CN114566221A - Automatic analysis and interpretation system for NGS data of genetic diseases - Google Patents
Automatic analysis and interpretation system for NGS data of genetic diseases Download PDFInfo
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- CN114566221A CN114566221A CN202210211910.9A CN202210211910A CN114566221A CN 114566221 A CN114566221 A CN 114566221A CN 202210211910 A CN202210211910 A CN 202210211910A CN 114566221 A CN114566221 A CN 114566221A
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- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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Abstract
The invention discloses an NGS (Next Generation Standard) data automatic analysis and interpretation system for genetic diseases, which relates to the technical field of gene sequencing and comprises a data import module, a database, a software library, a data analysis module and a report module; the database is used for providing various large databases; the software library is used for providing analysis software, the data analysis module is used for acquiring fastq original data imported by the data import module, screening the fastq original data by combining a big data algorithm, analyzing the fastq original data by combining a phenotype through an artificial intelligence algorithm, generating a data analysis report through the report module, and extracting software to be updated from the software library; the invention improves the automation level and the accuracy of the automatic analysis and interpretation system of the NGS data of the genetic disease by adding big data screening and artificial intelligence analysis, has the advantages of high automation degree, accordance with evidence-based medical diagnosis logic and the like, and has higher accuracy and better clinical application prospect in the aspect of gene diagnosis of rare and difficult diseases.
Description
Technical Field
The invention relates to the technical field of gene sequencing, in particular to an automatic analysis and interpretation system for NGS data of a genetic disease.
Background
High-throughput sequencing (NGS) is being used more and more widely for clinical molecular diagnosis of genetic diseases, but NGS can generate massive data; how to correctly and reasonably interpret gene (group) variation so as to identify reliable and clinically significant variation in huge data, so that the accurate and efficient application of the variation in clinical diagnosis becomes the most troublesome problem restricting accurate diagnosis and treatment of genetic diseases at present; therefore, the invention provides an automatic analyzing and reading system for NGS data of genetic diseases.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides an automatic analysis and interpretation system for NGS data of genetic diseases, which solves the problem that the most troublesome mutation which is reliable and has clinical significance is identified from huge data at present, so that the mutation can be correctly and efficiently applied to clinical diagnosis.
In order to achieve the above object, an embodiment according to a first aspect of the present invention provides an automatic analysis and interpretation system for NGS data of a genetic disease, which is applied to a hospital server and includes a data import module, a database, a software library, a data analysis module and a report module;
the data analysis module is respectively connected with the database and the software library;
the database is used for providing various large databases including a reference crowd database, a disease variation database, a disease phenotype database and a laboratory local gene and phenotype database;
the software library is used for providing analysis software, and the analysis software is bioinformatics calculation prediction software developed by an administrator; the data import module is used for importing fastq original data, the data analysis module is used for obtaining the fastq original data, screening the fastq original data by combining a big data algorithm, analyzing the fastq original data by combining an artificial intelligence algorithm and a phenotype, generating a data analysis report by the report module, and extracting software to be updated from a software library.
Further, the specific analysis steps of the data analysis module are as follows:
s1: importing fastq original data;
s2: comparing the fastq original data with the hg19 format human reference genome to obtain a bam format file;
s3: selecting sites which are inconsistent with bases in the hg19 format human reference genome in the bam format file, regarding the sites as mutated sites, and integrating to form a vcf format file;
s4: adding information of various databases to the vcf format file;
s5: screening by using a big data algorithm to obtain suspicious mutation;
s6: analyzing by using an artificial intelligence algorithm in combination with a phenotype to obtain a target mutation; the artificial intelligence algorithm is a Bayesian classification algorithm developed by management and self;
s7: generating an integrity report of the data analysis from the target mutation;
s8: and (6) ending.
Further, the specific process of screening by using the big data algorithm is as follows:
choosing mutations n with MAF <0.005 for Exac _ eas and MAF <0.005 for 1000_ ALL; among the mutations n, the remaining mutation types were mutation m1 except for the synonymous mutation, 0 and unknow; among mutations n, mutations m2 other than 0 in the scscscnn database were retained; m1+ m2 was considered a suspected mutation.
Further, the specific process of analyzing by using the artificial intelligence algorithm in combination with the phenotype is as follows:
genes in column m1+ m2, in conjunction with the omim database; matching the phenotype of the patient with the phenotype of the disease corresponding to the gene in the omim database, and finding out the gene mutation with the corresponding phenotype;
performing ACMG scoring on the mutations, and finally performing first generation verification on parents and then analyzing by combining a genetic pattern; if the three latitudes are all matched, marking the corresponding mutation as the target mutation.
Further, the bam format file is characterized in that: the sites in the file are aligned to carry absolute positions.
Further, the reference crowd database is used for acquiring information related to the occurrence frequency of a certain variation in a large-scale crowd, including gnomAD, ExAC and china map; a database of disease variations is used to obtain variations found in patients and an assessment of their pathogenicity, including ClinVar, OMIM, and HGMD; the disease phenotype database is used for processing and storing the phenotype of the patient by using the CHPO entries, and establishing a structural standardized phenotype-genotype sample library; the laboratory local gene and phenotype database was used to collect 5000 clearly diagnosed rare genetic disease genotypes and clinical phenotypes locally.
Further, the bioinformatics calculation prediction software is divided into two types: one class of methods for predicting whether missense variations will disrupt the function or structure of a protein includes REVEL, PolyPhen 2; another class used to predict whether splicing is affected includes GeneSplicer.
Compared with the prior art, the invention has the beneficial effects that:
the data analysis module is used for comparing the fastq original data with the human reference genome, picking out sites which are inconsistent with bases of the hg19 format human reference genome in the bam format file, and integrating to form a vcf format file; adding information of various databases to the vcf format file, and screening by using a big data algorithm to find out suspicious mutation; then, an artificial intelligence algorithm is combined with a phenotype for analysis, and an NGS gene data analysis and interpretation algorithm is further optimized through a Bayesian classification algorithm to realize automatic and accurate assessment of gene variation pathogenicity; the invention improves the automation level and the accuracy of the automatic analysis and interpretation system of the NGS data of the genetic disease by adding big data screening and artificial intelligence analysis, has the advantages of high automation degree, accordance with evidence-based medical diagnosis logic and the like, and has higher accuracy and better clinical application prospect in the aspect of gene diagnosis of rare and difficult diseases.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of the system of the present invention.
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 to 2, the automatic analysis and interpretation system for NGS data of hereditary disease is applied to hospital server, and comprises a data import module, a database, a software library, a data analysis module and a report module;
the data analysis module is respectively connected with the database and the software library;
the database is used for providing various large databases including a reference crowd database, a disease variation database, a disease phenotype database, a laboratory local gene and phenotype database and the like; the related information of the occurrence frequency of a certain variation in large-scale population, such as gnomAD, ExAC and the like, can be obtained by contrasting a population database, and China population high-depth whole genome sequencing information China MAP is particularly newly added; the disease variation database mainly contains variations found in patients and assessments of their pathogenicity, such as ClinVar, OMIM, HGMD, etc.; the disease phenotype database is characterized in that CHPO entries are used for processing and storing the phenotype of the patient, and a structural standardized phenotype-genotype sample library is established; the local gene and phenotype database of the laboratory collects and collates 5000 more than 5000 definitely diagnosed rare genetic disease genotypes and clinical phenotypes of Shanghai Children medical center;
the software library is used for providing analysis software, and the analysis software is bioinformatics calculation prediction software developed by an administrator, and mainly comprises two types: one can predict whether missense variation will destroy the function or structure of protein, such as REVEL, PolyPhen2, etc.; another possibility is to predict whether splicing is affected, e.g.GeneSplicer et al;
the data import module is used for importing the fastq original data, the data analysis module is used for acquiring the fastq original data for analysis, the report module is used for generating a data analysis report, and the software to be updated is extracted from the software library, and the specific analysis steps are as follows:
s1: importing fastq original data;
s2: comparing the fastq raw data with a human reference genome, wherein the human reference genome file is in a hg19 format; the fastq format file is compared with the hg19 format file to obtain a bam format file, wherein the bam format file is characterized in that: the sites in the file are aligned to carry absolute positions (e.g., chr2: 465415465);
s3: selecting sites which are inconsistent with bases of the hg19 format human reference genome in the bam format file, considering the sites as mutated sites, and integrating to form a vcf format file;
s4: adding information of various databases to the vcf format file;
s5: screening by using a big data algorithm: choosing mutations n with MAF <0.005 for Exac _ eas and MAF <0.005 for 1000_ ALL; among the mutations n, the remaining mutation types were the mutation m1 except for the synonymous mutation, 0 and unknow; among mutations n, mutations m2 other than 0 in the scscscnn database were retained; m1+ m2 was considered a suspected mutation;
s6: an artificial intelligence algorithm is utilized to analyze in combination with phenotype, wherein the artificial intelligence algorithm is a Bayesian classification algorithm which is managed and developed by self, and further optimizes NGS gene data analysis and interpretation algorithm to realize automatic and accurate assessment of pathogenicity of genetic variation; the method comprises the following specific steps:
genes in column m1+ m2, in conjunction with the omim database; matching the phenotype of the patient with the phenotype of the disease corresponding to the gene in the omim database, and finding out the gene mutation with the corresponding phenotype;
performing ACMG scoring on the mutations, and finally performing first generation verification on parents and then analyzing by combining a genetic pattern; if the three latitudes are all in accordance with each other, marking the corresponding mutation as a target mutation;
s7: generating an integrity report of the data analysis from the target mutation;
s8: and (6) ending.
According to the invention, the automation level and accuracy of the automatic analysis and interpretation system of the NGS data of the genetic disease are improved by adding big data screening and artificial intelligence analysis; the method has the advantages of high automation degree, accordance with evidence-based medical diagnosis logic and the like, and has high accuracy and good clinical application prospect in the aspect of gene diagnosis of rare stubborn diseases.
The working principle of the invention is as follows:
an NGS data automatic analysis and interpretation system for hereditary disease, during operation, the database is used for providing various large databases and providing data support for subsequent data analysis, a data import module is used for importing fastq original data, a data analysis module is used for acquiring the fastq original data for analysis, a data analysis report is generated through a report module, and software needing to be updated is extracted from a software library, wherein the software is bioinformatics calculation and prediction software developed by a manager, and the system mainly comprises two types: one can predict whether missense variation will destroy the function or structure of protein, such as REVEL, PolyPhen2, etc.; another possibility is to predict whether splicing is affected, e.g., GeneSplicer, etc.;
the data analysis module is used for comparing the fastq original data with the human reference genome, and picking out sites which are inconsistent with bases of the hg19 format human reference genome in the bam format file to integrate to form a vcf format file; adding information of various databases to the vcf format file, and screening by using a big data algorithm to find out suspicious mutation; then, an artificial intelligence algorithm is combined with a phenotype for analysis, and an NGS gene data analysis and interpretation algorithm is further optimized through a Bayesian classification algorithm to realize automatic and accurate assessment of gene variation pathogenicity; according to the invention, the automation level and the accuracy of the automatic analysis and interpretation system of the NGS data of the genetic disease are improved by adding big data screening and artificial intelligence analysis.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. An NGS data automatic analysis and interpretation system for genetic diseases is applied to hospital servers and is characterized by comprising a data import module, a database, a software library, a data analysis module and a report module;
the data analysis module is respectively connected with the database and the software library;
the database is used for providing various large databases including a reference crowd database, a disease variation database, a disease phenotype database and a laboratory local gene and phenotype database;
the software library is used for providing analysis software, and the analysis software is bioinformatics calculation prediction software developed by an administrator; the data import module is used for importing fastq original data, the data analysis module is used for obtaining the fastq original data, screening the fastq original data by combining a big data algorithm, analyzing the fastq original data by combining an artificial intelligence algorithm and a phenotype, generating a data analysis report by the report module, and extracting software to be updated from a software library.
2. The automatic NGS data analyzing and interpreting system as claimed in claim 1, wherein the specific analyzing steps of the data analyzing module are as follows:
s1: importing fastq original data;
s2: comparing the fastq original data with the hg19 format human reference genome to obtain a bam format file;
s3: selecting sites in the bam format file which are inconsistent with bases in the hg19 format human reference genome, considering the sites as mutated sites, and integrating to form a vcf format file;
s4: adding information of various databases to the vcf format file;
s5: screening by using a big data algorithm to obtain suspicious mutation;
s6: analyzing by using an artificial intelligence algorithm in combination with a phenotype to obtain a target mutation; the artificial intelligence algorithm is a Bayesian classification algorithm developed by management and self;
s7: generating an integrity report of the data analysis from the target mutation;
s8: and (6) ending.
3. The automatic NGS data analysis and interpretation system for genetic diseases as claimed in claim 2, wherein the screening by big data algorithm comprises the following steps:
choosing mutations n with MAF <0.005 for Exac _ eas and MAF <0.005 for 1000_ ALL; among the mutations n, the remaining mutation types were mutation m1 except for the synonymous mutation, 0 and unknow; among mutations n, mutations m2 other than 0 in the scscscnn database were retained; m1+ m2 was considered a suspected mutation.
4. The automatic genetic disease NGS data analysis and interpretation system according to claim 3, wherein the specific process of analyzing by using artificial intelligence algorithm and combining phenotype is as follows:
genes in column m1+ m2, in combination with the omim database; matching the phenotype of the patient with the phenotype of the disease corresponding to the gene in the omim database, and finding out the gene mutation with the corresponding phenotype;
performing ACMG scoring on the mutations, and finally performing first generation verification on parents and then analyzing by combining a genetic pattern; if the three latitudes are all matched, marking the corresponding mutation as the target mutation.
5. The automated genetic NGS data analysis and interpretation system of claim 2, wherein the bam format file is characterized by: sites in the file are compared and carry absolute positions.
6. The automated genetic NGS data analysis and interpretation system according to claim 1, wherein the control population database is used to obtain information about the frequency of occurrence of a certain variation in a large-scale population, including gnomAD, ExAC, and china map; a database of disease variations is used to obtain variations found in patients and an assessment of their pathogenicity, including ClinVar, OMIM, and HGMD; the disease phenotype database is used for processing and storing the phenotype of the patient by using the CHPO entries, and establishing a structural standardized phenotype-genotype sample library; the laboratory local gene and phenotype database was used to collect 5000 clearly diagnosed rare genetic disease genotypes and clinical phenotypes locally.
7. The automated genetic NGS data analysis and interpretation system according to claim 1, wherein the bioinformatics calculation prediction software is divided into two types: one class of compounds used to predict whether missense variations will disrupt the function or structure of a protein includes REVEL, PolyPhen 2; another class used to predict whether splicing is affected includes GeneSplicer.
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| CN117373696A (en) * | 2023-12-08 | 2024-01-09 | 神州医疗科技股份有限公司 | Automatic genetic disease interpretation system and method based on literature evidence library |
| CN117373696B (en) * | 2023-12-08 | 2024-03-01 | 神州医疗科技股份有限公司 | Automatic genetic disease interpretation system and method based on literature evidence library |
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