Structural variation detection with read pair information—an improved null-hypothesis reduces bias
2016 (English)In: 20th Annual Conference on Research in Computational Molecular Biology, RECOMB 2016, Springer, 2016, 176-188 p.Conference paper (Refereed)Text
Reads from paired-end and mate-pair libraries are often utilized to find structural variation in genomes, and one common approach is to use their fragment length for detection. After aligning readpairs to the reference, read-pair distances are analyzed for statistically significant deviations. However, previously proposed methods are based on a simplified model of observed fragment lengths that does not agree with data. We show how this model limits statistical analysis of identifying variants and propose a new model, by adapting a model we have previously introduced for contig scaffolding, which agrees with data. From this model we derive an improved null hypothesis that, when applied in the variant caller CLEVER, reduces the number of false positives and corrects a bias that contributes to more deletion calls than insertion calls. A reference implementation is freely available at https://github.com/ksahlin/GetDistr.
Place, publisher, year, edition, pages
Springer, 2016. 176-188 p.
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9649
Artificial intelligence, Computer science, Computers, False positive, Fragment lengths, Mate pairs, Null hypothesis, Reference implementation, Structural variations, Molecular biology
IdentifiersURN: urn:nbn:se:kth:diva-186750DOI: 10.1007/978-3-319-31957-5_13ScopusID: 2-s2.0-84964067887ISBN: 9783319319568OAI: oai:DiVA.org:kth-186750DiVA: diva2:937325
17 April 2016 through 21 April 2016
QC 201606152016-06-152016-05-132016-06-15Bibliographically approved