A copy number variation detection method based on next-generation sequencing

Abstract

The invention discloses a copy number variation detection method based on next generation sequencing. The method comprises the following steps: pre-processing copy number variation data, constructing a sliding window, calculating of statistics, implementing a replacement policy, constructing zero distribution, and carrying out performance evaluation of an algorithm. The performance evaluation of the algorithm comprises the steps of judging whether a relatively high correct positive rate can be acquired by the algorithm under the condition that a false positive rate is controllable, evaluating whether the algorithm can relatively accurately estimate a p value or not, detecting a boundary detection capability of copy number variation, and analyzing the calculation complexity of the algorithm. With the adoption of the copy number variation detection method, the problem of copy number variation detection errors, caused by the fact that sequencing platforms and sequencing levels are different, is solved, and a result is relatively accurate; data is normalized by utilizing characteristics of a multi-peak frequency histogram, so that a normal region and a copy number variation region are accurately divided; and a new model is established by a comprehensive effect of relevance between a variation reads number and a variation site, so that the inconsistency problem is solved, and the remarkable level of copy number variation is objectively estimated.

Description

technical field [0001] The invention belongs to the technical field of high-throughput sequencing for sequence determination of DNA molecules, and in particular relates to a copy number variation detection method based on next-generation sequencing. Background technique [0002] Copy number variation (CNV) is an important phenomenon in cancer genomes. It is mainly manifested in two states of copy number amplification and deletion, and is closely related to the occurrence and development of cancer cells. Detecting CNVs that co-occur in the same region in multiple cancer samples, and integrating and analyzing the impact of CNVs on the genome-wide expression level, and identifying those cancer genes that are affected by CNVs are of great significance for the study of cancer occurrence and metastasis. Although single-sample-based CNV detection methods have become more and more mature, these methods still cannot meet the detection sensitivity and accuracy of CNV regions in multi...

AI Technical Summary

Problems solved by technology

First, the difficulty of the problem itself: a) the number of sites is as high as 1.8 million and the number of samples is often small, forming a data pattern of high latitude and small samples; b) systematic errors caused by different sequencing platforms and sequencing levels, And normalize the samples of different sequencing levels; c) the reads signal (read depth, RD) corresponding to the gene locus is easily affected by noise such as sequencing errors and alignment errors; d) there are relatively large differences between CNV loci Strong correlation is not independent, so that there is an interaction between the detection factors; e) To detect the state of copy number amplification or deletion, two characteristics should be considered, that is, the number of reads corresponding to the site and the correlation between the sites, which requires a Reasonable trade-off mechanism of these two characteristics

Second, the theories and methods for solving the problem are challenging: a) The scale of data is large, and it is a challenge to effectively control the computational time and space complexity; b) How to fully consider the correlation between CNV sites and reduce the significance of CNV The conservatism of the estimation of the significance level is a difficult problem; c) How to establish a null hypothesis distribution that is consistent with the statistics and enhance the statistical significance of the estimation of the significance level is a key issue that has not yet been broken through

[0013] (1) Statistics based on a single CNV site as a primitive may easily lead to conservatism in the estimation of the significance level; although statistics based on CNV structural fragments retain the inherent structural characteristics of copy number to a certain extent, they ignore It is difficult to objectively estimate the significance level of the statistic CNV because of the correlation between internal sites

[0014] (2) There is no reasonable balance between the frequency of CNV and the correlation of mutation sites, making it difficult to locate the biological manifestations of CNV and cancer;

[0015] (3) When the method based on single-sample detection detects cCNV of multiple samples, the problem of systematic error or platform error is serious

[0016] (4) There is no automatic synthesis of multiple samples from different sequencing platforms or sequencing levels, which makes the detection of CNV functional patterns co-occurring in multiple samples have great limitations;

[0017] (5) For low-coverage level sample data, it is not sensitive and the detection effect is not good

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