@inproceedings{4322b54869fc40bbadd9278e83af3ff3,
title = "A Bayesian model for SNP discovery based on next-generation sequencing data",
abstract = "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.",
author = "Yanxun Xu and Xiaofeng Zheng and Yuan Yuan and Estecio, {Marcos R.} and Issa, {Jean Pierre} and Yuan Ji and Shoudan Liang",
year = "2012",
doi = "10.1109/GENSIPS.2012.6507722",
language = "English (US)",
isbn = "9781467352369",
series = "Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics",
pages = "42--45",
booktitle = "Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012",
note = "2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012 ; Conference date: 02-12-2012 Through 04-12-2012",
}