A simple data-adaptive probabilistic variant calling model

Algorithms for Molecular Biology : AMB
Steve HoffmannKorbinian Strimmer

Abstract

Several sources of noise obfuscate the identification of single nucleotide variation (SNV) in next generation sequencing data. For instance, errors may be introduced during library construction and sequencing steps. In addition, the reference genome and the algorithms used for the alignment of the reads are further critical factors determining the efficacy of variant calling methods. It is crucial to account for these factors in individual sequencing experiments. We introduce a simple data-adaptive model for variant calling. This model automatically adjusts to specific factors such as alignment errors. To achieve this, several characteristics are sampled from sites with low mismatch rates, and these are used to estimate empirical log-likelihoods. The likelihoods are then combined to a score that typically gives rise to a mixture distribution. From this we determine a decision threshold to separate potentially variant sites from the noisy background. In simulations we show that our simple model is competitive with frequently used much more complex SNV calling algorithms in terms of sensitivity and specificity. It performs specifically well in cases with low allele frequencies. The application to next-generation sequencing data r...Continue Reading

References

May 20, 2009·Bioinformatics·Heng Li, Richard Durbin
Jun 10, 2009·Bioinformatics·Heng LiUNKNOWN 1000 Genome Project Data Processing Subgroup
Sep 15, 2009·PLoS Computational Biology·Steve HoffmannJörg Hackermüller
Feb 18, 2012·BMC Genomics·Kerensa E McElroyTorsten Thomas
Jan 24, 2013·Briefings in Bioinformatics·Stephan PabingerZlatko Trajanoski
Sep 21, 2013·BMC Bioinformatics·Xiaoqing Yu, Shuying Sun

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Citations

Jan 18, 2018·BMC Bioinformatics·Audrey LegendreFariza Tahi

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Datasets Mentioned

BETA
SRR519713

Software Mentioned

BWA
haarz
GATK
SAMtools
GemSIM
segemehl

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