Group genome variation data processing method and device and electronic equipment

Abstract

The invention provides a group genome variation data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of chromosome variation data samples, determining an interval division mode corresponding to the plurality of chromosome variation data samples according to a plurality of group source identification information, dividing each chromosome variation data sample according to the interval division mode to obtain a plurality of variation data fragments corresponding to each chromosome variation data sample, and processing the plurality of variation data fragments based on variation data filtering parameters to obtain target group genome variation data. According to the method and the device, the variation data filtering task of the chromosome variation data sample can be divided into a plurality of sub-tasks with balanced data processing capacity based on the group source of the chromosome variation data sample, and the plurality of sub-tasks are processed in parallel, so that operation waiting caused by a sub-task trailing phenomenon can be avoided; therefore, computing resources are effectively saved, and the data processing speed is increased.

Description

Technical Field

[0001] 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 processing population genome variation data. Background Art

[0002] Currently, Variant Quality Score Recalibration (VQSR) is a key link in population genomics research. Through machine learning methods, a Gaussian mixture model is trained using multiple different data features to perform quality control on variant data in order to distinguish "good" variants from "bad" variants in genomic variant data and filter out high-quality variant data.

[0003] In the related art, the population genome analysis is usually performed directly using the resequencing data analysis tool (The Genome Analysis Toolkit, GATK) operation mode.

[0004] In this way, when the scale of genomic data rises to the scale of tens of thousands or hundreds of thousands of people, it is easy to encounter problems such as the difficulty in implementing ultra-large population genome analysis and the time consumption when processing population genome VQSR. Summary of the invention

[0005] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0006] To this end, the purpose of the present disclosure is to propose a method, device, electronic device, storage medium and computer program product for processing population genome variation data.

[0007] The first aspect of the present disclosure proposes a method for processing population genome variation data, comprising: obtaining multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information; determining interval division patterns corresponding to the multiple chromosome variation data samples according to the multiple population source identification information; dividing each of the chromosome variation data samples according to the interval division pattern to obtain multiple variation data segments corresponding to each of the chromosome variation data samples; and processing the multiple variation data segments based on variation data filtering parameters to obtain the target population genome variation data.

[0008] A second aspect of the present disclosure provides a population genome variation data processing device, comprising: an acquisition module, used to acquire multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information; a determination module, used to determine the interval division mode corresponding to the multiple chromosome variation data samples according to the multiple population source identification information; a first processing module, used to divide each of the chromosome variation data samples according to the interval division mode, so as to obtain multiple variation data fragments corresponding to each of the chromosome variation data samples; and a second processing module, used to process the multiple variation data fragments based on variation data filtering parameters to obtain the target population genome variation data.

[0009] The third aspect of the present disclosure proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the population genome variation data processing method proposed in the first aspect of the present disclosure.

[0010] The fourth aspect embodiment of the present disclosure proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the population genome variation data processing method proposed in the first aspect embodiment of the present disclosure.

[0011] The fifth embodiment of the present disclosure proposes a computer program product. When the instructions in the computer program product are executed by a processor, the method for processing population genome variation data proposed in the first embodiment of the present disclosure is executed.

[0012] The population genome variation data processing method, device, electronic device, storage medium and computer program product proposed in the embodiments of the present disclosure may at least include the following beneficial effects: by obtaining multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information, determining the interval division mode corresponding to the multiple chromosome variation data samples according to the multiple population source identification information, dividing each chromosome variation data sample according to the interval division mode to obtain multiple variation data fragments corresponding to each chromosome variation data sample, processing the multiple variation data fragments based on variation data filtering parameters to obtain the target population genome variation data, and being able to divide the variation data filtering task of the chromosome variation data sample into multiple subtasks with balanced data processing volume based on the population source of the chromosome variation data sample, and processing the multiple subtasks in parallel, thereby avoiding the running waiting caused by the subtask tailing phenomenon, thereby effectively saving computing resources and improving the data processing speed.

[0013] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0015] Figure 1 It is a flowchart of a method for processing population genome variation data proposed in one embodiment of the present disclosure;

[0016] Figure 2 It is a flowchart of the recalibration of the variant quality value;

[0017] Figure 3 is a flow chart of a method for processing population genome variation data proposed in another embodiment of the present disclosure;

[0018] Figure 4 is a flow chart of a method for processing population genome variation data proposed in another embodiment of the present disclosure;

[0019] Figure 5 is an example diagram of a gene density table in an embodiment of the present disclosure;

[0020] Figure 6 is a schematic diagram of a process for performing interval division based on a variation site density table in an embodiment of the present disclosure;

[0021] Figure 7 is a flow chart of a method for processing population genome variation data proposed in another embodiment of the present disclosure;

[0022] Figure 8 It is a structural schematic diagram of a population genome variation data processing device proposed in an embodiment of the present disclosure;

[0023] Fig. 9 is a schematic diagram of the structure of a population genome variation data processing device proposed in another embodiment of the present disclosure;

[0024] Fig.10 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0025] Embodiments of the present disclosure are described in detail below, examples of which 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 only used to explain the present disclosure, and are not to be construed as limitations of the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications, and equivalents that fall within the spirit and connotation of the appended claims.

[0026] Figure 1 It is a flowchart of a method for processing population genome variation data proposed in one embodiment of the present disclosure.

[0027] It should be noted that the executor of the population genome variation data processing method of this embodiment is a population genome variation data processing device, which can be implemented by software and / or hardware, and can be configured in an electronic device, without limitation.

[0028] like Figure 1 As shown, the method for processing population genome variation data includes:

[0029] S101: Acquire multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information.

[0030] Among them, the chromosome variation data sample refers to a data sample used to store the gene variation situation occurring at the gene locus on the chromosome. The chromosome variation data sample can be stored in a genome variation file (Variant CallFormat, VCF), and the chromosome variation data sample has corresponding population source identification information.

[0031] The population source identification information refers to the identification information used to identify the population source of the genome variation information contained in the chromosome variation data sample.

[0032] For example, when multiple population source identifiers are the same, it indicates that the corresponding multiple chromosome variation data samples come from closely related homologous groups. When multiple population source identifiers are different, it indicates that the corresponding multiple chromosome variation data come from large-scale groups with high relatedness and heterozygosity.

[0033] In the disclosed embodiment, when obtaining multiple chromosome variation data samples, the genome variation record files (Genome Variant Call Format, GVCF) of multiple single samples can be first obtained, and the genome variation record files of the multiple single samples can be jointly subjected to variation detection processing to obtain corresponding variation detection result files, and the variation detection result information belonging to the same numbered chromosome in the obtained variation detection result files can be merged to obtain chromosome variation data samples corresponding to 25 chromosomes, so as to obtain multiple chromosome variation data samples, or multiple chromosome variation data samples can be obtained from a publicly released database, without limitation, wherein each chromosome variation data sample has corresponding population source identification information for identifying the gene source population of the chromosome variation data sample.

[0034] S102: Determine interval division patterns corresponding to multiple chromosome variation data samples according to multiple population source identification information.

[0035] Among them, the interval division mode refers to a data grouping mode that can be used to divide the chromosome variation data sample into intervals to obtain multiple sub-data samples with smaller data volumes. The interval division mode, for example, can be an interval division mode that divides according to the density of gene variation sites on the chromosome, or can be an interval division mode that divides according to the density of alleles corresponding to the genes on the chromosome.

[0036] After obtaining the plurality of chromosome variation data samples and the group source identification information corresponding to the plurality of chromosome variation data samples, the embodiment of the present disclosure can determine the interval division mode corresponding to the plurality of chromosome variation data samples according to the plurality of group source identification information.

[0037] In an embodiment of the present disclosure, when determining the interval division pattern corresponding to multiple chromosome variation data samples based on multiple population source identification information, the multiple population source information can be analyzed and processed to obtain the population type to which the multiple chromosome variation data samples belong, and the interval division pattern corresponding to the chromosome variation data samples is determined based on the population type to which the multiple chromosome variation data samples belong. When the population type to which the multiple chromosome variation data samples belong is a homologous group, the multiple chromosome variation data samples can be divided according to the density of gene variation sites on the chromosome; when the population type to which the multiple chromosome variation data samples belong is a large-scale non-homologous group, the multiple chromosome variation data samples can be divided according to the allele density corresponding to the gene on the chromosome.

[0038] Optionally, in some embodiments, when determining the interval division patterns corresponding to multiple chromosome variation data samples based on multiple population source identification information, the multiple population source identification information can be compared to obtain comparison results, and the interval division pattern can be determined based on the comparison results. Since the density of gene variation sites in chromosome variation data samples from different groups is different, the interval division pattern is adaptively set for the different group sources of chromosome variation data samples to group the chromosome data samples, so as to generate multiple chromosome variation data analysis subtasks for parallel execution, thereby ensuring that the numbers of gene variation sites contained in the multiple divided sub-data samples are roughly the same, thereby ensuring the accuracy of the interval division.

[0039] In an embodiment of the present disclosure, when determining the interval division mode corresponding to multiple chromosome variation data samples based on multiple population source identification information, the multiple population source identification information can be compared to compare whether the multiple population source identification information are the same to obtain a comparison result. If the comparison result indicates that the multiple population source identification information are the same, it can be determined that the interval division mode is to divide the multiple chromosome variation data samples according to the density of gene variation sites on the chromosome. If the comparison result indicates that the multiple population source identification information are not the same, it can be determined that the interval division mode is to divide the multiple chromosome variation data samples according to the allele density corresponding to the gene on the chromosome.

[0040] S103: Divide each chromosome variation data sample according to the interval division mode to obtain a plurality of variation data segments corresponding to each chromosome variation data sample.

[0041] Among them, the variant data fragment refers to multiple sub-data samples obtained after the chromosome variant data samples are grouped according to the interval division mode, and the number of gene variant sites contained in the multiple variant data fragments after the division process can be kept balanced.

[0042] In the embodiment of the present disclosure, after obtaining multiple chromosome variation data samples and their corresponding population source identification information and determining the interval division patterns corresponding to the multiple chromosome variation data samples based on the multiple population source identification information, each chromosome variation data sample can be divided according to the interval division pattern to obtain multiple variation data segments corresponding to each chromosome variation data sample.

[0043] In the embodiment of the present disclosure, when each chromosome variation data sample is divided according to the interval division mode to obtain multiple variation data segments corresponding to each chromosome variation data sample, the number of intervals for which the chromosome data sample needs to be divided can be pre-set, and an estimated value of the number of gene variation sites contained in each variation data segment is obtained according to the number of intervals. Then, for each chromosome variation data sample, if the interval division mode is to divide according to the density of gene variation sites on the chromosome, the chromosome variation data sample can be divided into multiple sub-data samples containing the same number of gene variation sites according to the estimated value and the chromosome variation site density table, and the divided sub-data samples are used as variation data segments. If the interval division mode is to divide according to the allele density of the gene variation sites on the chromosome, the chromosome variation data sample can be divided into multiple sub-data samples containing the same number of gene variation sites according to the estimated value and the chromosome allele density table, and the divided sub-data samples are used as variation data segments, and each chromosome variation data sample is processed one by one to obtain multiple variation data segments corresponding to each chromosome variation data sample.

[0044] S104: Process multiple variant data segments based on variant data filtering parameters to obtain target population genome variant data.

[0045] The target population genome variation data refers to the genome variation data with better quality obtained by filtering after analyzing and processing the variation data information quality of the gene variation sites in each variation data fragment.

[0046] In the embodiment of the present disclosure, when processing multiple variant data segments based on variant data filtering parameters to obtain target population genome variant data, multiple variant data segments are subjected to variant quality value recalibration (VQSR) processing. First, a machine learning method can be used to obtain the variant quality value of the target population genome variant data based on the Gaussian Mixed Model (MGM). A Gaussian mixture model (GMM) is used to cluster multiple chromosome variation data samples obtained by combining with a standard set database for training, so as to train a classification model that can be used to evaluate the quality of gene variation result data of gene variation sites in multiple variation data fragments. The classification model includes multiple variation data filtering parameters, each variation data filtering parameter has a corresponding parameter threshold. Based on the classification model, the variation data fragments are scored for the variation data filtering parameters, and each score obtained is compared with the corresponding parameter threshold. The gene variation result data of the gene variation site that meets the parameter threshold is selected, and each variation data fragment is filtered one by one to obtain the gene variation site and its gene variation result data with better variation quality in each variation data fragment. The corresponding gene variation site and its gene variation result data obtained by filtering the variation data fragments belonging to the same chromosome variation data sample are merged to obtain the variation result data with better quality in each chromosome variation data sample, and the variation result data of the chromosome variation data sample are collectively used as the target population genome variation data.

[0047] In the disclosed embodiment, when processing multiple variant data fragments based on variant data filtering parameters to obtain the target population genome variant data, a single server or a computer cluster can be used for computational processing. Since the amount of variant data fragments involved in the population genome analysis data filtering is large, a data processing subtask can be established for each variant data fragment, and the data processing subtask can be put into a computer cluster. The computer cluster is used for computational processing. The computer cluster composed of multiple computer nodes performs parallel computational processing on multiple variant data fragments in batches to obtain the target population genome variant data.

[0048] For example, Figure 2 As shown, Figure 2: is a flowchart of variation quality value recalibration, in which the variation data record file VCF corresponding to each numbered chromosome is first obtained as a chromosome variation data sample, and then the variation data record file VCF is subjected to VQSR processing to obtain variation filtering parameters for filtering variation result information thereof, and then the chromosome variation data sample is subjected to interval division processing to obtain a plurality of variation data segments, wherein the number of gene variation sites contained in the plurality of variation data segments is substantially the same, and the variation data segments are filtered based on the variation filtering processing parameters to obtain a plurality of variation quality value recalibration result files, and then the plurality of variation quality value recalibration result files belonging to the same numbered chromosome are merged to obtain the final target population genome variation data.

[0049] In this embodiment, by obtaining multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information, the interval division mode corresponding to the multiple chromosome variation data samples is determined according to the multiple population source identification information, and each chromosome variation data sample is divided according to the interval division mode to obtain multiple variation data fragments corresponding to each chromosome variation data sample, and the multiple variation data fragments are processed based on variation data filtering parameters to obtain the target population genome variation data. Based on the population source of the chromosome variation data samples, the variation data filtering task of the chromosome variation data samples can be divided into multiple subtasks with balanced data processing volume, and the multiple subtasks can be processed in parallel, thereby avoiding the running wait caused by the subtask tailing phenomenon, thereby effectively saving computing resources and improving the data processing speed.

[0050] Figure 3 It is a flowchart of a method for processing population genome variation data proposed in another embodiment of the present disclosure.

[0051] like Figure 3 As shown, the method for processing population genome variation data includes:

[0052] S301: Acquire multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information.

[0053] S302: Compare multiple group source identification information to obtain comparison results.

[0054] For the detailed description of S301 , please refer to the above embodiment, which will not be repeated here.

[0055] S303: If the comparison result shows that the source identification information of multiple groups is the same, determine that the interval division mode is the first interval division mode, wherein the first interval division mode is a mode of division based on the density of variant sites.

[0056] The first interval division mode refers to a data mode for dividing sub-data samples based on the density of gene variation sites on chromosomes.

[0057] In the embodiment of the present disclosure, after the comparison process is performed on the plurality of group source identification information and the comparison result is obtained, the comparison result may be analyzed and processed to determine the interval division mode.

[0058] In the disclosed embodiment, if the comparison result is that the source identification information of multiple groups is the same, the interval division mode is determined to be the first interval division mode, indicating that the chromosome variation data sample interval division processing can be based on the density of gene variation sites on the chromosome.

[0059] S304: If the comparison result shows that the source identification information of the multiple populations is different, the interval division mode is determined to be the second interval division mode, wherein the second interval division mode is a mode for division based on allele density.

[0060] The second interval division mode refers to a data mode for dividing sub-data samples based on the allele density of gene variation sites on chromosomes.

[0061] In the embodiment of the present disclosure, after the comparison process is performed on the plurality of group source identification information and the comparison result is obtained, the comparison result may be analyzed and processed to determine the interval division mode.

[0062] In the disclosed embodiment, if the comparison result shows that the source identification information of multiple populations is different, the interval division mode is determined to be the second interval division mode, indicating that the chromosome variation data sample interval division processing can be based on the allele density of the gene variation site on the chromosome.

[0063] S305: Determine the number of reference sites of the variant sites included in each variant data segment according to the preset number of intervals.

[0064] The preset number of intervals refers to the number of planned intervals set in advance when executing the data filtering task.

[0065] The number of reference sites refers to the number of gene variation sites contained in the reference interval determined based on the number of preset intervals and the file size of the chromosome variation data sample.

[0066] In an embodiment of the present disclosure, when determining the number of reference sites of the variant sites contained in each variant data segment based on a preset number of intervals, the file length value of the chromosome variant data sample can be obtained, and the file length value can be divided by the number of predicted intervals to obtain an average file length. The average number of gene variant sites contained in the variant data segment is determined based on the average file length, and the average number is used as the reference number of variant sites contained in each variant data segment.

[0067] S306: When the interval division mode is the first interval division mode, each chromosome variation data sample is divided according to the number of reference sites and variation site density information to obtain a plurality of variation data segments corresponding to each chromosome variation data sample.

[0068] The variation site density information refers to a pre-acquired gene variation site density table.

[0069] After determining the number of reference sites of the variant sites contained in each variant data segment according to the preset number of intervals, the embodiment of the present disclosure can divide and process each chromosome variant data sample according to the interval division mode to obtain multiple variant data segments corresponding to each chromosome variant data sample.

[0070] In the disclosed embodiments, when each chromosome variation data sample is divided and processed according to the interval division mode, when the interval division mode is the first interval division mode, each chromosome variation data sample can be divided according to the number of reference sites and the variation site density information. For each chromosome variation data sample, the chromosome data sample can be divided into multiple sub-data samples containing the same number of gene variation sites according to the number of reference sites and the gene variation site density table, and the multiple sub-data samples obtained by division are used as multiple variation data fragments corresponding to the chromosome data sample. Each chromosome data sample is divided and processed one by one to obtain multiple variation data fragments obtained after the division of each chromosome data sample.

[0071] S307: When the interval division mode is the second interval division mode, each chromosome variation data sample is divided according to the number of reference sites and the allele density information to obtain a plurality of variation data segments corresponding to each chromosome variation data sample.

[0072] The allele density information refers to an allele density table, which is used to describe the alleles corresponding to the gene variation sites.

[0073] After determining the number of reference sites of the variant sites contained in each variant data segment according to the preset number of intervals, the embodiment of the present disclosure can divide and process each chromosome variant data sample according to the interval division mode to obtain multiple variant data segments corresponding to each chromosome variant data sample.

[0074] In the disclosed embodiment, when each chromosome variation data sample is divided and processed according to the interval division mode, when the interval division mode is the second interval division mode, each chromosome variation data sample is divided according to the number of reference sites and the allele density information. For each chromosome variation data sample, the chromosome data sample can be divided into multiple sub-data samples containing the same number of gene variation sites according to the number of reference sites and the allele density table, and the multiple sub-data samples obtained by division are used as multiple variation data fragments corresponding to the chromosome data sample. Each chromosome data sample is divided and processed one by one to obtain multiple variation data fragments obtained after the division of each chromosome data sample.

[0075] S308: Process multiple variant data segments based on the variant data filtering parameters to obtain the target population genome variant data.

[0076] Optionally, in some embodiments, when processing multiple variant data fragments based on variant data filtering parameters to obtain target population genome variant data, variant data filtering processing can be performed on the multiple variant data fragments based on the variant data filtering parameters to obtain multiple target variant data fragments, and the target variant data fragments belonging to the same chromosome variant data sample can be merged to obtain the target population genome variant data.

[0077] Among them, the target mutation data segment refers to the memory mutation site in the mutation data segment whose mutation quality meets the conditions and its corresponding mutation result information.

[0078] In the implementation of the present disclosure, when multiple variant data fragments are processed based on variant data filtering parameters to obtain target population genome variant data, variant data filtering processing can be performed on the multiple variant data fragments based on the variant data filtering parameters, and the variant result information of the gene variant sites contained in the variant data fragments can be scored for each variant data filtering parameter to obtain the variant result information score value corresponding to the variant data filtering parameter, and the variant data fragments are filtered based on the parameter threshold and the corresponding score value corresponding to the variant data filtering parameter to obtain the gene variant sites and their variant result information in each variant data fragment whose variant result information score value meets the corresponding parameter threshold, and the gene variant sites and their variant result information that meet the corresponding parameter threshold are generated into target variant data fragments, and then the target variant data fragments belonging to the same chromosome variant data sample can be merged to obtain the target population genome variant data.

[0079] In this embodiment, by obtaining multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information, according to the multiple population source identification information, the interval division mode corresponding to the multiple chromosome variation data samples is determined, and each chromosome variation data sample is divided according to the interval division mode to obtain multiple variation data fragments corresponding to each chromosome variation data sample, and the multiple variation data fragments are processed based on the variation data filtering parameters to obtain the target population genome variation data, and based on the population source of the chromosome variation data samples, the variation data filtering task of the chromosome variation data samples can be divided into multiple subtasks with balanced data processing amount, and the multiple subtasks can be processed in parallel Processing can avoid the tailing of subtasks and cause waiting for operation, thereby effectively saving computing resources and improving data processing speed. By comparing the identification information of multiple group sources, the comparison results are obtained. According to the comparison results, the interval division mode is determined. Since the density of gene variation sites in chromosome variation data samples of different groups is different, according to the different group sources of chromosome variation data samples, the interval division mode is adaptively set to group the chromosome data samples to generate multiple chromosome variation data analysis subtasks for parallel execution, so as to ensure that the number of gene variation sites contained in the multiple divided sub-data samples is roughly the same, and the accuracy of interval division is guaranteed.

[0080] Figure 4 It is a flowchart of a method for processing population genome variation data proposed in another embodiment of the present disclosure.

[0081] like Figure 4 As shown, the method for processing population genome variation data includes:

[0082] S401: Acquire multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information.

[0083] S402: Compare multiple group source identification information to obtain comparison results.

[0084] S403: If the comparison result shows that the source identification information of multiple groups is the same, determining that the interval division mode is the first interval division mode, wherein the first interval division mode is a mode of division based on the density of variant sites.

[0085] S404: If the comparison result shows that the source identification information of the multiple populations is different, the interval division mode is determined to be the second interval division mode, wherein the second interval division mode is a mode for division based on allele density.

[0086] S405: Determine the number of reference sites of the variant sites included in each variant data segment according to the preset number of intervals.

[0087] For the detailed description of S401 to S405 , please refer to the above embodiment, which will not be repeated here.

[0088] S406: Determine target interval information of each variant data segment on the chromosome variant data sample according to the number of reference sites and the variant site density information.

[0089] The target interval information refers to data information that can be used to determine the starting point of the interval of the variant data segment on the chromosome variant data sample.

[0090] In the embodiment of the present disclosure, when determining the target interval information of each variant data fragment on the chromosome variant data sample based on the number of reference sites and the variant site density information, all gene variant sites contained in the entire chromosome variant data sample can be divided into intervals based on the number of reference sites and the variant site density information, so that the number of gene variant sites contained in each interval obtained by the division is roughly consistent. When the number of gene variant sites contained in each interval is roughly consistent, the interval start point and interval end point corresponding to each data interval are obtained, and the interval start point and interval end point corresponding to each data interval are used as the target interval information of each variant data fragment on the chromosome variant data sample.

[0091] Optionally, in some embodiments, when determining the target interval information of each variant data fragment on the chromosome variant data sample based on the number of reference sites and the variant site density information, the initial interval information of each variant data fragment on the chromosome variant data sample can be determined based on the number of reference sites and the total length of the gene sites of the chromosome variant data sample, and the target interval information can be determined based on the variant site density information and the initial interval information.

[0092] Optionally, in some embodiments, when determining the target interval information of each variant data fragment on the chromosome variant data sample based on the number of reference sites and the variant site density information, the initial interval information of each variant data fragment can be determined based on the variant site density information and the total length of the gene sites of the chromosome variant data sample, and the target interval information can be determined based on the number of reference sites and the initial interval information.

[0093] The initial interval information refers to the data information describing the initial interval start point and the initial interval end point of the variation data segment after the chromosome variation data sample is initially divided and processed.

[0094] In the embodiment of the present disclosure, when determining the target interval information of each variant data fragment on the chromosome variant data sample according to the number of reference sites and the variant site density information, the chromosome variant data sample can be preliminarily divided. The preliminary division can be to first divide the chromosome variant data sample into average intervals according to the number of reference sites. The initial interval start point and the initial interval end point of the variant site interval obtained at this time are used as the initial interval information. Then, for the variant site interval described in the initial interval information, according to the variant site density table, the variant site interval described in the initial interval information is further subdivided into variant site intervals containing fewer and substantially the same number of gene variant sites, and the interval start point and the interval end point of the variant site interval are used as the target interval information.

[0095] In some other embodiments, when determining the target interval information of each variant data segment on the chromosome variant data sample, the preliminary division can also be to first perform average interval division on the chromosome variant data sample according to the variant site density table, and divide the chromosome variant data sample into variant site intervals containing substantially the same number of gene variant sites. The initial interval start point and initial interval end point of the variant site interval obtained at this time are used as the initial interval information, and then for the variant site interval described in the initial interval information, according to the number of reference sites, the variant site interval described in the initial interval information is further subdivided into variant site intervals containing fewer gene sites and substantially the same number, and the interval start point and interval end point of the variant site interval are used as the target interval information. For example, Figure 5 As shown, Figure 5 is an example diagram of a gene density table in an embodiment of the present disclosure.

[0096] In the embodiments of the present disclosure, a variety of interval division methods are combined to first perform a preliminary division according to a preset interval length within the data sample, and then further divide the divided data sample according to a density table. Alternatively, first perform a preliminary division according to a density table within the data sample, and then further divide the divided data sample according to a preset interval length. This can enrich the interval division methods, avoid the bias of a single division method, and avoid wasting computing resources due to the fact that some areas on the genome with no mutation sites for extremely long distances are divided into independent data intervals due to division entirely according to the density table, thereby achieving full utilization of computing resources used for filtering and analyzing gene mutation results.

[0097] S407: Divide the chromosome variation data sample into a plurality of variation data segments according to the target interval information.

[0098] After determining the target interval information of each variant data segment on the chromosome variant data sample according to the number of reference sites and the variant site density information, the disclosed embodiment can divide the chromosome variant data sample into multiple variant data segments according to the target interval information.

[0099] In the disclosed embodiments, when a chromosome variation data sample is divided into a plurality of variation data segments according to target interval information, an interval start point and an interval end point of each variation data segment on the chromosome variation data sample can be determined according to the target interval information, and based on each interval start point and the corresponding interval end point, the chromosome variation data sample is divided and processed according to each interval start point and the corresponding interval end point to obtain a plurality of interval data samples, and the obtained interval data samples are used as variation data segments, so as to realize the division of the chromosome variation data sample into a plurality of variation data segments according to the target interval information.

[0100] For example, Figure 6 As shown, Figure 6 It is a flow chart of interval division based on the variation site density table in the embodiment of the present disclosure. First, a human genome variation site density database can be constructed, and the number of intervals for executing subtasks is determined according to actual applications. According to the number of intervals to be executed, the average number of variation sites allocated to each interval is statistically calculated. According to the average number of variation points in each interval, the starting position allocated to each interval on the human genome is calculated to ensure that the number of variation sites in each interval is basically consistent. The total task is divided into intervals planned according to the variation site density to obtain multiple variation data fragments, and subtasks are executed in parallel in the divided intervals. Variation data filtering is performed on the variation data fragments based on each subtask.

[0101] S408: Process multiple variant data segments based on the variant data filtering parameters to obtain the target population genome variant data.

[0102] For the specific description of S408, please refer to the above embodiment, which will not be repeated here.

[0103] In this embodiment, by obtaining multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information, the interval division mode corresponding to the multiple chromosome variation data samples is determined according to the multiple population source identification information, and each chromosome variation data sample is divided according to the interval division mode to obtain multiple variation data fragments corresponding to each chromosome variation data sample, and the multiple variation data fragments are processed based on variation data filtering parameters to obtain the target population genome variation data. Based on the population source of the chromosome variation data sample, the variation data filtering task of the chromosome variation data sample can be divided into multiple subtasks with balanced data processing volume, and the multiple subtasks can be processed in parallel, thereby avoiding the existence of subtasks. The tailing phenomenon causes running waiting, thereby effectively saving computing resources and improving data processing speed. By combining a variety of interval division methods, the data sample can be first preliminarily divided according to the preset interval length, and then the divided data sample can be further divided according to the density table. Alternatively, the data sample can be first preliminarily divided according to the density table, and then the divided data sample can be further divided according to the preset interval length. In this way, the interval division method can be enriched, the bias of a single division method can be avoided, and the waste of computing resources caused by the division completely according to the density table is avoided. For example, some areas on the genome with no mutation sites for ultra-long distances are divided into independent data intervals, thereby achieving full utilization of the computing resources used for filtering and analyzing the results of gene mutations.

[0104] Figure 7 It is a flowchart of a method for processing population genome variation data proposed in another embodiment of the present disclosure.

[0105] like Figure 7 As shown, the method for processing population genome variation data includes:

[0106] S701: Acquire multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information.

[0107] S702: Compare multiple group source identification information to obtain comparison results.

[0108] S703: If the comparison result shows that the source identification information of multiple groups is the same, determine that the interval division mode is the first interval division mode, wherein the first interval division mode is a mode of division based on the density of variant sites.

[0109] S704: If the comparison result shows that the source identification information of the multiple populations is different, the interval division mode is determined to be the second interval division mode, wherein the second interval division mode is a mode for division based on allele density.

[0110] S705: Determine the number of reference sites of the variant sites included in each variant data segment according to the preset number of intervals.

[0111] For the detailed description of S701 to S705 , please refer to the above embodiment and will not be repeated here.

[0112] S706: Determine target interval information of each variation data segment on the chromosome variation data sample according to the number of reference sites and allele density information.

[0113] In the embodiment of the present disclosure, when determining the target interval information of each variant data fragment on the chromosome variant data sample based on the number of reference sites and the allele density information, all gene variant sites contained in the entire chromosome variant data sample can be divided into intervals based on the number of reference sites and the allele density information, so that the number of gene variant sites contained in each interval obtained by the division is roughly consistent. When the number of gene variant sites contained in each interval is roughly consistent, the interval start point and interval end point corresponding to each data interval are obtained, and the interval start point and interval end point corresponding to each data interval are used as the target interval information of each variant data fragment on the chromosome variant data sample.

[0114] Optionally, in some embodiments, when determining the target interval information of each variant data fragment on the chromosome variant data sample based on the number of reference sites and the allele density information, the initial interval information of each variant data fragment on the chromosome variant data sample can be determined based on the number of reference sites and the total length of the gene sites of the chromosome variant data sample, and the target interval information can be determined based on the allele density information and the initial interval information.

[0115] Optionally, in some embodiments, when determining the target interval information of each variant data fragment on the chromosome variant data sample based on the number of reference sites and allele density information, the initial interval information of each variant data fragment on the chromosome variant data sample can also be determined based on the allele density information and the total length of the gene sites of the chromosome variant data sample, and the target interval information can be determined based on the number of reference sites and the initial interval information.

[0116] In the embodiment of the present disclosure, when determining the target interval information of each variant data fragment on the chromosome variant data sample according to the number of reference sites and the allele density information, the chromosome variant data sample can be preliminarily divided. The preliminary division can be to first divide the chromosome variant data sample into average intervals according to the number of reference sites. The initial interval start point and the initial interval end point of the variant site interval obtained at this time are used as the initial interval information. Then, for the variant site interval described in the initial interval information, according to the allele density table, the variant site interval described in the initial interval information is further subdivided into variant site intervals containing fewer and substantially the same number of gene variant sites, and the interval start point and the interval end point of the variant site interval are used as the target interval information.

[0117] In some other embodiments, when determining the target interval information of each variant data fragment on the chromosome variant data sample, the preliminary division can also be to first perform average interval division on the chromosome variant data sample according to the allele density table, and divide the chromosome variant data sample into variant site intervals containing a substantially consistent number of gene variant sites. The initial interval start point and initial interval end point of the variant site interval obtained at this time are used as the initial interval information, and then for the variant site interval described in the initial interval information, according to the number of reference sites, the variant site interval described in the initial interval information is further subdivided into sections, and the variant site interval described in the initial interval information is divided into variant site intervals containing a smaller number of gene sites and a substantially consistent number, and the interval start point and interval end point of the variant site interval are used as the target interval information.

[0118] S707: Divide the chromosome variation data sample into a plurality of variation data segments according to the target interval information.

[0119] S708: Process multiple variant data segments based on the variant data filtering parameters to obtain the target population genome variant data.

[0120] For the detailed description of S708, please refer to the above embodiment and will not be repeated here.

[0121] In this embodiment, by obtaining multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information, the interval division mode corresponding to the multiple chromosome variation data samples is determined according to the multiple population source identification information, and each chromosome variation data sample is divided according to the interval division mode to obtain multiple variation data fragments corresponding to each chromosome variation data sample, and the multiple variation data fragments are processed based on variation data filtering parameters to obtain the target population genome variation data. Based on the population source of the chromosome variation data sample, the variation data filtering task of the chromosome variation data sample can be divided into multiple subtasks with balanced data processing amount, and the multiple subtasks are processed in parallel. This can avoid the tailing of subtasks and the resulting waiting for operation, thereby effectively saving computing resources and improving data processing speed. By taking into account the different population sources of chromosome data samples, an adaptive interval division rule is adopted to divide the chromosome data samples to obtain multiple sub-data samples. Since the number of mutation sites contained in the data intervals of the divided sub-samples is balanced, when using the VQSR algorithm, it is necessary to process all samples once for each mutation site to balance the computing pressure of each task, and balance the data processing volume of each subtask, which can effectively avoid the tailing of a few tasks and affect the progress of parallel processing, effectively improve the analysis speed, avoid running waiting, and make full use of computing resources.

[0122] Figure 8 It is a structural schematic diagram of a population genome variation data processing device proposed in another embodiment of the present disclosure.

[0123] like Figure 8 As shown, the population genome variation data processing device 80 comprises:

[0124] An acquisition module 801 is used to acquire a plurality of chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information;

[0125] A determination module 802 is used to determine interval division patterns corresponding to multiple chromosome variation data samples according to multiple population source identification information;

[0126] The first processing module 803 is used to divide each chromosome variation data sample according to the interval division mode to obtain a plurality of variation data segments corresponding to each chromosome variation data sample;

[0127] The second processing module 804 is used to process multiple variant data segments based on the variant data filtering parameters to obtain the target population genome variant data.

[0128] In some embodiments of the present disclosure, the determination module 803 is specifically configured to:

[0129] Compare and process multiple group source identification information to obtain comparison results;

[0130] According to the comparison results, the interval division mode is determined.

[0131] In some embodiments of the present disclosure, the determining module 803 is further configured to:

[0132] If the comparison result shows that the source identification information of multiple groups is the same, determining the interval division mode to be the first interval division mode, wherein the first interval division mode is a mode for division based on the density of variant sites;

[0133] If the comparison result shows that the identification information of multiple population sources is different, the interval division mode is determined to be the second interval division mode, wherein the second interval division mode is a mode for division based on allele density.

[0134] In some embodiments of the present disclosure, Fig. 9 As shown, Fig. 9 8 is a schematic diagram of the structure of a population genome variation data processing device proposed in another embodiment of the present disclosure, wherein the first processing module 803 includes:

[0135] A determination submodule 8031 ​​is used to determine the number of reference sites of the variant sites included in each variant data segment according to a preset number of intervals;

[0136] The first processing submodule 8032 is used to divide each chromosome variation data sample according to the number of reference sites and variation site density information when the interval division mode is the first interval division mode, so as to obtain a plurality of variation data segments corresponding to each chromosome variation data sample;

[0137] The second processing submodule 8033 is used to divide each chromosome variation data sample according to the number of reference sites and allele density information when the interval division mode is the second interval division mode, so as to obtain multiple variation data segments corresponding to each chromosome variation data sample.

[0138] In some embodiments of the present disclosure, the first processing submodule 8032 is specifically configured to:

[0139] According to the number of reference sites and the density of variant sites, the target interval information of each variant data fragment on the chromosome variant data sample is determined;

[0140] According to the target interval information, the chromosome variation data sample is divided into multiple variation data segments.

[0141] In some embodiments of the present disclosure, the first processing submodule 8032 is further configured to:

[0142] Determine the initial interval information of each variation data fragment on the chromosome variation data sample according to the number of reference sites and the total length of the gene sites of the chromosome variation data sample;

[0143] The target interval information is determined based on the variation site density information and the initial interval information.

[0144] In some embodiments of the present disclosure, the first processing submodule 8032 is further configured to:

[0145] Determine the initial interval information of each variation data segment according to the variation site density information and the total length of the gene site of the chromosome variation data sample;

[0146] According to the number of reference sites and the initial interval information, the target interval information is determined.

[0147] In some embodiments of the present disclosure, the second processing submodule 8033 is specifically used to:

[0148] According to the number of reference sites and allele density information, the target interval information of each variation data fragment on the chromosome variation data sample is determined;

[0149] According to the target interval information, the chromosome variation data sample is divided into multiple variation data segments.

[0150] In some embodiments of the present disclosure, the second processing submodule 8033 is further used to:

[0151] Determine the initial interval information of each variation data fragment on the chromosome variation data sample according to the number of reference sites and the total length of the gene sites of the chromosome variation data sample;

[0152] According to the allele density information and the initial interval information, the target interval information is determined.

[0153] In some embodiments of the present disclosure, the second processing submodule 8033 is further used to:

[0154] Determine the initial interval information of each variation data segment on the chromosome variation data sample according to the allele density information and the total length of the gene loci of the chromosome variation data sample;

[0155] According to the number of reference sites and the initial interval information, the target interval information is determined.

[0156] In some embodiments of the present disclosure, the second processing module 804 is specifically configured to:

[0157] Performing variant data filtering processing on multiple variant data segments based on variant data filtering parameters to obtain multiple target variant data segments;

[0158] The target variation data fragments belonging to the same chromosome variation data sample are merged to obtain the target population genome variation data.

[0159] It should be noted that the aforementioned explanation of the embodiment of the method for processing population genome variation data is also applicable to the population genome variation data processing device of this embodiment, and will not be repeated here.

[0160] In this embodiment, by obtaining multiple chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information, the interval division mode corresponding to the multiple chromosome variation data samples is determined according to the multiple population source identification information, and each chromosome variation data sample is divided according to the interval division mode to obtain multiple variation data fragments corresponding to each chromosome variation data sample, and the multiple variation data fragments are processed based on variation data filtering parameters to obtain the target population genome variation data. Based on the population source of the chromosome variation data samples, the variation data filtering task of the chromosome variation data samples can be divided into multiple subtasks with balanced data processing volume, and the multiple subtasks can be processed in parallel, thereby avoiding the running wait caused by the subtask tailing phenomenon, thereby effectively saving computing resources and improving the data processing speed.

[0161] In order to implement some of the above-mentioned embodiments, the present disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the population genome variation data processing method proposed in the aforementioned embodiments of the present disclosure.

[0162] In order to implement some of the above-mentioned embodiments, the present disclosure also proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the population genome variation data processing method proposed in the aforementioned embodiments of the present disclosure.

[0163] In order to implement some of the above embodiments, the present disclosure also proposes a computer program product. When the instruction processor in the computer program product is executed, it performs the population genome variation data processing method proposed in the aforementioned embodiments of the present disclosure.

[0164] Fig.10 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Fig.10 The electronic device 10 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0165] like Fig.10 As shown, the electronic device 10 is in the form of a general purpose computing device. Components of the electronic device 10 may include, but are not limited to: one or more processors or processing units 16, a memory 28, and a bus 18 that connects various system components (including the memory 28 and the processing unit 16).

[0166] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.

[0167] The electronic device 10 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 10, including volatile and non-volatile media, removable and non-removable media.

[0168] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 10 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Fig.10 Not shown, often called a "hard drive").

[0169] although Fig.10Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (hereinafter referred to as: CD-ROM), a digital versatile disc read only memory (hereinafter referred to as: DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present disclosure.

[0170] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described in the present disclosure.

[0171] The electronic device 10 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 10, and / or communicate with any device that enables the electronic device 10 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input / output (I / O) interface 22. Furthermore, the electronic device 10 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 10 through a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 10, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0172] The processing unit 16 executes various functional applications and data processing by running the programs stored in the memory 28, such as implementing the population genome variation data processing method mentioned in the above-mentioned embodiment.

[0173] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or applicability changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The description and examples are to be regarded as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0174] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

[0175] It should be noted that, in the description of the present disclosure, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present disclosure, unless otherwise specified, the meaning of "plurality" is two or more.

[0176] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0177] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0178] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0179] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0180] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0181] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means 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 disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0182] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A method for processing population genome variation data, characterized in that: include: Acquire a plurality of chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information; Determining interval division patterns corresponding to the plurality of chromosome variation data samples according to the plurality of population source identification information; Dividing each of the chromosome variation data samples according to the interval division mode to obtain a plurality of variation data segments corresponding to each of the chromosome variation data samples; The multiple variant data segments are processed based on the variant data filtering parameters to obtain the target population genome variant data.

2. The method according to claim 1, characterized in that Determining the interval division mode corresponding to the plurality of chromosome variation data samples according to the plurality of population source identification information includes: Comparing the plurality of group source identification information to obtain a comparison result; The interval division mode is determined according to the comparison result.

3. The method according to claim 2, characterized in that Determining the interval division mode according to the comparison result includes: If the comparison result shows that the plurality of group source identification information are the same, determining that the interval division mode is a first interval division mode, wherein the first interval division mode is a mode for division based on the density of variant sites; If the comparison result is that the plurality of population source identification information are not the same, the interval division mode is determined to be the second interval division mode, wherein the second interval division mode is a mode for division based on allele density.

4. The method according to claim 3, characterized in that The step of dividing each of the chromosome variation data samples according to the interval division mode to obtain a plurality of variation data segments corresponding to each of the chromosome variation data samples includes: Determining the number of reference sites of the variant sites included in each variant data segment according to the preset number of intervals; When the interval division mode is the first interval division mode, each of the chromosome variation data samples is divided according to the number of reference sites and the variation site density information to obtain a plurality of variation data segments corresponding to each of the chromosome variation data samples; When the interval division mode is the second interval division mode, each of the chromosome variation data samples is divided according to the number of reference sites and the allele density information to obtain a plurality of variation data segments corresponding to each of the chromosome variation data samples.

5. The method according to claim 4, characterized in that The step of dividing each of the chromosome variation data samples according to the number of reference sites and the variation site density information to obtain a plurality of variation data segments corresponding to each of the chromosome variation data samples comprises: Determining target interval information of each of the variant data segments on the chromosome variant data sample according to the number of reference sites and the variant site density information; According to the target interval information, the chromosome variation data sample is divided into a plurality of variation data segments.

6. The method according to claim 5, characterized in that The step of determining target interval information of each of the variant data segments on the chromosome variant data sample according to the number of reference sites and the variant site density information includes: Determining initial interval information of each of the variation data segments on the chromosome variation data sample according to the number of reference sites and the total length of gene sites of the chromosome variation data sample; The target interval information is determined according to the variation site density information and the initial interval information.

7. The method according to claim 5, characterized in that The determining, according to the number of reference sites and the variation site density information, target interval information of each variation data segment on the chromosome variation data sample further includes: Determining initial interval information of each of the variant data segments according to the variant site density information and the total length of the gene sites of the chromosome variant data sample; The target interval information is determined according to the number of reference sites and the initial interval information.

8. The method according to claim 4, characterized in that The step of dividing each of the chromosome variation data samples according to the number of reference sites and the allele density information to obtain a plurality of variation data segments corresponding to each of the chromosome variation data samples comprises: Determining target interval information of each of the variation data segments on the chromosome variation data sample according to the number of reference sites and the allele density information; According to the target interval information, the chromosome variation data sample is divided into a plurality of variation data segments.

9. The method according to claim 8, characterized in that Determining target interval information of each variation data segment on the chromosome variation data sample according to the number of reference sites and the allele density information includes: Determining initial interval information of each of the variation data segments on the chromosome variation data sample according to the number of reference sites and the total length of the gene sites of the chromosome variation data sample; The target interval information is determined according to the allele density information and the initial interval information.

10. The method according to claim 8, characterized in that The determining, according to the number of reference sites and the allele density information, target interval information of each variation data segment on the chromosome variation data sample further includes: Determining initial interval information of each of the variation data segments on the chromosome variation data sample according to the allele density information and the total length of the gene loci of the chromosome variation data sample; The target interval information is determined according to the number of reference sites and the initial interval information.

11. The method according to claim 1, characterized in that The step of processing the plurality of variant data segments based on the variant data filtering parameters to obtain the target population genome variant data includes: Performing variant data filtering processing on the multiple variant data segments based on the variant data filtering parameters to obtain multiple target variant data segments; The target variation data segments belonging to the same chromosome variation data sample are merged to obtain the target population genome variation data.

12. A population genome variation data processing device, characterized in that: include: An acquisition module, used for acquiring a plurality of chromosome variation data samples, wherein the chromosome variation data samples have corresponding population source identification information; A determination module, used to determine the interval division mode corresponding to the plurality of chromosome variation data samples according to the plurality of population source identification information; A first processing module, configured to divide each of the chromosome variation data samples according to the interval division mode, so as to obtain a plurality of variation data segments corresponding to each of the chromosome variation data samples; The second processing module is used to process the multiple variant data segments based on the variant data filtering parameters to obtain the target population genome variant data.

13. 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.

14. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and 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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