Butler enables rapid cloud-based analysis of thousands of human genomes

PCAWG Technical Working Group; PCAWG Consortium

doi:10.1038/s41587-019-0360-3

Butler enables rapid cloud-based analysis of thousands of human genomes

PCAWG Technical Working Group
, PCAWG Consortium

Department of Developmental Biology and Cancer Research

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

We present Butler, a computational tool that facilitates large-scale genomic analyses on public and academic clouds. Butler includes innovative anomaly detection and self-healing functions that improve the efficiency of data processing and analysis by 43% compared with current approaches. Butler enabled processing of a 725-terabyte cancer genome dataset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project in a time-efficient and uniform manner.

Original language	English
Pages (from-to)	288-292
Number of pages	5
Journal	Nature Biotechnology
Volume	38
Issue number	3
DOIs	https://doi.org/10.1038/s41587-019-0360-3
State	Published - 1 Mar 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1038/s41587-019-0360-3

Cite this

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title = "Butler enables rapid cloud-based analysis of thousands of human genomes",

abstract = "We present Butler, a computational tool that facilitates large-scale genomic analyses on public and academic clouds. Butler includes innovative anomaly detection and self-healing functions that improve the efficiency of data processing and analysis by 43\% compared with current approaches. Butler enabled processing of a 725-terabyte cancer genome dataset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project in a time-efficient and uniform manner.",

author = "\{PCAWG Technical Working Group\} and \{PCAWG Consortium\} and Sergei Yakneen and Waszak, \{Sebastian M.\} and Michael Gertz and Korbel, \{Jan O.\} and Brice Aminou and Javier Bartolome and Boroevich, \{Keith A.\} and Rich Boyce and Brooks, \{Angela N.\} and Alex Buchanan and Ivo Buchhalter and Butler, \{Adam P.\} and Byrne, \{Niall J.\} and Andy Cafferkey and Campbell, \{Peter J.\} and Zhaohong Chen and Sunghoon Cho and Wan Choi and Peter Clapham and Davis-Dusenbery, \{Brandi N.\} and \{De La Vega\}, \{Francisco M.\} and Jonas Demeulemeester and Dow, \{Michelle T.\} and Dursi, \{Lewis Jonathan\} and Juergen Eils and Roland Eils and Kyle Ellrott and Claudiu Farcas and Francesco Favero and Nodirjon Fayzullaev and Vincent Ferretti and Paul Flicek and Fonseca, \{Nuno A.\} and Gelpi, \{Josep L.L.\} and Gad Getz and Bob Gibson and Grossman, \{Robert L.\} and Olivier Harismendy and Heath, \{Allison P.\} and Heinold, \{Michael C.\} and Hess, \{Julian M.\} and Oliver Hofmann and Hong, \{Jongwhi H.\} and Hudson, \{Thomas J.\} and Barbara Hutter and Hutter, \{Carolyn M.\} and Daniel H{\"u}bschmann and Seiya Imoto and Berman, \{Benjamin P.\} and Carlos Caldas",

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doi = "10.1038/s41587-019-0360-3",

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AU - PCAWG Consortium

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AU - Korbel, Jan O.

AU - Aminou, Brice

AU - Bartolome, Javier

AU - Boroevich, Keith A.

AU - Boyce, Rich

AU - Brooks, Angela N.

AU - Buchanan, Alex

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AU - Butler, Adam P.

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AU - Cafferkey, Andy

AU - Campbell, Peter J.

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AU - Cho, Sunghoon

AU - Choi, Wan

AU - Clapham, Peter

AU - Davis-Dusenbery, Brandi N.

AU - De La Vega, Francisco M.

AU - Demeulemeester, Jonas

AU - Dow, Michelle T.

AU - Dursi, Lewis Jonathan

AU - Eils, Juergen

AU - Eils, Roland

AU - Ellrott, Kyle

AU - Farcas, Claudiu

AU - Favero, Francesco

AU - Fayzullaev, Nodirjon

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AU - Flicek, Paul

AU - Fonseca, Nuno A.

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AU - Getz, Gad

AU - Gibson, Bob

AU - Grossman, Robert L.

AU - Harismendy, Olivier

AU - Heath, Allison P.

AU - Heinold, Michael C.

AU - Hess, Julian M.

AU - Hofmann, Oliver

AU - Hong, Jongwhi H.

AU - Hudson, Thomas J.

AU - Hutter, Barbara

AU - Hutter, Carolyn M.

AU - Hübschmann, Daniel

AU - Imoto, Seiya

AU - Berman, Benjamin P.

AU - Caldas, Carlos

PY - 2020/3/1

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AB - We present Butler, a computational tool that facilitates large-scale genomic analyses on public and academic clouds. Butler includes innovative anomaly detection and self-healing functions that improve the efficiency of data processing and analysis by 43% compared with current approaches. Butler enabled processing of a 725-terabyte cancer genome dataset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project in a time-efficient and uniform manner.

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JF - Nature Biotechnology

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ER -

Butler enables rapid cloud-based analysis of thousands of human genomes

Abstract

Bibliographical note

UN SDGs

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