Method for detecting exome mononucleotide coverage rate

Abstract

The invention discloses a method and a system for unbiasedly evaluating whole genome SNP (single nucleotide polymorphism) chip exome and analyzing gene coverage rate, and evaluating SNP coverage rate condition of DNA (deoxyribose nucleic acid) fragments. False positive rate of the existing methods can be reduced and accuracy thereof can be improved while advantages of the existing evaluating methods are preserved. The technical scheme includes establishing models according to population genetics classical theory haplotype block and linkage disequilibrium theory, loading the established data models to a data processing unit, processing dataset to be detected or sequence position information, outputting image results and calculating the results, inputting the results to a calculating module to analyze, and acquiring annotation of coverage rate condition and r2 according to image information and data information.

Description

technical field [0001] The present invention relates to human genome single nucleotide tag coverage evaluation and analysis technology, in particular to a method and system for constructing and analyzing human genome using a haplotype domain model. Background technique [0002] Single nucleotide polymorphism (single nucleotide polymorphism, SNP) is the most abundant genetic variation in the human genome, accounting for more than 90% of the genetic polymorphisms in the human genome. SNP loci and their corresponding haplotype information on the genome have been widely used in disease susceptibility gene mapping and pharmacogenomics research. Recent studies have shown that there is a certain correlation between many SNP loci, and small-scale SNP loci - tag SNP (tagSNP) can provide the genetic pattern information of all SNPs. Applying label SNP loci to association analysis of complex genetic diseases and drug response can greatly reduce the cost of SNP genotyping and improve th...

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Problems solved by technology

but? The plausibility of this common assumption is debatable

like Figure 1 In the case shown, this algorithm clearly overestimates the coverage, especially when calculating partial coverage

[0016] In summary, although there are many evaluation methods for SNP coverage analysis of human genome, it is still a great challenge to accurately perform human genome-wide coverage analysis

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