Method for correcting and evaluating a detection result of variation detection software

Abstract

The invention relates to a method for correcting and evaluating a detection result of variation detection software. The method comprises the following steps: inputting a detection file; identifying and segmenting the polynucleotide variation in the detection file; carrying out duplicate removal and integration on variation results in the detection file after segmentation processing; and obtaininga correction detection result, and carrying out consistency evaluation on the variation result in the detection file and/or the correction detection result by taking the variation detection result ofthe reference software as a gold standard. According to the method for correcting and evaluating the detection result of the variation detection software, the detection result of any variation detection software can be corrected and evaluated by taking the result file of the variation detection software as input, so that the final variation detection rate can be improved.

Description

technical field [0001] The invention belongs to the technical field of gene detection, in particular to a method for correcting and evaluating detection results of variation detection software. Background technique [0002] Genes have multiple mutation types, the most common being single nucleotide mutation (SNV), DNA fragment insertion (Insertion) and deletion (Deletion), but in the process of mutation, polynucleotide variation (MNV) also often occurs. The polynucleotide is mutated into multiple SNPs or Indels in one block, such as: '1, 1289564, AGCT, CGCC', that is, the sequence AGCT (REF) is mutated to the sequence (ALT) CGCC at position 1289564 on chromosome 1 , in fact, a base substitution occurs at the beginning and end of the sequence, also known as SNP variation; another example: '2,56892445, TGGCTGCAA, CGGCGGCA', that is, a base substitution occurs at the beginning and end of the sequence, and at the same time Deletions occur at the end of the sequence, etc. In ac...

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Problems solved by technology

[0004] However, the calculation speed of the GATK algorithm is relatively slow, and there are still some defects in the detection of mutations in blood samples. First, it is not sensitive enough to detect extremely low mutation rate sites in blood.

Second, the model parameters used by GATK are trained using tissue data and are not suitable for blood samples

However, in the mutation detection results of these software, it is often found that there are a large numbe

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