Gene mutation detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a method for detecting gene mutation. The method comprises the following steps: acquiring a sequence read section which is compared with a reference genome; multiple assembling operations are executed to obtain multiple assembling results, each assembling operation comprises the steps that the sequence reads obtained after comparison are assembled, the assembling conditions of different assembling operations are different, and the assembling conditions comprise the fact that the number of the sequence reads obtained after comparison reaches a threshold value; performing first mutation detection on the multiple assembling results to obtain a plurality of first mutation information files; the mutation information file is analyzed, a gene mutation detection result is obtained, and the mutation information file comprises a plurality of first mutation information files. According to the detection method disclosed by the invention, a local assembly method is adopted, a global assembly process of assembling all reads is omitted, the influence of repeated reads on mutation detection in the global assembly process is reduced, and a multi-time assembly method is adopted, so that missing detection is prevented, and the accuracy of mutation detection is improved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing, and more particularly to a method and apparatus for detecting gene mutations, electronic devices, and storage media. Background Technology

[0002] Multiplex polymerase chain reaction (MPCR) is a technique that simultaneously amplifies multiple targets using a single polymerase chain reaction (PCR) reaction, and then detects the amplified products using specific detection methods to achieve diagnosis of multiple targets. Due to its high efficiency, high throughput, and low cost, MPCR technology has been extensively studied and widely applied in scientific research and disease diagnosis.

[0003] In the mutation detection process, existing MPCR products randomly assign sequence reads from the same reference genome to regions on the reference genome that are at different locations but have the same alignment score, resulting in a large number of duplicate reads. Then, all reads on the same reference genome are first globally assembled, and then mutation detection is performed on the results of the global assembly.

[0004] Therefore, the method of assembling reads globally first and then performing mutation detection cannot eliminate the interference of duplicate reads, which leads to a certain probability of false negatives or false positives in mutation detection during gene sequencing, resulting in a lower accuracy of gene sequencing. Summary of the Invention

[0005] This disclosure provides a method, apparatus, electronic device, and storage medium for gene mutation detection. Its main objective is to address the problem in existing technologies that employ a method of globally assembling reads before mutation detection. This method cannot eliminate interference from duplicate reads, leading to a certain probability of false negatives or false positives in gene sequencing mutation detection, and consequently, lower accuracy in gene sequencing.

[0006] According to a first aspect of this disclosure, a method for detecting gene mutations is provided, comprising:

[0007] Obtain sequence reads aligned with the reference genome;

[0008] Multiple assembly operations are performed to obtain multiple assembly results. Each assembly operation includes assembling the aligned sequence reads. The assembly conditions for different assembly operations are different, including the number of aligned sequence reads reaching a threshold.

[0009] The first mutation detection was performed on each of the multiple assembly results to obtain multiple first mutation information files;

[0010] The mutation information file is analyzed to obtain gene mutation detection results. The mutation information file includes multiple first mutation information files.

[0011] Alternatively, methods for detecting gene mutations may also include:

[0012] A second mutation detection is performed on the aligned sequence reads to obtain a second mutation information file;

[0013] The mutation information file also includes the second mutation information file.

[0014] Optionally, the mutation information file is analyzed to obtain gene mutation detection results, including:

[0015] If at least two files in the mutation information file contain the same site, then the same site is identified as the first target mutation site, and the gene mutation detection result is obtained.

[0016] Optionally, analyzing the mutation information file to obtain gene mutation detection results further includes:

[0017] If there are different sites among the files in the mutation information file, but the different sites are in the same region, then the mutation information file is compared with the gene mutation database to obtain the gene mutation detection result.

[0018] Optionally, the mutation information file is compared with a gene mutation database to obtain gene mutation detection results, including:

[0019] If at least two different sites in the mutation information file are located in the same target region as known mutation sites in the gene mutation database, then the known mutation sites are identified as the second target mutation sites, and gene mutation detection results are obtained.

[0020] Optionally, comparing the mutation information file with a gene mutation database to obtain gene mutation detection results further includes:

[0021] If none of the different sites in the mutation information file are in the same target region as the known mutation sites, then the site with the highest support for mutation depth among the different sites is determined as the second target mutation site, and the gene mutation detection result is obtained.

[0022] Optionally, the multiple assembly operations are two assembly operations, performing two assembly operations to obtain two assembly results;

[0023] The two assembly results were subjected to a first mutation detection to obtain two first mutation information files.

[0024] Optionally, in the two assembly operations, the first assembly operation is performed first, followed by the second assembly operation;

[0025] The number of the aligned sequence reads extracted in the first assembly operation and the second assembly operation is divided into a first preset threshold and a second preset threshold;

[0026] The first preset threshold is greater than the second preset threshold.

[0027] Optionally, obtain sequence reads aligned with the reference genome, including:

[0028] An optimized sequence alignment algorithm is invoked to align each sequence read with the reference genome, thereby obtaining a similarity score for each sequence read in each region of the reference genome. The optimized sequence alignment algorithm is obtained by optimizing alignment parameters in a preset sequence alignment algorithm.

[0029] Each sequence read is assigned to the region with the highest similarity score in the reference genome to obtain the aligned sequence read.

[0030] Optionally, the optimized alignment parameters include a minimum sequence matching length parameter and a sequence read insertion size parameter, and the optimized sequence alignment algorithm includes a data transformation algorithm and a data structure alignment algorithm;

[0031] The optimized sequence alignment algorithm is invoked to align each sequence read with a reference genome, obtaining similarity scores between the sequence read and various regions of the reference genome, including:

[0032] The data transformation algorithm is invoked to construct the genome index of the reference genome;

[0033] The data structure alignment algorithm is invoked to align the sequence read with the genome index of the reference genome based on the minimum sequence matching length parameter and the sequence read insertion size parameter, thereby obtaining the similarity score of the sequence read in each region of the reference genome.

[0034] According to a second aspect of this disclosure, a gene mutation detection device is provided, comprising:

[0035] The acquisition unit acquires sequence reads aligned with the reference genome.

[0036] The assembly unit performs multiple assembly operations to obtain multiple assembly results. Each assembly operation includes assembling the aligned sequence reads. The assembly conditions for different assembly operations are different, including the number of aligned sequence reads reaching a threshold.

[0037] The first detection unit performs a first mutation detection on each of the multiple assembly results to obtain multiple first mutation information files;

[0038] The first analysis unit analyzes the mutation information file to obtain gene mutation detection results. The mutation information file includes multiple first mutation information files.

[0039] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0040] At least one processor; and

[0041] A memory communicatively connected to the at least one processor; wherein,

[0042] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the detection method described in the first aspect of this disclosure.

[0043] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing the computer to perform the detection method described in the first aspect of this disclosure.

[0044] The gene mutation detection method provided in this disclosure adopts a local assembly method, which eliminates the global assembly process of assembling all reads, reduces the impact of repeated reads on mutation detection during the global assembly process, and the multiple assembly method of this disclosure prevents missed detection, accurately detects mutation sites, and improves the accuracy of mutation detection.

[0045] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0046] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0047] Figure 1 This is a schematic flowchart of a gene mutation detection method provided in an embodiment of the present disclosure;

[0048] Figure 2This is a schematic flowchart of the comparison method provided in the embodiments of this disclosure;

[0049] Figure 3 A schematic diagram of a comparison algorithm provided in an embodiment of this disclosure;

[0050] Figure 4 This is a schematic diagram of the structure of a gene mutation detection device provided in an embodiment of the present disclosure;

[0051] Figure 5 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0052] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0053] In this disclosure, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this disclosure is not necessarily to be construed as preferred or advantageous over other embodiments. Prefixes such as "first," "second," etc., in this disclosure are merely for distinguishing different descriptive objects and do not constitute limitations on the position, order, priority, number, or content of the descriptive objects. The description of the descriptive objects is given in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary limitations. It should be understood that those skilled in the art will recognize that the invention can be implemented without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0054] It should be noted that since the method of this embodiment is executed in a computing device, the processing objects of each computing device exist in the form of data or information, such as time, which is essentially time information. It is understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data that exist so that electronic devices can process them. Specific details will not be elaborated here.

[0055] The following description, with reference to the accompanying drawings, describes a method and apparatus for detecting gene mutations, an electronic device, and a storage medium according to embodiments of the present disclosure.

[0056] Figure 1This is a schematic flowchart of a gene mutation detection method provided in an embodiment of this disclosure. The method is applied in a computer, such as... Figure 1 As shown, the method includes the following steps:

[0057] Step 101: Obtain the sequence reads after alignment with the reference genome.

[0058] The reference genome (REF) is assembled, maintained, and updated collaboratively by multiple scientists and research institutions. It serves as a typical representative or case study of the whole genome sequence of an idealized individual of a species. Humans, viruses, bacteria, fungi, plants, and animals theoretically all have their own reference genomes. The reference genome is the foundation of bioinformatics analysis. Sequencing data such as MPCR product reads, microarrays, and transcriptomes must first be aligned with the reference genome before subsequent analysis can be performed. Since reads are fragments with short lengths, they cannot be directly analyzed. In practical applications, read alignment technology is typically used to compare the sequenced reads with the existing reference genome to find the corresponding positions where the reads match, thus obtaining the sequence alignment results.

[0059] In some embodiments provided in this disclosure, a read is a short sequencing fragment, which is sequencing data generated by a high-throughput sequencer. Obtaining reads aligned with a reference genome involves comparing the reads to be analyzed with the reference genome to locate the position of each read on the reference genome.

[0060] Step 102: Perform multiple assembly operations to obtain multiple assembly results. Each assembly operation includes assembling the aligned sequence reads. The assembly conditions for different assembly operations are different. The assembly conditions include: the number of aligned sequence reads reaches a threshold.

[0061] Understandably, this disclosure performs multiple assembly operations on the reads to be analyzed, with each assembly operation yielding a separate assembly result. The minimum number of reads extracted for assembly differs for each assembly, meaning the assembly threshold varies. Because a different assembly threshold is set for each assembly operation, only local assembly of reads is performed, eliminating the need for global assembly. This results in higher accuracy compared to global assembly methods, saving analysis resources and time. Furthermore, setting different assembly thresholds for each assembly operation filters out regions where the number of reads exceeds the threshold, and then assembles the reads in those regions. The assembly threshold can be flexibly set according to requirements. The different assembly thresholds in this disclosure result in a more comprehensive range of reads to be assembled, preventing a certain probability of missed detections and improving the accuracy of mutation detection results.

[0062] To assemble sequence reads, the detection parameters of the gene analysis software GATK (Genome Analyses Toolkit) can be set in, but are not limited to, the following ways. GATK uses a haplotype caller algorithm to identify active regions based on the detection parameters and assembles reads from those regions. The detection parameters in GATK include read assembly thresholds.

[0063] The number of assembly operations can be 1, 2, 3, ..., n, where n is a positive integer.

[0064] In some embodiments provided in this disclosure, the multiple assemblies are two assemblies; the results of the two assemblies are subjected to a first mutation detection to obtain two first mutation information files.

[0065] In some embodiments provided in this disclosure, the first assembly operation is performed first, followed by the second assembly operation; the number of the sequence reads extracted after the comparison in the first assembly operation and the second assembly operation is divided into a first preset assembly threshold and a second preset assembly threshold; the first preset assembly threshold and the second preset assembly threshold.

[0066] In some embodiments provided in this disclosure, the first preset assembly threshold is preferably 200 or higher. If the first preset threshold is set too high, it may lead to a longer time for gene mutation detection; if it is set too low, it may lead to a decrease in the accuracy of gene mutation detection. In a specific embodiment of this disclosure, the first preset assembly threshold is 200. Specifically, setting the first assembly threshold parameter to 200 in GATK indicates that regions with more than or equal to 200 reads to be assembled are extracted for assembly. Since multiplex PCR analysis has a higher depth, this disclosure improves the data used for assembly, making the assembly results more accurate, and eliminating the need to separately assemble all the data for analysis.

[0067] Because the first assembly process only assembles regions where the number of reads is greater than or equal to a first preset threshold, other regions where the number of sequence reads does not reach the first preset threshold are not assembled or tested for mutations, which can easily lead to missed detections. To solve the problem of missed detections and further improve the accuracy of gene mutation detection methods, this disclosure uses a second pre-assembly threshold, which is lower than the first preset threshold, to perform the assembly operation again, making the assembled regions more complete and avoiding missed detections. In a specific embodiment of this disclosure, the first preset assembly threshold is preferably 50, that is, the number of reads assembled is greater than or equal to 50, which is less than the first pre-assembly threshold in the above embodiment.

[0068] This disclosure does not specifically limit the first preset threshold and the second preset quantity threshold, nor does it limit the order of the first preset quantity threshold and the second preset quantity threshold.

[0069] Step 103: Perform a first mutation detection on each of the multiple assembly results to obtain multiple first mutation information files.

[0070] In this disclosure, each assembly result corresponds to a first mutation information file.

[0071] In some embodiments provided in this disclosure, the sequence read assembly software includes, but is not limited to, the following software: GATK software.

[0072] In some embodiments provided in this disclosure, when multiple assembly operations are performed as two assembly operations, two assembly operations are performed to obtain two assembly results. The two assembly results are then subjected to a first mutation detection to obtain two first mutation detection files.

[0073] In some embodiments provided in this disclosure, the assembly step and the mutation detection step can be performed by assembly software and mutation detection software respectively, or they can be performed by the same software, such as GATK, which can perform the assembly step and the mutation detection step simultaneously.

[0074] In some embodiments provided in this disclosure, to facilitate the processing of the first mutation information file after mutation detection, the first mutation information file may be, but is not limited to, a Variant Call Format (VCF) file. VCF is a file format used to store mutation records in a genomic sequence. For bioinformatics analysis, various programming languages ​​and toolkits can be used to process VCF files. The specific format of the first mutation information file is not limited in the embodiments of this disclosure.

[0075] Step 104: Analyze the mutation information file to obtain gene mutation detection results. The mutation information file includes multiple first mutation information files.

[0076] In some embodiments provided in this disclosure, multiple first mutation information files are analyzed, that is, the mutation information recorded in multiple first mutation information files is compared and analyzed, and finally the detection result of whether the sample to be tested contains mutation sites is obtained.

[0077] In some embodiments provided in this disclosure, the first mutation detection is performed using GATK software with the HaplotypeCaller algorithm, the working principle of which is as follows:

[0078] (1) Identify active areas

[0079] (2) Determining the monomer type by assembling active regions

[0080] (3) Determine the likelihood value of each read's monomer type.

[0081] (4) Determine the genotype

[0082] Because the GATK software uses the HaplotypeCaller algorithm, which assembles reads by identifying active regions based on detection parameters and assembling reads from those regions, mutations in inactive regions may be missed.

[0083] To further improve the accuracy of gene mutation detection, this disclosure provides some embodiments in which, based on the first mutation detection, a second mutation detection is performed directly on the aligned sequence read. The number of second mutation information files is one. Specifically, the second mutation detection is performed on the aligned sequence read to obtain a second mutation information file; the sites in multiple first and second mutation information files are analyzed to obtain the gene mutation detection result. The accuracy of mutation detection is improved by employing two detection methods. The order of the first and second mutation detection is not specifically limited in the embodiments of this disclosure.

[0084] The software used for the second mutation detection can be, but is not limited to, the bcftoolsmpileup software, which detects all sub-regions corresponding to the reference genome. Specific implementation includes: inputting the BAM files of sequence reads corresponding to all sub-regions of the reference genome into the bcftoolsmpileup software to obtain a BCF file storing information on all possible mutation sites in the reference genome; then using bcftools software to convert the BCF file into a third mutation information file in VCF format, ensuring that the second mutation information file has the same format as the first mutation information file, facilitating subsequent data analysis. bcftools is software for manipulating and processing VCF / BCF (storing VCF files in binary format results in BCF files); it is part of the samtools toolset and is used to detect SNPs and Indels in aligned BAM files. The BAM file (Binary Alignment / Map format) is a binary file format used to store large-scale sequencing data, particularly for storing sequence alignment results with the reference genome. It is a compressed version of the SAM (Sequence Alignment / Map) file, designed for more efficient storage and rapid data access.

[0085] This disclosure provides a gene mutation detection method that first aligns sequencing data and then assembles it. Compared to existing methods that assemble first and then align, this method saves analytical resources and time. Furthermore, during the multiple read assembly process, the different assembly thresholds at each stage result in more complete read assembly regions, facilitating accurate identification of reads generated by multiplex PCR, improving detection accuracy, and avoiding interference from duplicate reads.

[0086] In practical applications, MPCR products are characterized by a large number of repetitive reads. Existing detection methods typically use a sequence alignment tool (Burrows-Wheeler-Alignment Tool, BWA) to align reads to a reference genome. BWA is software capable of aligning reads with low differences to a reference genome with a longer read. This disclosure optimizes the sequence alignment algorithm to accurately identify reads and reduce the proportion of repetitive reads. The optimized sequence alignment algorithm is its core algorithm, including data transformation algorithms, such as the Burrows-Wheeler Transformation (BWT) algorithm, and data structure alignment algorithms, such as hash table-based data structure alignment algorithms. The BWT algorithm can be divided into encoding and decoding parts. After encoding, similar characters in the original string are placed in relatively adjacent positions; decoding is the process of restoring the encoded string to the original string. A key feature of the BWT algorithm is that the encoded string can be completely restored to the original string.

[0087] To better understand the BWT algorithm, Figure 2 This is a schematic flowchart illustrating a comparison method provided in an embodiment of this disclosure. Figure 2 As shown, the method includes the following steps:

[0088] Step 201: Invoke the optimized sequence alignment algorithm to align each sequence read with the reference genome to obtain the similarity score of each sequence read in each subregion of the reference genome. The optimized sequence alignment algorithm is obtained by optimizing the alignment parameters in the preset sequence alignment algorithm.

[0089] In the embodiments provided in this disclosure, commonly used data transformation algorithms, such as the BWT algorithm, first construct a genomic index corresponding to each subregion of the reference genome through BWT encoding. For the specific process of constructing the genomic index, please refer to [reference needed]. Figure 3Taking the original data base sequence string "ACAACG" as an example, first, add the character "$" to the end of the original data. Then, determine the value of each character in the string according to the order of "az", with characters appearing earlier having higher values ​​and those appearing later having lower values. The character "$" has a lower value than all other characters. Then, repeatedly move the first letter of the string "acaacg$" to the end of the string, resulting in... Figure 3 The matrix M shown is then sorted according to the value of the first character of each string to obtain the transformed matrix M'. Columns L and F of matrix M' are then determined, where column L is the last column of matrix M' and column F is the first column of matrix M. The genomic index of the original data "ACAACG" is column L and the relative position of each character in column L. The BWT algorithm can then be used to decode the data using columns L and F to reconstruct the original data.

[0090] Therefore, existing BWA software directly aligns reads to a reference genome and then calculates a similarity score. For example, aligning a read to region A of the reference genome yields the result: 13M1D8M2I12M (13 bases aligned, followed by a 1-base deletion, then an 8-base complete alignment, then a 2-base insertion, then a 12-base complete alignment). The similarity score is calculated as follows: 1 point for a complete alignment; a base deletion initially deducts 6 points, and 1 point is deducted for each subsequent base deletion; a base insertion initially deducts 6 points, and 1 point is deducted for each subsequent base insertion. Based on these rules, the score for this read is calculated as follows:

[0091] Score: 13 + 8 + 12 = 33

[0092] Penalty points: (6+1*1)+(6+1*2)=15

[0093] Final score: 33 - 15 = 18

[0094] After obtaining the similarity score, the sub-region to which the read should be assigned is determined according to the highest score of each read in each sub-region on the reference genome.

[0095] Because MPCR products typically contain many identical reads, the similarity scoring method described above will output many reads with the same score. The BWA software addresses this by randomly assigning these reads to regions on the reference genome that are located at different positions but have the same alignment score. If a mutated base occurs on these reads, there is a certain probability of detection anomalies (e.g., reads supporting the mutation are randomly placed in other regions, leading to a decrease in the number of reads supporting the mutation). Furthermore, this BWA-based scoring system and assignment method cannot distinguish between insertions, deletions, and base mutations.

[0096] Therefore, to reduce the proportion of duplicate reads and thus reduce their interference with mutation detection, this disclosure proposes a method for adding optimized alignment parameters to a preset sequence alignment algorithm. The optimized algorithm is used to align and assign the aforementioned sequence reads to the reference genome. The optimized alignment parameters include a minimum sequence matching length parameter and a read insertion size parameter. For example, the default minimum sequence matching length of the preset sequence alignment algorithm is 8, meaning that reads with gaps greater than 8 base pairs are discarded. In the embodiments provided in this disclosure, the minimum sequence matching length parameter can be set to 10000000000, the purpose of which is to align reads one by one when large gaps appear, rather than discarding gaps greater than 8 base pairs. Furthermore, the read insertion size parameter includes the mean, standard deviation (or 10% of the mean if not present), maximum value (or 4% of the mean if not present), and minimum value of the insertion size distribution. For example, setting the average insertion size to 100, the standard deviation to 50, and the maximum value to 250 means increasing the penalty for gaps and indirectly reducing the penalty for mutations.

[0097] After obtaining the optimized sequence alignment algorithm, the data transformation algorithm can be used, but is not limited to, to construct the genomic index of the reference genome; this process is the same as the aforementioned BWT encoding process, and will not be described again in this embodiment. Then, the data structure alignment algorithm is called to compare the sequence read with the genomic index corresponding to the reference genome based on the minimum sequence matching length parameter and the sequence read insertion size parameter, to obtain the similarity score of the sequence read in each region of the reference genome.

[0098] Step 202: Each sequence read is assigned to the region with the highest similarity score in the reference genome to obtain the sequence read after alignment with the reference genome.

[0099] The reads alignment method provided in the above embodiments optimizes the preset sequence alignment algorithm, reduces the penalty ratio for mutations, reduces the proportion of duplicate reads, thereby reducing the interference of duplicate reads on detection and improving the accuracy of subsequent mutation detection.

[0100] In some embodiments provided in this disclosure, analyzing the mutation information file to obtain gene mutation detection results specifically includes the following steps:

[0101] 1. If at least two files in the mutation information file contain the same site, the same site is identified as the first target mutation site, and the gene mutation detection result is obtained.

[0102] In some embodiments provided in this disclosure, the mutation information file is a plurality of first mutation information files and a second mutation information file, or a plurality of first mutation information files.

[0103] 2. If there are different sites among the files in the mutation information file, but the different sites are in the same region, then the mutation information file is compared with the gene mutation database to obtain the gene mutation detection result.

[0104] In one specific embodiment provided in this disclosure, the same region mentioned in this step is within 10 bp, that is, the distance between the sites in the two first mutation information files and the second mutation information file is within 10 bp.

[0105] In some embodiments provided in this disclosure, when the mutation information file consists of multiple first mutation information files and second mutation information files, if each of the multiple first mutation information files and second mutation information files contains different sites, and these different sites are located in the same region, then it is necessary to compare each first mutation information file with a gene mutation database. When the mutation information file consists of only multiple first mutation information files, the analysis method is the same and will not be described again here.

[0106] In some embodiments provided in this disclosure, the gene mutation database is used as reference information for gene mutation detection. The gene mutation database includes population frequency databases (also known as the Single Nucleotide Polymorphism Database (dbSNP)), the 1000 Genomes Project, the ClinVar database, etc. Among them, dbSNP is a commonly used database. dbSNP was established in cooperation with NCBI and the National Human Genome Research Institute. It includes data on SNPs, short insertion and deletion polymorphisms, microsatellite markers and short repetitive sequences, as well as their sources, detection and verification methods, genotype information, population frequencies, etc.

[0107] The method for comparison with gene mutation databases is as follows:

[0108] (1) If at least two different sites in the mutation information file are located in the same target region as known mutation sites in the gene mutation database, then the known mutation sites are identified as the second target mutation sites.

[0109] In one specific embodiment provided in this disclosure, the same target region is within 10bp.

[0110] Preferably, after determining that at least two different sites in the mutation information file are located in the same target region as known mutation sites in the gene mutation database, it is also necessary to perform a significance analysis on the mutation information files located in the same target region. Specifically, the number of reads of the sites in the mutation information files in the target region is analyzed for significance. If the difference is not significant, the known mutation site is determined as the target mutation site.

[0111] (2) If all the different sites in the mutation information file are not in the same target region as the known mutation sites, then the site with the highest support for mutation depth among the different sites is determined as the second target mutation site.

[0112] In some embodiments provided in this disclosure, when the mutation information file consists of multiple first mutation information files and second mutation information files, if these files contain different sites located in the same region, then the site with the highest supported mutation depth among these different sites is determined as the second target mutation site. When the mutation information file consists of only multiple first mutation information files, the analysis method is the same and will not be repeated here.

[0113] (3) If only one of the mutation information files has a site in the same target region as a known mutation site in the gene mutation database, then filter it out.

[0114] Finally, the detected target mutation sites are summarized to obtain the gene mutation detection results.

[0115] In order to better understand the analysis of the mutation information files and obtain gene mutation detection results, this disclosure provides detailed explanations using examples of multiple first mutation information files and multiple first mutation information files and second mutation information files.

[0116] Example 1:

[0117] When the mutation information file consists of multiple first mutation information files, and the number of these files is two, the first first mutation information file contains site A, and the second first mutation information file contains site B. The mutation information files are analyzed as follows:

[0118] 1. If site A and site B are the same, then site A and site B are identified as the first target mutation sites.

[0119] 2. If site A and site B are different, but the distance between them is within 10 bp, then the two first mutation information files are compared with a gene mutation database. That is, sites A and B are compared with known mutation sites in the gene mutation database to determine the target mutation site. The comparison method with the gene mutation database is as follows:

[0120] (1) If the distance between site A and site B and the known mutation site is within 10 bp, and the number of reads supporting the mutation of site A is not significantly different from the number of reads supporting the mutation of site B, then the known mutation site is determined as the second target mutation site.

[0121] (2) If site A and site B are different, and the distance between site A and site B and the known mutation site is not within 10bp, then the site with the most reads in site A and site B shall be taken as the second target mutation site.

[0122] By summarizing the target mutation sites, we can ultimately confirm whether the gene sites have mutated or are in a homozygous state.

[0123] Example 2:

[0124] When the mutation information file consists of multiple first mutation information files and second mutation information files, and the number of first mutation information files is two, the first first mutation information file contains site A, the second first mutation information file contains site B, and the second mutation information file contains site C. The mutation information files are analyzed as follows:

[0125] 1. If sites A, B and C are the same, then sites A, B and C are identified as the first target mutation sites.

[0126] 2. If sites A, B, and C are all different, and the distance between sites A, B, and C is within 10 bp, then the two first mutation information files are compared with the gene mutation database. That is, sites A, B, and C are compared with known mutation sites in the gene mutation database to determine the target mutation site. The comparison method with the gene mutation database is as follows:

[0127] (1) If the distance between site A, site B and the known mutation site is within 10 bp, the distance between site C and the known mutation site is not within 10 bp, and the number of reads supporting the mutation of site A is not significantly different from the number of reads supporting the mutation of site B, then the known mutation site is determined as the second target mutation site.

[0128] (2) If the distance between site A, site B and site C and the known mutation site is not within 10 bp, then the site with the most reads among site A, site B and site C shall be taken as the second target mutation site.

[0129] By summarizing the target mutation sites, we can ultimately confirm whether the gene sites have mutated or are in a homozygous state.

[0130] To further illustrate the accuracy of the gene mutation detection method provided in this embodiment, sequencing data from 384 clinical samples were obtained. After a conventional analysis process, VCF files were generated. The accuracy of these VCF file results was then compared with standard whole-genome sequencing results. In this embodiment, GATK and bcftoolsmileup software were used for mutation detection. Compared to this method, the conventional analysis process did not add optimized alignment parameters during read alignment, and only GATK software without a first preset threshold parameter was used for mutation detection. Sequencing data from the same 384 patients were analyzed using the gene mutation detection method provided in this embodiment, and the accuracy was calculated. The 384 samples were sequenced 12 times in parallel using the MPCR method, and point mutations were calculated using both the conventional and novel methods, with the results compared to the calculated accuracy. A comparison table of the accuracy of the conventional method and the gene detection method of this embodiment was obtained, as shown in Table 1.

[0131] Table 1. Accuracy Comparison of Conventional Methods and Gene Detection Methods of the Embodiments of this Disclosure

[0132]

[0133] As shown in Table 1, the gene mutation detection method of this embodiment clearly has a higher accuracy rate than the conventional procedure.

[0134] In summary, the embodiments disclosed herein have the following effects:

[0135] 1. By optimizing the preset sequence alignment algorithm, the penalty ratio for mutations was reduced, and the proportion of duplicate reads was reduced, thereby reducing the interference of duplicate reads on detection and improving the accuracy of subsequent mutation detection.

[0136] 2. It enhances the depth of MPCR product analysis, increases the number of locally assembled reads, and makes the assembly results more accurate. Simultaneously, it eliminates the need for a global assembly process that assembles all reads; instead, it assembles reads from sub-regions of the reference genome, improving the efficiency of mutation detection.

[0137] Corresponding to the gene mutation detection method described above, this disclosure also proposes a gene mutation detection device. Since the device embodiments of the present invention correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to the method embodiments described above, and will not be repeated here.

[0138] This disclosure provides a gene mutation detection device, comprising: an acquisition unit 41, an assembly unit 42, a first detection unit 43, and an analysis unit 44.

[0139] Acquisition unit 41 is used to acquire sequence reads aligned with the reference genome;

[0140] Assembly unit 42 is used to perform multiple assembly operations to obtain multiple assembly results. Each assembly operation includes assembling the aligned sequence reads. The assembly conditions for different assembly operations are different. The assembly conditions include: the number of aligned sequence reads reaches a threshold.

[0141] The first detection unit 43 is used to perform first mutation detection on the multiple assembly results respectively to obtain multiple first mutation information files;

[0142] Analysis unit 44 is used to analyze mutation information files to obtain gene mutation detection results. The mutation information files include multiple first mutation information files.

[0143] Compared with related technologies, the gene mutation detection device provided in this disclosure adopts local assembly and mutation detection, eliminating the global assembly process of assembling all reads, thereby reducing the impact of repeated reads on mutation detection during the global assembly process; moreover, this disclosure adopts a multiple assembly method with different assembly thresholds for different assembly operations, so as to assemble reads in different regions as much as possible, preventing missed detection and improving the accuracy of mutation detection.

[0144] Furthermore, in one possible implementation of this embodiment, the gene mutation detection device further includes:

[0145] The second detection unit 45 is used to perform a second mutation detection on the aligned sequence read segment to obtain a second mutation information file, wherein the mutation information file further includes a second mutation information file.

[0146] Furthermore, in one possible implementation of this embodiment, the analysis unit 44 includes:

[0147] First determining module 441: If at least two files in the mutation information file contain the same site, then the same site is determined as the first target mutation site, and the gene mutation detection result is obtained.

[0148] Furthermore, in one possible implementation of this embodiment, the analysis unit 44 further includes:

[0149] First comparison module 442: If there are different sites among the files in the mutation information file, but the different sites are in the same region, then the mutation information file is compared with the gene mutation database to obtain the gene mutation detection result.

[0150] Furthermore, in one possible implementation of this embodiment, the mutation information file is compared with a gene mutation database, and the analysis unit 44 further includes:

[0151] The second determining module 442 is used to determine the known mutation site as the second target mutation site if at least two different sites in the mutation information file are located in the same target region as the known mutation sites in the gene mutation database, thereby obtaining the gene mutation detection result.

[0152] Furthermore, in one possible implementation of this embodiment, the analysis unit 44 further includes:

[0153] The third determining module 443 is used to determine the site with the highest support for mutation depth among the different sites as the second target mutation site if all the different sites in the mutation information file are not in the same target region as the known mutation sites, and to obtain the gene mutation detection result.

[0154] Furthermore, in one possible implementation of this embodiment, in the assembly unit 42, the multiple assembly operations are two assembly operations, and two assembly operations are performed to obtain two assembly results;

[0155] The two assembly results were subjected to a first mutation detection to obtain two first mutation information files.

[0156] Furthermore, in one possible implementation of this embodiment, in the assembly unit 42, the first assembly operation is performed first, followed by the second assembly operation;

[0157] The number of the aligned sequence reads extracted in the first assembly operation and the second assembly operation is divided into a first preset threshold and a second preset threshold;

[0158] The first preset threshold is greater than the second preset threshold.

[0159] Furthermore, in one possible implementation of this embodiment, the acquisition unit 41 includes:

[0160] The alignment module 411 is used to call an optimized sequence alignment algorithm to align each sequence read with the reference genome to obtain the similarity score of each sequence read in each region of the reference genome. The optimized sequence alignment algorithm is obtained by optimizing the alignment parameters in a preset sequence alignment algorithm.

[0161] The allocation module 412 is used to allocate each sequence read to the region with the highest similarity score in the reference genome to obtain the aligned sequence read.

[0162] Furthermore, in one possible implementation of this embodiment, the optimized alignment parameters include a minimum sequence matching length parameter and a sequence read insertion size parameter, and the optimized sequence alignment algorithm includes a data transformation algorithm and a data structure alignment algorithm.

[0163] Furthermore, in one possible implementation of this embodiment, the allocation module 412 is also used for:

[0164] The data structure alignment algorithm is invoked to align the sequence read with the genome index of the reference genome based on the minimum sequence matching length parameter and the sequence read insertion size parameter, thereby obtaining the similarity score of the sequence read in each region of the reference genome.

[0165] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0166] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0167] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0168] like Figure 5As shown, device 500 includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 502 or a computer program loaded from storage unit 508 into RAM (Random Access Memory) 503. RAM 503 can also store various programs and data required for the operation of device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. I / O (Input / Output) interface 505 is also connected to bus 504.

[0169] Multiple components in device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0170] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as gene mutation detection methods. For example, in some embodiments, the gene mutation detection method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the aforementioned gene mutation detection method by any other suitable means (e.g., by means of firmware).

[0171] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0172] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0173] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0174] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0175] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0176] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0177] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0178] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0179] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for detecting a genetic mutation, characterized by, The method comprises the following steps: obtaining sequence reads aligned with a reference genome; performing multiple assembly operations to obtain multiple assembly results, each assembly operation comprising assembling the aligned sequence reads, and the assembly conditions of different assembly operations being different, wherein the assembly conditions comprise that the number of the aligned sequence reads reaches a threshold value; performing first mutation detection on the multiple assembly results respectively to obtain multiple first mutation information files; analyzing the mutation information files to obtain a gene mutation detection result, wherein the mutation information files comprise the multiple first mutation information files.

2. The method of claim 1, wherein, The method further comprises the following steps: performing second mutation detection on the aligned sequence reads to obtain a second mutation information file; the mutation information files further comprise the second mutation information file.

3. The detection method according to claim 1 or 2, characterized in that, analyzing the mutation information files to obtain a gene mutation detection result, comprising the following steps: if at least two files in the mutation information files contain the same site, the same site is determined as a first target mutation site to obtain the gene mutation detection result.

4. The detection method according to claim 2, characterized in that, analyzing the mutation information files to obtain a gene mutation detection result, further comprising the following steps: if there are different sites between the files in the mutation information files, and the different sites are in the same region, the mutation information files are aligned with a gene mutation database to obtain the gene mutation detection result.

5. The detection method according to claim 4, characterized in that, aligning the mutation information files with a gene mutation database to obtain a gene mutation detection result, comprising the following steps: if at least two of the different sites in the mutation information files are in the same target region as known mutation sites in the gene mutation database, the known mutation sites are determined as second target mutation sites to obtain the gene mutation detection result.

6. The detection method according to claim 4, characterized in that, aligning the mutation information files with a gene mutation database to obtain a gene mutation detection result, further comprising the following steps: if the different sites in the mutation information files are not in the same target region as the known mutation sites, the site with the highest support variant depth in the different sites is determined as a second target mutation site to obtain the gene mutation detection result.

7. The assay method according to any one of claims 1 to 6, characterized by, the multiple assembly operations are two assembly operations, and two assembly results are obtained by performing the two assembly operations; performing first mutation detection on the two assembly results respectively to obtain two first mutation information files.

8. The detection method according to claim 7, wherein: in the two assembly operations, the first assembly operation is performed first, and then the second assembly operation is performed; the number of the aligned sequence reads extracted by the first assembly operation and the second assembly operation is divided into a first preset threshold value and a second preset threshold value; the first preset threshold value is greater than the second preset threshold value.

9. The method of claim 1, wherein, The method comprises the following steps: calling an optimized sequence alignment algorithm to align each sequence read with a reference genome respectively to obtain a similarity score of each sequence read in each region of the reference genome, wherein the optimized sequence alignment algorithm is obtained by optimizing alignment parameters in a preset sequence alignment algorithm. assigning each of the sequence reads to a region of the reference genome having a highest similarity score, to obtain aligned sequence reads.

10. The detection method according to claim 9, characterized in that, The optimized alignment parameters include a minimum sequence match length parameter and a sequence read insert size parameter, and the optimized sequence alignment algorithm includes a data transformation algorithm and a data structure alignment algorithm. The optimized sequence alignment algorithm is called to align each sequence read with the reference genome to obtain a similarity score of each sequence read with each region of the reference genome, including: The data transformation algorithm is called to construct a genome index of the reference genome. The data structure alignment algorithm is called to align the sequence reads with the genome index of the reference genome based on the minimum sequence match length parameter and the sequence read insert size parameter to obtain a similarity score of the sequence reads in each region of the reference genome.

11. A device for detecting a genetic mutation, comprising: comprising: an obtaining unit, configured to obtain sequence reads aligned with a reference genome; an assembling unit, configured to perform multiple assembling operations to obtain multiple assembling results, each assembling operation including assembling the aligned sequence reads, and assembling conditions of different assembling operations being different, the assembling conditions including that a quantity of the aligned sequence reads reaches a threshold; a first detecting unit, configured to perform first mutation detection on the multiple assembling results respectively to obtain multiple first mutation information files; a first analyzing unit, configured to analyze the mutation information files to obtain a gene mutation detection result, the mutation information files including the multiple first mutation information files.

12. An electronic device, comprising: comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the detection method in any one of claims 1-10.

13. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the detection method in any one of claims 1-10.

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