swMATH ID: 33128
Software Authors: Ryan M. Layer, Colby Chiang, Aaron R. Quinlan, Ira M. Hall
Description: LUMPY: A probabilistic framework for structural variant discovery. 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.
Homepage: https://arxiv.org/abs/1210.2342
Source Code: https://github.com/arq5x/lumpy-sv
Keywords: Genomics; arXiv_q-bio.GN; structural variation; SV; human genomes; DNA sequencing; Structural variation; genome variation; paired-end mapping; split-read mapping; variant discovery; genome sequencing; somatic mutation; tumor heterogeneity; signal integration
Related Software: Pindel; Delly; wgsim; Samtools; YAHA; GitHub; GASV; svclassify; MetaSV; Vaquita; Bowtie 2; SeqAn; BLAT
Cited in: 1 Publication

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LUMPY: A probabilistic framework for structural variant discovery
Ryan M. Layer, Ira M. Hall, Aaron R. Quinlan

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