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DELLY: structural variant discovery by integrated paired-end and split-read analysis.


ABSTRACT: MOTIVATION: The discovery of genomic structural variants (SVs) at high sensitivity and specificity is an essential requirement for characterizing naturally occurring variation and for understanding pathological somatic rearrangements in personal genome sequencing data. Of particular interest are integrated methods that accurately identify simple and complex rearrangements in heterogeneous sequencing datasets at single-nucleotide resolution, as an optimal basis for investigating the formation mechanisms and functional consequences of SVs. RESULTS: We have developed an SV discovery method, called DELLY, that integrates short insert paired-ends, long-range mate-pairs and split-read alignments to accurately delineate genomic rearrangements at single-nucleotide resolution. DELLY is suitable for detecting copy-number variable deletion and tandem duplication events as well as balanced rearrangements such as inversions or reciprocal translocations. DELLY, thus, enables to ascertain the full spectrum of genomic rearrangements, including complex events. On simulated data, DELLY compares favorably to other SV prediction methods across a wide range of sequencing parameters. On real data, DELLY reliably uncovers SVs from the 1000 Genomes Project and cancer genomes, and validation experiments of randomly selected deletion loci show a high specificity. AVAILABILITY: DELLY is available at www.korbel.embl.de/software.html CONTACT: tobias.rausch@embl.de.

SUBMITTER: Rausch T 

PROVIDER: S-EPMC3436805 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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DELLY: structural variant discovery by integrated paired-end and split-read analysis.

Rausch Tobias T   Zichner Thomas T   Schlattl Andreas A   Stütz Adrian M AM   Benes Vladimir V   Korbel Jan O JO  

Bioinformatics (Oxford, England) 20120901 18


<h4>Motivation</h4>The discovery of genomic structural variants (SVs) at high sensitivity and specificity is an essential requirement for characterizing naturally occurring variation and for understanding pathological somatic rearrangements in personal genome sequencing data. Of particular interest are integrated methods that accurately identify simple and complex rearrangements in heterogeneous sequencing datasets at single-nucleotide resolution, as an optimal basis for investigating the format  ...[more]

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