This article is part of the supplement: Proceedings of the Third Annual RECOMB Satellite Workshop on Massively Parallel Sequencing (RECOMB-seq 2013)
Discovering motifs that induce sequencing errors
1 Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Germany
2 Life Sciences Group, Centrum Wiskunde & Informatica, Amsterdam, Netherlands
3 Bioinformatics, Computer Science XI, TU Dortmund, Germany
4 Interdisciplinary Centre for Clinical Research (IZKF) & Institute for Biomedical Engineering, RWTH University Medical School, Aachen, Germany
5 Genome Informatics, Faculty of Medicine, University of Duisburg-Essen, Germany
Citation and License
BMC Bioinformatics 2013, 14(Suppl 5):S1 doi:10.1186/1471-2105-14-S5-S1Published: 10 April 2013
Elevated sequencing error rates are the most predominant obstacle in single-nucleotide polymorphism (SNP) detection, which is a major goal in the bulk of current studies using next-generation sequencing (NGS). Beyond routinely handled generic sources of errors, certain base calling errors relate to specific sequence patterns. Statistically principled ways to associate sequence patterns with base calling errors have not been previously described. Extant approaches either incur decisive losses in power, due to relating errors with individual genomic positions rather than motifs, or do not properly distinguish between motif-induced and sequence-unspecific sources of errors.
Here, for the first time, we describe a statistically rigorous framework for the discovery of motifs that induce sequencing errors. We apply our method to several datasets from Illumina GA IIx, HiSeq 2000, and MiSeq sequencers. We confirm previously known error-causing sequence contexts and report new more specific ones.
Checking for error-inducing motifs should be included into SNP calling pipelines to avoid false positives. To facilitate filtering of sets of putative SNPs, we provide tracks of error-prone genomic positions (in BED format).