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

Abstract

The invention provides an analysis method and device for detecting point mutation based on third-generation sequencing data. The analysis method comprises the following steps: 1) extracting a first sequence subset containing to-be-detected point mutation; 2) extracting a seed sequence from the first sequence subset to obtain a second sequence subset; 3) obtaining an original data set with expected quality; 4) using the seed sequence pair of the second sequence subset to obtain N data sets containing target sequences; 5) carrying out point mutation detection analysis on the N data sets containing the target sequence; 6) allocating a weight W to the result of each point mutation in the N detection results; and 7) calculating a point mutation result and frequency thereof according to a formula. The invention further provides a device for detecting point mutation based on third-generation sequencing data. By using the method provided by the invention, the problem of false negative caused by low comparison rate due to random indel or relatively high sequencing error is effectively avoided from the aspect of data features, and meanwhile, the false positive result can be more effectively controlled.

Description

technical field [0001] The invention belongs to the field of sequencing technology and biological information technology analysis of sequencing data, and in particular relates to a method for detecting point mutations based on third-generation sequencing data, and also relates to a device and system for detecting point mutations based on third-generation sequencing data. Background technique [0002] A point mutation is a change in only one base pair. General point mutations can be base substitutions, single base insertions or base deletions; narrow sense point mutations are also called single base substitutions. Base substitutions are further divided into two types: transitions and transversions. Common methods for detecting gene point mutations include PCR, Sanger sequencing (first-generation sequencing) and next-generation sequencing. The PCR method has the characteristics of high sensitivity, and the technology is mature, but each pair of primers can only detect one mu...

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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