Title |
Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics
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Published in |
Genome Biology, January 2015
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DOI | 10.1186/s13059-014-0577-x |
Pubmed ID | |
Authors |
Benjamin J Kelly, James R Fitch, Yangqiu Hu, Donald J Corsmeier, Huachun Zhong, Amy N Wetzel, Russell D Nordquist, David L Newsom, Peter White |
Abstract |
While advances in genome sequencing technology make population-scale genomics a possibility, current approaches for analysis of this data rely upon parallelization strategies that have limited scalability, complex implementation and lack reproducibility. Churchill, a balanced regional parallelization strategy, overcomes these challenges, fully automating the multiple steps required to go from raw sequencing reads to variant discovery. Through implementation of novel deterministic parallelization techniques, Churchill allows computationally efficient analysis of a high-depth whole genome sample in less than two hours. The method is highly scalable, enabling full analysis of the 1000 Genomes raw sequence dataset in a week using cloud resources. http://churchill.nchri.org/. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | 35% |
India | 2 | 8% |
United Kingdom | 2 | 8% |
Sweden | 1 | 4% |
France | 1 | 4% |
Germany | 1 | 4% |
Comoros | 1 | 4% |
Mexico | 1 | 4% |
China | 1 | 4% |
Other | 1 | 4% |
Unknown | 6 | 23% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 13 | 50% |
Scientists | 12 | 46% |
Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 4% |
France | 2 | 1% |
United Kingdom | 2 | 1% |
Luxembourg | 2 | 1% |
China | 1 | <1% |
Italy | 1 | <1% |
Japan | 1 | <1% |
Brazil | 1 | <1% |
Unknown | 139 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 47 | 30% |
Student > Ph. D. Student | 37 | 24% |
Other | 14 | 9% |
Student > Bachelor | 13 | 8% |
Student > Master | 13 | 8% |
Other | 19 | 12% |
Unknown | 12 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 67 | 43% |
Computer Science | 24 | 15% |
Biochemistry, Genetics and Molecular Biology | 23 | 15% |
Medicine and Dentistry | 10 | 6% |
Engineering | 7 | 5% |
Other | 10 | 6% |
Unknown | 14 | 9% |