Data analysis method for genetic disease gene testing, system thereof and storage medium

Abstract

The invention discloses a data analysis method for genetic disease gene testing, a system thereof and a storage medium. The method comprises the steps of inputting sample information and sequencing information of a testee; performing bioinformatics analysis on the sequencing data and obtaining an annotation result and a statistics result; performing quality control auditing on a quality index in astatistics result; performing interpretation personnel and flow distribution on a result which passes quality control auditing; determining a variation condition factor pool and performing variationevidence scoring, wherein the variation evidence scoring is used for assisted analysis for a genetic disease variation factor of the testee. The method and the system thereof perform bioinformatics analysis and quality control auditing based on the sequencing data, and performs interpretation personnel and flow distribution based on clinical phenotype information. Furthermore through variation evidence scoring, semi-automatic interpolation is realized and working efficiency is improved. Furthermore, a one-generation verifying primer database can be introduced, thereby greatly saving a primer designing process and reducing resource consumption. The data analysis method, the system thereof and the storage medium can be widely used for genetic disease gene sequencing data analysis and interpretation.

Description

technical field [0001] The invention relates to the technical field of data analysis, in particular to a data analysis method, system and storage medium for gene detection of genetic diseases. Background technique [0002] According to the data released by the World Health Organization, there are about 7,000 rare diseases that have been confirmed in the world, accounting for about 10% of human diseases, and about 80% of rare diseases are genetic diseases. With the development of molecular biology techniques, more and more genetic diseases can be diagnosed, but genetic diseases involve multiple disciplines, and the clinical symptoms are complex, making diagnosis more difficult. Traditional diagnostic techniques often have the risk of missed diagnosis and misdiagnosis, which may cause patients to miss the best opportunity for treatment, while genetic testing can achieve early diagnosis, early intervention, and early treatment of genetic diseases. [0003] At present, with the...

AI Technical Summary

Problems solved by technology

With the development of molecular biology techniques, more and more genetic diseases can be diagnosed, but genetic diseases involve multiple disciplines, and the clinical symptoms are complex, making it difficult to diagnose

Traditional diagnostic techniques often have the risk of missed diagnosis and misdiagnosis, which may cause patients to miss the best opportunity for treatment, while genetic testing can achieve early diagnosis, early intervention, and early treatment of genetic diseases

[0003] At present, with the continuous increase in the sample size of genetic disease genetic testing, the sample information and genetic testing data are also explosively increasing, which is prone to the following problems: 1) sample information or testing data are confused, resulting in errors in clinical testing reports; 2) The traceability of information is poor; 3) Genetic testing data needs to rely on professionals to manually query various databases and documents to interpret the correlation and pathogenicity of gene mutations and clinical phenotypes of subjects. This analysis often consumes a lot of manpower and Time; 4) The first-generation verification of mutation sites after the interpretation of genetic disease genetic test results often requires the design of primers to carry out verification. At present, many laboratories rely on artificially designed primers, making it difficult to reuse the primers accumulated in the past. This results in a waste of money and manpower

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