biostudies-literature00530Xu YNCI NIH HHS42-45https://www.ebi.ac.uk/biostudies/studies/S-EPMC4697941biostudies-literatureUnknown2012A single-nucleotide polymorphism (SNP) is a single base change in the DNA sequence and is the most common polymorphism. Since some SNPs have a major influence on disease susceptibility, detecting SNPs plays an important role in biomedical research. To take fully advantage of the next-generation sequencing (NGS) technology and detect SNP more effectively, we propose a Bayesian approach that computes a posterior probability of hidden nucleotide variations at each covered genomic position. The position with higher posterior probability of hidden nucleotide variation has a higher chance to be a SNP. We apply the proposed method to detect SNPs in two cell lines: the prostate cancer cell line PC3 and the embryonic stem cell line H1. A comparison between our results with dbSNP database shows a high ratio of overlap (>95%). The positions that are called only under our model but not in dbSNP may serve as candidates for new SNPs.IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and StatisticsA Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data.PMC4697941K25 CA123344R01 CA132897Liang SZheng XEstecio MRIssa JPJi YXu YYuan Y53falseA Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data.A single-nucleotide polymorphism (SNP) is a single base change in the DNA sequence and is the most common polymorphism. Since some SNPs have a major influence on disease susceptibility, detecting SNPs plays an important role in biomedical research. To take fully advantage of the next-generation sequencing (NGS) technology and detect SNP more effectively, we propose a Bayesian approach that computes a posterior probability of hidden nucleotide variations at each covered genomic position. The position with higher posterior probability of hidden nucleotide variation has a higher chance to be a SNP. We apply the proposed method to detect SNPs in two cell lines: the prostate cancer cell line PC3 and the embryonic stem cell line H1. A comparison between our results with dbSNP database shows a high ratio of overlap (>95%). The positions that are called only under our model but not in dbSNP may serve as candidates for new SNPs.2012-01-01T00:00:00Z2012 Dec2024-10-18T08:46:54.938Z2019-03-27T02:06:06ZS-EPMC46979412672630410.1109/gensips.2012.650772210.1109/GENSIPS.2012.6507722