Copy number variation detection method based on single-sample next-generation sequencing data

Abstract

The invention belongs to the technical field of copy number variation (CNV) detection, and discloses a copy number variation detection method based on single-sample next-generation sequencing data. The method comprises the steps of: performing preprocessing of data in an earlier stage, filtering invalid locations, performing gC content calibration, data equalization and data denoising, performingsegmentation processing on the data, wherein part of data is used for fitting a model, the other part of data is used as detected data, and the cross detection of two parts of the data enables variation to be detected in the model; calculating the probability value of each piece of the data, selecting a significance level ([alpha]), and predicting the CNV by adopting a hypothesis testing method. In order to further verify the effectiveness of the method, simulation data samples are detected and compared with several existing popular methods, and the best performance is shown. The method is efficient in detection, accurate, easy to operate and high in detection speed; and the accuracy and recall rate of low-purity data testing are greatly superior to those of a comparison algorithm.

Description

technical field [0001] The invention belongs to the technical field of copy number variation detection, in particular to a copy number variation detection method based on single-sample next-generation sequencing data. Background technique [0002] At present, the closest existing technology: At present, the copy number variation detection methods of the second generation sequencing technology mainly include the following types: paired-endmapping (PEM): use paired-end sequencing reads to detect copy number variation, due to the The size of the obtained fragments is basically fixed. When the paired-end reads are aligned to the reference genome, if the distance between the reads changes, copy number variation occurs. Depth ofcoverage (DOC): The most commonly used detection method in second-generation sequencing, which detects copy number variation by analyzing the difference in reads depth signals at different positions. Split-Read: When the reads are compared to the reference...

AI Technical Summary

Problems solved by technology

[0008](1) Existing detection methods are applied to the detection of low-purity samples, with low accuracy and sensitivity and high false positives

[0009](2) DOC-based copy number variation detection method is not sensitive to the detection of copy number deletion

[0010]Difficulty in solving the above technical problems: in low-purity samples, normal cells account for a large proportion, resulting in inconspicuous abnormal signals, which brings great challenges to detection

The DOC-based copy number variation detection method is difficult to detect CNV deletions. Generally, the deleted copy number is close to the normal copy number, which is easy to cause false positives.

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