Title |
WEP: a high-performance analysis pipeline for whole-exome data
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Published in |
BMC Bioinformatics, April 2013
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DOI | 10.1186/1471-2105-14-s7-s11 |
Pubmed ID | |
Authors |
Mattia D'Antonio, Paolo D'Onorio De Meo, Daniele Paoletti, Berardino Elmi, Matteo Pallocca, Nico Sanna, Ernesto Picardi, Graziano Pesole, Tiziana Castrignanò |
Abstract |
The advent of massively parallel sequencing technologies (Next Generation Sequencing, NGS) profoundly modified the landscape of human genetics.In particular, Whole Exome Sequencing (WES) is the NGS branch that focuses on the exonic regions of the eukaryotic genomes; exomes are ideal to help us understanding high-penetrance allelic variation and its relationship to phenotype. A complete WES analysis involves several steps which need to be suitably designed and arranged into an efficient pipeline.Managing a NGS analysis pipeline and its huge amount of produced data requires non trivial IT skills and computational power. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 43% |
France | 1 | 14% |
Montenegro | 1 | 14% |
Sweden | 1 | 14% |
Unknown | 1 | 14% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 6 | 86% |
Members of the public | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Italy | 4 | 3% |
Brazil | 3 | 2% |
China | 2 | 1% |
United States | 2 | 1% |
Germany | 1 | <1% |
Ghana | 1 | <1% |
South Africa | 1 | <1% |
Netherlands | 1 | <1% |
Canada | 1 | <1% |
Other | 3 | 2% |
Unknown | 141 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 42 | 26% |
Researcher | 40 | 25% |
Student > Master | 17 | 11% |
Student > Bachelor | 12 | 8% |
Professor > Associate Professor | 9 | 6% |
Other | 25 | 16% |
Unknown | 15 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 64 | 40% |
Biochemistry, Genetics and Molecular Biology | 33 | 21% |
Computer Science | 18 | 11% |
Medicine and Dentistry | 9 | 6% |
Engineering | 6 | 4% |
Other | 9 | 6% |
Unknown | 21 | 13% |