Method and apparatus for performing joint-calling acceleration on high-performance sequencing data, and electronic device and storage medium

Abstract

Provided in the present disclosure are a method and apparatus for performing joint-calling acceleration on high-performance sequencing data, and an electronic device and a storage medium. The method comprises: determining a division mode for genome variation data according to population information of a plurality of samples; dividing the genome variation data according to the division mode, so as to obtain a plurality of variation data segments corresponding to each piece of genome variation data; merging variation data segments, which have the same rank, in the genome variation data, so as to obtain merged variation data; performing population variation detection on each piece of merged variation data, so as to obtain population genome variation data of each piece of merged variation data; and merging the population genome variation data, so as to obtain target population genome variation data of the plurality of samples. Thus, during population variation detection, by means of performing population anomaly detection on a plurality of pieces of local merged data, and merging detection results, the efficiency of acquiring complete population genome variation data can be improved.

Description

A method, device, electronic device and storage medium for accelerating high-performance sequencing data joint-calling

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure claims priority to the international application number "PCT / CN2023 / 100428" filed by Shenzhen BGI Intelligent Manufacturing Technology Co., Ltd. on June 15, 2023, with the invention name "Population variation detection method, device, electronic device and storage medium". Technical Field

[0003] The present disclosure relates to the field of biological information processing technology, and in particular to a method, device, electronic device, and storage medium for accelerating joint-calling of high-performance sequencing data. Background Art

[0004] Population genomics: By analyzing DNA sequence polymorphism data, we infer the various forces that have acted on the genome and thus explore the process of biological evolution. Population genomics primarily encompasses changes in population structure and population size, population mutations, natural selection, genetic drift, and their relationship to genetic variation within the genome. Medical research in population genetics aims to investigate the frequency and mode of inheritance of genetic diseases, as well as the patterns of gene frequency and variation. This research aims to understand the patterns of occurrence and spread of genetic diseases within populations, providing crucial information and measures for the prevention, monitoring, and treatment of genetic diseases.

[0005] Joint population variation detection is a key step in population genomics research. In this process, how to efficiently perform joint population variation detection on large-scale samples to obtain corresponding population variation data is a pressing technical challenge.

[0006] Summary of the Invention

[0007] The present disclosure provides a method, device, electronic device, and storage medium for accelerating joint-calling of high-performance sequencing data.

[0008] In one aspect, an embodiment of the present disclosure proposes a method for accelerating joint-calling of high-performance sequencing data, the method comprising: obtaining genome variation data corresponding to each of a plurality of samples; determining a division method of the genome variation data based on population information of the plurality of samples; dividing each of the genome variation data according to the division method to obtain a plurality of variation data segments corresponding to each of the genome variation data, wherein the order of the variation data segments is determined based on the position of the variation data segments in the genome variation data; merging variation data segments with the same order in each of the genome variation data to obtain merged variation data; performing population variation detection on each of the merged variation data to obtain population genome variation data of each of the merged variation data; and merging each of the population genome variation data to obtain target population genome variation data of the plurality of samples.

[0009] In another aspect, an embodiment of the present disclosure proposes a device for accelerating joint-calling of high-performance sequencing data, the device comprising: an acquisition module for acquiring genomic variation data corresponding to each of a plurality of samples; a first determination module for determining a division method of the genomic variation data based on population information of the plurality of samples; a first division module for dividing each of the genomic variation data according to the division method to obtain a plurality of variation data segments corresponding to each of the genomic variation data, wherein the order of the variation data segments is determined based on the position of the variation data segments in the genomic variation data; a first merging processing module for merging variation data segments with the same order in each of the genomic variation data to obtain merged variation data; a first population variation detection module for performing population variation detection on each of the merged variation data to obtain population genomic variation data of each of the merged variation data; and a second merging processing module for merging each of the population genomic variation data to obtain target population genomic variation data of the plurality of samples.

[0010] Another embodiment of the present disclosure provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, a method for accelerating Joint-Calling of high-performance sequencing data disclosed in an embodiment of the present disclosure is implemented.

[0011] Another aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method disclosed in the embodiment of the present disclosure for accelerating Joint-Calling of high-performance sequencing data is implemented.

[0012] According to the technical solution provided by the embodiment of the present disclosure, in the process of processing the genomic variation data corresponding to each of the multiple samples, the division method of the genomic variation data is determined according to the population information of the multiple samples, and according to the division method, each genomic variation data is divided respectively to obtain multiple variation data fragments corresponding to each genomic variation data and the variation data fragments with the same order in each genomic variation data are merged to obtain merged variation data; group variation detection is performed on each merged variation data respectively to obtain the group genomic variation data of each merged variation data, and each group genomic variation data is merged to obtain the target group genomic variation data of multiple samples. Thus, in the process of group variation detection, by performing group anomaly detection on multiple local merged data respectively and merging the detection results, the efficiency of obtaining complete group genomic variation data of multiple samples can be improved.

[0013] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0015] FIG1 is a flow chart of a method for accelerating high-performance sequencing data Joint-Calling according to an embodiment of the present disclosure;

[0016] FIG2 is a flow chart of a method for accelerating high-performance sequencing data Joint-Calling according to another embodiment of the present disclosure;

[0017] FIG3 is a flow chart of a method for accelerating high-performance sequencing data Joint-Calling according to another embodiment of the present disclosure;

[0018] FIG4 is a flow chart of a method for accelerating high-performance sequencing data Joint-Calling according to another embodiment of the present disclosure;

[0019] FIG5 is a flow chart of a method for accelerating high-performance sequencing data Joint-Calling according to another embodiment of the present disclosure;

[0020] FIG6 is a flow chart of a method for accelerating high-performance sequencing data Joint-Calling according to another embodiment of the present disclosure;

[0021] FIG7 is a schematic structural diagram of an apparatus for accelerating high-performance sequencing data Joint-Calling according to an embodiment of the present disclosure;

[0022] FIG8 is a schematic structural diagram of an apparatus for accelerating high-performance sequencing data Joint-Calling according to another embodiment of the present disclosure;

[0023] FIG9 is a block diagram of an electronic device according to one embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention, but are not to be construed as limiting the present invention.

[0025] As disclosed herein, the term "sample" refers to any sample of a subject, which can reflect a biological state associated with the subject, and includes cell-free DNA. Examples of samples include, but are not limited to, blood, whole blood, plasma, serum, urine, cerebrospinal fluid, saliva, sweat, and tears of the subject. A biological sample can include any tissue or material derived from a living or dead subject. The sample can be a cell-free sample. In some embodiments, biological tissue or fluid can be or include amniotic fluid, aqueous humor, ascites, bile, bone marrow, blood, breast milk, cerebrospinal fluid, cerumen, chyle, chyme, a single ejaculate of semen (ejaculate), endolymph, exudate, feces, gastric acid, gastric juice, lymph, mucus, pericardial fluid, perilymph, peritoneal fluid, pleural fluid, pus, inflammatory secretions, saliva, sebum, semen, serum, smegma, sputum, synovial fluid, sweat, tears, urine, vaginal secretions, vitreous humor, vomitus, and / or combinations thereof or one or more components. In some embodiments, biological fluid can be or include intracellular fluid, extracellular fluid, intravascular fluid (plasma), interstitial fluid, lymph and / or transcellular fluid. In some embodiments, biological fluid can be or include plant exudate. In some embodiments, biological tissue or sample can be obtained, for example, by suction, biopsy (for example, fine needle or tissue biopsy), swab (for example, oral, nasal, skin or vaginal swab), scraping, surgery, washing or lavage (for example, bronchoalveolar, duct, nose, eye, oral cavity, uterus, vagina or other washing or lavage). In some embodiments, biological sample is or includes the cell obtained from individual. In some embodiments, sample is " the first sample " obtained directly from the source of interest by any suitable means. In some embodiments, as will be apparent from the context, term " sample " refers to the product obtained by processing the first sample (for example, by removing one or more components thereof and / or by adding one or more agents thereto). For example, using semipermeable membrane filtration. Such "processed samples" may include, for example, nucleic acids (including DNA, RNA), proteins or chromatin extracted from the sample or obtained by subjecting the primary sample to one or more techniques (such as amplification or reverse transcription of nucleic acids, separation and / or purification of certain components, etc.).

[0026] The following describes the method, device, electronic device, and storage medium for accelerating high-performance sequencing data Joint-Calling according to embodiments of the present disclosure with reference to the accompanying drawings.

[0027] Figure 1 is a flow chart illustrating a method for accelerating high-performance sequencing data by joint-calling according to an embodiment of the present disclosure. It should be noted that the method for accelerating high-performance sequencing data by joint-calling provided in this embodiment is performed by an apparatus for accelerating high-performance sequencing data by joint-calling. The apparatus for accelerating high-performance sequencing data by joint-calling in this embodiment can be implemented by software and / or hardware, and can be an electronic device or be configured in an electronic device.

[0028] The electronic device may include but is not limited to a terminal device, a server, a computer cluster, etc., which is not specifically limited in this embodiment.

[0029] The terminal device may include but is not limited to a personal computer (PC), a mobile device, a tablet computer, etc., and this embodiment does not specifically limit this.

[0030] As shown in FIG1 , the method for accelerating the high-performance sequencing data Joint-Calling may include:

[0031] Step 101: Obtain genome variation data corresponding to each of a plurality of samples.

[0032] In some exemplary embodiments, variation detection may be performed on multiple samples separately to obtain genome variation call format (GVCF) files corresponding to each of the multiple samples, wherein the GVCF files may include genome variation data.

[0033] Step 102: Determine a division method for genome variation data based on population information of multiple samples.

[0034] The group information is used to indicate the group to which the sample belongs. For example, the group information may include but is not limited to group identification information, group name information, etc.

[0035] In some exemplary embodiments, one possible implementation method for determining the division method of genomic variation data based on the population information of multiple samples may include comparing the population information of the multiple samples to determine whether it is the same, obtaining a comparison result, and determining the division method of the genomic variation data based on the comparison result. In other words, the multiple samples may be determined based on their corresponding population information to determine whether they are from the same population, and the division method of the genomic variation data may be determined based on whether the multiple samples are from the same population.

[0036] As an example, when the comparison result shows that the population information of multiple samples is the same, the first partitioning method is determined to be used for partitioning the genomic variation data. That is, when the multiple samples are determined to be from the same population based on their corresponding population information, i.e., the multiple samples belong to the same population, and since the multiple samples from the same population have a common origin, the first partitioning method can be used to partition the genomic variation data. The first partitioning method is used to indicate a partitioning method based on variant site density.

[0037] The density of variant sites refers to the number of variant sites per unit length of sample.

[0038] Among them, the unit length is a reference standard set according to demand. The unit length is a standard for reference. It has no fixed value and changes according to the setting.

[0039] In other examples, when the comparison result indicates that the population information of the multiple samples is different, the second partitioning method is determined to be used for partitioning the genomic variation data. That is, when the multiple samples are determined to be from different populations based on the population information of the multiple samples, because the multiple samples are from different populations with a wide range of population sources, high population heterozygosity, and a large number of alleles at the same locus, the second partitioning method can be determined for partitioning the genomic variation data, where the second partitioning method is used to indicate partitioning based on allele density.

[0040] Among them, heterozygosity: Heterozygosity is also called the average heterozygosity or heterozygosity of a population. It is another measurement parameter of genetic variation in a population, which refers to the frequency of heterozygous alleles at a certain locus.

[0041] Among them, allele density refers to the number of alleles per unit length of sample.

[0042] Alleles are genes that control different forms of the same trait at the same position on a pair of homologous chromosomes. Different alleles produce variations in genetic traits such as hair color or blood type. When there are more than two alleles at the same locus on homologous chromosomes within a population, it is called a multiple allele. For example, the human ABO blood type gene locus is at the end of the long arm of chromosome 9. The alleles at this locus are A, B, and O. Therefore, the human ABO blood type is determined by three multiple alleles. When joint-calling is used to detect population genomic variation, the number of alleles at the same locus can reach dozens.

[0043] Step 103 : Divide each genome variation data according to the division method to obtain multiple variation data segments corresponding to each genome variation data, wherein the order of the variation data segments is determined based on the position of the variation data segments in the genome variation data.

[0044] In some exemplary embodiments, when the division method is the first division method, each genome variation data is divided according to the division method to obtain multiple variation data fragments corresponding to each genome variation data. One possible implementation method is: for each genome variation data, the genome variation data is divided according to the variation site density to obtain multiple variation data fragments corresponding to the genome variation data.

[0045] In this example, the mutation sites in the genomic mutation data are not evenly distributed. In order to make full use of computing resources and reduce computing time, for each genomic mutation data, in this example, the distribution of the mutation sites in the genomic mutation data is statistically analyzed to obtain the mutation site statistics of the genomic mutation data, and according to the mutation site statistics, the genomic mutation data is divided based on the mutation site density to obtain multiple mutation data segments corresponding to the genomic mutation data, wherein the lengths corresponding to the multiple mutation data segments are different, but the difference in the number of mutation sites on the multiple mutation data segments is within a first preset number, for example, the number of mutation sites on the multiple mutation data segments is basically the same.

[0046] The first preset number may be pre-set.

[0047] In some examples, the average value of the variation sites assigned to each variation data segment can be determined based on the preset number of segments and the total number of variation sites in the genomic variation data, and the starting and ending positions of each variation data segment in the genomic variation data can be determined based on the average value. The genomic variation data can be divided based on the starting and ending positions of each variation data segment to obtain a preset number of variation data segments.

[0048] In other exemplary embodiments, when the division method is the second division method, each genomic variation data is divided according to the division method to obtain multiple variation data segments corresponding to each genomic variation data. One possible implementation method is: for each genomic variation data, the genomic variation data is divided according to the allele density to obtain multiple variation data segments corresponding to the genomic variation data.

[0049] Specifically, for each genomic variation data, the allele information corresponding to each variation site in the genomic variation data can be determined, and the allele information in the genomic variation data can be statistically analyzed to obtain the statistical results of the allele information of the genomic variation data. According to the statistical results, the genomic variation data is divided based on the allele density to obtain multiple variation data segments corresponding to the genomic variation data, wherein the lengths corresponding to the variation data segments are different, and the difference between the numbers of alleles in the multiple variation data segments is within a second preset number. For example, the numbers of alleles in the multiple variation data segments can be basically the same.

[0050] In some examples, the average value of the alleles assigned to each variation data segment can be determined based on the preset number of segments and the total number of alleles in the genomic variation data, and the starting and ending positions of each variation data segment in the genomic variation data can be determined based on the average value. The genomic variation data can be divided based on the starting and ending positions of each variation data segment to obtain a preset number of variation data segments.

[0051] Step 104 : Merge the variation data segments with the same order in each genome variation data to obtain merged variation data.

[0052] It is understandable that there are multiple merged variation data in this example, and the number of merged variation data is the same as the number of gene variation data segments obtained by dividing a genome variation data.

[0053] Step 105 , performing population variation detection on each merged variation data to obtain the population genome variation data of each merged variation data.

[0054] In some exemplary embodiments, multiple idle computing nodes may be called, and each computing node performs population variation detection (Joint-Calling) on ​​a merged variation data, wherein the merged variation data processed by different nodes are different. That is, in some examples, population variation detection may be performed on multiple merged variation data respectively by multiple computing nodes, wherein the merged variation data processed by different computing nodes are different. Thus, by performing population variation detection on multiple merged variation data respectively by multiple computing nodes, the joint variation detection process can be accelerated, and the efficiency of obtaining the population genomic variation data corresponding to each of the multiple merged variation data can be improved.

[0055] In other exemplary embodiments, when population variation detection is performed on multiple merged variation data through multiple computing nodes, a possible implementation method of performing population variation detection on each merged variation data separately to obtain the population genome variation data of each merged variation data is: for each computing node: obtain an unprocessed target merged variation data from multiple merged variation data; call the computing node to perform population variation detection on the target merged variation data to obtain the population genome variation data corresponding to the target merged variation data; obtain the next unprocessed target merged variation data from the multiple merged variation data until population variation detection is completed for all multiple merged variation data.

[0056] Step 106 : Merge the genome variation data of each population to obtain the genome variation data of the target population of multiple samples.

[0057] The method for accelerating the joint-calling of high-performance sequencing data of the disclosed embodiment is as follows: in the process of processing the genomic variation data corresponding to each of the multiple samples, the division method of the genomic variation data is determined according to the population information of the multiple samples, and each genomic variation data is divided according to the division method to obtain multiple variation data fragments corresponding to each genomic variation data and the variation data fragments with the same order in each genomic variation data are merged to obtain merged variation data; group variation detection is performed on each merged variation data to obtain the group genomic variation data of each merged variation data, and each group genomic variation data is merged to obtain the target group genomic variation data of multiple samples. Thus, in the process of group variation detection, by performing group anomaly detection on multiple local merged data respectively and merging the detection results, the efficiency of obtaining complete group genomic variation data of multiple samples can be improved.

[0058] Figure 2 is a flow chart of a method for accelerating high-performance sequencing data Joint-Calling according to another embodiment of the present disclosure. It should be noted that this embodiment is a further refinement of the above embodiment.

[0059] As shown in FIG2 , the method for accelerating the joint-calling of high-performance sequencing data may include:

[0060] Step 201: Obtain genome variation data corresponding to each of a plurality of samples.

[0061] Step 202: Determine a division method for genome variation data based on population information of multiple samples.

[0062] Step 203 : divide each genome variation data according to the division method to obtain multiple variation data segments corresponding to each genome variation data, wherein the order of the variation data segments is determined based on the position of the variation data segments in the genome variation data.

[0063] Step 204 : Merge the variation data segments with the same order in each genome variation data to obtain merged variation data.

[0064] Step 205 , performing population variation detection on each merged variation data to obtain the population genome variation data of each merged variation data.

[0065] It should be noted that, for the specific implementation of steps 201 to 205, please refer to the relevant description of the embodiment of the present disclosure, which will not be repeated here.

[0066] Step 206: Determine the chromosome region corresponding to the genome variation data of each population.

[0067] Step 207 : merging the population genome variation data in the same chromosome region to obtain the target population genome variation data of multiple samples.

[0068] The target population genomic variation data may be, but is not limited to, the population gene merging results corresponding to each of the 25 stained regions. The population gene merging results corresponding to each stained region are obtained by merging the population genomic variation data corresponding to the corresponding stained region.

[0069] In this example, during the processing of genomic variation data corresponding to multiple samples, a partitioning method for the genomic variation data is determined based on the population information of the multiple samples. Each genomic variation data is partitioned based on the partitioning method to obtain multiple variation data segments corresponding to each genomic variation data segment. Variation data segments with the same order in each genomic variation data segment are merged to obtain merged variation data. Population variation detection is performed on each merged variation data segment to obtain population genomic variation data for each merged variation data segment. The genomic variation data for each population segment are then merged by chromosome region to obtain target population genomic variation data for the multiple samples. Thus, by merging the local population genomic variation data by chromosome region, the complete population genomic variation data corresponding to each stained region of the multiple samples can be conveniently and simply obtained.

[0070] Based on any of the above embodiments, in order to improve the accuracy of the obtained population genome difference data, in some examples, the population information of multiple samples can be combined to determine the method of dividing each genome variation data, and the each genome variation data can be divided based on the determined division method, and subsequent analysis can be performed based on the divided data. In order to clearly understand the process, the process is exemplarily described below in conjunction with Figure 3.

[0071] FIG3 is a flowchart of a method for accelerating high-performance sequencing data Joint-Calling according to another embodiment of the present disclosure.

[0072] As shown in FIG3 , the method for accelerating the high-performance sequencing data Joint-Calling may include:

[0073] Step 301: Obtain genomic variation data corresponding to each of a plurality of samples.

[0074] For the specific implementation of step 301, please refer to the relevant description of the embodiment of the present disclosure, which will not be repeated here.

[0075] Step 302 : Compare the group information of multiple samples to see if they are the same, to obtain a comparison result.

[0076] Step 303: When the comparison result shows that the population information of the multiple samples is the same, determine that the division method of the genome variation data is the first division method.

[0077] The first division method is used to indicate a division method based on the density of variant sites.

[0078] Step 304 , when the division method is the first division method, for each genomic variation data, the genomic variation data is divided according to the variation site density to obtain a plurality of variation data segments corresponding to the genomic variation data.

[0079] For the specific implementation of step 304, please refer to the relevant description in the embodiment of the present disclosure, which will not be repeated here.

[0080] Step 305 : When the comparison result shows that the population information of the multiple samples is different, determining that the division method of the genome variation data is the second division method.

[0081] The second division method is used to indicate a division method based on allele density.

[0082] Step 306 , when the division method is the second division method, for each genomic variation data, the genomic variation data is divided according to the allele density to obtain a plurality of variation data segments corresponding to the genomic variation data.

[0083] The order of the variant data segments is determined based on the positions of the variant data segments in the genome variant data.

[0084] It should be noted that, for the specific implementation of step 306, reference may be made to the relevant description of the embodiment of the present disclosure, which will not be repeated here.

[0085] Step 307 : Merge the variation data segments with the same order in each genome variation data to obtain merged variation data.

[0086] For the specific implementation of step 307, please refer to the relevant description of the embodiment of the present disclosure, which will not be repeated here.

[0087] Step 308 : Perform population variation detection on each merged variation data to obtain population genome variation data of each merged variation data.

[0088] Step 309 : Merge the genome variation data of each population to obtain the genome variation data of the target population of multiple samples.

[0089] It should be noted that, for the specific implementation of steps 307 to 309, please refer to the relevant description of the embodiment of the present disclosure, which will not be repeated here.

[0090] In this example, based on the population information of multiple samples, it is determined whether the multiple samples come from the same population, and based on the determination result, a division method for the genomic variation data is determined. The genomic variation data are divided based on the determined division method to obtain multiple variation data segments corresponding to each genomic variation data, and the variation data segments with the same order in each genomic variation data are merged to obtain merged variation data. Population variation detection is performed on each merged variation data to obtain the population genomic variation data of each merged variation data. The genomic variation data of each population are merged according to chromosome region to obtain the target population genomic variation data of multiple samples. In this way, the accuracy of the obtained population genomic variation data can be improved.

[0091] In one embodiment of the present disclosure, in some scenarios, there may be a long region on the genome without a mutation site. In order to facilitate the subsequent merging of genomic variation data of each local population based on chromosome regions, before dividing the genomic data using a division method determined based on population information of multiple samples, the genomic data can also be divided based on a preset division length. In order to clearly understand the present disclosure, the method of this embodiment is exemplarily described below in conjunction with Figure 4.

[0092] FIG4 is a flowchart of a method for accelerating high-performance sequencing data Joint-Calling according to another embodiment of the present disclosure.

[0093] As shown in FIG4 , the method for accelerating the high-performance sequencing data Joint-Calling may include:

[0094] Step 401: Obtain genome variation data corresponding to each of a plurality of samples.

[0095] Step 402: Determine a division method for genome variation data based on population information of multiple samples.

[0096] It should be noted that, for the specific implementation of step 401 and step 402, please refer to the relevant description of the embodiment of the present disclosure, which will not be repeated here.

[0097] Step 403 : For each genome variation data, the genome variation data is divided according to a preset length to obtain a plurality of variant sub-data corresponding to the genome variation data.

[0098] The preset length is a length pre-set in the population variation detection device. In actual applications, the value of the preset length can be determined according to actual application requirements, and this embodiment does not specifically limit this.

[0099] It should be noted that the execution order of step 402 and step 403 is not particular.

[0100] Step 404 : Divide the plurality of variant sub-data according to the division method to obtain a plurality of variant data segments corresponding to the genome variation.

[0101] The order of the variant data segments is determined based on the positions of the variant data segments in the genome variant data.

[0102] Step 405 : Merge the variation data segments with the same order in each genome variation data to obtain merged variation data.

[0103] Step 406 , performing population variation detection on each merged variation data to obtain population genome variation data of each merged variation data.

[0104] Step 407: Determine the chromosome region corresponding to the genome variation data of each population.

[0105] Step 408 : Merge the population genome variation data in the same chromosome region to obtain the target population genome variation data of multiple samples.

[0106] It should be noted that, for the specific implementation of steps 405 to 408, please refer to the relevant description of the embodiment of the present disclosure, which will not be repeated here.

[0107] In this example, the genomic variation data is divided based on a preset length to obtain multiple variant sub-data of the genomic variation data, and each variant sub-data is further divided based on the division method determined by the population information of multiple samples to obtain variant data segments, and the variant data segments with the same order in each genomic variation data are merged to obtain merged variation data, and population variation detection is performed on each merged variation data to obtain the population genomic variation data of each merged variation data, and the chromosome region corresponding to each population genomic variation data is determined. The population genomic variation data in the same chromosome region are merged to obtain the target population genomic variation data of multiple samples. Thus, by dividing the genomic data in two ways and performing subsequent population variation detection based on the divided data, the accuracy of the target population genomic variation data obtained subsequently can be improved.

[0108] In one embodiment of the present disclosure, FIG5 is a flowchart of a method for accelerating Joint-Calling of high-performance sequencing data according to another embodiment of the present disclosure.

[0109] As shown in FIG5 , the method may further include:

[0110] Step 501 : for each genome variation data, the multiple variation data segments of the genome variation data are divided again according to preset lengths to obtain multiple variation data sub-segments corresponding to the genome variation data.

[0111] The order of the variant data sub-segments is determined based on the positions of the variant data sub-segments in the variant data segment to which they belong.

[0112] The preset length is a length pre-set in the population variation detection device. In actual applications, the value of the preset length can be determined according to actual application requirements, and this embodiment does not specifically limit this.

[0113] Step 502: Merge the variant data sub-segments with the same order in each genome variant data to obtain a merged variant data segment.

[0114] Step 503 : Perform population variation detection on each merged variation data segment to obtain population genome variation data of each merged variation data segment.

[0115] Step 504 : Merge the population genome variation data of each merged variation data segment to obtain merged genome variation data corresponding to multiple samples.

[0116] In this example, the genomic data is divided based on a division method determined by population information of multiple samples to obtain multiple variant data segments of genomic variation data. The multiple variant data segments of the genomic variation data are then divided based on preset lengths to obtain variant data sub-segments. The variant data sub-segments with the same order in each genomic variation data are merged to obtain merged variant data segments. Population variation detection is performed on each merged variant data segment to obtain population genomic variation data for each merged variant data segment. The population genomic variation data for each merged variant data segment are merged to obtain a merged genomic variation number corresponding to multiple samples. Thus, by dividing the genomic data using two division methods and performing subsequent population variation detection based on the divided data, the accuracy of the subsequently obtained population genomic variation data can be improved.

[0117] In order to clearly understand the present disclosure, the method for accelerating the high-performance sequencing data Joint-Calling of this embodiment is exemplarily described below with reference to FIG6 .

[0118] FIG6 is a flowchart of a method for accelerating high-performance sequencing data Joint-Calling according to another embodiment of the present disclosure.

[0119] As shown in FIG6 , the method for accelerating the high-performance sequencing data Joint-Calling may include:

[0120] In step 601 , the genome variation data corresponding to each of the multiple samples are divided to obtain multiple variation data segments corresponding to each of the genome variation data.

[0121] It should be noted that the division method of the genomic variation data can be determined based on the population information of multiple samples.

[0122] Among them, for a specific description of determining a division method for genome variation data based on population information of multiple samples, please refer to the relevant description of the embodiment of the present disclosure, which will not be repeated here.

[0123] Step 602: Merge the variation data segments with the same order in each genome variation data to obtain merged variation data.

[0124] Step 603 : Perform population variation detection on each merged variation data to obtain population genome variation data of each merged variation data.

[0125] Step 604 , based on the chromosome regions corresponding to the respective population genome variation data, the population genome variation data in the same chromosome region are merged to obtain the target population genome variation data of the corresponding chromosome regions of the multiple samples.

[0126] It should be noted that, for the specific implementation of steps 602 to 604, please refer to the relevant description of the embodiment of the present disclosure, which will not be repeated here.

[0127] In this example, during the process of population variation detection, population anomaly detection is performed on multiple local merged data separately to obtain multiple local population genome anomaly data, and each local population genome anomaly data is merged to obtain complete population genome data, thereby improving the efficiency of obtaining complete population genome data.

[0128] In some exemplary embodiments, after obtaining complete population genome variation data corresponding to multiple samples, if new samples are collected, in order to save a lot of computing resources and time consumption, in this example, the new samples can also be processed in the manner disclosed herein to obtain population genome variation data corresponding to the new samples. Then, the population genome variation data corresponding to the original multiple samples and the population gene variation data corresponding to the new samples are merged and processed to obtain the final population genome variation data of the population.

[0129] Corresponding to the population variation detection methods provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides a device for accelerating the joint-calling of high-performance sequencing data. Since the device for accelerating the joint-calling of high-performance sequencing data provided in the embodiment of the present disclosure corresponds to the methods for accelerating the joint-calling of high-performance sequencing data provided in the above-mentioned embodiments, the implementation of the method for accelerating the joint-calling of high-performance sequencing data is also applicable to the device for accelerating the joint-calling of high-performance sequencing data in this embodiment, and will not be described in detail in this embodiment.

[0130] FIG7 is a schematic diagram of the structure of an apparatus for accelerating high-performance sequencing data Joint-Calling according to an embodiment of the present disclosure.

[0131] As shown in FIG7 , the apparatus 700 for accelerating high-performance sequencing data by joint calling includes: an acquisition module 701 , a first determination module 702 , a first division module 703 , a first merging module 704 , a first population variation detection module 705 , and a second merging module 706 , wherein:

[0132] An acquisition module 701 is used to obtain genome variation data corresponding to each of the multiple samples;

[0133] A first determining module 702 is configured to determine a division method for genome variation data based on population information of multiple samples;

[0134] A first partitioning module 703 is configured to partition each genomic variation data according to a partitioning method to obtain a plurality of variation data segments corresponding to each genomic variation data, wherein the order of the variation data segments is determined based on the position of the variation data segments in the genomic variation data;

[0135] A first merging processing module 704 is configured to merge variant data segments with the same order in each genome variant data to obtain merged variant data;

[0136] A first population variation detection module 705 is configured to perform population variation detection on each merged variation data to obtain population genome variation data of each merged variation data;

[0137] The second merging processing module 706 is used to merge the genome variation data of each population to obtain the genome variation data of the target population of multiple samples.

[0138] In one embodiment of the present disclosure, the second merging processing module 706 is specifically used to: determine the chromosome region corresponding to each population genome variation data; merge the population genome variation data in the same chromosome region to obtain the target population genome variation data of multiple samples.

[0139] In one embodiment of the present disclosure, based on the apparatus embodiment shown in FIG. 7 , as shown in FIG. 8 , the first determining module 702 may include:

[0140] Comparison unit 7021, used to compare whether the group information of multiple samples is the same to obtain a comparison result;

[0141] The determination unit 7022 is used to determine the division method of the genome variation data according to the comparison result.

[0142] In one embodiment of the present disclosure, the determination unit 7022 is specifically used to: when the comparison result shows that the population information of multiple samples is the same, determine that the division method of the genomic variation data is a first division method; when the comparison result shows that the population information of multiple samples is different, determine that the division method of the genomic variation data is a second division method, wherein the first division method is used to indicate a division method based on the density of the variation sites, and the second division method is used to indicate a division method based on the density of the alleles.

[0143] In one embodiment of the present disclosure, when the division method is the first division method, the first division module 703 is specifically used to: for each genome variation data, divide the genome variation data according to the variation site density to obtain multiple variation data segments corresponding to the genome variation data.

[0144] In one embodiment of the present disclosure, when the division method is the second division method, the first division module 703 is specifically used to: for each genomic variation data, divide the genomic variation data according to the allele density to obtain multiple variation data segments corresponding to the genomic variation data.

[0145] In one embodiment of the present disclosure, as shown in FIG8 , the apparatus may further include:

[0146] The second partitioning module 707 is configured to partition each genome variation data according to a preset length to obtain a plurality of variant sub-data corresponding to the genome variation data;

[0147] The first partitioning module 703 is specifically configured to partition the plurality of variant sub-data according to a partitioning method to obtain a plurality of variant data segments corresponding to the genome variation.

[0148] In one embodiment of the present disclosure, as shown in FIG8 , the apparatus may further include:

[0149] a third partitioning module 708 for further partitioning the multiple variant data segments of the genomic variant data according to preset lengths for each genomic variant data to obtain multiple variant data sub-segments corresponding to the genomic variant data, wherein the order of the variant data sub-segments is determined based on the position of the variant data sub-segments within the variant data segment to which they belong;

[0150] The third merging processing module 709 is used to merge the variant data sub-segments with the same order in each genome variant data to obtain a merged variant data segment;

[0151] The second population variation detection module 710 is configured to perform population variation detection on each merged variation data segment to obtain population genome variation data of each merged variation data segment.

[0152] In one embodiment of the present disclosure, when population variation detection is performed on multiple merged variation data through multiple computing nodes, the first population variation detection module 705 is specifically used to: for each computing node: obtain an unprocessed target merged variation data from the multiple merged variation data; call the computing node to perform population variation detection on the target merged variation data to obtain population genome variation data corresponding to the target merged variation data; obtain the next unprocessed target merged variation data from the multiple merged variation data, until population variation detection is completed for all the multiple merged variation data.

[0153] The device for accelerating the joint-calling of high-performance sequencing data of the disclosed embodiment, in the process of processing the genomic variation data corresponding to each of the multiple samples, determines the division method of the genomic variation data according to the population information of the multiple samples, and divides each genomic variation data according to the division method to obtain multiple variation data fragments corresponding to each genomic variation data and merges the variation data fragments with the same order in each genomic variation data to obtain merged variation data; performs population variation detection on each merged variation data to obtain the population genomic variation data of each merged variation data, and merges each population genomic variation data to obtain the target population genomic variation data of multiple samples. Thus, in the process of population variation detection, by performing population anomaly detection on multiple local merged data respectively and merging the detection results, the efficiency of obtaining complete population genomic variation data of multiple samples can be improved.

[0154] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0155] FIG9 is a structural block diagram of an electronic device according to an embodiment of the present disclosure.

[0156] As shown in FIG9 , the electronic device 900 includes a memory 910 , a processor 920 , and computer instructions stored in the memory 910 and executable on the processor 920 .

[0157] When the processor 920 executes the instructions, the method for accelerating the high-performance sequencing data Joint-Calling provided in the above embodiment is implemented.

[0158] Furthermore, the electronic device 900 further includes:

[0159] The communication interface 930 is used for communication between the memory 910 and the processor 920 .

[0160] The memory 910 is used to store computer instructions that can be executed on the processor 920 .

[0161] The memory 910 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0162] The processor 920 is configured to implement the method for accelerating the high-performance sequencing data Joint-Calling according to the above embodiment when executing the program.

[0163] If the memory 910, processor 920, and communication interface 930 are implemented independently, the communication interface 930, memory 910, and processor 920 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of illustration, FIG9 shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0164] Optionally, in a specific implementation, if the memory 910, the processor 920 and the communication interface 930 are integrated on a chip, the memory 910, the processor 920 and the communication interface 930 can communicate with each other through an internal interface.

[0165] The processor 920 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present disclosure.

[0166] Another aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the device for accelerating the Joint-Calling of high-performance sequencing data according to any of the embodiments of the present disclosure is implemented.

[0167] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0168] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0169] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for accelerating joint-calling of high-performance sequencing data, characterized in that: The method comprises: Obtain genome variation data corresponding to multiple samples; Determining a division method of the genome variation data according to the population information of the multiple samples; According to the division method, each of the genome variation data is divided respectively to obtain a plurality of variation data segments corresponding to each of the genome variation data, wherein the order of the variation data segments is determined based on the positions of the variation data segments in the genome variation data; Merging the variation data fragments with the same order in each of the genome variation data to obtain merged variation data; Performing population variation detection on each of the merged variation data respectively to obtain population genome variation data of each of the merged variation data; The genome variation data of each population are merged to obtain the genome variation data of the target population of the multiple samples.

2. The method according to claim 1, characterized in that The merging of the genome variation data of each population to obtain the genome variation data of the target population of the multiple samples includes: Determining the chromosome region corresponding to each of the population genome variation data; The population genome variation data in the same chromosome region are merged to obtain the target population genome variation data of the multiple samples.

3. The method according to claim 1, characterized in that Determining the division method of the genome variation data according to the population information of the multiple samples includes: Comparing whether the group information of the multiple samples is the same to obtain a comparison result; According to the comparison result, a division method of the genome variation data is determined.

4. The method according to claim 3, characterized in that Determining the division method of the genome variation data according to the comparison result includes: When the comparison result shows that the population information of the multiple samples is the same, determining that the division method of the genome variation data is the first division method; When the comparison result is that the population information of the multiple samples is different, the division method of the genome variation data is determined to be a second division method, wherein the first division method is used to indicate a division method based on variation site density, and the second division method is used to indicate a division method based on allele density.

5. The method according to claim 4, characterized in that In the case where the division method is the first division method, the division of each genome variation data according to the division method to obtain a plurality of variation data segments corresponding to each genome variation data includes: For each of the genome variation data, the genome variation data is divided according to the variation site density, To obtain multiple variation data segments corresponding to the genome variation data.

6. The method according to claim 5, characterized in that For each of the genome variation data, the genome variation data is divided according to the variation site density to obtain a plurality of variation data segments corresponding to the genome variation data, including: For each genome variation data, statistics are collected on the distribution of variation sites in the genome variation data to obtain variation site statistics results for the genome variation data; According to the variation site statistics, the genome variation data is divided based on variation site density to obtain a plurality of variation data segments corresponding to the genome variation data, wherein the lengths corresponding to the plurality of variation data segments are different, and the difference in number between the number of variation sites on the plurality of variation data segments is within a first preset number.

7. The method according to claim 4, characterized in that In the case where the division method is the second division method, the division of each genome variation data according to the division method to obtain a plurality of variation data segments corresponding to each genome variation data includes: For each genome variation data, the genome variation data is divided according to the allele density to obtain a plurality of variation data segments corresponding to the genome variation data.

8. The method according to claim 7, characterized in that For each of the genome variation data, the genome variation data is divided according to the allele density to obtain a plurality of variation data segments corresponding to the genome variation data, including: For the genome variation data, determining the allele information corresponding to each variation site in the genome variation data; Performing statistics on the allele information in the genome variation data to obtain a statistical result of the allele information of the genome variation data; According to the statistical results, the genomic variation data is divided based on gene allele density to obtain a plurality of variation data segments corresponding to the genomic variation data, wherein the lengths corresponding to the plurality of variation data segments are different, and the difference between the numbers of alleles in the plurality of variation data segments is within a second preset number.

9. The method according to claim 2, characterized in that According to the division method, before dividing each of the genome variation data respectively to obtain a plurality of variation data segments corresponding to each of the genome variation data, the method further includes: For each genome variation data, the genome variation data is divided according to a preset length to obtain a plurality of variation sub-data corresponding to the genome variation data; According to the division method, each of the genome variation data is divided respectively to obtain a plurality of variation data segments corresponding to each of the genome variation data, including: According to the division method, the plurality of variant sub-data are divided to obtain the corresponding Multiple variant data fragments.

10. The method according to claim 1, characterized in that The method further comprises: For each genome variation data, the multiple variation data segments of the genome variation data are divided again according to preset lengths to obtain multiple variation data sub-segments corresponding to the genome variation data, wherein the order of the variation data sub-segments is determined based on the positions of the variation data sub-segments in the variation data segment to which they belong; Merging the variation data sub-segments with the same order in each of the genome variation data to obtain a merged variation data segment; Population variation detection is performed on each of the merged variation data segments respectively to obtain population genome variation data of each of the merged variation data segments.

11. The method according to any one of claims 1 to 10, characterized in that In the case where a plurality of the merged variation data are subjected to population variation detection by using a plurality of computing nodes, the population variation detection is performed on each of the merged variation data to obtain the population genome variation data of each of the merged variation data, including: For each compute node: Acquire an unprocessed target merged variation data from the plurality of merged variation data; Calling the computing node to perform population variation detection on the target merged variation data to obtain population genome variation data corresponding to the target merged variation data; The next unprocessed target merged variation data is obtained from the plurality of merged variation data until the plurality of merged variation data completes the population variation detection.

12. A device for accelerating high-performance sequencing data Joint-Calling, characterized in that: The device comprises: An acquisition module, used to obtain genome variation data corresponding to each of the multiple samples; A first determination module, configured to determine a division method of the genome variation data according to the population information of the plurality of samples; A first partitioning module is used to partition each of the genome variation data according to the partitioning method to obtain a plurality of variation data segments corresponding to each of the genome variation data, wherein the order of the variation data segments is determined based on the positions of the variation data segments in the genome variation data; A first merging processing module is used to merge the variation data segments with the same order in each of the genome variation data to obtain merged variation data; A first population variation detection module is used to perform population variation detection on each of the merged variation data to obtain population genome variation data of each of the merged variation data; The second merging processing module is used to merge the genome variation data of each population to obtain the genome variation data of the target population of the multiple samples.

13. The device according to claim 12, characterized in that The second merging processing module is specifically used to: Determining the chromosome region corresponding to each of the population genome variation data; The population genome variation data in the same chromosome region are merged to obtain the target population genome variation data of the multiple samples.

14. The device according to claim 12, characterized in that The first determining module includes: A comparison unit, used for comparing whether the group information of the plurality of samples is the same to obtain a comparison result; A determination unit is used to determine a division method of the genome variation data according to the comparison result.

15. The device according to claim 14, characterized in that The determining unit is specifically configured to: When the comparison result shows that the population information of the multiple samples is the same, determining that the division method of the genome variation data is the first division method; When the comparison result is that the population information of the multiple samples is different, the division method of the genome variation data is determined to be a second division method, wherein the first division method is used to indicate a division method based on variation site density, and the second division method is used to indicate a division method based on allele density.

16. The device according to claim 15, characterized in that When the division mode is the first division mode, the first division module is specifically used to: For each genome variation data, the genome variation data is divided according to variation site density to obtain a plurality of variation data segments corresponding to the genome variation data.

17. The device according to claim 16, characterized in that The first division module is specifically used for: For each genome variation data, statistics are collected on the distribution of variation sites in the genome variation data to obtain variation site statistics results for the genome variation data; According to the variation site statistics, the genome variation data is divided based on the variation site density to obtain a plurality of variation data segments corresponding to the genome variation data, wherein the lengths corresponding to the plurality of variation data segments are different, and the difference in number between the number of variation sites on the plurality of variation data segments is within a first preset number.

18. The device according to claim 15, characterized in that When the division mode is the second division mode, the first division module is specifically used to: For each genome variation data, the genome variation data is divided according to the allele density to obtain a plurality of variation data segments corresponding to the genome variation data.

19. The device according to claim 18, characterized in that The first division module is specifically used for: For the genome variation data, determining the allele information corresponding to each variation site in the genome variation data; Performing statistics on the allele information in the genome variation data to obtain a statistical result of the allele information of the genome variation data; According to the statistical result, the genome variation data is divided based on the gene allele density to obtain a plurality of variation data segments corresponding to the genome variation data, wherein the lengths corresponding to the plurality of variation data segments are different, and the difference between the number of alleles in the plurality of variation data segments is within a second preset number. Inside.

20. The device according to claim 14, characterized in that The device also includes: A second division module is used to divide each genome variation data according to a preset length to obtain a plurality of variant sub-data corresponding to the genome variation data; The first division module is specifically used for: According to the division method, the multiple variant sub-data are divided to obtain multiple variant data segments corresponding to the genome variation.

21. The device according to claim 15, characterized in that The device also includes: a third division module, configured to divide the multiple variation data segments of the genome variation data again according to preset lengths for each genome variation data, so as to obtain multiple variation data sub-segments corresponding to the genome variation data, wherein the order of the variation data sub-segments is determined based on the positions of the variation data sub-segments in the variation data segment to which they belong; A third merging processing module is used to merge the variation data sub-segments with the same order in each of the genome variation data to obtain a merged variation data segment; The second population variation detection module is used to perform population variation detection on each of the merged variation data segments respectively to obtain population genome variation data of each of the merged variation data segments.

22. The device according to any one of claims 12 to 21, characterized in that In the case where group variation detection is performed on the plurality of merged variation data by using the plurality of computing nodes, the first group variation detection module is specifically used to: For each compute node: Acquire an unprocessed target merged variation data from the plurality of merged variation data; Calling the computing node to perform population variation detection on the target merged variation data to obtain population genome variation data corresponding to the target merged variation data; The next unprocessed target merged variation data is obtained from the plurality of merged variation data until the plurality of merged variation data completes the population variation detection.

23. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 11 is implemented.

24. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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