs at the 3' end), the search direction is upstream. Starting from the first candidate breakpoint, a soft-splitting sequence is searched upstream within a window of a preset length. During the search, a dynamic programming algorithm is used for similarity matching. If a highly similar soft-splitting sequence is detected in the first reference sequence, it is recorded as the first candidate repeating sequence. By searching for soft-splitting sequences again from the second alignment results based on the first candidate breakpoint, the utilization rate of comparison information is improved, and missed detections are prevented.

[0066] Further, the step of constructing a first local reference sequence based on the first candidate repeating sequence, the first candidate breakpoint, and the first reference sequence includes:

[0067] The correct alignment sequence is determined from the first reference sequence based on the second comparison result;

[0068] The splicing point of the correctly aligned sequence is determined based on the first candidate breakpoint;

[0069] The first candidate repeating sequence and the correctly aligned sequence are spliced ​​together according to the splicing point to obtain the first local reference sequence.

[0070] In practical applications, from the second alignment results, a read segment that can continuously and correctly match the first reference sequence is selected, and this read segment is taken as the correct alignment sequence. The splicing point of the correct alignment sequence can be determined based on the first candidate breakpoint, and the first candidate breakpoint is taken as the splicing point of the correct alignment sequence. After searching within a window of a preset length to obtain the first candidate repeating sequence, the first candidate repeating sequence and the correct alignment sequence are spliced ​​together according to the splicing point. For example, if two correct alignment sequences are selected, namely correct alignment sequence A and correct alignment sequence B, and the first candidate breakpoint is located between correct alignment sequence A and correct alignment sequence B, then the resulting local rough sequence is [correct alignment sequence A] + [first candidate repeating sequence] + [correct alignment sequence B].

[0071] By constructing a first local reference sequence, potential ITD sequences (i.e., first candidate repetitive sequences) from downstream or upstream sources are spliced ​​with correctly aligned sequences to construct a reference sequence containing the ITD. Because some sequencing sequences containing ITDs are of low quality or have ambiguous ITD boundaries, very short matching portions may occur during the alignment of unaligned sequences to the first reference sequence. These matching portions may be smaller than the length threshold. By re-aligning using the first local reference sequence, sequencing sequences smaller than the length threshold can be matched with the reference sequence. This allows for the screening of more sequencing sequences supporting the same ITD, reducing false negatives and improving detection sensitivity and accuracy.

[0072] Further, the step of selecting a second candidate breakpoint from the first candidate breakpoints based on the second alignment result and the first local reference sequence includes:

[0073] Based on the second comparison result and the first local reference sequence, the number of alignment sequences in the first local reference sequence that are aligned with the first candidate breakpoint is calculated.

[0074] Compare the number of alignment sequences of the first candidate breakpoint with the number threshold, and select the first candidate breakpoint whose number of alignment sequences is not less than the number threshold as the second candidate breakpoint.

[0075] In practical applications, the sequencing sequence in the second alignment result is compared with the first local reference sequence. The number of aligned sequences that align with the first candidate breakpoint is calculated, and the threshold for this number can be set to 3. The first candidate breakpoint with at least 3 aligned sequences is selected as the second candidate breakpoint. By setting the threshold to 3, the first candidate breakpoint with more than 3 aligned sequences is selected, increasing the possibility that the aligned sequences originate from multiple different original RNA molecules. This eliminates false positive signals caused by random sequencing errors, rare alignment errors, or single PCR errors, preventing misjudgment of ITD and ensuring high confidence.

[0076] Further, the calculation of the allele frequency of the second candidate breakpoint using a local alignment algorithm on the second candidate repeat sequence includes:

[0077] Extract all sequencing sequences within the genomic region corresponding to the second candidate repeat sequence as candidate sequencing sequences;

[0078] Each of the candidate sequencing sequences is compared with the first reference sequence to obtain a first alignment score;

[0079] Each of the candidate sequencing sequences is aligned with a second local reference sequence to obtain a second alignment score; wherein, the second local reference sequence is the first local reference sequence corresponding to the second candidate repeat sequence;

[0080] Calculate the score difference between each second alignment score and the corresponding first alignment score, and determine whether each candidate sequencing sequence is a supporting sequence based on each score difference;

[0081] The number of supporting sequences corresponding to the second candidate breakpoint is counted to obtain the number of target sequences. The allele frequency of the second candidate breakpoint is calculated based on the number of target sequences and the sequencing depth.

[0082] In practical applications, during PCR amplification, DNA polymerase may introduce single-base mismatches or minor insertions / deletions when replicating the ITD sequence, and the sequencer itself may also generate random base errors. As a result, the sequences of multiple PCR copies of the same original RNA molecule with the same true ITD may have subtle differences. Therefore, there may be multiple different second candidate repeat sequences at the second candidate breakpoint. All sequencing sequences in the genomic region corresponding to each second candidate repeat sequence are extracted from the second alignment results, and the sequencing sequence is used as the candidate sequencing sequence. All candidate sequencing sequences corresponding to each second candidate repeat sequence are sequentially aligned to the first reference sequence and the second local reference sequence corresponding to each second candidate repeat sequence. The first alignment score and the second alignment score of all candidate sequencing sequences corresponding to each second candidate repeat sequence are obtained. The score difference between the second alignment score and the corresponding first alignment score is calculated, which is obtained by subtracting the first alignment score from the second alignment score of the candidate sequencing sequence. The score difference is compared with a score difference threshold. If the score difference is greater than the threshold, the candidate sequencing sequence is determined to be a supporting sequence. Then, the number of supporting sequences at the second candidate breakpoint for each second candidate repeat sequence is counted. The number of supporting sequences at the second candidate breakpoint for each second candidate repeat sequence is divided by the sequencing depth to calculate the allele frequency of each second candidate repeat sequence at the second candidate breakpoint. Calculating the allele frequency improves the quantitative accuracy.

[0083] Specifically, after calculating the allele frequency of each second candidate repeat sequence at the second candidate breakpoint, the second candidate repeat sequences are filtered according to the allele frequency at the second candidate breakpoint. At the same candidate breakpoint, the allele frequencies corresponding to each second candidate repeat sequence are compared, and the second candidate repeat sequence with the highest allele frequency, i.e. the highest number of target sequences, is taken as the internal tandem repeat sequence to obtain the first detection result.

[0084] Furthermore, when the repetitive fragments in the sequencing sequence are short or the insertion position is simple, it usually shows as an Indel insertion in the second alignment result. For sequencing sequences showing Indel insertion, Indel mutation detection is performed on the second alignment result to obtain the mutation detection result. False positive Indel mutations are filtered out from the mutation detection result to obtain the Indel insertion detection result. If the second alignment result is 50M24I76M, it means that there are 50 base matches at the 5' end, 76 base matches at the 3' end, and 24 bases are inserted. At this time, the first candidate breakpoint is in the middle, that is, both the 3' end sequence and the 5' end sequence are correctly aligned. Then the correctly aligned sequence is the correctly aligned sequence of the 5' end sequence and the correctly aligned sequence of the 3' end. [The correctly aligned sequence of the 5' end sequence] + [the first candidate repetitive sequence] + [the correctly aligned sequence of the 3' end sequence], which gives the first local reference sequence. Based on the first local reference sequence and the second alignment result, it is determined whether the first candidate breakpoint is a second candidate breakpoint. If so, a local alignment algorithm is used on the second candidate repeat sequence corresponding to the first candidate breakpoint to calculate the allele frequency of the second candidate repeat sequence at the corresponding second candidate breakpoint. At the second candidate breakpoint, the allele frequencies of each second candidate repeat sequence corresponding to the second candidate breakpoint are compared. The second candidate repeat sequence with the highest allele frequency, i.e., the highest number of target sequences, is taken as the internal tandem repeat sequence, and the Indel insertion detection result is obtained. The Indel insertion detection result is merged into the first detection result. By detecting Indel mutations and filtering false positives, it is possible to detect internal tandem repeat sequences with short repeat fragments or simple insertion positions, thereby reducing missed detections.

[0085] Further, the step of filtering false positives from the first detection result to obtain the second detection result includes:

[0086] The first detection result is filtered for false positives based on a preset blacklist database to obtain the filtered detection result.

[0087] The filtered test results are then filtered based on the batch frequency and total sample frequency of historical test samples to obtain a second test result.

[0088] In practical applications, false positive sites corresponding to recurring proto-oncogenes in clinical sequencing results are pre-analyzed. A blacklist is established based on these false positive sites. The first test result is then filtered against this blacklist to obtain the filtered test result. Clinical sequencing results are pre-stored in a database, such as PostgreSQL. Based on the internal tandem repeat (ITD) variation information of proto-oncogenes in the database, the batch frequency and total sample frequency of ITDs in each clinical sequencing result are calculated. The batch frequency represents the proportion of samples with the exact same ITD in the current batch, while the total sample frequency represents the proportion of samples with the exact same ITD in all historical samples in the database. The filtered test result is then further filtered based on the batch frequency and total sample frequency to obtain the second test result. Filtered test results with batch frequencies exceeding a batch frequency threshold and those with total sample frequencies exceeding a total sample frequency threshold are considered false positives. All false positives are removed from the filtered test result to obtain the second test result.

[0089] The method for detecting internal tandem repeats of the present invention removes residual DNA sequences in transcriptome data by aligning sequencing data to a second reference sequence and then extracting unaligned sequences from the sequencing data. This removes false soft-splitting signals caused by intron sequences, greatly reducing false positives and ensuring the accuracy and reliability of subsequent analyses. Furthermore, by filtering the first detection results for false positives, the false positives in the detection results are further reduced.

[0090] like Figure 2 As shown, this embodiment of the invention also provides a system for detecting internal series repetitions, including:

[0091] The reference sequence generation module 10 is used to obtain a first reference sequence based on at least one exon sequence corresponding to the proto-oncogene and a second reference sequence based on at least one intron sequence corresponding to the proto-oncogene.

[0092] The data alignment module 20 is used to align the sequencing data of the sample to be tested to a second reference sequence to obtain a first alignment result, extract unaligned sequences from the sequencing data based on the first alignment result, and align the unaligned sequences to the first reference sequence to obtain a second alignment result.

[0093] The result detection module 30 is used to determine a first detection result based on the second comparison result and the first reference sequence, filter false positives from the first detection result, and obtain a second detection result.

[0094] The various modules of the aforementioned internal serial repetitive detection system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules and units can be embedded in hardware within or independently of the processor in a computer device, or stored in software within the memory of the computer device, so that the processor can invoke and execute the corresponding operations of each module.

[0095] like Figure 3 As shown, an embodiment of the present invention discloses a computer device, including a memory and a processor, wherein the memory stores a computer program;

[0096] The computer device can be a server, and its internal structure diagram can be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the method for detecting internal serial repetitions described in the above embodiments.

[0097] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0098] This invention also discloses a computer-readable storage medium storing a computer program that causes a computer to perform the method for detecting internal serial repetitions described in the above embodiments.

[0099] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0100] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for detecting internal series repetitions, characterized in that, include: The first reference sequence is obtained based on at least one target exon sequence corresponding to the proto-oncogene, and the second reference sequence is obtained based on at least one target intron sequence corresponding to the proto-oncogene. The sequencing data of the sample to be tested is aligned to the second reference sequence to obtain the first alignment result. Based on the first alignment result, the unaligned sequence is extracted from the sequencing data and aligned to the first reference sequence to obtain the second alignment result. The first detection result is determined based on the second comparison result and the first reference sequence. False positives are filtered out from the first detection result to obtain the second detection result.

2. The method for detecting internal tandem repeats as described in claim 1, characterized in that, The step of determining the first detection result based on the second alignment result and the first reference sequence includes: The target soft shearing sequence is determined based on the second alignment result; A first candidate breakpoint is determined based on the target soft shearing sequence, and a first candidate repeating sequence is determined based on the first candidate breakpoint, the second alignment result, and the first reference sequence. A first local reference sequence is constructed based on the first candidate repeating sequence, the first candidate breakpoint, and the first reference sequence. A second candidate breakpoint is selected from the first candidate breakpoints based on the second alignment result and the first local reference sequence. A local alignment algorithm is used to calculate the allele frequency of each second candidate repeat sequence at the second candidate breakpoint, wherein the second candidate repeat sequence is the first candidate repeat sequence corresponding to the second candidate breakpoint; the second candidate repeat sequences are filtered to obtain internal tandem repeat sequences, and the internal tandem repeat sequences are used as the first detection result.

3. The method for detecting internal tandem repeats as described in claim 2, characterized in that, The step of determining the first candidate repeating sequence based on the first candidate breakpoint, the second alignment result, and the first reference sequence includes: Determine the search direction based on the first candidate breakpoint; From the first candidate breakpoint along the search direction, search for a soft shear sequence from the second alignment results within a window of a preset length; The searched soft-splitting sequence is matched with the first reference sequence for similarity, and the successfully matched soft-splitting sequence is used as the first candidate repeat sequence.

4. The method for detecting internal tandem repeats as described in claim 2, characterized in that, The step of constructing a first local reference sequence based on the first candidate repeating sequence, the first candidate breakpoint, and the first reference sequence includes: The correct alignment sequence is determined from the first reference sequence based on the second comparison result; The splicing point of the correctly aligned sequence is determined based on the first candidate breakpoint; The first candidate repeating sequence and the correctly aligned sequence are spliced ​​together according to the splicing point to obtain the first local reference sequence.

5. The method for detecting internal tandem repeats as described in claim 2, characterized in that, The step of selecting a second candidate breakpoint from the first candidate breakpoints based on the second comparison result and the first local reference sequence includes: Based on the second comparison result and the first local reference sequence, the number of alignment sequences in the first local reference sequence that are aligned with the first candidate breakpoint is calculated. Compare the number of alignment sequences of the first candidate breakpoint with the number threshold, and select the first candidate breakpoint whose number of alignment sequences is not less than the number threshold as the second candidate breakpoint.

6. The method for detecting internal tandem repeats as described in claim 2, characterized in that, The local alignment algorithm is used to calculate the allele frequency of the second candidate breakpoint for the second candidate repeat sequence, including: Extract all reads within the genomic region corresponding to the second candidate repeat sequence as candidate reads; Each candidate read segment is compared with the first reference sequence to obtain a first comparison score; Each candidate read segment is compared with the first local reference sequence to obtain a second comparison score; Calculate the score difference between each second alignment score and the corresponding first alignment score, and determine whether each candidate read segment is a supporting sequence based on each score difference; The number of supporting sequences corresponding to the second candidate breakpoint is counted to obtain the number of target sequences. The allele frequency of the second candidate breakpoint is calculated based on the number of target sequences and the sequencing depth.

7. The method for detecting internal tandem repeats as described in any one of claims 1-6, characterized in that, The step of filtering false positives from the first detection result to obtain the second detection result includes: The first detection result is filtered for false positives based on a preset blacklist database to obtain the filtered detection result. The filtered test results are then filtered based on the batch frequency and total sample frequency of historical test samples to obtain a second test result.

8. A detection system for internal series repeating, characterized in that, include: The reference sequence generation module is used to obtain a first reference sequence based on at least one exon sequence corresponding to the proto-oncogene and a second reference sequence based on at least one intron sequence corresponding to the proto-oncogene. The data alignment module is used to align the sequencing data of the sample to be tested to a second reference sequence to obtain a first alignment result, extract unaligned sequences from the sequencing data based on the first alignment result, and align the unaligned sequences to the first reference sequence to obtain a second alignment result. The result detection module is used to determine a first detection result based on the second alignment result and the first reference sequence, filter false positives from the first detection result, and obtain a second detection result.

9. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-7.

10. 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 steps of the method according to any one of claims 1-7.

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