Acceleration method and apparatus for genetic testing and electronic device

Abstract

The present invention relates to the field of biological information, and provides an acceleration method and apparatus for genetic testing and an electronic device. The method of the present invention mainly comprises: obtaining a first variation data set and a second variation data set according to a first genetic sample set; and obtaining a variation quality control model according to the second variation data set, and processing the first variation data set according to the variation quality control model to obtain a first variation detection result corresponding to the first variation data set. According to the technical solution of the present application, variation detection data is divided into a first variation data set and a second variation data set by means of temporary interval information, so that processing is directly carried out with respect to intervals during subsequent quality control, thereby achieving the effect accelerating computation. Compared with a mode of dividing data and allocating tasks during quality control in the past, the method reduces testing time consumption, and effectively improves the computational efficiency of population genome analysis.

Description

Acceleration method, device and electronic equipment for genetic testing Technical Field

[0001] The present disclosure relates to the field of bioinformatics, and in particular to an acceleration method, device, and electronic device for gene detection. Background Art

[0002] In the biomedical field, whole genome sequencing (WGS) is a commonly used genetic analysis method that can quickly and cost-effectively determine the complete genome sequence of an organism and detect the collection of variants within its genome. However, when the scale of whole genome data increases to tens of thousands or even hundreds of thousands of people, genomic analysis of such large populations faces serious challenges such as memory constraints and excessive processing time. Therefore, how to efficiently utilize computing and storage resources to quickly and accurately analyze large-scale genomic samples is a pressing issue.

[0003] Summary of the Invention

[0004] The present disclosure provides an acceleration method, device, and electronic device for genetic testing to solve problems in related technologies and improve the speed of population genetic testing.

[0005] A first aspect embodiment of the present disclosure proposes an acceleration method for genetic testing, the method comprising: obtaining a first variation data set and a second variation data set based on a first genetic sample set, wherein the first genetic sample set includes a plurality of genetic sample variation data, and the first genetic sample set is obtained by preprocessing the original data; obtaining a variation quality control model based on the second variation data set, and processing the first variation data set according to the variation quality control model to obtain a first variation detection result corresponding to the first variation data set.

[0006] In some embodiments of the present disclosure, obtaining the first variant dataset includes: obtaining a gene variation density table; and merging the first gene sample set according to the gene variation density table to obtain the first variant dataset.

[0007] In some embodiments of the present disclosure, merging the first gene sample set according to the gene variation density table to obtain the first variation data set includes: determining multiple intervals of the first gene sample set according to the gene variation density table; processing the gene sample variation data in the same interval to obtain multiple first variation data, and the multiple first variation data form the first variation data set.

[0008] In some embodiments of the present disclosure, obtaining the second variant dataset includes: acquiring gene chromosome information; and merging the first variant dataset according to the gene chromosome information to obtain the second variant dataset.

[0009] In some embodiments of the present disclosure, merging the first variant data set according to gene chromosome information to obtain the second variant data set includes: determining multiple intervals of multiple first variant data according to the gene chromosome information; processing the first variant data in the same interval to obtain multiple second variant data, and the multiple second variant data form the second variant data set.

[0010] In some embodiments of the present disclosure, a variation quality control model is obtained based on a second variation data set, and a first variation data set is processed based on the variation quality control model to obtain a first variation detection result corresponding to the first variation data set, including: training a preset model using the second variation data set to obtain the variation quality control model; and screening the first variation data set based on the variation quality control model to obtain the first variation detection result.

[0011] In some embodiments of the present disclosure, obtaining a variation quality control model includes: obtaining a standard dataset for gene variation detection; and training and constructing a variation quality control model based on a second variation dataset and the standard dataset for gene variation detection.

[0012] In some embodiments of the present disclosure, the method further includes: obtaining gene chromosome information; and merging the first variation detection results according to the gene chromosome information.

[0013] In some embodiments of the present disclosure, after processing the first variant data set according to the variant quality control model to obtain the first variant detection result, or after obtaining the first variant data set, the method also includes: obtaining a second gene sample set; obtaining a third variant data set based on the second gene sample set; processing the third variant data set according to the variant quality control model to obtain a second variant detection result corresponding to the third variant data set.

[0014] A second aspect of the present disclosure provides an acceleration device for genetic testing, comprising: an acquisition unit for obtaining a first variation data set and a second variation data set based on a first gene sample set, wherein the first gene sample set includes a plurality of gene sample variation data, and the first gene sample set is obtained by preprocessing the original data; a processing unit for obtaining a variation quality control model based on the second variation data set, and processing the first variation data set according to the variation quality control model to obtain a first variation detection result corresponding to the first variation data set.

[0015] The third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the first aspect embodiment of the present disclosure.

[0016] In some embodiments of the present disclosure, the electronic device is a single server or a computer cluster.

[0017] The fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method described in the first aspect embodiment of the present disclosure.

[0018] A fifth embodiment of the present disclosure provides a computer program product, including a computer program, which executes the method described in the first embodiment of the present disclosure when executed by a processor.

[0019] The acceleration method, device, electronic device and storage medium for genetic testing provided by the present disclosure include: obtaining a first variation data set and a second variation data set based on a first gene sample set, wherein the first gene sample set includes a plurality of gene sample variation data, and the first gene sample set is obtained by preprocessing the original data; obtaining a variation quality control model based on the second variation data set, and processing the first variation data set according to the variation quality control model to obtain a first variation detection result corresponding to the first variation data set. The present disclosure utilizes temporary interval information to divide the variation detection data into the first and second variation data sets, so that in the subsequent application of quality control, the interval processing is directly oriented to achieve the effect of speeding up parallel computing. Compared with the previous method of re-splitting data and assigning tasks when applying quality control, this method reduces the time consumption of detection and effectively improves the efficiency of population genome analysis and calculation.

[0020] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0022] FIG1 is a flow chart of an acceleration method for genetic testing provided by an embodiment of the present disclosure;

[0023] FIG2 is a flow chart of an acceleration method for genetic testing provided by an embodiment of the present disclosure;

[0024] FIG3 is a flow chart of an acceleration method for genetic testing provided by an embodiment of the present disclosure;

[0025] FIG4 is a flow chart of an acceleration method for genetic testing provided by an embodiment of the present disclosure;

[0026] FIG5 is a flow chart of an acceleration method for genetic testing provided by an embodiment of the present disclosure;

[0027] FIG6 is a flow chart of an acceleration method for genetic testing provided by an embodiment of the present disclosure;

[0028] FIG7 is a flow chart of an acceleration method for genetic testing provided by an embodiment of the present disclosure;

[0029] FIG8 is a flow chart of an acceleration method for genetic testing provided by an embodiment of the present disclosure;

[0030] FIG9 is a flow chart of an acceleration method for genetic testing provided by an embodiment of the present disclosure;

[0031] FIG10 is a schematic diagram of a process for performing genetic testing according to an embodiment of the present disclosure;

[0032] FIG11 is a schematic diagram of a process for performing genetic testing according to an embodiment of the present disclosure;

[0033] FIG12 is a block diagram of a composition of an acceleration device for gene detection provided by an embodiment of the present disclosure;

[0034] FIG13 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0035] The following describes in detail embodiments of the present disclosure, 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 intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0036] In the biomedical field, whole genome sequencing (WGS) is a commonly used genetic analysis method that can quickly and cost-effectively determine the complete genome sequence of an organism and detect the collection of variants within its genome. However, when the scale of whole genome data increases to tens of thousands or even hundreds of thousands of people, genomic analysis of such large populations faces serious challenges such as memory constraints and excessive processing time. Therefore, how to efficiently utilize computing and storage resources to quickly and accurately analyze large-scale genomic samples is a pressing issue.

[0037] Existing testing methods, when applying quality control algorithms, require splitting the test data and redistributing the tasks to meet the requirements of parallel computing. However, this approach also increases the processing time of the test tasks and does not integrate the pre-quality control testing process.

[0038] To address the issues in related technologies, this paper utilizes temporary interval information to split variant detection data into first and second variant datasets. This allows for direct interval-based processing during subsequent quality control applications, accelerating parallel computation. Compared to previous approaches to re-splitting data and assigning tasks during quality control, this approach reduces detection time and effectively improves the efficiency of population genomic analysis and computation.

[0039] As used herein, the term "sample" generally refers to an aliquot of a material obtained or derived from a source of interest, and a collection of data, charts, etc. formed by the labeling, identification, or analysis results of these samples. In some embodiments, the source of interest is a biological or environmental source. In some embodiments, the source of interest can be or include a cell or organism, such as a microorganism, a plant, or an animal (e.g., a human). In some embodiments, the source of interest is or includes a biological tissue or fluid. In some embodiments, the biological tissue or fluid can be or include amniotic fluid, aqueous humor, ascites, bile, bone marrow, blood, breast milk, cerebrospinal fluid, cerumen, chyle, chime, 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 or one or more components thereof. In some embodiments, the biological fluid can be or include intracellular fluid, extracellular fluid, intravascular fluid (plasma), interstitial fluid, lymph, and / or transcellular fluid. In some embodiments, the biological fluid can be or include plant exudate. In some embodiments, biological tissue or sample can be obtained, for example, by aspiration, biopsy (e.g., fine needle or tissue biopsy), swab (e.g., oral, nasal, skin or vaginal swab), scraping, surgery, washing or lavage (e.g., bronchoalveolar, duct, nasal, ocular, oral, uterine, vaginal or other washing or lavage). In some embodiments, the biological sample is or includes cells obtained from an individual. In some embodiments, the sample is a "primary sample" obtained directly from a source of interest by any suitable means. In some embodiments, as will be apparent from the context, the term "sample" refers to a product obtained by processing a primary sample (e.g., by removing one or more components thereof and / or by adding one or more agents thereto). For example, filtering using a semipermeable membrane. Such "processed samples" may include, for example, nucleic acids (including DNA, RNA), proteins or chromatin, etc., 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.).

[0040] As used herein, the term "sequencer" generally refers to a sequencer used to determine the sequence of the genetic material of a sample. A sequencer can function in a variety of ways and based on a variety of technologies, including sequencing by primer extension using labeled or unlabeled nucleotides, such as sequencing-by-ligation or pyrosequencing, for example, using any of the Sanger dideoxy sequencing, nanopore, or "NexGen" sequencing methods known in the art (e.g., using the sequencing platform of MGI, the ROCHE 454 sequencing platform, the ILLUMINA™ SOLEXA™ sequencing platform, the SOLID™ sequencing platform of LIFE TECHNOLOGIES / APPLIED BIOSYSTEMS, the SMRT™ sequencing platform of PACIFIC BIOSCIENCES, the POLLONATOR Polony sequencing platform, the COMPLETE GENOMICS sequencing platform, the INTELLIGENT BIOSYSTEMS sequencing platform, the HELICOS sequencing platform, or any other sequencer and system known in the art).

[0041] FIG1 is a flow chart of an acceleration method for gene detection provided by an embodiment of the present disclosure, including steps 101-102.

[0042] Step 101: Obtain a first variation dataset and a second variation dataset based on a first gene sample set.

[0043] It should be noted that the embodiments of the present disclosure include the following: the first gene sample set, including multiple gene sample variation data, is obtained by preprocessing the original data. The original data is the collected sample to be tested, which can be a single sample or multiple samples. The specific biological species and the specific number of samples are not limited by the present disclosure. The preprocessing is to process the data of the collected samples in sequence to obtain the variation data of each gene sample. The processing flow can include whole genome sequencing WGS completed by a sequencer. The main steps include sequencing data quality control, sequence alignment, sorting, tag deduplication, base quality calibration, variation detection and other related processing processes. The specific processing flow and processing method are not limited by the present disclosure.

[0044] In addition, it should be noted that in the embodiments of the present disclosure, the first variant dataset, when acquired, is obtained by first processing the variant data of the multiple gene samples to obtain the variation results of the interval, and then forming a dataset of the variation results of the interval, which is the first variant dataset. The second variant dataset is obtained by merging the first variant dataset according to a certain merging rule. The merging rule can be based on gene chromosome information or other gene information. The specific merging rule is not limited by this disclosure.

[0045] Step 102 : obtaining a variation quality control model according to the second variation data set, and processing the first variation data set according to the variation quality control model to obtain a first variation detection result corresponding to the first variation data set.

[0046] It should be noted that in the embodiments of the present disclosure, the variation quality control model is a model trained to screen out correct variation prediction results. It can be a Gaussian mixture model, a K-means model, or a density clustering model, such as the DBSCAN model (Density-Based Spatial Clustering of Applications with Noise, DBSCAN), the OPTICS model (Ordering points to identify the clustering structure, OPTICS), etc., or a hierarchical clustering model, such as the BIRCH model (Balanced Iterative Reducing and Clustering Using Hierarchies, BIRCH), etc., or other models such as the affinity propagation model, the aggregation clustering model, and the spectral clustering model. The specific model is not limited in this disclosure.

[0047] In summary, the acceleration method for genetic testing provided by the present disclosure includes: obtaining a first variation data set and a second variation data set based on a first gene sample set, wherein the first gene sample set includes a plurality of gene sample variation data, and the first gene sample set is obtained by preprocessing the original data; obtaining a variation quality control model based on the second variation data set, and processing the first variation data set according to the variation quality control model to obtain a first variation detection result corresponding to the first variation data set. The present disclosure utilizes temporary interval information to divide the variation detection data into the first and second variation data sets, so that in the subsequent application of quality control, the interval processing is directly oriented to achieve the effect of speeding up parallel computing. Compared with the previous method of re-splitting data and assigning tasks when applying quality control, this method reduces the time consumption of detection and effectively improves the efficiency of population genome analysis and calculation.

[0048] Furthermore, in some embodiments of the present disclosure, when executing step 101 to obtain the first variant dataset and the second variant dataset based on the first gene sample set, obtaining the first variant dataset may be achieved by, but not limited to, the following method, as shown in FIG2 , including the following steps:

[0049] Step 201: Obtain a gene variation density table.

[0050] It should be noted that the genetic variation density table described in the embodiments of this disclosure is obtained using a pre-constructed human genome variant site density database. This database is obtained by utilizing both general high-quality genetic variation datasets and large-scale population variation information generated by joint-calling of mixed samples from multiple large populations. This combined information is used to construct the human genome variant site density database. The specific method for obtaining genetic mutation density is not limited by this disclosure.

[0051] Step 202: Merge the first gene sample set according to the gene variation density table to obtain a first variation data set.

[0052] It should be noted that the processing of the first genetic sample set, specifically, involves dividing the variation data of multiple genetic samples in the first genetic sample set into different intervals based on a genetic variation density table. Joint variation detection is then performed on each of these intervals to obtain variation data for multiple intervals. The resulting dataset consisting of the variation data for these multiple intervals constitutes the first variation dataset. The interval division can be based on a genetic variation density table, other genetic information, or other predefined division rules. This disclosure does not limit the specific division rules.

[0053] Furthermore, in some embodiments of the present disclosure, when executing step 202 to merge the first gene sample set according to the gene variation density table to obtain the first variation data, this can be achieved by, but not limited to, the following method, as shown in FIG3 , including the following steps:

[0054] Step 301: Determine multiple intervals of a first gene sample set according to a gene variation density table.

[0055] It should be noted that the embodiments of the present disclosure have been explained in the above steps and will not be repeated here. When determining the interval using the gene variation density table, the numerical rules for division can be predefined. The specific division rules are not limited by the present disclosure.

[0056] Step 302 : Process the variation data of the gene samples in the same interval to obtain a plurality of first variation data, and the plurality of first variation data form a first variation data set.

[0057] It should be noted that in the embodiments of the present disclosure, when processing genetic sample variation data in the same interval, the joint variation detection method adopted can utilize relevant SNP calling tools in genetic research, such as the GATK detection tool, the MegaBOLT detection tool, or other related detection tools. The specific detection tools and detection methods are not limited by this disclosure.

[0058] Furthermore, in some embodiments of the present disclosure, when executing step 101 to obtain the first variant dataset and the second variant dataset based on the first gene sample set, obtaining the second variant dataset may be achieved by, but not limited to, the following method, as shown in FIG4 , including the following steps:

[0059] Step 401: Obtain gene chromosome information.

[0060] It should be noted that the embodiments of the present disclosure herein describe gene chromosome information that is determined by different organisms being tested, and the present disclosure does not limit the specific chromosome types and numbers.

[0061] Step 402: Merge the first variant data set according to the gene chromosome information to obtain a second variant data set.

[0062] It should be noted that in the embodiments of the present disclosure, the merging of the first joint interval variation dataset based on gene chromosome information can be performed based on different chromosome types, or the merging of the first joint data variation data based on different chromosomes. The specific method of utilizing chromosome information is not limited by this disclosure. In addition to gene chromosome information, the merging process can also be performed based on other gene information, and the specific type of gene information is not limited by this disclosure.

[0063] Furthermore, in some embodiments of the present disclosure, when executing step 402 to merge the first variant dataset according to the gene chromosome information to obtain the second variant dataset, it can be achieved by, but not limited to, the following method, as shown in FIG5 , including the following steps:

[0064] Step 501: Determine multiple intervals of multiple first variation data based on gene chromosome information.

[0065] What needs to be explained in the present disclosure has been described in the above steps and will not be repeated here. When determining intervals using the gene variation density table, division rules can be predefined. The present disclosure does not limit the specific division rules.

[0066] Step 502 : Process the first variation data in the same interval to obtain a plurality of second variation data, and the plurality of second variation data form a second variation data set.

[0067] It should be noted that the embodiments of the present disclosure may be merging processing, and a data set consisting of a plurality of second variant data is the second variant data set. The merging processing may also be based on different merging methods or different merging orders. The specific merging rules, specific merging methods and orders are not limited by the present disclosure.

[0068] As shown above, the first gene sample set is processed according to the gene variation density table and the gene chromosome information to obtain the first variation data set and the second variation data set. Then, according to step 102, a variation quality control model is obtained according to the second variation data set, and the first variation data set is processed according to the variation quality control model to obtain a first variation detection result corresponding to the first variation data set. This can be achieved by, but not limited to, the following method, as shown in FIG6 , including the following steps:

[0069] Step 601: Use the second variation data set to train the preset model to obtain a variation quality control model.

[0070] It should be noted here in the embodiments of the present disclosure that when obtaining the variation quality control model, it is obtained by training by comprehensively utilizing the known high-quality variation standard data set and the second variation data set. During training, different training methods and different training platforms can be used. The specific training methods and platforms are not limited by the present disclosure.

[0071] Step 602: Screen the first variation data set according to the variation quality control model to obtain a first variation detection result.

[0072] It should be noted that the use of a model for screening in the embodiments of this disclosure involves utilizing a trained variation quality control model to discriminate the data in the obtained first interval dataset, thereby eliminating inaccurate data and retaining high-quality data. When applied, the model can be deployed on different computing platforms and invoked in various ways. This disclosure does not limit the specific model deployment or invocation methods.

[0073] Furthermore, in some embodiments of the present disclosure, when executing step 601 to obtain the variation quality control model, it can be achieved by, but not limited to, the following method, as shown in FIG7 , including the following steps:

[0074] Step 701: Obtain a standard dataset for gene variation detection.

[0075] It should be noted that the standard dataset for genetic variation detection in the embodiments of this disclosure is a commonly available, high-quality genetic variation dataset, which can be a HapMap dataset, an Omni dataset, a dbSNP dataset, or other high-quality genetic variation dataset. When acquiring a standard dataset, one or more of these datasets can be obtained. The specific dataset and number of datasets are not limited in this disclosure.

[0076] Step 702: Train and construct a variation quality control model based on the second variation dataset and the gene variation detection standard dataset.

[0077] What needs to be explained in this disclosure has been explained in the above steps and will not be repeated here. This disclosure does not limit the specific training data combination method and the specific model training method.

[0078] Furthermore, in some embodiments of the present disclosure, after executing step 102 to obtain a variation quality control model based on the second variation data set, and processing the first variation data set based on the variation quality control model to obtain a first variation detection result corresponding to the first variation data set, further processing of the first variation detection result is also included. This processing can be implemented by, but is not limited to, the following method, as shown in FIG8 , including the following steps:

[0079] Step 801: Obtain gene chromosome information.

[0080] It should be noted that the embodiments of the present disclosure herein describe gene chromosome information that is determined by different organisms being tested, and the present disclosure does not limit the specific chromosome types and numbers.

[0081] Step 802: Merge the first variation detection results according to the gene chromosome information.

[0082] It should be noted here in the embodiments of the present disclosure that after merging, the variation detection results in chromosome units can be obtained, and the merging process can also be based on different merging methods or different merging orders. The specific merging rules, specific merging methods and orders are not limited by the present disclosure.

[0083] Furthermore, some embodiments of the present disclosure also include the addition of samples. When adding, the sample can be added after the variation quality control model processes the first variation data set to obtain the first variation detection result, or after the first variation data set is obtained. The specific addition time is not limited by the present disclosure. The addition operation can be implemented by, but is not limited to, the following method, as shown in Figure 9, including the following steps:

[0084] Step 901: Acquire a second gene sample set.

[0085] It should be noted that the embodiment of the present disclosure here that the second gene sample set, including multiple gene sample variation data, is obtained by preprocessing the original data. The original data, that is, the collected samples to be tested, can be a single sample or multiple samples. The specific biological species and the specific number of samples are not limited by the present disclosure. The preprocessing is to process the data of the collected samples in sequence to obtain the variation data of each gene sample. The processing flow can include whole genome sequencing WGS completed by a sequencer. The main steps include sequencing data quality control, sequence alignment, sorting, tag deduplication, base quality calibration, variation detection and other related processing processes. The specific processing flow and processing method are not limited by the present disclosure.

[0086] Step 902: Obtain a third variation dataset based on the second gene sample set.

[0087] It should be noted that the operation of obtaining the third variation data set is consistent with the operation of obtaining the first variation data set described above, and the specific steps are not repeated here.

[0088] Step 903 : Process the third variation data set according to the variation quality control model to obtain a second variation detection result corresponding to the third variation data set.

[0089] It should be noted that the embodiment of the present disclosure processes the third variant data set according to the variant quality control model in the same manner as the operation of obtaining the first variant detection result as described above, and the specific steps are not repeated here.

[0090] In summary, this paper utilizes temporary interval information to split variant detection data into first and second variant datasets, allowing for direct interval-based processing during subsequent quality control applications, accelerating parallel computation. Compared to previous approaches that re-split data and assign tasks during quality control, this method reduces detection time and effectively improves the efficiency of population genomic analysis and computation.

[0091] In related studies, when accelerating genetic testing, such as the Autism 10,000 Genomes Project, genetic data analysis acceleration software, such as MegaBOLT software, is used to process the project's whole genome samples. The specific process is to process and generate multiple genetic sample variation data based on the original sequencing file (FASTQ file) obtained after sequencing each sample, and then perform joint variation detection. When performing joint variation detection, this method uses all genetic sample variation data as input at one time to complete variation detection of all samples, and all samples are passed through the variation quality control model to produce the final result.

[0092] In this embodiment, the variation quality control step fully utilizes the interval data generated in the joint variation detection step (i.e., the first variation data set segmented based on the variation density table). This disclosure performs variation quality control on the first variation data set, effectively increasing detection speed through parallel computing. Furthermore, segmenting interval data based on the variation density table ensures segmentation quality, thereby guaranteeing detection effectiveness. These two strategies are unique to the method disclosed in this patent.

[0093] The following is an example of the method described in Figures 1-9 through the specific embodiments shown in Figures 10 and 11.

[0094] Figure 10 is a schematic diagram of the overall process of performing variant detection in the present disclosure, including data acquisition and preprocessing, obtaining a joint variant detection dataset, and executing a quality control algorithm. Figure 11 is a schematic diagram of obtaining a joint variant detection dataset and executing a quality control algorithm in the present disclosure when performing variant detection. Specifically, when performing variant detection on a population genome, the following steps may be used, but are not limited to:

[0095] 1. Obtain the genome variation record file GVCF of a single sample.

[0096] In this embodiment, whole genome sequencing (WGS) is used to obtain a genomic variation record file (GVCF) for each single sample. Whole genome sequencing (WGS) is the use of high-throughput sequencing technology to sequence the entire genome of an individual or a population to discover information such as sequence or structural variations.

[0097] 2. Divide the intervals by the variation density table and assign calculation subtasks.

[0098] In this embodiment, when allocating computing tasks, since variant sites are unevenly distributed across the genome, if conventional methods were used to allocate tasks evenly, a serious tailing phenomenon would occur for a small number of tasks, resulting in a waste of computing resources and time. Therefore, this disclosure proposes to allocate tasks based on variant density.

[0099] In addition, the variation density table is obtained through a pre-constructed human genome variation site density database. When obtaining the database, on the one hand, general high-quality gene variation data set information is utilized, and on the other hand, large-scale population variation information generated by joint variation detection of multiple large population mixed samples is also utilized. The two aspects of information are integrated to construct a human genome variation site density database. The specific method of obtaining gene mutation density is not limited by this disclosure.

[0100] 3. Perform the joint mutation detection (Joint-Calling) step.

[0101] In this embodiment, when performing joint variation detection, that is, when executing the Joint-calling step, the merging module (Combine GVCF module) will be used to merge the GVCF data by interval, and the genotype module (Genotype module) will be used to perform joint variation detection on the merged GVCF data to obtain the joint variation detection result of the interval.

[0102] After obtaining the interval results, the fusion module (Merge VCF module) of the Joint-Calling step is responsible for merging the joint variation detection results of the interval into 25 chromosome result files and outputting Chr1, Chr2, ..., Chr22, ChrM, ChrX, ChrY.

[0103] 4. Perform the variation quality control (VQSR) step.

[0104] In this embodiment, when implementing the variation quality control (VQSR) step, the VCF data of Chr1VCF, Chr2VCF, ...ChrY generated in step 3 will be input into the variation quality control model of the VQSR step (i.e., the VariantRecalibrator module). This model is obtained through machine learning methods, such as a model trained based on a Gaussian Mixed Model (GMM).

[0105] Based on this variation quality control model, the interval segment data generated by the Joint-Calling step (indicated by the dotted arrows) are processed in parallel to generate the VQSR VCF results of the interval segments respectively, and then the final result is obtained by merging the VQSR VCF results of the interval segments.

[0106] Therefore, this solution has the following beneficial effects: It utilizes temporary interval information during the testing process to obtain the first and second variant datasets, allowing subsequent quality control to directly process the intervals. Because processing is performed on existing intervals, it avoids the time-consuming process of splitting and assigning tasks. It also leverages information from the testing process, making the testing process more collaborative, thereby improving the overall effectiveness of the population genomics analysis and computational process.

[0107] FIG12 is a schematic diagram of the structure of an acceleration device 1200 for gene detection provided by an embodiment of the present disclosure. As shown in FIG12 , the acceleration device for gene detection includes:

[0108] An acquisition unit 1201 is configured to obtain a first variation data set and a second variation data set based on a first gene sample set, wherein the first gene sample set includes a plurality of gene sample variation data, and the first gene sample set is obtained by preprocessing original data;

[0109] The processing unit 1202 is configured to obtain a variation quality control model based on the second variation data set, and process the first variation data set based on the variation quality control model to obtain a first variation detection result corresponding to the first variation data set;

[0110] In some embodiments of the present disclosure, the acquiring unit 1201 includes:

[0111] The first acquisition module is used to obtain a gene variation density table.

[0112] The first processing module is configured to merge the first gene sample set according to the gene variation density table to obtain a first variation data set.

[0113] In some embodiments of the present disclosure, the processing module is further configured to:

[0114] According to the gene variation density table, multiple intervals of the first gene sample set are determined; the gene sample variation data in the same interval are processed to obtain multiple first variation data, and the multiple first variation data form a first variation data set.

[0115] In some embodiments of the present disclosure, the acquiring unit 1201 further includes:

[0116] The second acquisition module is used to obtain gene chromosome information.

[0117] The second processing module is configured to merge the first variant data set according to the gene chromosome information to obtain a second variant data set.

[0118] In some embodiments of the present disclosure, the second processing module is further configured to:

[0119] According to the gene chromosome information, multiple intervals of multiple first variation data are determined; the first variation data in the same interval are processed to obtain multiple second variation data, and the multiple second variation data form a second variation data set.

[0120] In some embodiments of the present disclosure, the processing unit 1202 includes:

[0121] The training module is used to train the preset model using the second variation data set to obtain a variation quality control model.

[0122] The screening module is used to screen the first variant data set according to the variant quality control model to obtain a first variant detection result.

[0123] In some embodiments of the present disclosure, the training module is further configured to:

[0124] Obtain a standard dataset for gene variation detection; and train and construct a variation quality control model based on the second variation dataset and the standard dataset for gene variation detection.

[0125] In some embodiments of the present disclosure, the method further comprises:

[0126] The acquisition unit 1201 is also used to acquire gene chromosome information.

[0127] The processing unit 1202 is further configured to merge the first variation detection results according to the gene chromosome information.

[0128] In some embodiments of the present disclosure, the method further comprises:

[0129] The acquiring unit 1201 is further configured to: acquire a second gene sample set; and obtain a third variation data set based on the second gene sample set.

[0130] The processing unit 1202 is further configured to process the third variation data set according to the variation quality control model to obtain a second variation detection result corresponding to the third variation data set.

[0131] In an embodiment of the present disclosure, when detecting gene variation in a population, a first variation data set and a second variation data set are obtained based on a first gene sample set, wherein the first gene sample set includes a plurality of gene sample variation data, and the first gene sample set is obtained by preprocessing the original data; a variation quality control model is obtained based on the second variation data set, and the first variation data set is processed according to the variation quality control model to obtain a first variation detection result corresponding to the first variation data set. The present disclosure utilizes temporary interval information to divide the variation detection data into the first and second variation data sets, so that in the subsequent application of quality control, the interval processing is directly oriented to achieve the effect of speeding up parallel computing. Compared with the previous method of re-splitting data and assigning tasks when applying quality control, this method reduces the time consumption of detection and effectively improves the efficiency of population genome analysis and calculation.

[0132] In the embodiments provided above, the methods and devices provided in the embodiments of the present application are introduced. In order to implement the various functions of the methods provided in the embodiments of the present application, the electronic device may include a hardware structure and a software module, and implement the aforementioned functions in the form of a hardware structure, a software module, or a hardware structure plus a software module. One of the aforementioned functions may be executed in the form of a hardware structure, a software module, or a hardware structure plus a software module.

[0133] FIG13 is a block diagram of an electronic device 1300 for implementing the aforementioned acceleration method for genetic testing, according to an exemplary embodiment. For example, electronic device 1300 may be a sequencer, a mobile phone, a computer, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like.

[0134] The method proposed in this disclosure can be applied to a single server, that is, all functions are provided by a single server, and the computational tasks of genetic testing can be performed on this single server. Due to the large amount of data in population genome analysis in practice, the method proposed in this disclosure can also be applied to a computer cluster. A computer cluster is a distributed computing system composed of multiple servers. In the computational tasks of genetic testing, the computation can be performed by assigning the computational tasks to different servers. Therefore, the electronic device can be a single server or a computer cluster. In other words, the electronic device can be a server, or it can be a server group, and the server group includes at least two servers.

[0135] 13 , the electronic device 1300 may include one or more of the following components: a processing component 1302 , a memory 1304 , a power component 1306 , a multimedia component 1308 , an audio component 1310 , an input / output (I / O) interface 1312 , a sensor component 1314 , and a communication component 1316 .

[0136] Processing component 1302 generally controls the overall operation of electronic device 1300, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. Processing component 1302 may include one or more processors 1320 to execute instructions to perform all or part of the steps of the above-described method. In addition, processing component 1302 may include one or more modules to facilitate interaction between processing component 1302 and other components. For example, processing component 1302 may include a multimedia module to facilitate interaction between multimedia component 1308 and processing component 1302.

[0137] The memory 1304 is configured to store various types of data to support operations on the electronic device 1300. Examples of such data include instructions for any application or method operating on the electronic device 1300, contact data, phone book data, messages, pictures, videos, etc. The memory 1304 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0138] The power supply component 1306 provides power to the various components of the electronic device 1300. The power supply component 1306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 1300.

[0139] The multimedia component 1308 includes a screen that provides an output interface between the electronic device 1300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1308 includes a front camera and / or a rear camera. When the electronic device 1300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.

[0140] The audio component 1310 is configured to output and / or input audio signals. For example, the audio component 1310 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 1300 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 1304 or transmitted via the communication component 1316. In some embodiments, the audio component 1310 also includes a speaker for outputting audio signals.

[0141] I / O interface 1312 provides an interface between processing component 1302 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to, a home button, volume buttons, a start button, and a lock button.

[0142] The sensor assembly 1314 includes one or more sensors for providing various aspects of the status assessment of the electronic device 1300. For example, the sensor assembly 1314 can detect the open / closed state of the electronic device 1300, the relative positioning of components, such as the display and keypad of the electronic device 1300. The sensor assembly 1314 can also detect changes in the position of the electronic device 1300 or a component of the electronic device 1300, the presence or absence of user contact with the electronic device 1300, the orientation or acceleration / deceleration of the electronic device 1300, and changes in the temperature of the electronic device 1300. The sensor assembly 1314 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 1314 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1314 can also include an accelerometer, a gyroscope, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0143] The communication component 1316 is configured to facilitate wired or wireless communication between the electronic device 1300 and other devices. The electronic device 1300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, 4G LTE, 5G NR (New Radio) or a combination thereof. In an exemplary embodiment, the communication component 1316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0144] In an exemplary embodiment, the electronic device 1300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, graphics processing units (GPUs), computer clusters, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0145] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1304 including instructions. The instructions can be executed by the processor 320 of the electronic device 1300 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0146] An embodiment of the present disclosure further provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the acceleration method for genetic testing described in the above embodiments of the present disclosure.

[0147] An embodiment of the present disclosure further provides a computer program product, including a computer program, which, when executed by a processor, performs the acceleration method for gene detection described in the above embodiment of the present disclosure.

[0148] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure as detailed in the appended claims.

[0149] Throughout this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" indicate that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative uses of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0150] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising 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 invention includes alternative implementations in which functions may be performed out of 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 invention pertain.

[0151] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection having one or more wires (control method), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0152] It should be understood that various parts of the embodiments of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0153] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0154] Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing module, each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in either hardware or software functional modules. If the integrated modules are implemented as software functional modules and sold or used as standalone products, they may also be stored in a computer-readable storage medium. The aforementioned storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc.

[0155] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary 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. An acceleration method for gene detection, characterized in that: include: Obtaining a first variation data set and a second variation data set according to a first gene sample set, wherein the first gene sample set includes a plurality of gene sample variation data, and the first gene sample set is obtained by preprocessing original data; A variation quality control model is obtained according to the second variation data set, and the first variation data set is processed according to the variation quality control model to obtain a first variation detection result corresponding to the first variation data set.

2. The method according to claim 1, characterized in that The obtaining of the first variation dataset comprises: Get gene variation density table; The first gene sample set is merged according to the gene variation density table to obtain the first variation data set.

3. The method according to claim 2, characterized in that The merging of the first gene sample set according to the gene variation density table to obtain the first variation data set includes: determining a plurality of intervals of the first gene sample set according to the gene variation density table; The gene sample variation data in the same interval are processed to obtain a plurality of first variation data, and the plurality of first variation data form the first variation data set.

4. The method according to claim 1, wherein The obtaining of the second variation data set comprises: Obtain gene chromosome information; The first variation data set is merged according to the gene chromosome information to obtain the second variation data set.

5. The method according to claim 4, characterized in that The merging of the first variation data set according to the gene chromosome information to obtain the second variation data set includes: Determining multiple intervals of the multiple first variation data according to the gene chromosome information; The first variation data in the same interval are processed to obtain a plurality of second variation data, and the plurality of second variation data form the second variation data set.

6. The method according to any one of claims 1 to 5, characterized in that The step of obtaining a variation quality control model based on the second variation data set, and processing the first variation data set based on the variation quality control model to obtain a first variation detection result corresponding to the first variation data set includes: Using the second variation data set to train a preset model to obtain the variation quality control model; The first variation data set is screened according to the variation quality control model to obtain the first variation detection result.

7. The method according to claim 6, characterized in that The obtaining of the variation quality control model comprises: Obtain a standard dataset for gene variation detection; The variation quality control model is trained and constructed based on the second variation dataset and the gene variation detection standard dataset.

8. The method according to any one of claims 1 to 7, characterized in that The method further comprises: Obtain gene chromosome information; The first variation detection results are merged according to the gene chromosome information.

9. The method according to any one of claims 1 to 8, characterized in that After processing the first variant data set according to the variant quality control model to obtain a first variant detection result, or after obtaining the first variant data set, the method further includes: obtaining a second genetic sample set; Obtaining a third variation dataset based on the second gene sample set; The third variation data set is processed according to the variation quality control model to obtain a second variation detection result corresponding to the third variation data set.

10. An acceleration device for gene detection, characterized in that: include: an acquisition unit, configured to obtain a first variation data set and a second variation data set based on a first gene sample set, wherein the first gene sample set includes a plurality of gene sample variation data, and the first gene sample set is obtained by preprocessing original data; A processing unit is configured to obtain a variation quality control model based on the second variation data set, and process the first variation data set based on the variation quality control model to obtain a first variation detection result corresponding to the first variation data set.

11. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 9.

12. The electronic device according to claim 11, wherein: The electronic device is a single server or a computer cluster.

13. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 9.

14. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 9.

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