Copy number variation and deletion type detection method and computer based on single tumor sample

Abstract

The invention belongs to the technical field of copy number variation detection, and discloses a copy number variation and deletion type detection method and computer based on a single tumor sample. The method includes the steps of building a dynamic balancing mechanism of a copy number extension and deletion range, iterating the detection process to constantly correct the benchmark of reading thesegment number, correcting the parameters of statistical test distribution, and objectively detecting significant copy number variation and weakly-significant copy number variation; building a Bayesian inference model, and correctly detecting the copy number variation state and the copy number deletion type. According to the method and the computer, the copy number variation state and the copy number deletion type are correctly detected, and the information of heterozygote deletion and homologous deletion is provided; the comparation quality and the comparation error problem are considered, and the whole genome GC content is reasonably corrected; the dynamic balancing mechanism of the copy number extension and deletion range is built to accurately position the benchmark of the copy numberand accurately detect the variation state of the copy number.

Description

technical field [0001] The invention belongs to the technical field of copy number variation detection, and in particular relates to a single tumor sample-based copy number variation and deletion type detection method and a computer. Background technique [0002] At present, experts at home and abroad have proposed a variety of copy number variation detection methods, which can be roughly divided into three types: copy number variation detection based on multiple tumor samples, the first type focuses on the detection of consistent copies in multiple samples The number variation pattern is used to discover the common variation pattern for the same type of cancer and provide a scientific basis for the discovery and research of disease-causing genes; the second and third types of detection methods focus on the copy number variation of a patient's genome Detection to observe whether the disease-causing gene or potential disease-causing gene is mutated, and provide a scientific b...

AI Technical Summary

Problems solved by technology

[0003] In summary, the problems existing in the existing technology are: it is difficult to locate the benchmark for the number of genome reads; the existing method based on a single tumor sample does not consider the purity of the tumor tissue and does not consider the copy number variation detection process. The detection of multiple deletion types reduces the accuracy of copy number variation and cannot provide information on heterozygous deletions and homologous deletions

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