Copy number variation detection method and system, storage medium and computer equipment

Abstract

The invention belongs to the technical field of gene sequencing, and discloses a copy number variation detection method and system, a storage medium and computer equipment. The method comprises the steps of extracting a read depth value of each window from a bam file, and performing GC correction on data; carrying out segment calculation on the processed data; calculating the data generated by thesegment calculation by using a density peak detection algorithm; obtaining standardized two-dimensional data of density and distance, and screening out a variation region through two characteristicsof the density and the distance of each seg; obtaining a variation region, and determining whether variation is lost or added. The method is simple, easy to implement and low in resource overhead, a final result can be detected through a given bam file and a reference sequence file, data can be rapidly processed through a density peak algorithm, then Gaussian distribution statistics is conducted on the data, and a variation region is obtained according to probability density judgment.

Description

technical field [0001] The invention belongs to the technical field of gene sequencing, and in particular relates to a copy number variation detection method, system, storage medium and computer equipment. Background technique [0002] Currently: The density peak algorithm is a density-based algorithm that can quickly find global outliers. The most important part of the whole algorithm is the following three concepts: d ij : Indicates the Euclidean distance between the i-th point and the j-th point. ρ i : The number of points whose distance to the i-th point is less than dc, where dc is a threshold. σ i : In the set of points with higher density than the i-th point, the minimum Euclidean distance to the i-th point, if the density of the i-th point is the largest, then σ i Equal to the largest Euclidean distance to the i-th point. The main idea of ​​the density peak algorithm: first calculate the density of each data point and the Euclidean distance between them, and fi...

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Problems solved by technology

However, when the proportion of normal cells in the sample is too high, the detection results of these methods will be inaccurate due to the small signal of tumor cells and the influence of noise.

[0006] Although the existing technology has good performance in the detection of copy number variation, there are generally the following shortcomings: the detection method is too dependent on the original data, resulting in inaccurate detection results for low-coverage data; the threshold used by the detection method is manually determined , resulting in low sensitivity of the detection results; the detection method involves an iterative or recursive process, resulting in a large computational complexity

[0007] Through the above analysis, the existing problems and defects of the existing technology are: the existing technology has low sensitivity to copy number variation detection, inaccurate copy number variation detection, and large computational complexity

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