BioinformaticsBioinformaticsPeer-reviewed journal article
Manta: Rapid detection of structural variants and indels for clinical sequencing applications
Xiaoyu Chen, Ole Schulz-Trieglaff, Richard Shaw, Bret Barnes, Felix Schlesinger, Anthony J. Cox, Semyon Kruglyak, Christopher T. SaundersiD
Abstract
SummaryWe describe Manta, a method to discover structural variants and indels from next generation sequencing data. Manta is optimized for rapid clinical analysis, calling structural variants, medium-sized indels and large insertions on standard compute hardware in less than a tenth of the time that comparable methods require to identify only subsets of these variant types: for example NA12878 at 50x genomic coverage is analyzed in less than 20 minutes. Manta can discover and score variants based on supporting paired and split-read evidence, with scoring models optimized for germline analysis of diploid individuals and somatic analysis of tumor-normal sample pairs. Call quality is similar to or better than comparable methods, as determined by pedigree consistency of germline calls and comparison of somatic calls to COSMIC database variants. Manta consistently assembles a higher fraction of its calls to basepair resolution, allowing for improved downstream annotation and analysis of clinical significance. We provide Manta as a community resource to facilitate practical and routine structural variant analysis in clinical and research sequencing scenarios.\n\nAvailabilityManta source code and Linux binaries are available from http://github.com/sequencing/manta.\n\nContactcsaunders@illumina.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: variant effect prediction
- approach: deep learning
- approach: statistical model
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