Somatic cell gene variation analysis method, apparatus and device, and storage medium
By obtaining somatic cell detection sample data and matching gene tree grouping, the problem of inefficient somatic cell mutation judgment in the prior art is solved, and rapid and accurate gene mutation analysis and the formulation of personalized treatment plans are achieved.
Patent Information
- Application Number
- CN202411937529.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-26
- Publication Date
- 2025-04-29
AI Technical Summary
In the prior art, the judgment of somatic cell mutation is inefficient and it is impossible to make a quick judgment.
By obtaining sample data for somatic cell detection, primary bond information is determined, gene tree grouping is matched based on primary bond information, oncogenicity results and mutation site information are obtained, and somatic cell gene mutation analysis results are output.
It realizes rapid analysis and judgment of somatic cell variant data, ensures the integrity and consistency of the data, provides clinicians with accurate gene variant interpretation reports, helps to formulate personalized treatment plans, and improves diagnosis and treatment effects.
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Figure CN119905143A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of gene variation databases, and in particular to somatic gene variation analysis methods, devices, equipment and storage media. Background Art
[0002] With the rapid development of high-throughput sequencing technology, the detection of somatic variants has become more and more common. Somatic variants usually refer to gene mutations that occur in non-germline cells. These mutations are closely related to the occurrence and development of many diseases, especially cancer. There are five main types of somatic variants: single-base point mutations SNV, short fragment insertion and deletion indel, copy number amplification, copy number deletion, and fusion mutation. Therefore, accurate interpretation of somatic variants is of great significance for disease diagnosis, treatment selection, and prognosis assessment. At present, there are some databases for somatic variants, such as COSMIC (Catalogue of Somatic Mutations in Cancer), TCGA (The Cancer Genome Atlas), and ICGC (International Cancer Genome Consortium). These databases provide researchers with a large amount of somatic variant information to a certain extent. Although they provide basic support for the study of somatic variants, the analysis efficiency of somatic variant information is low and it is impossible to make a quick judgment.
[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention
[0004] The main purpose of this application is to provide a somatic gene variation analysis method, device, equipment and storage medium, aiming to solve the technical problem of low efficiency in judging somatic variation in the prior art.
[0005] To achieve the above objectives, the present application proposes a somatic gene variation analysis method, which comprises:
[0006] Acquire sample data of somatic cell detection, and determine primary key information according to the sample data;
[0007] Determine ID information according to the primary key information, match gene tree groups based on the ID information, and obtain gene tree group information;
[0008] Matching the sample data with the gene tree grouping information to determine the carcinogenicity results and mutation site information corresponding to the sample data;
[0009] The carcinogenicity result and the mutation site information are output as a somatic gene mutation analysis result.
[0010] In one embodiment, the step of obtaining sample data for somatic cell detection and determining primary key information according to the sample data includes:
[0011] Obtaining a somatic cell test report, and extracting transcript information and nucleotide mutation information from sample data in the somatic cell test report;
[0012] Based on a message digest algorithm, primary key information is generated according to the transcript information and the nucleotide information.
[0013] In one embodiment, the step of determining ID information according to the primary key information, matching gene tree groups based on the ID information, and obtaining gene tree group information comprises:
[0014] Matching the primary key information with the gene information entry, and when the match is successful, determining the gene information entry containing the primary key information, and obtaining the ID information of the gene information entry;
[0015] The ID information is matched with the gene tree grouping to obtain the gene tree grouping information corresponding to the ID information.
[0016] In one embodiment, after the step of matching the primary key information with the gene information entry, the method further includes:
[0017] When the primary key information fails to match the gene information entry, generating ID information according to the primary key information;
[0018] Determine gene tree grouping information according to the sample data corresponding to the primary key information;
[0019] Gene information entries are generated according to the gene tree grouping information, the primary key information and the ID information.
[0020] In one embodiment, the step of matching the sample data with the gene tree grouping information to determine the carcinogenicity result and mutation site information corresponding to the sample data includes:
[0021] Determine the variation type in the sample data, and determine the variation type grouping;
[0022] In the variation type grouping, determining the gene type to which the sample data belongs;
[0023] Detecting the protein variation type in the sample data based on the gene type to obtain a carcinogenicity result corresponding to the sample data;
[0024] Determining the protein variation type category;
[0025] The protein variation category is accurately matched with the variation type to obtain the variation point information.
[0026] In one embodiment, the step of accurately matching the protein variation category with the variation type to obtain the variation point information includes:
[0027] According to the protein variation category and the variation type, the variation analysis result is determined;
[0028] The variation analysis result is analyzed to obtain variation point information.
[0029] In one embodiment, the step of outputting the carcinogenicity result and the variation point information as a somatic gene variation analysis result comprises:
[0030] Combining the carcinogenicity result with the mutation site information to obtain somatic mutation information;
[0031] Combining the somatic variation information with the sample data to determine variation annotation;
[0032] A somatic gene variation analysis result is obtained according to the sample data, the somatic variation information and the variation annotation, and the somatic gene variation analysis result is output.
[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a somatic gene variation analysis device, the somatic gene variation analysis device comprising:
[0034] A data acquisition module, used to acquire sample data of somatic cell detection and determine primary key information according to the sample data;
[0035] Grouping location information, used to determine ID information according to the primary key information, match gene tree groups based on the ID information, and obtain gene tree grouping information;
[0036] A variation analysis module, used to match the sample data with the gene tree grouping information to determine the carcinogenicity results and variation site information corresponding to the sample data;
[0037] A result output module is used to output the carcinogenicity result and the variation point information as a somatic gene variation analysis result.
[0038] In addition, to achieve the above-mentioned objectives, the present application also proposes a somatic gene variation analysis device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the somatic gene variation analysis method as described above.
[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the somatic gene variation analysis method described above are implemented.
[0040] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the somatic gene variation analysis method described above.
[0041] One or more technical solutions proposed in the present application have at least the following technical effects: obtaining sample data for somatic cell detection, and determining primary key information based on the sample data; determining ID information based on the primary key information, matching gene tree groupings based on the ID information, and obtaining gene tree grouping information; matching the sample data with the gene tree grouping information, and determining the carcinogenicity results and mutation site information corresponding to the sample data; outputting the carcinogenicity results and the mutation site information as somatic gene variation analysis results. The present application can process somatic variation data containing multiple fields, and perform effective data integration to ensure the integrity and consistency of the data, and can provide clinicians with accurate gene variation interpretation reports to help formulate personalized treatment plans and improve diagnosis and treatment effects. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.
[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0044] Figure 1 A schematic diagram of the process provided in Example 1 of the method for analyzing somatic gene variation of the present application;
[0045] Figure 2 A schematic diagram of sample data fields provided for an embodiment of a somatic gene variation analysis method of the present application;
[0046] Figure 3 A schematic diagram of sample data fields provided for an embodiment of a somatic gene variation analysis method of the present application;
[0047] Figure 4 A schematic diagram of sample data fields provided for an embodiment of a somatic gene variation analysis method of the present application;
[0048] Figure 5 A flow chart for determining the carcinogenicity of tumor somatic mutations provided in an embodiment of the somatic gene variation analysis method of the present application;
[0049] Figure 6 A flow chart for determining the carcinogenicity of tumor somatic mutations provided in an embodiment of the somatic gene variation analysis method of the present application;
[0050] Figure 7 A schematic diagram of automatic generation of variation analysis provided in accordance with an embodiment of the somatic gene variation analysis method of the present application;
[0051] Figure 8 A schematic diagram of automatic generation of variation analysis provided in accordance with an embodiment of the somatic gene variation analysis method of the present application;
[0052] Fig. 9 This is a schematic diagram of the module structure of the somatic gene variation analysis device according to an embodiment of the present application;
[0053] Fig.10 Schematic diagram of the device structure of the hardware operating environment involved in the somatic gene variation analysis method in the embodiment of the present application.
[0054] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION
[0055] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.
[0056] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.
[0057] The main solution of the embodiment of the present application is: obtain sample data for somatic cell detection, determine primary key information based on the sample data; determine ID information based on the primary key information, match gene tree grouping based on the ID information, and obtain gene tree grouping information; match the sample data with the gene tree grouping information to determine the carcinogenicity result and mutation site information corresponding to the sample data; output the carcinogenicity result and the mutation site information as somatic gene variation analysis results.
[0058] In this embodiment, for ease of description, the following description is made by taking the device for identifying somatic gene variation analysis as the execution subject.
[0059] According to the existing technology, there are five main types of somatic gene variation: single base point mutation SNV, short fragment insertion and deletion indel, copy number amplification, copy number deletion, and fusion mutation fusion. Therefore, accurate interpretation of somatic gene variation is of great significance for disease diagnosis, treatment selection and prognosis assessment. At present, there are some databases for somatic gene variation, such as COSMIC (Catalogue of Somatic Mutations in Cancer), TCGA (The Cancer Genome Atlas), ICGC (International Cancer Genome Consortium), etc. These databases provide researchers with a large amount of somatic variation information to a certain extent. Although they provide basic support for the study of somatic gene variation, the analysis efficiency of somatic variation information is low and it is impossible to make a quick judgment.
[0060] It can be seen from the above embodiments that the present application obtains sample data for somatic cell detection, determines primary key information based on the sample data; determines ID information based on the primary key information, matches gene tree grouping based on the ID information, and obtains gene tree grouping information; matches the sample data with the gene tree grouping information, and determines the carcinogenicity result and mutation site information corresponding to the sample data; outputs the carcinogenicity result and the mutation site information as somatic gene variation analysis results. The present application can process somatic variation data containing multiple fields, and perform effective data integration to ensure the integrity and consistency of the data, and can provide clinicians with accurate gene variation interpretation reports to help formulate personalized treatment plans and improve diagnosis and treatment effects.
[0061] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a somatic cell gene variation analysis device, etc. The following takes the somatic cell gene variation analysis device as an example to illustrate this embodiment and the following embodiments.
[0062] Based on this, the present application embodiment provides a somatic gene variation analysis method, referring to Figure 1 , Figure 1 This is a schematic diagram of the process of the first embodiment of the somatic gene variation analysis method of the present application.
[0063] In this embodiment, the somatic gene variation analysis method includes steps S10 to S40:
[0064] Step S10, obtaining sample data of somatic cell detection, and determining primary key information according to the sample data.
[0065] It should be noted that the sample data of somatic cell testing refers to the genetic test results obtained by the patient after genetic testing, which includes multiple fields, such as gene name, transcript, nucleotide mutation, protein mutation, VEP mutation type, Region, mutation type, protein mutation type, splicing mutation type, Position, PositionEnd, CdsStart, CdsEnd, ProteinStart, ProteinEnd, fusion partner region, domain, fusion partner, fusion main gene position, fusion gene coding direction, fusion reading frame, MET exon14 skipping, mutation coordinates, etc. For details, please refer to Figure 2 , Figure 3 and Figure 4 , sample data can be passed in from the database interface.
[0066] It should be understood that the primary key information is a database primary key generated based on the transcripts and nucleotide mutations in the sample data. The database primary key is MutationMD5 data and is unique.
[0067] In a feasible embodiment, the step of obtaining sample data for somatic cell detection and determining primary key information according to the sample data includes:
[0068] Obtaining a somatic cell test report, and extracting transcript information and nucleotide mutation information from sample data in the somatic cell test report;
[0069] Based on a message digest algorithm, primary key information is generated according to the transcript information and the nucleotide information.
[0070] In a specific implementation, a somatic cell test report is obtained, and the data of the somatic cell test report is extracted to obtain the transcript information and nucleotide mutation information in the sample data of the somatic cell test report. The transcript information and nucleotide mutation information are then formatted according to preset rules to ensure that the same input produces the same output, and MD5 is selected as the target hash function to generate a 128-bit fixed-length hash value for the transcript information and nucleotide mutation information. The hash value is unique, and since MD5 is usually a 32-character hexadecimal string, the 128-bit hash value can be converted into a 32-character MD5 value, and the MD5 value is used as the primary key information.
[0071] Step S20, determining ID information according to the primary key information, matching gene tree groupings based on the ID information, and obtaining gene tree grouping information.
[0072] It should be noted that the ID information is generated based on the primary key information, and there is a one-to-one correspondence between the primary key information and the ID information. Each pair of primary key information and ID information can mark a variation record to ensure the uniqueness and traceability of each variation record.
[0073] In a specific implementation, the steps of determining the ID information according to the primary key information, matching the gene tree grouping based on the ID information, and obtaining the gene tree grouping information include: matching the primary key information with the gene information item, and when the match is successful, determining the gene information item containing the primary key information, and obtaining the ID information of the gene information item; matching the ID information with the gene tree grouping to obtain the gene tree grouping information corresponding to the ID information. Specifically, matching the gene information item according to the primary key information to obtain a matching result. The gene information item is a data item stored in a database, including relevant information of a gene mutation. After obtaining the matching result, the matching result can be judged. When it is determined that the match is successful, the currently matched gene item information can be used as the target gene item information, and the ID information in the gene item information can be extracted, and the ID information is matched with the gene tree grouping to determine the gene tree grouping information corresponding to the ID information.
[0074] In a feasible embodiment, after the step of matching the primary key information with the gene information entry, the method further includes:
[0075] When the primary key information fails to match the gene information entry, generating ID information according to the primary key information;
[0076] Determine gene tree grouping information according to the sample data corresponding to the primary key information;
[0077] Gene information entries are generated according to the gene tree grouping information, the primary key information and the ID information.
[0078] In the specific implementation, when the primary key information and the gene information entry fail to match, it means that there is no record result of the relevant gene variation in the current sample data in the current database, which belongs to a new variation type. Therefore, the current sample information can be used as data for recording and the gene information entry in the current database can be expanded. ID information can be generated based on the primary key information. At this time, a new ID information can be added based on the last ID information of the current gene entry information, and the new ID information currently obtained can be matched with the primary key information to form a new gene entry information. Then, the grouping of the gene tree is determined according to the specific data of the sample data, the specific position of the current sample data in the gene tree grouping is determined, and the new gene entry information is enriched to complete the inclusion of the new data and expand the data volume of the gene database.
[0079] Step S30, matching the sample data with the gene tree grouping information to determine the carcinogenicity results and mutation site information corresponding to the sample data.
[0080] It should be noted that the carcinogenicity result is a summary of the results of the gene variation types shown in the sample data that clinically cause cancer. The carcinogenicity results can be divided into multiple categories. For example, in this embodiment, the carcinogenicity results can be divided into 5 categories, such as Oncogenic, Likely Oncogenic, Variants of uncertain significance, Likely Benign and Benign. The variant site information can reflect the specific location of the gene variation.
[0081] In a specific implementation, the step of matching the sample data with the gene tree grouping information to determine the carcinogenicity result and variant site information corresponding to the sample data includes: determining the variant type in the sample data and determining the variant type grouping; in the variant type grouping, determining the gene type to which the sample data belongs; detecting the protein variant type in the sample data based on the gene type to obtain the carcinogenicity result corresponding to the sample data; judging the protein variant type category to determine the protein variant category; accurately matching the protein variant category with the variant type to obtain the variant site information. Reference Figure 5 and Figure 6 , Figure 5 and Figure 6The flowchart for judging the carcinogenicity of tumor somatic cell mutations. First, the mutation type summarized in the sample data can be judged. In this embodiment, the mutation type can be divided into snv indel, amplification, deletion, fusion, GeneTree grouping and others. After determining each mutation type, it can be classified and accurately matched in the corresponding mutation type, where the matching results can be such as oncogenic, likely oncogenic, resistance, uncertain, likely neutral, and neutral. When matching the carcinogenicity according to the matching results, it can be matched according to the following corresponding relationship:
[0082] Oncogenic corresponds to Oncogenic;
[0083] Likely Oncogenic corresponds to Likely Oncogenic;
[0084] Resistance corresponds to Likely Oncogenic;
[0085] Inconclusive corresponds to variants of uncertain significance;
[0086] Likely Neutral corresponds to Likely Benign;
[0087] Neutral corresponds to Benign.
[0088] At the same time, it is also possible to Figure 7 and Figure 8 The variation analysis automatic generation schematic diagram shown in the figure automatically analyzes the current sample data. When performing the automatic analysis, the variation analysis result can be determined by accurately matching the protein variation category with the variation type; the variation analysis result is analyzed to obtain the variation point information. This embodiment gives two examples for auxiliary explanation.
[0089] Example 1: snv (single base mutation)
[0090] Gene name: "POLE"
[0091] Transcript: "NM_006231"
[0092] Nucleotide mutation: "c.857C>A"
[0093] Protein mutation protein: "p.P286H"
[0094] VEP variant type: "missense_variant"
[0095] Region:"exon9"
[0096] Mutation type: "snv"
[0097] Protein mutation type: "Missense"
[0098] Shear mutation type: ""
[0099] Position:"133253184"
[0100] PositionEnd:"133253184"
[0101] CdsStart: "857"
[0102] CdsEnd: "857"
[0103] ProteinStart: "286"
[0104] ProteinEnd: "286"
[0105] Fusion partner region: ""
[0106] Domain: "DEDDy 3\'-5\'exonuclease domain of eukaryotic DNA polymeraseepsilon'; 'DNA-directed DNA polymerase"
[0107] Fusion Partner: ""
[0108] Fusion major gene position: ""
[0109] Fusion gene encoding direction: ""
[0110] Fusion reading frame: ""
[0111] MET exon14 skipping: ""
[0112] Mutation coordinates: "12_133253184_G_T"
[0113] Automatically generate calculations and read out:
[0114] ID: "13186255"
[0115] MutationMD5: "f9f865adaaeae8f8872b94fa1ffa4fe3"
[0116] Genetree grouping: "POLE=>Hotspot HM"
[0117] Carcinogenicity: "Oncogenic"
[0118] Site analysis: "POLE p.P286H is a missense mutation located in exon 9. After the mutation, the 286th amino acid of the encoded protein mutates from P (proline) to H (histidine). It has been reported that this mutation is a carcinogenic mutation (Oncogenic) (PMID: 25228659)."
[0119] Example 2: fusion
[0120] Gene name: "ALK"
[0121] Transcript: "NM_004304"
[0122] Nucleotide mutation: "DCTN1-exon26-ALK-exon19 fusion"
[0123] Protein mutation protein:"."
[0124] VEP variation type: "."
[0125] Region: "exon19"
[0126] Mutation type: "fusion"
[0127] Protein variant type: "Unclassified"
[0128] Shear mutation type: ""
[0129] Position:"-1"
[0130] PositionEnd: "-1"
[0131] CdsStart: "0"
[0132] CdsEnd: "0"
[0133] ProteinStart: "0"
[0134] ProteinEnd: "0"
[0135] Fusion partner region: "exon26"
[0136] Domain: ""
[0137] Fusion partner: "DCTN1"
[0138] Fusion major gene position: "3"
[0139] Fusion gene encoding direction: "<<"
[0140] Fusion reading frame: ""
[0141] MET exon14 skipping: ""
[0142] Mutation coordinates: ""
[0143] Automatically generate calculations and read out:
[0144] ID: "13248259"
[0145] MutationMD5: "1f42913912f8b5ca5471f3290140c243"
[0146] Genetree grouping: "ALK=>Fusion=>Uncommon fusion"
[0147] Carcinogenicity: "Likely Oncogenic"
[0148] Site analysis: "ALK fusion was detected, and the mutation is currently a likely oncogenic mutation (Likely Oncogenic)."
[0149] Step S40, outputting the carcinogenicity result and the mutation site information as a somatic gene mutation analysis result.
[0150] It should be noted that the results of somatic gene variation analysis refer to the comprehensive analysis data after variation analysis of sample data, including carcinogenicity results and variation point information.
[0151] In a specific implementation, the carcinogenicity result is combined with the variation point information to obtain somatic variation information; the somatic variation information is combined with the sample data to determine the variation annotation;
[0152] The somatic gene variation analysis result is obtained according to the sample data, the somatic variation information and the variation annotation, and the somatic gene variation analysis result is output. Specifically, the important information in the sample data and the variation point information according to the carcinogenicity result can be output in a preset output format, such as a gene analysis report, which can include the analyzed source data and analysis results, and can give a clear detection analysis report in an intuitive form.
[0153] In this embodiment, through the preset logical rules, the system can automatically determine the carcinogenicity of somatic mutations without manual intervention, greatly improving the processing speed and efficiency. It can also automatically generate a detailed mutation site analysis report according to the logical rules, including information such as mutation type, location, functional impact, etc., reducing the workload of manual analysis. Through automated processing, the workload of manual analysis is reduced, the labor cost is reduced, the calculation efficiency is improved, and the operating cost is reduced. And it can quickly process large-scale somatic gene mutation data, support efficient storage and retrieval, and ensure the timeliness and accuracy of data processing. By generating MutationMD5 (database primary key) and automatically generating ID, the uniqueness and traceability of each mutation record are ensured to avoid data duplication and confusion. It can process somatic mutation data containing multiple fields (such as gene name, transcript, nucleotide mutation, protein mutation, etc.), and perform effective data integration to ensure the integrity and consistency of the data. Through the preset logical rules, the data is standardized to ensure the consistency of data from different sources in format and content, which is convenient for subsequent analysis and comparison. An intuitive and easy-to-use user interface is also provided, allowing users to easily enter the gene mutation data to be analyzed and view the analysis results. It can also analyze the results, making it easier for users to understand and use, improving the user experience. It also supports multiple data input methods, and can pass in data through the database interface to adapt to different data sources and data formats. It also has good scalability, and can add new functional modules or update existing logic rules as needed to adapt to changing research needs and technological advances. Accuracy and reliability. The judgment of variant carcinogenicity and site analysis are carried out through preset logic rules, which reduces human errors and improves the accuracy and reliability of judgments. Data verification and validation can be performed during data processing to ensure the quality and consistency of the data. It can process newly uploaded data in real time and generate analysis results in a timely manner to meet real-time needs in clinical and research. It supports dynamic updating of data, and can update information in the database regularly or on demand to keep the data up to date and valid.
[0154] The present embodiment provides a somatic gene variation analysis method, which obtains sample data of somatic cell detection, determines primary key information according to the sample data; determines ID information according to the primary key information, matches gene tree grouping based on the ID information, and obtains gene tree grouping information; matches the sample data with the gene tree grouping information, and determines the carcinogenicity result and mutation site information corresponding to the sample data; outputs the carcinogenicity result and the mutation site information as a somatic gene variation analysis result. The present application can process somatic variation data containing multiple fields and perform effective data integration to ensure the integrity and consistency of the data, and can provide clinicians with accurate gene variation interpretation reports to help formulate personalized treatment plans and improve diagnosis and treatment effects.
[0155] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the somatic gene variation analysis method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.
[0156] This application also provides a somatic gene variation analysis device, please refer to Fig. 9 , the somatic gene variation analysis device comprises:
[0157] A data acquisition module 10 is used to acquire sample data of somatic cell detection and determine primary key information according to the sample data;
[0158] Grouping location information 20, used to determine ID information according to the primary key information, match gene tree groups based on the ID information, and obtain gene tree grouping information;
[0159] A variation analysis module 30, used to match the sample data with the gene tree grouping information to determine the carcinogenicity results and variation site information corresponding to the sample data;
[0160] The result output module 40 is used to output the carcinogenicity result and the mutation site information as a somatic gene mutation analysis result.
[0161] In one embodiment, the data acquisition module 10 is also used to obtain a somatic cell detection report, extract transcript information and nucleotide mutation information in the sample data of the somatic cell detection report; and generate primary key information based on the transcript information and the nucleotide information based on a message digest algorithm.
[0162] In one embodiment, the grouping location information 20 is also used to match the primary key information with the gene information entry. When the match is successful, the gene information entry containing the primary key information is determined, and the ID information of the gene information entry is obtained; the ID information is matched with the gene tree grouping to obtain the gene tree grouping information corresponding to the ID information.
[0163] In one embodiment, the grouping location information 20 is also used to generate ID information based on the primary key information when the primary key information fails to match the gene information entry; determine the gene tree grouping information based on the sample data corresponding to the primary key information; and generate a gene information entry based on the gene tree grouping information, the primary key information and the ID information.
[0164] In one embodiment, the variation analysis module 30 is further used to determine the variation type in the sample data and determine the variation type grouping; in the variation type grouping, determine the gene type to which the sample data belongs; detect the protein variation type in the sample data based on the gene type to obtain the carcinogenicity result corresponding to the sample data; judge the protein variation type category to determine the protein variation category; accurately match the protein variation category with the variation type to obtain the variation point information.
[0165] In one embodiment, the variation analysis module 30 is further used to accurately match the protein variation category with the variation type to determine the variation analysis result; and analyze the variation analysis result to obtain variation point information.
[0166] In one embodiment, the result output module 40 is also used to combine the carcinogenicity result with the variation point information to obtain somatic variation information; combine the somatic variation information with the sample data to determine the variation annotation; obtain the somatic gene variation analysis result according to the sample data, the somatic variation information and the variation annotation, and output the somatic gene variation analysis result.
[0167] The somatic gene variation analysis device provided by the present application adopts the somatic gene variation analysis method in the above embodiment, which can solve the technical problem of low efficiency in judging somatic variation in the prior art. Compared with the prior art, the beneficial effects of the somatic gene variation analysis device provided by the present application are the same as the beneficial effects of the somatic gene variation analysis method provided by the above embodiment, and the other technical features of the somatic gene variation analysis device are the same as the features disclosed in the above embodiment method, which will not be repeated here.
[0168] The present application provides a somatic gene variation analysis device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the somatic gene variation analysis method in the above-mentioned embodiment 1.
[0169] Reference below Fig.10 , which shows a schematic diagram of the structure of a somatic gene variation analysis device suitable for implementing the embodiment of the present application. The somatic gene variation analysis device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.10 The somatic gene variation analysis device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.
[0170] like Fig.10 As shown, the somatic gene variation analysis device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the somatic gene variation analysis device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the somatic gene variation analysis device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a somatic gene variation analysis device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.
[0171] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.
[0172] The somatic gene variation analysis device provided by the present application adopts the somatic gene variation analysis method in the above embodiment, which can solve the technical problem of low efficiency in judging somatic variation in the prior art. Compared with the prior art, the beneficial effects of the somatic gene variation analysis device provided by the present application are the same as the beneficial effects of the somatic gene variation analysis method provided by the above embodiment, and the other technical features in the somatic gene variation analysis device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.
[0173] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.
[0174] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
[0175] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the somatic gene variation analysis method in the above-mentioned embodiment.
[0176] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.
[0177] The computer-readable storage medium may be included in the somatic cell gene variation analysis device; or may exist independently without being assembled into the somatic cell gene variation analysis device.
[0178] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the somatic cell gene variation analysis device, the somatic cell gene variation analysis device:
[0179] Acquire sample data of somatic cell detection, and determine primary key information according to the sample data;
[0180] Determine ID information according to the primary key information, match gene tree groups based on the ID information, and obtain gene tree group information;
[0181] Matching the sample data with the gene tree grouping information to determine the carcinogenicity results and mutation site information corresponding to the sample data;
[0182] The carcinogenicity result and the mutation site information are output as a somatic gene mutation analysis result.
[0183] Computer program code for performing the operations of the present application may be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0184] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
[0185] The modules involved in the embodiments of the present application may be implemented by software or hardware, wherein the name of the module does not limit the unit itself in some cases.
[0186] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned somatic gene variation analysis method, and can solve the technical problem of low efficiency in judging somatic variation in the prior art. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the somatic gene variation analysis method provided in the above-mentioned embodiment, and will not be repeated here.
[0187] The present application also provides a computer program product, including a computer program, which implements the steps of the somatic gene variation analysis method as described above when executed by a processor.
[0188] The computer program product provided in this application can solve the technical problem of low efficiency in judging somatic cell variation in the prior art. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the somatic cell gene variation analysis method provided in the above embodiment, which will not be repeated here.
[0189] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.
Claims
1. A method for analyzing somatic gene variation, characterized in that: The somatic gene variation analysis method comprises: Acquire sample data of somatic cell detection, and determine primary key information according to the sample data; Determine ID information according to the primary key information, match gene tree groups based on the ID information, and obtain gene tree group information; Matching the sample data with the gene tree grouping information to determine the carcinogenicity results and mutation site information corresponding to the sample data; The carcinogenicity result and the mutation site information are output as a somatic gene mutation analysis result.
2. The method according to claim 1, characterized in that The step of obtaining sample data for somatic cell detection and determining primary key information according to the sample data comprises: Obtaining a somatic cell test report, and extracting transcript information and nucleotide mutation information from sample data in the somatic cell test report; Based on a message digest algorithm, primary key information is generated according to the transcript information and the nucleotide information.
3. The method according to claim 1, characterized in that The steps of determining ID information according to the primary key information, matching gene tree groups based on the ID information, and obtaining gene tree grouping information include: Matching the primary key information with the gene information entry, and when the match is successful, determining the gene information entry containing the primary key information, and obtaining the ID information of the gene information entry; The ID information is matched with the gene tree grouping to obtain the gene tree grouping information corresponding to the ID information.
4. The method according to claim 3, characterized in that After the step of matching the primary key information with the gene information entry, the method further includes: When the primary key information fails to match the gene information entry, generating ID information according to the primary key information; Determine gene tree grouping information according to the sample data corresponding to the primary key information; Gene information entries are generated according to the gene tree grouping information, the primary key information and the ID information.
5. The method according to claim 1, characterized in that The step of matching the sample data with the gene tree grouping information to determine the carcinogenicity result and mutation site information corresponding to the sample data comprises: Determine the variation type in the sample data, and determine the variation type grouping; In the variation type grouping, determining the gene type to which the sample data belongs; Detecting the protein variation type in the sample data based on the gene type to obtain a carcinogenicity result corresponding to the sample data; Determining the protein variation type category; The protein variation category is accurately matched with the variation type to obtain the variation point information.
6. The method according to claim 5, characterized in that The step of accurately matching the protein variation category with the variation type to obtain the variation point information comprises: According to the protein variation category and the variation type, the variation analysis result is determined; The variation analysis result is analyzed to obtain variation point information.
7. The method according to claim 1, characterized in that The step of outputting the carcinogenicity result and the mutation site information as a somatic gene mutation analysis result comprises: Combining the carcinogenicity result with the mutation site information to obtain somatic mutation information; Combining the somatic variation information with the sample data to determine variation annotation; A somatic gene variation analysis result is obtained according to the sample data, the somatic variation information and the variation annotation, and the somatic gene variation analysis result is output.
8. A somatic gene variation analysis device, characterized in that: The device comprises: A data acquisition module, used to acquire sample data of somatic cell detection and determine primary key information according to the sample data; Grouping location information, used to determine ID information according to the primary key information, match gene tree groups based on the ID information, and obtain gene tree grouping information; A variation analysis module, used to match the sample data with the gene tree grouping information to determine the carcinogenicity results and variation site information corresponding to the sample data; A result output module is used to output the carcinogenicity result and the variation point information as a somatic gene variation analysis result.
9. A somatic gene variation analysis device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the somatic gene variation analysis method according to any one of claims 1 to 7.
10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the somatic gene variation analysis method according to any one of claims 1 to 7 are implemented.