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A single-nucleotide polymorphism (SNP) is a sole base change in the DNA sequence and is the most common polymorphism. Detection and annotation of SNPs are among the central topics in biomedical research as SNPs are believed to play important roles on the manifestation of phenotypic events, such as disease susceptibility. To take full advantage of the nextgeneration sequencing (NGS) technology, we propose a Bayesian approach, BM-SNP, to identify SNPs based on the posterior inference using NGS data. In particular, BM-SNP computes the posterior probability of nucleotide variation at each covered genomic position using the contents and frequency of the mapped short reads. The position with a high posterior probability of nucleotide variation is flagged as a potential SNP. We apply BM-SNP to two cell-line NGS data, and the results show a high ratio of overlap (> 95%) with the dbSNP database. Compared with MAQ, BM-SNP identifies more SNPs that are in dbSNP, with higher quality. The SNPs that are called only by BM-SNP but not in dbSNP may serve as new discoveries. The proposed BMSNP method integrates information from multiple aspects of NGS data, and therefore achieves high detection power. BM-SNP is fast, capable of processing whole genome data at 20-fold average coverage in a short amount of time.
Shoudan Liang, "BM-SNP: A Bayesian Model for SNP Calling using High Throughput Sequencing Data", IEEE/ACM Transactions on Computational Biology and Bioinformatics, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TCBB.2014.2321407
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