| Literature DB >> 28132028 |
Richard Meier1,2, Stefan Graw1,2, Peter Beyerlein1, Devin Koestler3, Julian R Molina4, Jeremy Chien2.
Abstract
Structural variations (SVs) in genomic DNA can have profound effects on the evolution of living organisms, on phenotypic variations and on disease processes. A critical step in discovering the full extent of structural variations is the development of tools to characterize these variations accurately in next generation sequencing data. Toward this goal, we developed a software pipeline named digit that implements a novel measure of mapping ambiguity to discover interchromosomal SVs from mate-pair and pair-end sequencing data. The workflow robustly handles the high numbers of artifacts present in mate-pair sequencing and reduces the false positive rate while maintaining sensitivity. In the simulated data set, our workflow recovered 96% of simulated SVs. It generates a self-updating library of common translocations and allows for the investigation of patient- or group-specific events, making it suitable for discovering and cataloging chromosomal translocations associated with specific groups, traits, diseases or population structures.Entities:
Mesh:
Year: 2017 PMID: 28132028 PMCID: PMC5435966 DOI: 10.1093/nar/gkx010
Source DB: PubMed Journal: Nucleic Acids Res ISSN: 0305-1048 Impact factor: 16.971