A comprehensive strategy for identifying somatic mutations

Abstract

The invention discloses a comprehensive strategy for identifying somatic mutations. Taking comparison results as basic data, SNV and Indel identification are carried out on the basic data at the same time using the SNV identification tool set and the Indel identification tool set. The SNV data is compared and filtered with the set conditions, and the reliable SNV data that meets the conditions are taken out, and all the Indel data obtained by using the Indel identification tool set are compared and filtered with the set conditions, and the reliable Indel data that meets the conditions are taken out, and the reliable SNV data Combined with reliable Indel data to get reliable somatic mutation results. The invention comprehensively uses multiple mutation identification tools to simultaneously reduce false positives and false negatives when identifying mutations to realize accurate identification of somatic mutations, and applies the comprehensive strategy for identifying somatic mutations to tools for production, with high application value.

Description

Technical field [0001] The present invention relates to a method of identifying somatic mutations, in particular to a comprehensive strategy for identifying somatic mutations. Background [0002] As referred to in the present invention, "somatic mutation" is a mutation in tumor tissue that does not occur in a normal control mutation, types including SNV (Single Nucleotide Variation, single nucleotide variant) and Indel (Insertion and Deletion, insertion and deletion). [0003] There are currently many tools for somatic mutation identification, including MuTect2, VarScan2, Radia, SomaticSniper, Strelka, MuSE, etc. for SNV identification, and VarScan2, MuTect2, Strelka, Pindel, Indelocator, etc. for Indel identification. Each of these pieces of software has its own strengths and weaknesses, and none of them can achieve the perfect mutation results when used alone. The following is a list of two of the more widely used software, which introduce the advantages and disadvantages of in...

AI Technical Summary

Problems solved by technology

Its disadvantage is that it uses Fisher’s Exact Test (Fisher’s exact test) to perform a statistical test on whether the mutation is a somatic cell. This test method is relatively simple and is a purely mathematical method. It is not based on existing knowledge like MuTect2

In addition, there are some mutations that have been omitted for no reason

[0006] Each of these individual tools suffers from false positives and false negatives when identifying somatic mutations

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