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Xu Y, Zheng X, Yuan Y, et al. A Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data. IEEE Int Workshop Genomic Signal Process Stat. 2012;2012:42-45doi: 10.1109/GENSIPS.2012.6507722.
Xu, Y., Zheng, X., Yuan, Y., Estecio, M. R., Issa, J. P., Ji, Y., & Liang, S. (2012). A Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data. IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics, 201242-45. https://doi.org/10.1109/GENSIPS.2012.6507722
Xu, Yanxun, et al. "A Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data." IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics vol. 2012 (2012): 42-45. doi: https://doi.org/10.1109/GENSIPS.2012.6507722
Xu Y, Zheng X, Yuan Y, Estecio MR, Issa JP, Ji Y, Liang S. A Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data. IEEE Int Workshop Genomic Signal Process Stat. 2012 Dec;2012:42-45. doi: 10.1109/GENSIPS.2012.6507722. PMID: 26726304; PMCID: PMC4697941.
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IEEE Int Workshop Genomic Signal Process Stat. 2012 Dec;2012:42-45. doi: 10.1109/GENSIPS.2012.6507722.

A Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data.

IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics

Yanxun Xu, Xiaofeng Zheng, Yuan Yuan, Marcos R Estecio, Jean-Pierre Issa, Yuan Ji, Shoudan Liang

Affiliations

  1. Department of Statistics, Rice University Houston, TX.
  2. Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center Houston, TX.
  3. Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine Houston, TX.
  4. Department of Biochemistry and Molecular Biology, The University of Texas, MD Anderson Cancer Center Houston, TX.
  5. Fels Institute for Cancer Research and Molecular Biology, Temple University Philadelphia, PA.
  6. CCRI, NorthShore University HealthSystem Chicago, IL.

PMID: 26726304 PMCID: PMC4697941 DOI: 10.1109/GENSIPS.2012.6507722

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.

References

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Publication Types

Grant support