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          Institute: MPI für molekulare Genetik     Collection: Research Group Development and Disease     Display Documents

ID: 541681.0, MPI für molekulare Genetik / Research Group Development and Disease
Microindel detection in short-read sequence data.
Authors:Krawitz, P.; Rödelsperger, C.; Jäger, M.; Jostins, L.; Bauer, S.; Robinson, P. N.
Research Context:Berlin-Brandenburg Center for Regenerative Therapies (BCRT) (Bundesministerium für Bildung und Forschung, project number 0313911); Deutsche Forschungsgemeinschaft (DFG SFB 760).
Date of Publication (YYYY-MM-DD):2010-03-15
Title of Journal:Bioinformatics
Journal Abbrev.:Bioinformatics
Issue / Number:6
Start Page:722
End Page:729
Copyright:2010 Oxford University Pres.
Review Status:not specified
Audience:Not Specified
Abstract / Description:MOTIVATION: Several recent studies have demonstrated the effectiveness of resequencing and single nucleotide variant (SNV) detection by deep short-read sequencing platforms. While several reliable algorithms are available for automated SNV detection, the automated detection of microindels in deep short-read data presents a new bioinformatics challenge. RESULTS: We systematically analyzed how the short-read mapping tools MAQ, Bowtie, Burrows-Wheeler alignment tool (BWA), Novoalign and RazerS perform on simulated datasets that contain indels and evaluated how indels affect error rates in SNV detection. We implemented a simple algorithm to compute the equivalent indel region eir, which can be used to process the alignments produced by the mapping tools in order to perform indel calling. Using simulated data that contains indels, we demonstrate that indel detection works well on short-read data: the detection rate for microindels (<4 bp) is >90%. Our study provides insights into systematic errors in SNV detection that is based on ungapped short sequence read alignments. Gapped alignments of short sequence reads can be used to reduce this error and to detect microindels in simulated short-read data. A comparison with microindels automatically identified on the ABI Sanger and Roche 454 platform indicates that microindel detection from short sequence reads identifies both overlapping and distinct indels. CONTACT: peter.krawitz@googlemail.com; peter.robinson@charite.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Free Keywords:*Algorithms; Base Sequence; Genomics/*methods; INDEL Mutation; Molecular Sequence Data; Sequence Analysis, DNA/methods
External Publication Status:published
Document Type:Article
Communicated by:Stefan Mundlos
Affiliations:MPI für molekulare Genetik
External Affiliations:1.Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany;
2.Berlin-Brandenburg Center for Regenerative Therapies, Augustenburger Platz 1, 13353 Berlin, Germany;
3.Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK.
Identifiers:ISSN:) 1367-4803 [ID No:1]
URL:http://www.ncbi.nlm.nih.gov/pubmed/20144947 [ID No:2]
DOI:10.1093/bioinformatics/btq027 [ID No:3]
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