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Bioinformatics Pipelines for Targeted Resequencing and Whole-Exome Sequencing of Human and Mouse Genomes: A Virtual Appliance Approach for Instant Deployment

Overview of attention for article published in PLOS ONE, April 2014
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Title
Bioinformatics Pipelines for Targeted Resequencing and Whole-Exome Sequencing of Human and Mouse Genomes: A Virtual Appliance Approach for Instant Deployment
Published in
PLOS ONE, April 2014
DOI 10.1371/journal.pone.0095217
Pubmed ID
Authors

Jason Li, Maria A. Doyle, Isaam Saeed, Stephen Q. Wong, Victoria Mar, David L. Goode, Franco Caramia, Ken Doig, Georgina L. Ryland, Ella R. Thompson, Sally M. Hunter, Saman K. Halgamuge, Jason Ellul, Alexander Dobrovic, Ian G. Campbell, Anthony T. Papenfuss, Grant A. McArthur, Richard W. Tothill

Abstract

Targeted resequencing by massively parallel sequencing has become an effective and affordable way to survey small to large portions of the genome for genetic variation. Despite the rapid development in open source software for analysis of such data, the practical implementation of these tools through construction of sequencing analysis pipelines still remains a challenging and laborious activity, and a major hurdle for many small research and clinical laboratories. We developed TREVA (Targeted REsequencing Virtual Appliance), making pre-built pipelines immediately available as a virtual appliance. Based on virtual machine technologies, TREVA is a solution for rapid and efficient deployment of complex bioinformatics pipelines to laboratories of all sizes, enabling reproducible results. The analyses that are supported in TREVA include: somatic and germline single-nucleotide and insertion/deletion variant calling, copy number analysis, and cohort-based analyses such as pathway and significantly mutated genes analyses. TREVA is flexible and easy to use, and can be customised by Linux-based extensions if required. TREVA can also be deployed on the cloud (cloud computing), enabling instant access without investment overheads for additional hardware. TREVA is available at http://bioinformatics.petermac.org/treva/.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 5 4%
United Kingdom 3 3%
Italy 1 <1%
Brazil 1 <1%
Sweden 1 <1%
Germany 1 <1%
Netherlands 1 <1%
Czechia 1 <1%
China 1 <1%
Other 1 <1%
Unknown 104 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 41 34%
Student > Master 17 14%
Student > Ph. D. Student 16 13%
Other 14 12%
Student > Bachelor 8 7%
Other 17 14%
Unknown 7 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 32%
Biochemistry, Genetics and Molecular Biology 27 23%
Computer Science 18 15%
Medicine and Dentistry 13 11%
Engineering 3 3%
Other 9 8%
Unknown 12 10%