The invention relates to a method for distinguishing
gene mutation types an individual
tumor sample based on second-generation sequencing. A
tumor tissue sample and a
normal tissue sample are used forlibrary construction and NGS sequencing respectively, strand bias and different types of base frequencies of
mutation sites stored in an intermediate file BAM for biological
information analysis of the
tumor tissue sample are analyzed, the quality of base comparison and the frequency of
noise are used as the training characteristics of
machine learning, meanwhile, type information of corresponding
mutation sites of the
normal tissue sample is paired to serve as a prediction
mutation type, a classification prediction model is constructed to distinguish somatic mutation from
germline mutation,the model is used to distinguish somatic mutation from
germline mutation, the detection efficiency is high, the specificity is high, and after the model is established, the individual
tumor sample canbe used for NGS sequencing and
mutation detection, the detection cost of a normal or
cancer sample can be well saved, and meanwhile, the problem that normal tissues of tumor patients with specific types are difficult to obtain can be solved.