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Bioinformatics. 2012 Mar 1;28(5):643-50. doi: 10.1093/bioinformatics/bts001. Epub 2012 Jan 16.

SNP calling using genotype model selection on high-throughput sequencing data.

Author information

1
Department of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, China.

Abstract

MOTIVATION:

A review of the available single nucleotide polymorphism (SNP) calling procedures for Illumina high-throughput sequencing (HTS) platform data reveals that most rely mainly on base-calling and mapping qualities as sources of error when calling SNPs. Thus, errors not involved in base-calling or alignment, such as those in genomic sample preparation, are not accounted for.

RESULTS:

A novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which accounts for the errors that occur during the preparation of the genomic sample. Simulations and real data analyses indicate that GeMS has the best performance balance of sensitivity and positive predictive value among the tested SNP callers.

AVAILABILITY:

The GeMS package can be downloaded from https://sites.google.com/a/bioinformatics.ucr.edu/xinping-cui/home/software or http://computationalbioenergy.org/software.html.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
22253293
PMCID:
PMC3338331
DOI:
10.1093/bioinformatics/bts001
[Indexed for MEDLINE]
Free PMC Article
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