A single-sample tumor DNA copy number variation detection method and device

Abstract

The present application discloses a method and a device for detecting the variation of the copy number of tumor DNA in a single sample. The method and device of the present application use the dynamic baseline fluctuation level to simulate the reference population reference level, and calculate the clinical tissue sample construction training set sample RC value relative to the Z of the reference population in each capture area based on the reference population reference level fluctuation in each capture area ‑score value, use the statistical scoring value to train the SVR model of each target capture region; when detecting single-sample tumor DNA copy number variation, use the SVR model of each region to calculate the Ratio value of the region, and finally follow the annotation and filter rule, outputs regions where copy number variation occurs. This application solves the dilemma that the existing copy number variation detection methods and software cannot detect copy number variation in the case of a single sample, and overcomes the defects of low sensitivity and low accuracy caused by factors in the sequencing environment.

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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