Genetic database updating method, classification method and system based on sequencing data

Abstract

The embodiment of the present invention relates to the field of bioinformatics, and discloses a genetic database update method based on sequencing data, including: extracting variant site data from filtered VCF data in a proband sample; threshold matching the significance level value with a pre-set feature threshold to obtain variant site data that meets the corresponding feature threshold; matching each reference variant site in a feature database according to the determined significant variant site, if there is a reference variant site matching the corresponding significant variant site in the feature database, adjusting the weight parameter of the corresponding reference variant site, if there is no corresponding significant variant site in the feature database, adding the corresponding significant variant site in the feature database. The genetic database update method based on sequencing data realizes dynamic update and optimization of the database through the steps of screening significant variant sites, adjusting weight parameters, and adding new variant sites.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics, and in particular to a genetic database updating method, a classification method and a system based on sequencing data. Background Art

[0002] At present, with the development of high-throughput sequencing technology, genomic research has entered a new era. At present, the Illumina sequencing platform is still one of the most widely used high-throughput sequencing technologies on the market, providing extremely high sequencing depth and data quality. However, Illumina sequencing technology still has some limitations in practical applications, especially in dealing with complex genomic structural variations, splicing and alignment of short reads, and data processing efficiency. To this end, PIMS-SR (Precision Integrated Multi-omics Sequencing System - Short Read) sequencing technology came into being, aiming to provide more efficient and accurate sequencing solutions. PIMS-SR sequencing technology combines the advantages of high-throughput sequencing and short-read sequencing, and is particularly suitable for the analysis of large-scale genomic data. Compared with traditional short-read sequencing platforms such as Illumina, PIMS-SR technology exhibits superiority in many aspects:

[0003] Higher sequencing depth and accuracy: PIMS-SR technology can provide more accurate sequencing results by optimizing sequencing chemistry and imaging technology, especially in high-complexity regions of the genome, and can better detect variants. This is of great significance for studying the genetic mechanisms of complex diseases. Higher throughput and cost-effectiveness: PIMS-SR sequencing technology can not only generate a large amount of sequence data, but also has a lower cost, making large-scale genomic research more economical and feasible. This makes it possible for the popularization and large-scale application of genomic research. Flexible sequencing mode: PIMS-SR sequencing technology allows researchers to choose the appropriate sequencing mode according to specific research needs, from whole genome sequencing to targeted sequencing, which can meet a variety of research needs. This flexibility enables it to perform well in different types of genomic research.

[0004] Although PIMS-SR sequencing technology has significant advantages in data acquisition, existing genomic data analysis methods still face challenges in processing these data, especially in the process of sample typing and haplotype construction. There are some technical difficulties:

[0005] First, inaccurate haplotype assembly: Existing typing algorithms usually rely on the splicing and alignment of short-read sequences when constructing haplotypes. However, due to the short length of short-read data fragments, splicing errors are prone to occur when assembling haplotypes, especially in repetitive sequence regions or regions with complex genomic structures. The variant sites in these regions are easily confused during assembly, resulting in inaccurate haplotype assembly. This inaccuracy may lead to erroneous genetic linkage analysis results in clinical and research, which in turn affects the location of disease-related genes.

[0006] Second, ignoring structural variation: Many traditional typing algorithms focus on the detection of single nucleotide polymorphisms (SNPs), but pay insufficient attention to structural variations in the genome (such as copy number variations CNVs, large insertions / deletions Indels, etc.). These structural variations play a vital role in the genetic mechanisms of complex diseases, but due to the low sensitivity of existing algorithms in detecting these variations, important genetic information may be missed, affecting the comprehensive understanding of the disease.

[0007] Third, insufficient noise processing capabilities: Although PIMS-SR sequencing technology has high accuracy, in actual applications, sequencing data still contains a certain proportion of noise and erroneous data. If these noises are not handled properly, they may interfere with the accurate detection of variant sites and lead to an increase in false positive results. Existing typing algorithms usually rely on a large number of data cleaning and preprocessing steps when facing noise, which not only increases the complexity and time cost of data processing, but also may lose some potential real signals.

[0008] Fourth, low computational efficiency: When faced with the massive data generated by PIMS-SR sequencing technology, computational efficiency often becomes a bottleneck for existing genomic data analysis methods. The processing of large-scale data requires efficient algorithms and powerful computing resources, but traditional algorithms often find it difficult to complete the analysis within a reasonable time due to the high computational complexity of processing these data. This low efficiency not only affects the timeliness of data analysis, but also limits its application in large-scale genomic research.

[0009] Fifth, lack of flexibility: Traditional typing algorithms usually rely on fixed parameter settings and lack the ability to dynamically adjust according to data sets and research needs. In the context of increasingly complex and diverse genomic research, researchers need analytical tools that can adapt to different types of data and research backgrounds. However, the lack of flexibility of existing algorithms limits their application in diverse research.

[0010] In summary, although PIMS-SR sequencing technology has significant advantages in data acquisition, existing typing algorithms still have many deficiencies in processing these data. Therefore, it is particularly important to develop a new genomic data analysis method to overcome the deficiencies of existing algorithms in haplotype assembly, structural variation detection, noise processing, and computational efficiency. Summary of the invention

[0011] In view of the above-mentioned defects, the embodiments of the present invention disclose a genetic database updating method and a classification method based on sequencing data, which can realize accurate classification of samples and efficient screening of genetic variation sites.

[0012] The first aspect of the embodiment of the present invention discloses a method for updating a genetic database based on sequencing data, comprising:

[0013] Obtain the data marked as the proband sample in the PED file, and extract the variant site data in the filtered VCF data of the proband sample;

[0014] Using the chi-square test to calculate the significance level value between each variant site data and the disease phenotype, and threshold matching the significance level value with a preset characteristic threshold to obtain variant site data that meets the corresponding characteristic threshold, and determining the variant site data that meets the corresponding characteristic threshold as a significant variant site;

[0015] The determined significant variation sites are matched with the reference variation sites in the feature database. If there is a reference variation site matching the corresponding significant variation site in the feature database, the weight parameter of the corresponding reference variation site is adjusted. If there is no corresponding significant variation site in the feature database, the corresponding significant variation site is added to the feature database.

[0016] As an optional implementation, in the first aspect of the embodiment of the present invention, extracting variant site data from the filtered VCF data in the proband sample includes:

[0017] Extract the genotype information and sequencing depth value of the variant sites in the filtered VCF data of the proband sample;

[0018] The genotype information of the variant site in the VCF data is compared with the reference genome. If the comparison is inconsistent, the corresponding sequencing depth value is compared with the set depth value. If the sequencing depth value is greater than the set depth value, the corresponding variant site is determined as a candidate variant site;

[0019] According to the variant type of interest set by the user, the variant sites that meet the conditions will be obtained with the variant type of interest to obtain the determined variant site data.

[0020] As an optional implementation, in the first aspect of the embodiment of the present invention, the filtering operation in the filtered VCF data includes variant site range screening, CNV filtering and Mendelian filtering;

[0021] The step of performing threshold matching on the significance level value and a preset characteristic threshold to obtain variable site data that meets the corresponding characteristic threshold includes:

[0022] Perform threshold matching on the significance level value and a preset first characteristic threshold value to obtain a first variation number of the variation site data that meets the first characteristic threshold value;

[0023] Matching the first variation quantity with a first quantity threshold, and if the first variation quantity is within the first quantity threshold interval, outputting corresponding variation site data;

[0024] If the first variation quantity is greater than a larger value in a first quantity threshold interval, adjusting the first feature threshold to a second feature threshold, wherein the second feature threshold is less than the first feature threshold;

[0025] If the first variation quantity is smaller than a smaller value in a first quantity threshold interval, the first feature threshold is adjusted to a third feature threshold, wherein the third feature threshold is greater than the first feature threshold.

[0026] A second aspect of an embodiment of the present invention discloses a method for classifying embryo samples based on sequencing data, comprising:

[0027] Obtaining a data-verified embryo sample data file input by a user, wherein the embryo sample data file includes a VCF file and a PED file;

[0028] Determine whether there is label data in the embryo sample data file, if so, write the variation site associated with the label data into the variation data sequence; if not, perform feature extraction operation on the sample data file to obtain corresponding matching feature data;

[0029] The matching feature data is matched with the proband features stored in a pre-constructed database to obtain variant site details, wherein the variant site details include the number of variant sites, and the corresponding variant matching rate is calculated based on the number of variant sites and the total number of variant sites;

[0030] If the variation matching rate exceeds the preset classification threshold, the corresponding embryo sample is determined to be a diseased sample; if the variation matching rate does not exceed the preset classification threshold, the corresponding embryo sample is determined to be a healthy sample.

[0031] As an optional implementation, in the second aspect of the embodiment of the present invention, after determining that the corresponding embryo sample is a diseased sample if the variation matching rate exceeds the preset classification threshold, and determining that the corresponding embryo sample is a healthy sample if the variation matching rate does not exceed the preset classification threshold, the method further includes:

[0032] Output the classification results of each sample data file, the classification results include sample classification labels, a list of significant variant sites, matching result information and analysis result information, the matching result information includes matched variant sites and variant matching rates; wherein the list of significant variant sites includes significant variant sites, variant chromosome positions, reference bases, alternative bases and displayed variant values.

[0033] As an optional implementation, in the second aspect of the embodiment of the present invention, the performing a feature extraction operation on the sample data file to obtain corresponding matching feature data includes:

[0034] Extract the genotype information and sequencing depth value of the variant sites in the filtered VCF data in the sample data file;

[0035] The genotype information of the variant site in the VCF data is compared with the reference genome. If the comparison is inconsistent, the corresponding sequencing depth value is compared with the set depth value. If the sequencing depth value is greater than the set depth value, the corresponding variant site is determined as a candidate variant site;

[0036] According to the variant type of interest set by the user, eligible variant sites will be obtained according to the variant type of interest to obtain determined matching feature data.

[0037] As an optional implementation manner, in the second aspect of the embodiment of the present invention, the obtaining of the sample data file input by the user and after data verification, wherein the sample data file includes a VCF file and a PED file, includes:

[0038] Reading the gene sequencing data of the corresponding sample that has undergone data verification from the VCF file information; wherein the gene sequencing data includes chromosome position, reference base, variant base and genotype information of the sample;

[0039] Read the pedigree structure and phenotypic information of the sample from the PED file, wherein the PED file includes family member information, and the family member information includes the gender of the sample, the relationship between family members, and the phenotypic characteristics associated with the family members; during the data reading process, the system automatically matches the sample ID in the PED file with the sample ID in the VCF file;

[0040] The variant site details also include specific variant sites.

[0041] A third aspect of an embodiment of the present invention discloses a genetic database updating system based on sequencing data, comprising:

[0042] Acquisition module: used to obtain the data marked as proband samples in the PED file, and extract the variant site data in the filtered VCF data of the proband sample;

[0043] Feature determination module: used to calculate the significance level value between each variant site data and the disease phenotype using a chi-square test, and threshold match the significance level value with a preset feature threshold to obtain variant site data that meets the corresponding feature threshold, and determine the variant site data that meets the corresponding feature threshold as a significant variant site;

[0044] Database update module: used to match the determined significant variation sites with the reference variation sites in the feature database. If there is a reference variation site matching the corresponding significant variation site in the feature database, the weight parameter of the corresponding reference variation site is adjusted. If there is no corresponding significant variation site in the feature database, the corresponding significant variation site is added to the feature database.

[0045] The fourth aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the genetic database update method based on sequencing data disclosed in the first aspect of the embodiment of the present invention.

[0046] A fifth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the genetic database updating method based on sequencing data disclosed in the first aspect of an embodiment of the present invention.

[0047] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0048] The genetic database updating method based on sequencing data in the embodiment of the present invention extracts significant variant sites according to the actual data of the proband sample and dynamically updates the feature set in the database. By combining real data for feature extraction, the system can more accurately reflect the genetic differences between samples, thereby improving the accuracy of subsequent classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 It is a flow chart of a genetic database updating method based on sequencing data disclosed in an embodiment of the present invention;

[0051] Figure 2 It is a schematic diagram of a process for feature extraction disclosed in an embodiment of the present invention;

[0052] Figure 3 is a schematic diagram of a flow chart of weight adjustment disclosed in an embodiment of the present invention;

[0053] Figure 4 It is a flowchart of an embryo sample classification method based on sequencing data disclosed in an embodiment of the present invention;

[0054] Figure 5 It is a schematic diagram of the process of data reading and feature screening disclosed in an embodiment of the present invention;

[0055] Figure 6 is a schematic diagram of a specific process of sample classification disclosed in an embodiment of the present invention;

[0056] Figure 7 It is a flowchart of the filtering logic disclosed in the embodiment of the present invention;

[0057] Figure 8 It is a structural schematic diagram of a genetic database updating system based on sequencing data provided by an embodiment of the present invention;

[0058] Fig. 9 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0061] Although PIMS-SR sequencing technology has significant advantages in data acquisition, existing typing algorithms still have many deficiencies in processing these data. Based on this, the embodiments of the present invention disclose a genetic database update method, system, electronic device and storage medium based on sequencing data, extract its significant variant sites according to the actual data of the proband sample, and dynamically update the feature set in the database. By combining real data for feature extraction, the system can more accurately reflect the genetic differences between samples, thereby improving the accuracy of subsequent classification.

[0062] Embodiment 1

[0063] See also Figure 1 , Figure 1 It is a flow chart of the genetic database update method based on sequencing data disclosed in the embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and / or hardware, and the execution subject can receive relevant information by wired or / and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices. For example Figure 1 As shown, the genetic database updating method based on sequencing data comprises the following steps:

[0064] S101: Obtain data marked as proband samples in the PED file, and extract variant site data from the filtered VCF data in the proband sample;

[0065] S102: using a chi-square test to calculate the significance level value between each variant site data and the disease phenotype, and threshold matching the significance level value with a preset characteristic threshold to obtain variant site data that meets the corresponding characteristic threshold, and determining the variant site data that meets the corresponding characteristic threshold as a significant variant site;

[0066] S103: Match the determined significant variation sites with the reference variation sites in the feature database. If there is a reference variation site matching the corresponding significant variation site in the feature database, adjust the weight parameter of the corresponding reference variation site. If there is no corresponding significant variation site in the feature database, add the corresponding significant variation site to the feature database.

[0067] The above-mentioned genetic database update method based on sequencing data has the following technical effects: First, improve the accuracy and pertinence of database update, data screening: by extracting data marked as proband samples from PED files, and further extracting variant site data from filtered VCF data, the accuracy and representativeness of the data are ensured. Proband samples are usually the first cases discovered in family genetic disease research, and their variant site data are of great significance for disease research. Significance test: the chi-square test is used to calculate the significance level value between each variant site and the disease phenotype, and by matching with the pre-set feature threshold, the significant variant sites are screened out. This process helps to identify variant sites that are highly associated with the disease, thereby improving the accuracy of database update. Second, realize dynamic update and optimization of the database, weight adjustment: for reference variant sites that match the significant variant sites that already exist in the feature database, adjust their weight parameters to reflect their important role in the occurrence of the disease. This weight adjustment mechanism helps to optimize the reference variant site information of the database and improve its application value in genetic disease diagnosis and research. Third, add new variant sites: for significant variant sites that do not exist in the feature database, add them to the database. This step realizes the continuous updating and expansion of the database, so that the database can continuously include new and significant variant site information to maintain its timeliness and comprehensiveness. Fourth, promote the development of genetic disease research and clinical application, research support: This method provides an efficient and accurate database update method for genetic disease research, which helps researchers better understand and analyze the occurrence mechanism of genetic diseases, and provides strong support for the prevention, diagnosis and treatment of genetic diseases. By continuously updating and optimizing the genetic database, the accuracy and efficiency of genetic disease diagnosis can be improved, providing clinicians with more reliable genetic information references, and helping to develop more personalized treatment plans and intervention measures. In summary, this genetic database update method based on sequencing data realizes the dynamic update and optimization of the database through steps such as screening significant variant sites, adjusting weight parameters and adding new variant sites, and improves the level of genetic disease research and clinical application.

[0068] More preferably, Figure 2 As shown, the step of extracting variant site data from the filtered VCF data in the proband sample includes:

[0069] S1011: Extract the genotype information and sequencing depth value of the variant sites in the filtered VCF data of the proband sample;

[0070] S1012: comparing the genotype information of the variant site in the VCF data with the reference genome. If the comparison is inconsistent, comparing the corresponding sequencing depth value with the set depth value. If the sequencing depth value is greater than the set depth value, determining the corresponding variant site as a candidate variant site.

[0071] S1013: According to the variant type of interest set by the user, the variant sites that meet the conditions are obtained together with the variant type of interest to obtain determined variant site data.

[0072] The solution of the embodiment of the present invention extracts the variant sites in the filtered VCF data of the samples marked as probands in the PED file. The extraction process includes the following key steps:

[0073] Genotype and depth value (DP) screening: The system analyzes the genotype information of each proband sample in the VCF file and combines it with the sequencing depth value (DP). Only variant sites with a DP value greater than 10 will be extracted as candidate features. This screening criterion was selected based on research experience. Variant sites with higher DP values ​​are usually more reliable and reduce the probability of false positive variant sites.

[0074] Variant type screening: Depending on the research needs, the system may further screen out specific types of variants (such as SNPs, Indels). Users can specify the variant type of interest, and the system will only extract eligible variant sites for subsequent analysis. In this way, the system can focus on variant types with biological significance or known associations, further improving the pertinence of the analysis.

[0075] More preferably, the filtering operations in the filtered VCF data include variant site range screening, CNV filtering and Mendelian filtering; the data filtering in the embodiment of the present invention includes:

[0076] Variant site range screening: The system first performs preliminary filtering based on the variant site range specified by the user. Typically, researchers will specify one or more chromosomal intervals of interest or specific types of variant sites to narrow the scope of the analysis. In this step, the system will prioritize and focus on the variant data in these key areas, but will not completely exclude variant sites that are not within these ranges. Instead, they will be marked as secondary data for further analysis or re-evaluation in subsequent steps. This screening method effectively reduces the complexity of data processing while retaining flexibility, ensuring that subsequent analysis can be focused on key areas without completely excluding potentially important information. This method not only improves the efficiency of data processing, but also retains the integrity of the research data, providing a basis for possible subsequent in-depth analysis.

[0077] CNV filtering: In this step, the system will filter out all variant sites with a proband sample genotype of "0 / 0". This means that for these variants, all proband samples have not mutated and are therefore considered invalid variants. At the same time, the system will delete any variant site with an embryo sample genotype of ". / ." and all proband sample genotypes are not all ". / .". This step ensures that only variant sites with potential significance in the proband or embryo are retained. This screening method can significantly reduce the interference of noise data and improve the accuracy and reliability of subsequent analysis.

[0078] Mendelian filtering: This filtering verifies the genotypes of parents and children in each family based on Mendel's laws of inheritance. The system checks the inheritance pattern of variant sites in family members to ensure that the transmission of variants conforms to Mendel's laws of inheritance. For variant sites that do not conform to the laws of inheritance, the system excludes them to reduce false positive results. This step not only ensures that the data conforms to the basic principles of genetics, but also further improves the accuracy of the analysis.

[0079] like Figure 3 As shown, the threshold matching of the significance level value with a preset feature threshold to obtain variant site data that meets the corresponding feature threshold includes:

[0080] S1021: threshold matching the significance level value with a preset first characteristic threshold to obtain a first variation number of variation site data that meets the first characteristic threshold;

[0081] S1022: Matching the first variation quantity with a first quantity threshold, and if the first variation quantity is within the first quantity threshold interval, outputting corresponding variation site data;

[0082] S1023: If the first variation quantity is greater than a larger value in a first quantity threshold interval, adjusting the first feature threshold to a second feature threshold, wherein the second feature threshold is less than the first feature threshold;

[0083] S1024: If the first variation quantity is smaller than a smaller value in a first quantity threshold interval, adjusting the first feature threshold to a third feature threshold, wherein the third feature threshold is larger than the first feature threshold.

[0084] In order to determine which variant sites are significantly associated with the phenotype, the system performs statistical analysis on the extracted features. The specific steps are as follows:

[0085] Chi-square test: The system uses the chi-square test to calculate the P value between each variant site and the phenotype. The P value reflects the strength of the association between the variant site and the disease phenotype. A lower P value (usually set to 0.05 or lower) means that there is a significant association between the variant site and the disease phenotype.

[0086] Automatic adjustment of P-value threshold: In order to ensure that significant variant sites can be effectively screened out in a variety of situations, the system has designed a mechanism for automatically adjusting the P-value threshold. The initial P-value threshold is usually set to 0.05, and the system first uses this threshold to screen out variant sites. If the number of variant sites screened out is too small, the system will gradually relax the P-value threshold (such as increasing it to 0.1, 0.2) to ensure that enough significant features can be captured for analysis. Similarly, if too many variant sites are screened out, the system will gradually tighten the P-value threshold (such as reducing it to 0.01, 0.005) to improve the accuracy of the screening. Through this dynamic adjustment mechanism, the system can adapt to the characteristics of different data sets and optimize the feature screening effect.

[0087] Significant variation screening: The system automatically screens out the variation sites that ultimately meet the P value threshold requirements and marks them as significant features for subsequent analysis. This process ensures the accuracy of feature screening while retaining sufficient features for subsequent classification and analysis.

[0088] In the embodiment of the present invention, database insertion and updating: the significant variant sites involved in the classification process will be inserted into the database. The system first checks whether the same variant site already exists in the database. If the variant site already exists, the system will update its usage count (i.e., weight) to reflect the change in its importance in different analyses. If the variant site does not exist, the system will insert it into the database as a new record. This mechanism ensures that the information in the database can be dynamically updated as the analysis progresses.

[0089] Batch operations and performance optimization: To improve the efficiency of database operations, the system supports batch insert and update operations. Compared with record-by-record processing, batch operations can significantly increase the speed of data writing and updating, especially in large-scale data analysis. This optimization greatly improves system performance.

[0090] Data query and analysis: In order to support researchers to conduct in-depth analysis of variant site data, the system provides a convenient data query function. Researchers can query records in the database based on specific chromosome location, variant type, genotype and other conditions. The system also supports basic statistical analysis of query results, such as calculating variant frequency, genotype distribution, etc. These functions provide researchers with powerful data analysis support and help to explore potential genetic laws.

[0091] The genetic database updating method based on sequencing data in the embodiment of the present invention extracts significant variant sites according to the actual data of the proband sample and dynamically updates the feature set in the database. By combining real data for feature extraction, the system can more accurately reflect the genetic differences between samples, thereby improving the accuracy of subsequent classification.

[0092] Embodiment 2

[0093] like Figures 4 to 7 As shown, the second aspect of the embodiment of the present invention discloses a method for classifying embryo samples based on sequencing data, comprising:

[0094] S201: Acquire a data file of an embryo sample input by a user and after data verification, wherein the embryo sample data file includes a VCF file and a PED file;

[0095] S202: Determine whether there is label data in the embryo sample data file, if so, write the variation site associated with the label data into the variation data sequence; if not, perform feature extraction operation on the sample data file to obtain corresponding matching feature data;

[0096] S203: matching the matching feature data with the proband features stored in a pre-constructed database to obtain variant site details, wherein the variant site details include the number of variant sites, and calculating the corresponding variant matching rate according to the number of variant sites and the total number of variant sites; the proband features stored in the pre-constructed database here are updated and determined by the method of the first embodiment;

[0097] S204: If the variation matching rate exceeds a preset classification threshold, the corresponding embryo sample is determined to be a diseased sample; if the variation matching rate does not exceed the preset classification threshold, the corresponding embryo sample is determined to be a healthy sample.

[0098] The technical effects of the above-mentioned embryo sample classification method based on sequencing data are mainly reflected in the following aspects: improving the accuracy and reliability of classification, data verification: at the beginning of the method, by obtaining the embryo sample data file (including VCF file and PED file) input by the user and verified by data, the accuracy and completeness of the input data are ensured, laying a solid foundation for the subsequent analysis and classification of variant sites. Feature extraction and matching: for unlabeled embryo samples, the corresponding matching feature data is obtained through feature extraction operation, and it is matched with the proband features stored in the pre-built database. This feature-based matching method can fully consider the genetic information of the embryo sample, so as to more accurately judge its health status. Calculation of variation matching rate: by calculating the variation matching rate (i.e., the ratio of the number of matched variant sites to the total number of variant sites), the similarity between the embryo sample and the known disease characteristics can be quantified. When the variation matching rate exceeds the preset classification threshold, the embryo sample can be determined as a diseased sample, otherwise it is a healthy sample. This quantitative analysis method improves the accuracy and reliability of classification.

[0099] Flexible response to different sample characteristics, adaptive classification: This method can flexibly respond to embryo samples with different genetic characteristics. Whether it is a sample with a known label or a sample without a label, accurate classification results can be obtained through the corresponding processing flow. Adapt to different diseases: Since the proband characteristics of various diseases are stored in the database, this method can be applied to the screening and diagnosis of various genetic diseases and has wide applicability.

[0100] Improve the efficiency of genetic disease screening and diagnosis, automatic processing: the entire classification process is automated, and no manual intervention is required from data input to classification result output, which greatly improves processing efficiency. Quick feedback: By calculating the variation matching rate and comparing it with the preset threshold, the classification results of embryo samples can be quickly obtained, providing clinicians with timely diagnostic basis.

[0101] The solution of the embodiment of the present invention first matches the variant sites of the embryo sample with the proband characteristics stored in the database. The matching criteria include the complete consistency of chromosome position, reference base and variant base. Through this process, the system can identify which variant sites in the embryo sample are consistent with the known pathological characteristics.

[0102] The scheme of the embodiment of the present invention calculates the matching rate based on the matching of the embryo sample with the known features in the database. The matching rate is calculated based on the ratio of the number of matched variant sites to the total number of variant sites. If the matching rate of the embryo sample exceeds the preset classification threshold (such as 10%), the system will determine that the embryo sample has a similar variation pattern to the proband and classify it as "diseased"; otherwise, it will be classified as "healthy". This classification result is not only based on the matching of a single variant site, but also takes into account the similarity of the overall variation pattern.

[0103] More preferably, after determining that the corresponding embryo sample is a diseased sample if the variation matching rate exceeds the preset classification threshold, and determining that the corresponding embryo sample is a healthy sample if the variation matching rate does not exceed the preset classification threshold, the method further includes:

[0104] Output the classification results of each sample data file, the classification results include sample classification labels, a list of significant variant sites, matching result information and analysis result information, the matching result information includes matched variant sites and variant matching rates; wherein the list of significant variant sites includes significant variant sites, variant chromosome positions, reference bases, alternative bases and displayed variant values.

[0105] The solution of the embodiment of the present invention outputs the classification results: the classification results of each sample (including classification labels, matched variant sites and matching rates, etc.) are written into an output file. These data not only provide direct results of sample classification, but also provide data support for subsequent research and verification. At the same time, the system also generates visualization charts of the classification results, such as tree diagrams and classification diagrams, to intuitively display the relationship between samples and classification results.

[0106] Generate analysis report and visualization, a) Generate a comprehensive report. The system will generate a comprehensive report based on the analysis results of the sequencing data. The report content includes the following parts: Sample classification results: List the classification labels of all samples (such as diseased or healthy), and briefly explain the classification basis of each sample. The system will provide clear classification results for each sample based on the number of matched variant sites and the matching rate. List of significant variant sites: List the significant variant sites screened out during the analysis process. These variant sites are considered to be significantly associated with phenotypes (such as diseases). The list includes detailed information such as the chromosome position of the variant, reference base, alternative base, P value, etc., so that researchers can further investigate the biological significance of these variants. Matching details: For each embryo sample, the system will provide matching details with existing features in the database. Matching details include information such as the specific variant sites matched and the matching rate, which helps researchers assess the genetic risk of embryo samples. Analysis summary: The system will summarize the entire analysis process, explain the screening and classification criteria used, and the main conclusions drawn. This section will also include a brief explanation of the analysis results and suggestions for the next step of research. After the report is generated, the system will output it in text format, which is convenient for researchers to save and consult. Visual display: the system generates a variety of visual charts to intuitively display the analysis results. It mainly includes the following chart forms: Tree diagram: The system generates a tree diagram based on the sample classification results to show the genetic relationship between samples. Through the tree diagram, researchers can intuitively see how the samples are clustered and which samples are significantly different from other samples. This kind of chart helps to understand the genetic similarities or differences between samples, especially in family studies, it can clearly show the genetic pattern. Sample classification diagram: Use a simple dot chart or bar chart to show the classification results of all samples. In the chart, the samples are grouped according to the classification labels, and the diseased samples and healthy samples are distinguished by color or shape. This type of chart can quickly summarize the classification results and is easy for non-professionals to understand.

[0107] More preferably, the performing a feature extraction operation on the sample data file to obtain corresponding matching feature data includes:

[0108] Extract the genotype information and sequencing depth value of the variant sites in the filtered VCF data in the sample data file;

[0109] The genotype information of the variant site in the VCF data is compared with the reference genome. If the comparison is inconsistent, the corresponding sequencing depth value is compared with the set depth value. If the sequencing depth value is greater than the set depth value, the corresponding variant site is determined as a candidate variant site;

[0110] According to the variant type of interest set by the user, eligible variant sites will be obtained according to the variant type of interest to obtain determined matching feature data.

[0111] More preferably, the obtaining of the sample data file input by the user and after data verification, wherein the sample data file includes a VCF file and a PED file, comprises:

[0112] Reading the gene sequencing data of the corresponding sample that has undergone data verification from the VCF file information; wherein the gene sequencing data includes chromosome position, reference base, variant base and genotype information of the sample;

[0113] Read the pedigree structure and phenotypic information of the sample from the PED file, wherein the PED file includes family member information, and the family member information includes the gender of the sample, the relationship between family members, and the phenotypic characteristics associated with the family members; during the data reading process, the system automatically matches the sample ID in the PED file with the sample ID in the VCF file;

[0114] The variant site details also include specific variant sites.

[0115] The present invention provides a genetic variation site analysis and sample classification method based on PIMS-SR sequencing data. By combining a variety of advanced data processing and analysis technologies, it can efficiently and accurately screen out significant disease-related variation sites in large-scale genomic data and classify samples as diseased or healthy. This method significantly improves the efficiency and accuracy of genetic analysis in many aspects, with the following specific effects:

[0116] Efficient data filtering and processing: Variant site range screening: Through the user-specified variant site range, the system can quickly focus on key areas, reducing the complexity and computational cost of data processing. While ensuring data processing efficiency, the system also retains secondary data that is not within the specified range for subsequent analysis.

[0117] CNV and Mendelian filtering: The system combines copy number variation (CNV) filtering and Mendelian genetic law verification in data preprocessing, which not only reduces the interference of noise data, but also improves the accuracy of analysis data. This rigorous filtering mechanism ensures the quality of the data and lays a solid foundation for subsequent feature extraction and classification.

[0118] Accurate feature extraction and screening: P-value screening and dynamic adjustment: The system calculates the P-value between each variant site and the phenotype through the chi-square test, and automatically screens out disease-related variant sites based on the significance of the P-value. The system has designed an automatic adjustment mechanism for the P-value threshold, which can dynamically optimize the results of feature screening under different data sets to ensure that enough significant variant sites are screened for subsequent analysis. This flexible and accurate screening strategy significantly improves the effectiveness of feature extraction.

[0119] Feature extraction based on actual data: The system extracts significant variant sites based on the actual data of the proband sample and dynamically updates the feature set in the database. By combining real data for feature extraction, the system can more accurately reflect the genetic differences between samples, thereby improving the accuracy of subsequent classification.

[0120] Reliable sample classification: Match rate determination and classification: The system calculates the match rate based on the match between the embryo sample and the known features in the database, and classifies the sample as "diseased" or "healthy" based on the preset threshold. This classification method is not only based on the match of a single variant site, but also takes into account the similarity of the overall variant pattern, making the classification results more accurate and comprehensive.

[0121] Weight adjustment and classification optimization: During the classification process, the system can gradually optimize the composition of the feature set by dynamically updating the number of times the features are used (weights) in the database, so that the system can make more accurate classification decisions when facing new samples. This adaptive feature adjustment mechanism helps the system continuously improve classification performance and reduce the occurrence of misclassification.

[0122] Strong learning ability and adaptability: Deep learning and accumulation of family data: By processing a large amount of family data, the system can gradually learn and accumulate the characteristic patterns of each family. As the amount of family data increases, the system's understanding of the association between genetic variation and disease will become more and more in-depth. This learning ability enables the system to not only process samples of the current family, but also accurately analyze new samples based on historical data.

[0123] Autonomous classification without a proband: Through in-depth learning of previous family data, the system can independently classify new embryo samples without proband samples. This means that even if the proband sample is not available, the system can still judge the health status or genetic risk of the embryo sample based on the learned family characteristic patterns. This function significantly expands the application scope of the system, especially in some special cases (such as when the proband cannot provide samples). It has important practical value.

[0124] Efficiency of data management and operation: Batch operation and performance optimization: The system supports batch operation when inserting and updating data, which greatly improves the efficiency of data management compared to the way of processing one by one. This has significant performance advantages for processing large-scale genomic data and complex family sample analysis. Convenient data query and analysis: The system provides powerful data query functions, and researchers can quickly retrieve variant site information in the database according to specific conditions and perform basic statistical analysis. These functions provide researchers with flexible data operation methods and support more in-depth genetic research.

[0125] Intuitive visualization: Multiple chart displays: The system generates tree diagrams, sample classification diagrams, and matching rate bar charts, which can intuitively display the genetic relationship and classification results between samples. Through these charts, researchers can quickly understand the core information and analysis results of the data, and support subsequent research and decision-making. Comprehensive report and chart export: The system automatically generates a comprehensive analysis report, and supports exporting charts and reports into multiple formats, which is convenient for researchers to display, share, and publish. This function not only improves the readability and ease of use of the data, but also enhances the application value of the system in actual research.

[0126] In summary, the present invention significantly improves the efficiency, accuracy and reliability of genomic data analysis, and can provide strong support for genetic research and clinical decision-making. This method is particularly suitable for scenarios that require processing large-scale and diverse genetic data, such as disease research, family analysis and personalized medicine.

[0127] The genetic database updating method based on sequencing data in the embodiment of the present invention extracts significant variant sites according to the actual data of the proband sample and dynamically updates the feature set in the database. By combining real data for feature extraction, the system can more accurately reflect the genetic differences between samples, thereby improving the accuracy of subsequent classification.

[0128] Embodiment 3

[0129] See also Figure 8 , Figure 8 Schematic diagram of the structure of the genetic database update system based on sequencing data disclosed in the embodiment of the present invention. Figure 8 As shown, the genetic database updating system based on sequencing data may include:

[0130] Acquisition module 21: used to acquire data marked as proband samples in the PED file, and extract variant site data in the filtered VCF data of the proband sample;

[0131] Feature determination module 22: used to calculate the significance level value between each variant site data and the disease phenotype using a chi-square test, and threshold match the significance level value with a preset feature threshold to obtain variant site data that meets the corresponding feature threshold, and determine the variant site data that meets the corresponding feature threshold as a significant variant site;

[0132] Database updating module 23: used to match the determined significant variation sites with the reference variation sites in the feature database. If there is a reference variation site matching the corresponding significant variation site in the feature database, the weight parameter of the corresponding reference variation site is adjusted. If there is no corresponding significant variation site in the feature database, the corresponding significant variation site is added to the feature database.

[0133] Traditional genomic data analysis methods usually rely on software algorithms on general-purpose computing platforms, such as parallel computing on CPUs (central processing units) or GPUs (graphics processing units). However, as the scale of data increases, these general-purpose platforms show obvious bottlenecks in handling complex analysis tasks. Even high-performance GPU clusters consume a lot of time and energy when processing whole genome sequencing data, especially in the stages of data preprocessing, feature extraction, and classification analysis, where the problem of inefficiency is particularly prominent.

[0134] In addition, existing genomic data analysis algorithms often have difficulty balancing accuracy and efficiency. For example, commonly used haplotype analysis methods may sacrifice accuracy when processing complex genotype data due to limited computing resources. In addition, these methods often require complex data preprocessing and conversion processes when faced with multimodal data (such as data fusion generated by different sequencing technologies), which further increases the difficulty and time cost of analysis.

[0135] In response to the above problems, hardware acceleration technologies such as FPGA and ASIC provide new ideas for solving the bottleneck of large-scale data processing. FPGA can significantly improve data processing speed through parallel computing and pipeline processing, while ASIC can provide extremely high computing efficiency and energy efficiency for specific tasks. However, there is currently a lack of dedicated hardware solutions for genomic data analysis on the market, especially when dealing with complex feature extraction and classification analysis tasks, and existing hardware accelerators still have a lot of room for improvement.

[0136] The main highlights of the present invention include:

[0137] Hardware acceleration and efficient parallel processing: By using hardware accelerators such as FPGA or ASIC, the present invention realizes efficient parallel processing of genomic data, especially in data filtering and feature extraction. Compared with traditional CPU / GPU computing, hardware accelerators can process large amounts of data in a very short time while significantly reducing energy consumption.

[0138] Dynamic threshold adjustment and real-time analysis: The system has the ability to dynamically adjust the P value screening threshold according to the characteristics of the input data, ensuring that the most representative genetic features are extracted during real-time analysis. This function is particularly suitable for dynamically adjusting analysis strategies when studying different populations or complex diseases to improve the accuracy and reliability of results.

[0139] Modular design and flexible configuration: The system adopts modular design, and users can flexibly configure different hardware modules according to actual needs, including data preprocessing module, feature extraction module and classification analysis module. This flexibility not only enables the system to adapt to data processing needs of different scales and types, but also allows easy expansion of system functions when hardware is upgraded.

[0140] Efficient data processing and low energy consumption: By utilizing the high parallel computing capability of hardware accelerators, the present invention performs well in processing large-scale genomic data, significantly improving the efficiency of data filtering and classification analysis. Compared with traditional software algorithms, the present system consumes less energy and is particularly suitable for high-throughput sequencing projects that need to process massive amounts of data in a short period of time.

[0141] The hardware accelerated genome data filtering and classification system of the present invention is composed of multiple modules, and the modules are connected by a high-speed data bus to ensure the efficient circulation and stability of data during the processing.

[0142] System components of the embodiment of the present invention: Data acquisition module: including a data input interface and a data preprocessing unit, responsible for acquiring raw genome data from a sequencer or storage device, and formatting and converting the data into a standard binary format for subsequent processing. Hardware acceleration processing module: The core module of this system, composed of one or more FPGAs, ASICs or GPUs, responsible for performing core tasks such as data filtering, feature extraction and classification analysis. The acquisition module 21 is essentially a data acquisition module.

[0143] This module includes the following submodules: Data filtering submodule: Rapidly screen variant sites that do not meet the analysis requirements, using parallel computing technology to process large amounts of data in a short time. Feature extraction submodule (i.e., feature determination module 22): Calculate the P value of genomic data and dynamically adjust the screening threshold to ensure that the most representative genetic features are extracted. Classification submodule: Based on the classification algorithm implemented in hardware, classify and analyze the samples to determine their health status or genetic characteristics. Storage and management module (database update module 23): Includes high-speed memory and data management software, responsible for storing analysis results and intermediate data, and provides data backup and recovery functions. This module is connected to the hardware acceleration processing module via a high-speed bus to ensure fast access to data.

[0144] Result output module: supports multiple output interfaces, such as HDMI, USB, Ethernet, etc., and outputs the analysis results in charts, text or other forms for researchers to view and further analyze.

[0145] Structural connection relationship: The data acquisition module is connected to the hardware acceleration processing module through a high-speed data bus, and the pre-processed data is transmitted to the hardware accelerator for analysis. The submodules of the hardware acceleration processing module are connected through an internal bus. The data transmission efficiency between the submodules is high, and there is no obvious delay during the processing. The storage and management module is connected to the hardware acceleration processing module through a memory bus, which is responsible for receiving and storing the processed data and ensuring the integrity of the data. The result output module is directly connected to the storage module, supports multiple output formats, and can export the results to external devices according to user needs.

[0146] Working principle and dynamic process: After the system is started, the data acquisition module first obtains genome data from external devices (such as sequencers) and performs preliminary preprocessing. The processed data is transmitted to the hardware acceleration processing module through the bus. In the hardware acceleration processing module, the data filtering submodule first screens the input data to remove low-quality or non-compliant variant sites. This step is implemented by the parallel processing unit of the FPGA, which is fast and accurate. Then, the feature extraction submodule calculates the P value of the filtered data and screens out statistically significant variant sites based on preset or dynamically adjusted thresholds. This process also uses hardware acceleration technology to improve computing efficiency. The classification submodule classifies and analyzes the samples based on the extracted features. The hardware-implemented classification algorithm enables the system to process a large number of samples in a short time and output classification results.

[0147] Finally, the analysis results are transmitted to the storage and management module for storage and backup. The result output module outputs the analysis results in the form of charts, reports, etc., and supports real-time display or export.

[0148] The present invention proposes a genome data processing system and its implementation method. The system realizes efficient analysis of large-scale genome data by combining multiple data filtering and feature extraction algorithms through hardware acceleration. The specific steps are as follows: a. Data acquisition and preprocessing: Obtain the original genome data from the sequencing device or storage device. The data format is usually a standard VCF (variation call format) file. In the data acquisition module, the data is preliminarily formatted to exclude low-quality reads and obvious noise signals to ensure the integrity of the data and the accuracy of the analysis. This step includes reading, parsing and basic quality control of the data to ensure the quality of the data entering the subsequent processing flow.

[0149] b. Variant site range screening: Users can specify the chromosome interval of interest or a specific type of variant site, and the system automatically screens out eligible sites based on these parameters. The data filtering submodule receives these screening conditions and uses the hardware accelerator to perform high-speed screening on the input data. This module can process large amounts of data in parallel and quickly exclude variant sites that are not within the specified range, thereby reducing the complexity of data processing and concentrating computing resources on key areas.

[0150] c. Data filtering: After the initial screening, the data will be filtered more finely by the data filtering submodule in the hardware acceleration processing module. This step includes filtering based on quality values ​​to exclude low-quality or unreliable variant sites. The specific filtering logic includes: Quality filtering: exclude sites with quality scores below the specified threshold to ensure the data quality of subsequent analysis. Depth filtering: filter variant sites according to sequencing depth (DP value) and exclude sites with low coverage to reduce the impact of false positive results. Mendel's genetic rule filtering: For samples with family data, by comparing the genotypes of the proband and parents, exclude variant sites that do not conform to Mendel's genetic rules to further improve the accuracy of the data.

[0151] d. Feature extraction and P-value calculation: After completing data filtering, the feature extraction submodule performs parallel calculations on large-scale data through a hardware accelerator to extract the features of each variant site, such as allele frequency, genotype distribution, etc. P-value calculation is a key step in feature extraction. The system screens out statistically significant variant sites based on the set or dynamically adjusted P-value threshold. Through the parallel computing capabilities of the hardware accelerator, the P-value calculation of large-scale data can be completed quickly. Specific steps for feature extraction and P-value screening: Dynamic P-value threshold adjustment: The system will automatically adjust the P-value screening threshold according to data characteristics and user needs to ensure that statistically significant variant sites are screened out. Feature selection: By selecting the K best features (such as the SelectKBest method mentioned above), combined with the P-value for screening, the variant sites that have the greatest impact on the classification results are retained.

[0152] e. Classification analysis: After feature extraction, the system uses the classification submodule in the hardware acceleration processing module to classify and analyze the samples. The classification algorithm determines the health status or genetic characteristics of the sample based on the extracted significant features. The system supports a variety of classification algorithms, such as statistical methods (such as logistic regression, decision trees, etc.) and machine learning methods (such as support vector machines, neural networks, etc.). Users can select or adjust the classification algorithm as needed. This classification step is not only fast but also accurate. It can process large-scale samples and automatically update the classification model based on previous family data to improve the accuracy and reliability of classification.

[0153] f. Result storage and output: After the classification is completed, the system stores the results and analysis data in the storage and management module. The data storage includes raw data, processed variant sites, classification results and analysis reports. The system supports multiple ways to output data, such as exporting result files through the USB interface, uploading data to a remote server through the Ethernet interface, or displaying analysis results in real time through a graphical interface (such as HDMI). The result output module displays the analysis results in a user-friendly form, supporting multiple forms such as charts and text reports, which are convenient for researchers to view and interpret.

[0154] g. Data backup and recovery: To ensure data security and integrity, the system automatically backs up data after each processing step. The storage and management module is responsible for regularly backing up key data in processing and provides recovery functions to prevent data loss or processing errors. Users can restore to the previous processing steps and re-analyze data through simple interface operations, ensuring the flexibility and controllability of the analysis process.

[0155] Embodiment 4

[0156] See also Fig. 9 , Fig. 9 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may be a computer, a server, etc. Of course, in certain circumstances, it may also be a smart device such as a mobile phone, a tablet computer, a monitoring terminal, and an image acquisition device with processing functions. Fig. 9 As shown, the electronic device may include:

[0157] A memory 510 storing executable program codes;

[0158] a processor 520 coupled to the memory 510;

[0159] The processor 520 calls the executable program code stored in the memory 510 to execute part or all of the steps in the genetic database updating method based on sequencing data in the first embodiment.

[0160] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute part or all of the steps in the method for updating a genetic database based on sequencing data in the first embodiment.

[0161] The embodiment of the present invention further discloses a computer program product, wherein when the computer program product is run on a computer, the computer is enabled to execute part or all of the steps in the method for updating a genetic database based on sequencing data in the first embodiment.

[0162] An embodiment of the present invention further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the genetic database updating method based on sequencing data in embodiment one.

[0163] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0164] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0165] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0166] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a memory and includes several requests to enable a computer device (which can be a personal computer, server or network device, etc., specifically a processor in a computer device) to perform some or all steps of the method described in each embodiment of the present invention.

[0167] In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0168] A person of ordinary skill in the art may understand that part or all of the steps in the various methods of the embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0169] The genetic database updating method, system, electronic device and storage medium based on sequencing data disclosed in the embodiments of the present invention are introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for updating a genetic database based on sequencing data, characterized in that: include: Obtain the data marked as the proband sample in the PED file, and extract the variant site data in the filtered VCF data of the proband sample; The step of extracting variant site data from the filtered VCF data in the proband sample includes: Extract the genotype information and sequencing depth value of the variant sites in the filtered VCF data of the proband sample; The genotype information of the variant site in the VCF data is compared with the reference genome. If the comparison is inconsistent, the corresponding sequencing depth value is compared with the set depth value. If the sequencing depth value is greater than the set depth value, the corresponding variant site is determined as a candidate variant site; According to the variant type of interest set by the user, eligible variant sites are obtained to obtain variant site data; Using a chi-square test to calculate the significance level value between each variant site data and the disease phenotype, and threshold matching the significance level value with a preset feature threshold to obtain variant site data that meets the corresponding feature threshold, and determining the variant site data that meets the corresponding feature threshold as a significant variant site; threshold matching the significance level value with a preset feature threshold to obtain variant site data that meets the corresponding feature threshold, including: Perform threshold matching on the significance level value and a preset first characteristic threshold value to obtain a first variation number of the variation site data that meets the first characteristic threshold value; Matching the first variation quantity with a first quantity threshold, and if the first variation quantity is within the first quantity threshold interval, outputting corresponding variation site data; If the first variation quantity is greater than a larger value in a first quantity threshold interval, adjusting the first feature threshold to a second feature threshold, wherein the second feature threshold is less than the first feature threshold; If the first variation quantity is less than a smaller value in the first quantity threshold interval, adjusting the first feature threshold to a third feature threshold, wherein the third feature threshold is greater than the first feature threshold; The determined significant variation sites are matched with the reference variation sites in the feature database. If there is a reference variation site matching the corresponding significant variation site in the feature database, the weight parameter of the corresponding reference variation site is adjusted. If there is no corresponding significant variation site in the feature database, the corresponding significant variation site is added to the feature database.

2. The method for updating a genetic database based on sequencing data according to claim 1, characterized in that: The filtering operations in the filtered VCF data include variant site range screening, CNV filtering, and Mendelian filtering.

3. A method for classifying embryo samples based on sequencing data, characterized in that: include: Obtaining a data-verified embryo sample data file input by a user, wherein the embryo sample data file includes a VCF file and a PED file; Determine whether there is label data in the embryo sample data file, if so, write the variation site associated with the label data into the variation data sequence; if not, perform feature extraction operation on the embryo sample data file to obtain corresponding matching feature data; The matching feature data is matched with the proband features stored in a pre-constructed database to obtain variant site details, wherein the variant site details include the number of variant sites, and the corresponding variant matching rate is calculated based on the number of variant sites and the total number of variant sites; wherein the proband features stored in the pre-constructed database are obtained by the method according to any one of claims 1 to 2; If the variation matching rate exceeds the preset classification threshold, the corresponding embryo sample is determined to be a diseased sample; if the variation matching rate does not exceed the preset classification threshold, the corresponding embryo sample is determined to be a healthy sample.

4. The method for classifying embryo samples based on sequencing data as claimed in claim 3, characterized in that: After determining that the corresponding embryo sample is a diseased sample if the variation matching rate exceeds the preset classification threshold, and determining that the corresponding embryo sample is a healthy sample if the variation matching rate does not exceed the preset classification threshold, the method further includes: Output the classification results of each embryo sample data file, the classification results include sample classification labels, a list of significant variant sites, matching result information and analysis result information, the matching result information includes matched variant sites and variant matching rates; wherein the list of significant variant sites includes significant variant sites, variant chromosome positions, reference bases, alternative bases and significant variant values.

5. The method for classifying embryo samples based on sequencing data as claimed in claim 3, characterized in that: The step of performing a feature extraction operation on the embryo sample data file to obtain corresponding matching feature data includes: Extract the genotype information and sequencing depth value of the variant sites in the filtered VCF data in the embryo sample data file; The genotype information of the variant site in the VCF data is compared with the reference genome. If the comparison is inconsistent, the corresponding sequencing depth value is compared with the set depth value. If the sequencing depth value is greater than the set depth value, the corresponding variant site is determined as a candidate variant site; According to the variant type of interest set by the user, eligible variant sites will be obtained according to the variant type of interest to obtain determined matching feature data.

6. The method for classifying embryo samples based on sequencing data as claimed in claim 3, characterized in that: The step of obtaining a data-verified embryo sample data file input by a user, wherein the embryo sample data file includes a VCF file and a PED file, includes: Reading the gene sequencing data of the corresponding sample that has undergone data verification from the VCF file information; wherein the gene sequencing data includes chromosome position, reference base, variant base and genotype information of the sample; Read the pedigree structure and phenotypic information of the sample from the PED file, wherein the PED file includes family member information, and the family member information includes the gender of the sample, the relationship between family members, and the phenotypic characteristics associated with the family members; during the data reading process, the system automatically matches the sample ID in the PED file with the sample ID in the VCF file; The variant site details also include specific variant sites.

7. A genetic database updating system based on sequencing data, characterized in that: include: Acquisition module: used to obtain the data marked as proband samples in the PED file, and extract the variant site data in the filtered VCF data of the proband sample; The step of extracting variant site data from the filtered VCF data in the proband sample includes: Extract the genotype information and sequencing depth value of the variant sites in the filtered VCF data of the proband sample; The genotype information of the variant site in the VCF data is compared with the reference genome. If the comparison is inconsistent, the corresponding sequencing depth value is compared with the set depth value. If the sequencing depth value is greater than the set depth value, the corresponding variant site is determined as a candidate variant site; According to the variant type of interest set by the user, eligible variant sites are obtained to obtain variant site data; Feature determination module: used to calculate the significance level value between each variant site data and the disease phenotype using a chi-square test, and threshold match the significance level value with a preset feature threshold to obtain variant site data that meets the corresponding feature threshold, and determine the variant site data that meets the corresponding feature threshold as a significant variant site; threshold matching the significance level value with a preset feature threshold to obtain variant site data that meets the corresponding feature threshold, including: Perform threshold matching on the significance level value and a preset first characteristic threshold value to obtain a first variation number of the variation site data that meets the first characteristic threshold value; Matching the first variation quantity with a first quantity threshold, and if the first variation quantity is within the first quantity threshold interval, outputting corresponding variation site data; If the first variation quantity is greater than a larger value in a first quantity threshold interval, adjusting the first feature threshold to a second feature threshold, wherein the second feature threshold is less than the first feature threshold; If the first variation quantity is less than a smaller value in the first quantity threshold interval, adjusting the first feature threshold to a third feature threshold, wherein the third feature threshold is greater than the first feature threshold; Database update module: used to match the determined significant variation sites with the reference variation sites in the feature database. If there is a reference variation site matching the corresponding significant variation site in the feature database, the weight parameter of the corresponding reference variation site is adjusted. If there is no corresponding significant variation site in the feature database, the corresponding significant variation site is added to the feature database.

8. An electronic device, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the genetic database updating method based on sequencing data according to any one of claims 1 to 2.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program enables a computer to execute the genetic database updating method based on sequencing data according to any one of claims 1 to 2.

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