Analysis method and device for detecting point mutations based on third-generation sequencing data

Abstract

The invention provides an analysis method and device for detecting point mutations based on three-generation sequencing data. The analysis method of the present invention includes: 1) extracting the first sequence subset containing the point mutation to be detected; 2) extracting the seed sequence from the first sequence subset to obtain the second sequence subset; 3) obtaining the original sequence subset with expected quality Data set; 4) Use the seed sequence pairs of the second sequence subset to obtain N data sets containing the target sequence; 5) Perform point mutation detection and analysis on the N data sets containing the target sequence; 6) Analyze the N detection results The result of each point mutation in is assigned a weight W; 7) Calculate the point mutation result and its frequency according to the formula. The invention also provides a device for detecting point mutations based on three-generation sequencing data. Using the method of the present invention not only effectively avoids the problem of false negatives caused by low alignment rates caused by random indels or high sequencing errors from the perspective of data characteristics, but also can more effectively control the results of false positives.

Description

technical field [0001] The invention belongs to the field of sequencing technology and biological information technology analysis of sequencing data, in particular to a method for detecting point mutation based on third-generation sequencing data, and also relates to a device and system for detecting point mutation based on third-generation sequencing data. Background technique [0002] A point mutation is when only one base pair is changed. A general point mutation can be a base substitution, a single base insertion or a base deletion; a narrow point mutation is also called a single base substitution. Base substitutions are divided into two categories: transitions and transversions. At present, common methods for detecting gene point mutations include PCR, Sanger sequencing (first-generation sequencing) and second-generation sequencing. PCR method has the characteristics of high sensitivity, and the technology is mature, but each pair of primers can only detect one mutati...

AI Technical Summary

Problems solved by technology

The PCR method has the characteristics of high sensitivity, and the technology is mature, but each pair of primers can only detect one mutation, cannot detect too many samples and sites at the same time, and the throughput is low

The cost of Sanger sequencing is low, but the amount of sample required is large, and the detection sensitivity for low-frequency mutations is low

Next-generation sequencing has the characteristics of high throughput, and the cost of sequencing is also decreasing year by year. However, the current methods and tools commonly used to detect point mutations are not high in specificity (such as Varscan) and low in sensitivity to low-frequency detection (such as Mutect). Or the use of partial assembly steps leads to too long running time (such as Mutect2), which cannot well meet the needs of point mutation detection

Although there are some methods for detecting point mutations developed based on three-generation sequencing data at this stage (as mentioned above), their respective shortcomings are also very obvious. The most important ones are limited by the quality of sequencing and the comparison algorithm or deep learning training set they rely on. The data distribution, etc., and the applicable scenarios are not wide enough, and the robustness (robust) is not enough

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