Somatic cell detection method

Abstract

The invention discloses a somatic cell detection method. The detection method comprises the following steps of firstly, acquiring a somatic cell sequence set; extracting the characteristic for describing a genome candidate mutation site, of each somatic cell sequence in the somatic cell sequence, acquiring a sample dataset, and extracting a first training set, a second training set and a testing set from the sample dataset; then establishing a full-connecting neural network model; training and verifying the full-connecting neural network model by means of the first training set, the second training set and the testing set, obtaining a trained full-connecting neural network model; finally, obtaining a to-be-detected complete genome sequence, and extracting the characteristic for describingthe genome candidate mutation site, of the to-be-detected complete genome sequence, and obtaining to-be-detected data; and inputting the to-be-detected data into the trained full-connecting neural network model for detecting. The somatic cell detection method realizes detection of a mutation gene and further improves tumor disease diagnosis accuracy.

Description

technical field [0001] The invention relates to the field of gene detection, in particular to a method for detecting somatic cell mutations. Background technique [0002] Current studies of cancer genomes typically use Next Generation Sequencing (NGS) to analyze tumors with single nucleotide variant (SNV) somatic mutations. Detection of somatic mutations in cancer using NGS typically involves sequencing tumor DNA and DNA from non-malignant (or normal) tissue (usually blood) from the same patient. Thus, cancer-focused NGS experiments differ significantly in experimental design from studies of Mendelian disorders or normal human variation. In cancer research, where reads from two matched samples need to be aligned with a reference human genome, unpredictable errors are likely to occur during the sequencing and alignment process, and mutations in some genes do not affect one determinant of cancer. Samtools, SOAPsnp, VarScan, SNVMix, GATK, VipR and other tools compare tumor a...

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Problems solved by technology

However, these simple alignment methods are not very accurate

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