LUMPY: A probabilistic framework for structural variant discovery

LUMPY: A probabilistic framework for structural variant discovery
Ryan M. Layer, Ira M. Hall, Aaron R. Quinlan
(Submitted on 8 Oct 2012)
Comprehensive discovery of structural variation (SV) in human genomes from DNA sequencing requires the integration of multiple alignment signals including read-pair, split-read and read-depth. However, owing to inherent technical challenges, most existing SV discovery approaches utilize only one signal and consequently suffer from reduced sensitivity, especially at low sequence coverage and for smaller SVs. We present a novel and extremely flexible probabilistic SV discovery framework that is capable of integrating any number of SV detection signals including those generated from read alignments or prior evidence. We demonstrate improved sensitivity over extant methods by combining paired-end and split-read alignments and emphasize the utility of our framework for comprehensive studies of structural variation in heterogeneous tumor genomes. We further discuss the broader utility of this approach for probabilistic integration of diverse genomic interval datasets.

Haplotype-based variant detection from short-read sequencing

Haplotype-based variant detection from short-read sequencing
Erik Garrison, Gabor Marth
(Submitted on 17 Jul 2012 (v1), last revised 20 Jul 2012 (this version, v2))

The direct detection of haplotypes from short-read DNA sequencing data requires changes to existing small-variant detection methods. Here, we develop a Bayesian statistical framework which is capable of modeling multiallelic loci in sets of individuals with non-uniform copy number. We then describe our implementation of this framework in a haplotype-based variant detector, FreeBayes.