Dataset Information

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

ABSTRACT: 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.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.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 data are available at Bioinformatics online.

SUBMITTER: You N

PROVIDER: S-EPMC3338331 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

You Na N Murillo Gabriel G Su Xiaoquan X Zeng Xiaowei X Xu Jian J Ning Kang K Zhang Shoudong S Zhu Jiankang J Cui Xinping X

Bioinformatics (Oxford, England) 20120116 5

<h4>Motivation</h4>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.<h4>Results</h4>A novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which ...[more]

PMID: 22253293

Similar Datasets

Project description:BACKGROUND: Many Single Nucleotide Polymorphism (SNP) calling programs have been developed to identify Single Nucleotide Variations (SNVs) in next-generation sequencing (NGS) data. However, low sequencing coverage presents challenges to accurate SNV identification, especially in single-sample data. Moreover, commonly used SNP calling programs usually include several metrics in their output files for each potential SNP. These metrics are highly correlated in complex patterns, making it extremely difficult to select SNPs for further experimental validations. RESULTS: To explore solutions to the above challenges, we compare the performance of four SNP calling algorithm, SOAPsnp, Atlas-SNP2, SAMtools, and GATK, in a low-coverage single-sample sequencing dataset. Without any post-output filtering, SOAPsnp calls more SNVs than the other programs since it has fewer internal filtering criteria. Atlas-SNP2 has stringent internal filtering criteria; thus it reports the least number of SNVs. The numbers of SNVs called by GATK and SAMtools fall between SOAPsnp and Atlas-SNP2. Moreover, we explore the values of key metrics related to SNVs' quality in each algorithm and use them as post-output filtering criteria to filter out low quality SNVs. Under different coverage cutoff values, we compare four algorithms and calculate the empirical positive calling rate and sensitivity. Our results show that: 1) the overall agreement of the four calling algorithms is low, especially in non-dbSNPs; 2) the agreement of the four algorithms is similar when using different coverage cutoffs, except that the non-dbSNPs agreement level tends to increase slightly with increasing coverage; 3) SOAPsnp, SAMtools, and GATK have a higher empirical calling rate for dbSNPs compared to non-dbSNPs; and 4) overall, GATK and Atlas-SNP2 have a relatively higher positive calling rate and sensitivity, but GATK calls more SNVs. CONCLUSIONS: Our results show that the agreement between different calling algorithms is relatively low. Thus, more caution should be used in choosing algorithms, setting filtering parameters, and designing validation studies. For reliable SNV calling results, we recommend that users employ more than one algorithm and use metrics related to calling quality and coverage as filtering criteria.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets