[HTML][HTML] LUMPY: a probabilistic framework for structural variant discovery

RM Layer, C Chiang, AR Quinlan, IM Hall�- Genome biology, 2014 - Springer
Genome biology, 2014Springer
Comprehensive discovery of structural variation (SV) from whole genome sequencing data
requires multiple detection signals including read-pair, split-read, read-depth and prior
knowledge. Owing to technical challenges, extant SV discovery algorithms either use one
signal in isolation, or at best use two sequentially. We present LUMPY, a novel SV discovery
framework that naturally integrates multiple SV signals jointly across multiple samples. We
show that LUMPY yields improved sensitivity, especially when SV signal is reduced owing to�…
Abstract
Comprehensive discovery of structural variation (SV) from whole genome sequencing data requires multiple detection signals including read-pair, split-read, read-depth and prior knowledge. Owing to technical challenges, extant SV discovery algorithms either use one signal in isolation, or at best use two sequentially. We present LUMPY, a novel SV discovery framework that naturally integrates multiple SV signals jointly across multiple samples. We show that LUMPY yields improved sensitivity, especially when SV signal is reduced owing to either low coverage data or low intra-sample variant allele frequency. We also report a set of 4,564 validated breakpoints from the NA12878 human genome. https://github.com/arq5x/lumpy-sv .
Springer
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