Method and device for detecting DNA copy number variation of single sample tumor

Abstract

The invention discloses a single-sample tumor DNA copy number variation detection method and device. According to the method and the device, the reference population benchmark level is simulated by utilizing the dynamic baseline fluctuation level, and the Z-score value of the RC value of the training set sample constructed by the clinical tissue sample relative to the reference population in each capture region is calculated based on the reference population benchmark level fluctuation of each capture region; an SVR model of each target capture area is trained by using the statistical score; when single-sample tumor DNA copy number variation detection is carried out, the SVR model of each region is used to calculate the ratio value of the region, and finally, the region with copy number variation is output according to annotation and filtering rules. According to the invention, the problem that the existing copy number variation detection method and software cannot perform copy number variation detection under the condition of a single sample is solved, and the defects of low sensitivity, low accuracy and the like caused by factors in the aspect of sequencing environment are overcome.

Description

technical field [0001] The present application relates to the technical field of tumor DNA copy number variation detection, in particular to a method and device for single-sample tumor DNA copy number variation detection. Background technique [0002] Copy number variation (CNV) is caused by genome rearrangement and is common in the population. Copy number variation detection can detect the variation of large fragments of DNA sequence in the genome early, so as to provide a reference for the diagnosis and treatment of diseases. The current methods for detecting CNV mainly include microarray comparative genomic hybridization (aCGH), droplet digital PCR (ddPCR), fluorescence in situ hybridization (FISH), etc., and each method has its own characteristics. [0003] Target region sequencing (Target region sequencing) is a research strategy for high-throughput sequencing by customizing the probe of the genomic region of interest, hybridizing with genomic DNA, enriching the DNA in...

AI Technical Summary

Problems solved by technology

[0005] At present, traditional copy number variation detection methods, such as FISH, ddPCR, aCGH, etc., have problems such as cumbersome operation and low resolution.

FISH is currently the gold standard method for clinical and pathological detection of gene CNV, but this method has many steps, which is easy to cause signal loss and false negative results; in addition, it can only detect qualitatively, not quantitatively

ddPCR is an absolute quantitative technique for nucleic acid molecules, but its throughput is not high, the operation is complicated, it can only be analyzed qualitatively, and it is easy to contaminate, etc.

Although aCGH has relatively high resolution, sensitivity, and throughput, it cannot identify the specific location of the breakpoint

[0006] Moreover, most of the existing analysis software is only suitable for whole exome sequencing, and is applicable to the scenario of two samples, that is, tumor samples and paired normal samples; in addition, some software does not eliminate the experimental time, environmental The error caused by other factors will affect the accuracy of the test results.

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