A system and method for automated analysis of genomic copy number variation
By designing an automated genome copy number variation analysis system, the problem of low efficiency in CNV data analysis in existing technologies has been solved. It enables automated querying of multiple databases and literature, improves the efficiency and accuracy of CNV data interpretation, and meets the needs of clinical genetic counseling.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NANJING MATERNITY & CHILD HEALTH CARE HOSPITAL
- Filing Date
- 2022-08-05
- Publication Date
- 2026-07-31
AI Technical Summary
Existing CNV data analysis software suffers from inefficiency and incomplete information in data querying and literature retrieval, making it difficult to meet the genetic counseling needs of clinicians, especially the requirements for accuracy and timeliness in assessing CNV pathogenicity.
An automated genome copy number variation analysis system was designed, comprising an information input module, an offline database query module, and an online database query module. It supports automated querying of multiple databases and literature retrieval, adopts segmented input and ISCN standard input format, and combines Python's Selenium library for automated browser driving to achieve efficient data interpretation and real-time updates.
It achieves highly efficient automation of CNV data analysis, enabling rapid querying of various key databases and literature, improving the efficiency and accuracy of genetic counseling, and meeting the clinical diagnostic needs of clinicians.
Smart Images
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Abstract
Description
Technical Field
[0001] This invention belongs to the field of copy number variation detection technology, specifically relating to an automated analysis system and method for genomic copy number variations. Background Technology
[0002] Genome copy number variants (CNVs) are associated with a variety of human diseases. Whole-genome CNV testing has been used as a first-line clinical method to identify the genetic causes of cases such as children with developmental delays, intellectual disabilities, and multiple malformations, patients with recurrent spontaneous abortions, and fetuses with structural malformations detected by prenatal ultrasound.
[0003] In recent years, various technologies have been developed for CNV detection, mainly including chromosomal microarray (CMA) analysis and copy number variation sequencing (CNV-seq) based on high-throughput sequencing. However, the massive amounts of CNVs detected by these technologies pose significant challenges to data analysis and genetic counseling.
[0004] CNV data analysis is highly complex, often requiring searches across multiple databases, such as the Database of Genomic Variants (DGV), the Clinical Genome (ClinGen), and the Online Mendelian Inheritance in Man (OMIM). Manually querying these databases is tedious, and the different databases have varying data submission formats for CNVs, causing significant inconvenience. Therefore, automated CNV data analysis software is urgently needed.
[0005] Currently, there are four automated CNV analysis software programs: AnnotSV, X-CNV, ClassifyCNV, and AutoCNV. While these programs can automate data analysis to some extent, they also have limitations. AnnotSV and X-CNV algorithms are not based on the ACMG guidelines for CNV pathogenicity assessment, making their assessed CNV pathogenicity levels difficult to directly use as a basis for clinical genetic counseling. ClassifyCNV and AutoCNV, although based on the ACMG guidelines, are only helpful for interpreting CNVs with particularly clear pathogenicity. Genetic data analysts are often very familiar with these CNVs, and for the vast majority of CNVs requiring time-consuming analysis, these programs offer very limited assistance. CNV analysis typically requires literature review, but none of these four software programs provide literature search assistance. Furthermore, they can only query past data in databases. However, in actual data analysis, analysts often need to retrieve the latest information from databases, especially since the "half-dose inadequacy" and "triple-dose sensitivity" scores in the ClinGen database are crucial for determining CNV pathogenicity. Summary of the Invention
[0006] To achieve the above objectives, the technical solution of the present invention is as follows: an automated genomic copy number variation analysis system, the system comprising: Information input module: Users input the CNV data to be analyzed through the information input module; The offline database query module allows users to perform localized queries on the offline database based on automatically parsed CNV data annotations, and returns the corresponding results; and The online database query module allows users to perform real-time online queries on the database based on the type information of the automatically parsed CNV data, and returns the corresponding results.
[0007] Based on the above technical solutions, this solution provides an automated genome copy number variation analysis system. It can automatically query multiple databases required for CNV data interpretation by only inputting CNV information once. It can also automatically query several commonly used key databases for CNV data in real time, as well as query literature databases in real time. This system can greatly improve the efficiency of CNV data analysis and better assist clinicians in genetic counseling.
[0008] As an improvement of the present invention, the information input module provides a segmented input data submission method, which is used by data analysts to temporarily analyze a specific CNV data. In the information input module, the information categories of CNV data submitted by the user through the segmented input data submission method include chromosome, upstream position, downstream position, and copy number.
[0009] As an improvement of the present invention, the information input module also provides an ISCN (International System for Nomenclature of Human Cell Genetics) standard format input data submission method. The ISCN standard format input data submission method is used for the user's existing ISCN standard format CNV data. The user pastes the ISCN standard format CNV data into the system, and the system automatically analyzes the ISCN standard format CNV data into a segmented input data submission format.
[0010] As an improvement of the present invention, the offline database query module includes the DGV database, ISCA database, ClinGen database, OMIM phenotypic database, HGMD database, Clinvar database, G2P database, and Genereview database.
[0011] Based on the above technical solution, after a user submits CNV data to be analyzed, the system will automatically query the local MySQL database and return the corresponding query results. The local MySQL database is based on a previously established dataset, and the data primarily comes from CNV analysis-related databases, including DGV, ISCA, ClinGen, OMIM, HGMD, Clinvar, G2P, and Genereview. The MySQL database is updated regularly (every 3 months) to ensure data timeliness.
[0012] As an improvement of the present invention, the DGV database records CNV data of normal populations, the ISCA database records CNV records of diseased populations, the ClinGen database records whether the search segment involves genes that are haplo-insufficient or triplo-sensitive, the OMIM phenotype database records whether the search segment contains the Morbid gene (a gene associated with human diseases) from the OMIM database, the HGMD database records whether the gene in the search segment contains pathogenic mutations and the types of mutations, the Clinvar database records whether the gene in the search segment contains pathogenic mutations and the types of mutations, the G2P database records the mechanism by which the gene in the search segment exerts its pathogenic effect, and the Genereview database records whether the search segment contains genes already included in the Genereview database.
[0013] As an improvement of the present invention, if the DGV database contains the CNV record to be retrieved, the result will be retrieved; if the ISCA database contains a case smaller than or similar to the CNV to be retrieved, the result will be retrieved.
[0014] As an improvement of the present invention, in the ClinGen database, if the Haplo_Score (haplo-insufficiency score) of a gene is sufficient (indicating that the gene heterozygous deletion is clearly pathogenic), then the gene is a haplo-insufficiency pathogenic gene; if the Triplo_Score (triplo-sensitivity score) of a gene is sufficient (indicating that the gene heterozygous duplication is clearly pathogenic), then the gene is a triplo-sensitivity pathogenic gene; if the Haplo_Score (haplo-insufficiency score) of a gene is Recessive (indicating that the gene is a recessive gene), then the gene is a recessive pathogenic gene.
[0015] As an improvement of the present invention, the system performs online real-time queries through the PubMed database, using "Refseq gene AND keyword" as the keyword (Refseq gene is a gene annotated by the National Center for Biotechnology Information in the United States; keyword is a search term derived from CNV type. If the CNV is missing, the keyword is "deletion", "loss of function", or "haploinsufficiency". If the CNV is duplicated, the keyword is "duplication", "gain of function", or "triplosensitivity".
[0016] As an improvement of the present invention, the system drives a browser to perform automated online real-time retrieval of key CNV analysis databases.
[0017] Based on the above technical solution, the system provides an online database query module. When a user submits a CNV to be analyzed, the system parses the Refseq gene contained within the CNV and performs a real-time query in the PubMed database using "Refseq gene AND keyword" as the keywords, according to the CNV type (missing or duplicate). Furthermore, the software uses Python's Selenium library to drive a browser to perform real-time automated online retrieval of key databases, thereby allowing real-time viewing of annotation information related to the CNV to be analyzed.
[0018] As an improvement to the present invention, an analysis method based on an automated genome copy number variation analysis system is provided, the method comprising the following steps: 1) Users submit CNV data to be analyzed. Users select either segmentation mode or ISCN standard format to input the CNV data to be analyzed; 2) After a user submits a CNV to be analyzed, the system will automatically query the local MySQL database and return the corresponding query results; 3) When a user submits a CNV to be analyzed, the system will parse the Refseq gene contained in the CNV and perform real-time queries in the PubMed database based on the missing or duplicate information of the CNV, according to the "Refseq gene AND keyword" keyword. Alternatively, the software can drive the browser to perform real-time online automated retrieval of key databases through the Selenium library of Python.
[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: 1) The automated genome copy number variation analysis system provided in this solution can automatically query multiple databases required for CNV data interpretation by only inputting CNV information once. It can also automatically query several commonly used key databases for CNV data in real time, as well as query literature databases in real time, thereby better assisting clinicians in genetic counseling.
[0020] 2) This system adopts a top-down, stepwise refinement structured software design method, is geared towards the medical industry, and is developed to enable intelligent genome querying, assisting genetic variation data analysts in efficiently interpreting CNV data. This system can greatly improve the efficiency of CNV data analysis. Attached Figure Description
[0021] Figure 1 Analysis flowchart of the automated genome copy number variation analysis system.
[0022] Figure 2 Offline database query module architecture diagram.
[0023] Figure 3 Architecture diagram of the online database query module.
[0024] Figure 4 A diagram illustrating two ways for users to submit CNV data.
[0025] Figure 5 Example of an offline database query interface Figure 1 (DGV, ISCA, ClinGen, OMIM databases).
[0026] Figure 6Example of an offline database query interface Figure 2 (HGMD, Clinvar, G2P, Genereview databases).
[0027] Figure 7 Example of an online database query interface Figure 1 (Pubmed database).
[0028] Figure 8 Example of an online database query interface Figure 2 (DGV database).
[0029] Figure 9 Example of an online database query interface Figure 3 (Decipher database - case query).
[0030] Figure 10 Example of an online database query interface Figure 4 (Decipher database - gene query).
[0031] Figure 11 Example of an online database query interface Figure 5 (ClinGen database).
[0032] Figure 12 Example of an online database query interface Figure 6 (UCSC database). Detailed Implementation
[0033] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. The annotations in the accompanying drawings are as follows: Figure captions: [CNV: Copy number variation; Refseq gene: Gene annotated by the National Center for Biotechnology Information; Gene: Gene name; deletion: Deletion; loss of function: Loss of function; haploinsufficiency: Haploinsufficiency; duplication: Duplication; gain of function: Gain of function; triplosensitivity: Triple-dose sensitivity; Python Entrez: Entrez search library in Python, used for online searching of the PubMed database; PubMed: National Library of Medicine; Python Selenium: Selenium library in Python, a library for simulating dynamic web crawling; DGV: Genomic Variation Database; Decipher Database: Disease-related CNV database; ClinGen: Clinical Genomics Database; UCSC: University of California, Santa Cruz Database].
[0034] Example: Figure 1 As shown, the analysis process of an automated genome copy number variation analysis system is as follows: First, start the software; the user submits the CNV data to be analyzed; the user selects the segmentation mode to input the CNV data to be analyzed (e.g., chromosome 17, upstream position 34822465, downstream position 36307773, copy number 1); the user selects the ISCN standard notation of the CNV data to input the CNV data to be analyzed (e.g., arr[hg19] 17q12(34822465_36307773)×1); the software automatically parses the input CNV data and performs a localized query of the CNV data related annotation database; the software automatically parses the input CNV data and performs an online real-time query of the CNV data related annotation database; the software ends operation.
[0035] Furthermore, such as Figure 2 and Figure 3 As shown, the automated genome copy number variation analysis system (hereinafter referred to as the system) includes: an information input module, in which the user inputs the CNV data to be analyzed; an offline database query module, in which the user performs localized queries of the offline database based on the relevant annotations of the automatically parsed CNV data and returns the corresponding results; and an online database query module, in which the user performs online real-time queries of the database based on the type information of the automatically parsed CNV data and returns the corresponding results.
[0036] Furthermore, the information input module provides a segmented input data submission method, which is used by data analysts to temporarily analyze specific CNV data. In the information input module, the information categories of CNV data submitted by users through the segmented input data submission method include chromosome, upstream position, downstream position, and copy number. The information input module also provides an ISCN standard format input data submission method, which is used for users' existing ISCN standard format CNV data. Users paste ISCN standard format CNV data into the system, and the system automatically analyzes the ISCN standard format CNV data into a segmented input data submission format.
[0037] Furthermore, the offline database query module includes the DGV, ISCA, ClinGen, OMIM, HGMD, Clinvar, G2P, and Genereview databases. After a user submits CNV data to be analyzed, the system will automatically query the local MySQL database and return the corresponding query results. The local MySQL database is based on a previously established dataset, and the data mainly comes from CNV analysis-related databases, including DGV, ISCA, ClinGen, OMIM, HGMD, Clinvar, G2P, and Genereview. The MySQL database is updated regularly (every 3 months) to ensure data timeliness.
[0038] Furthermore, users submit CNV data: the system provides two CNV data submission formats (Input Mode 1: Segmented Input; Input Mode 2: ISCN Standard Format Input) (e.g. Figure 4 As shown in A). Segmented input method (such as...) Figure 4 (As shown in B) This method is suitable for situations where data analysts need to perform temporary analysis on a specific CNV (e.g., in a genetic counseling clinic, a patient has a CNV assessment report from another hospital and needs the hospital's data analysts to reassess the CNV). Users only need to submit "chromosome," "upstream location," "downstream location," and "copy number" respectively. ISCN standard format input method (e.g.) Figure 4 As shown in C, this is suitable for users who have readily available ISCN standard format CNVs (such as CNVs generated by ChAS software after installation). Users can paste the ISCN standard format CNVs into the system at once, and the system will automatically parse the ISCN standard format CNVs into the "segmented input method" format for subsequent data analysis.
[0039] Furthermore, the DGV database (such as...) Figure 5As shown in Figure A, these databases record some CNVs in the normal population. If these databases contain cases containing the CNVs to be retrieved, these results will be retrieved. The ISCA database (e.g., Figure 5 Figure B shows records of some CNVs in the disease population. If there are smaller or similar cases than the CNV being searched in these databases, these results will be retrieved. An "Empty DataFrame" in the figure indicates that there are no similar CNVs in that database. The ClinGen database (such as...) Figure 5 (As shown in C) records whether the search segment involves genes with insufficient haploid doses or sensitive triploid doses. In the ClinGen database, if a gene's Haplo_Score is sufficient, it indicates that the gene is a pathogenic gene with insufficient haploid doses; if a gene's Triplo_Score is sufficient, it indicates that the gene is a pathogenic gene sensitive to triploid doses; and if a gene's Haplo_Score is Recessive, it indicates that the gene is a recessive pathogenic gene. Furthermore, the system allows viewing the results of the ClinGen database at different times, facilitating data analysts' understanding of changes in a gene's Haplo_Score or Triplo_Score. The OMIM phenotypic database (such as...) Figure 5 (As shown in D) Records whether the segment to be searched contains the Morbid gene from the OMIM database. It displays genes that are relatively more important and clearly associated with a certain phenotype within a segment. A phenoMapKey of 1 for a gene indicates that the gene is associated with one phenotype. A phenoMapKey of 3 for a gene indicates that the gene is associated with three phenotypes. The HGMD database (e.g., Figure 6 As shown in Figure A, the record indicates whether the genes in the searched region are recorded by HGMD. Some mutations in these genes may be associated with diseases. The Clinvar database (e.g., ...) records whether the genes in the searched region are recorded by HGMD. Mutations in these genes may be associated with diseases. Figure 6 (As shown in B) records whether the genes in the searched region are recorded by clinvar; some mutations in these genes may be associated with diseases. The G2P database (such as...) Figure 6 As shown in C, this records the mechanism by which genes in the searched segment exert their pathogenic effects. If mutation_consequence is loss of function, it indicates that the gene exerts its biological function through a "loss of function" mechanism. The Genereview database (e.g., Figure 6 (As shown in D) This record indicates whether the segment to be searched contains genes already included in the Genereview database. The functions or corresponding phenotypes of these genes have been thoroughly studied and can be used as a key reference.
[0040] Furthermore, the system can perform online real-time queries of the PubMed database: it can perform real-time searches of all "Refseq genes + deletion / duplication" in the target segment within the PubMed database, thereby helping data analysts to more quickly identify important genes (such as...) in the target segment. Figure 7 As shown in the figure, this improves the efficiency of literature retrieval.
[0041] In addition, the system can also drive a browser to perform automated online real-time retrieval of key CNV analysis databases (Pubmed database, DGV database, Decipher database - case query, Decipher database - gene query, ClinGen database, UCSC database) (such as... Figure 8-12 (As shown). Before generating reports, data analysts often need to check the latest data in various databases. If they manually perform online queries for these databases, the efficiency will be very low due to the different CNV submission formats across different databases. This system combines Python's Selenium library with CNV information, allowing users to view relevant database information in real time by entering the CNV information only once.
[0042] It should be noted that the above content merely illustrates the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. For those skilled in the art, various improvements and modifications can be made without departing from the principle of the present invention, and all such improvements and modifications fall within the scope of protection of the claims of the present invention.
Claims
1. A system for automated analysis of genomic copy number variation, comprising: The system includes: Information input module: Users input the CNV data to be analyzed through the information input module; The information input module includes a segmented input data submission method, which is used by data analysts to temporarily analyze a specific CNV data. The information categories of CNV data submitted by users through the segmented input data submission method in the information input module include chromosome, upstream position, downstream position, and copy number. The information input module also includes an ISCN standard format input data submission method. The ISCN standard format input data submission method is used for the user's existing ISCN standard format CNV data. The user pastes the ISCN standard format CNV data into the system, and the system analyzes the ISCN standard format CNV data into a segmented input data submission format. The offline database query module allows users to perform localized queries on the offline database based on automatically parsed CNV data annotations, and returns the corresponding results. The offline database query module includes the DGV database, ISCA database, ClinGen database, OMIM phenotype database, HGMD database, Clinvar database, G2P database, and Genereview database; It also includes an online database query module, which allows users to perform real-time online queries on the database based on the type information of the automatically parsed CNV data, and returns the corresponding results. The system performs online real-time queries through the PubMed database, searching for all Refseq genes in the region to be searched. The system also drives a browser to perform automated online real-time retrieval of key databases for CNV data analysis.
2. The system for automated analysis of genomic copy number variations according to claim 1, wherein, The DGV database records CNV data from healthy individuals; the ISCA database records CNV records from diseased individuals; the ClinGen database records whether the search segment involves genes that are haplo-inadequate or triplo-sensitive; the OMIM phenotype database records whether the search segment contains the Morbid gene from the OMIM database; the HGMD database records whether the gene in the search segment contains pathogenic mutations and the types of mutations; the Clinvar database records whether the gene in the search segment contains pathogenic mutations and the types of mutations; the G2P database records the mechanism by which the gene in the search segment exerts its pathogenic effect; and the Genereview database records whether the search segment contains genes already included in the Genereview database.
3. The system for automated analysis of genomic copy number variations according to claim 2, wherein, If the DGV database contains the CNV record to be retrieved, the result will be retrieved. If the ISCA database contains a smaller or similar CNV record to be retrieved, the result will be retrieved.
4. The system for automated analysis of genomic copy number variations according to claim 3, wherein, In the ClinGen database, if a gene's Haplo_Score result is sufficient, it indicates that the gene is a haplo-insufficient pathogenic gene; if a gene's Triplo_Score result is sufficient, it indicates that the gene is a triple-dose-sensitive pathogenic gene; and if a gene's Haplo_Score result is Recessive, it indicates that the gene is a recessive pathogenic gene.