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SpeedSeq: Ultra-fast personal genome analysis and interpretation

Colby Chiang, Ryan M Layer, Gregory G Faust, Michael R Lindberg, David B Rose, Erik P Garrison, Gabor T Marth, Aaron R Quinlan, Ira M Hall
doi: https://doi.org/10.1101/012179
Colby Chiang
1The Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA.
2Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
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Ryan M Layer
2Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
3Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
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Gregory G Faust
2Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
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Michael R Lindberg
2Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
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David B Rose
2Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
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Erik P Garrison
4Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
5Wellcome Trust Sanger Institute, Hinxton, UK.
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Gabor T Marth
6Department of Biology, Boston College, Chestnut Hill, MA, USA.
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Aaron R Quinlan
3Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
7Department of Human Genetics and USTAR Center for Genetic Discovery, University of Utah School of Medicine, Salt Lake City, UT, USA.
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Ira M Hall
1The Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA.
8Department of Medicine, Washington University School of Medicine, St. Louis, MO 63108, USA.
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  • For correspondence: ihall@genome.wustl.edu
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Abstract

Comprehensive interpretation of human genome sequencing data is a challenging bioinformatic problem that typically requires weeks of analysis, with extensive hands-on expert involvement. This informatics bottleneck inflates genome sequencing costs, poses a computational burden for large-scale projects, and impedes the adoption of time-critical clinical applications such as personalized cancer profiling and newborn disease diagnosis, where the actionable timeframe can measure in hours or days. We developed SpeedSeq, an open-source genome analysis platform that vastly reduces computing time. SpeedSeq accomplishes read alignment, duplicate removal, variant detection and functional annotation of a 50X human genome in <24 hours, even using one low-cost server. SpeedSeq offers competitive or superior performance to current methods for detecting germline and somatic single nucleotide variants (SNVs), indels, and structural variants (SVs) and includes novel functionality for SV genotyping, SV annotation, fusion gene detection, and rapid identification of actionable mutations. SpeedSeq will help bring timely genome analysis into the clinical realm.

Availability: SpeedSeq is available at https://github.com/cc2qe/speedseq.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 05, 2014.
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SpeedSeq: Ultra-fast personal genome analysis and interpretation
Colby Chiang, Ryan M Layer, Gregory G Faust, Michael R Lindberg, David B Rose, Erik P Garrison, Gabor T Marth, Aaron R Quinlan, Ira M Hall
bioRxiv 012179; doi: https://doi.org/10.1101/012179
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SpeedSeq: Ultra-fast personal genome analysis and interpretation
Colby Chiang, Ryan M Layer, Gregory G Faust, Michael R Lindberg, David B Rose, Erik P Garrison, Gabor T Marth, Aaron R Quinlan, Ira M Hall
bioRxiv 012179; doi: https://doi.org/10.1101/012179

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