Tumor-related gene variation pathogenicity classification method and device and storage medium

Abstract

The invention discloses a tumor-related gene variation pathogenicity classification method and device and a storage medium. The method comprises the following steps: acquiring a tumor-related gene, and annotating the tumor-related gene according to a fixed transcript according to an HGVS rule to obtain position information; on the basis of an ACMG guide and in combination with expert group suggestion, interpreting a process evaluation evidence item, and dividing evidences into pathogenicity evidences and benign variation evidences; according to an evidence item general rule and a specific gene special rule, judging the graded evidence to obtain variation grade classification; and interpreting the graded evidence according to an interpretation rule, and outputting an interpretation result. According to the method, suggestions of guidelines and expert groups are strictly followed, evidence grading is carded and determined, result differences caused by inconsistency of evidence items are avoided, and tumor-related gene variation can be efficiently, conveniently, normatively and accurately interpreted; manual repeated work is reduced, the normalization and uniformity of interpretation are improved, and the working efficiency is improved.

Description

technical field [0001] The present application relates to the technical field of interpretation of tumor-related gene variants, and in particular, to a method, device and storage medium for classifying the pathogenicity of tumor-related gene variants. Background technique [0002] Sequencing technology has developed rapidly in the past decade with the advent of next-generation high-throughput sequencing. Utilizing next-generation sequencing technology, clinical laboratories have a growing portfolio of products for detecting inherited diseases, including genotyping, single-gene, gene-packaging, exome, genome, transcriptome, and epigenetic tests. With the increasing sophistication of technology, genetic testing continues to face new challenges in sequence interpretation. The American College of Medical Genetics and Genomics (ACMG) has developed a guideline for the interpretation of sequence variants, providing interpretation classification and interpretation algorithms for se...

AI Technical Summary

Problems solved by technology

Although these databases or tools have greatly helped clinicians reduce some duplication and data integration and prediction work, such as the relevant information of open source databases such as Clinvar and gnomAD; however, the guidelines themselves cannot incorporate the details and parameters of each evaluation standard They are all clearly specified, and there are differences in certain data of different races, such as disease information, and different interpreters may have certain understanding or cognition differences in specific operations, resulting in a high degree of inconsistency in interpretation conclusions

In addition, for each clinical test sample, interpreters need to manually repeatedly query various database resources and compare the information of relevant evidence items one by one, making the entire data processing process very cumbersome and inefficient, and prone to errors

Moreover, most of the current databases or tools are displayed in English, which requires high professional understanding ability of practitioners or clinical workers, and the consistency of information needs to be checked repeatedly during the implementation process

[0004] It can be seen that the practicability of existing technical methods is relatively limited, the degree of automation and systematization is not high, and there is a lack of efficient sequencing data interpretation solutions, which cannot meet the needs of third-party laboratories for fast processing of large-scale sample data and consistent results.

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