Title |
EVA: Exome Variation Analyzer, an efficient and versatile tool for filtering strategies in medical genomics
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Published in |
BMC Bioinformatics, September 2012
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DOI | 10.1186/1471-2105-13-s14-s9 |
Pubmed ID | |
Authors |
Sophie Coutant, Chloé Cabot, Arnaud Lefebvre, Martine Léonard, Elise Prieur-Gaston, Dominique Campion, Thierry Lecroq, Hélène Dauchel |
Abstract |
Whole exome sequencing (WES) has become the strategy of choice to identify a coding allelic variant for a rare human monogenic disorder. This approach is a revolution in medical genetics history, impacting both fundamental research, and diagnostic methods leading to personalized medicine. A plethora of efficient algorithms has been developed to ensure the variant discovery. They generally lead to ~20,000 variations that have to be narrow down to find the potential pathogenic allelic variant(s) and the affected gene(s). For this purpose, commonly adopted procedures which implicate various filtering strategies have emerged: exclusion of common variations, type of the allelics variants, pathogenicity effect prediction, modes of inheritance and multiple individuals for exome comparison. To deal with the expansion of WES in medical genomics individual laboratories, new convivial and versatile software tools have to implement these filtering steps. Non-programmer biologists have to be autonomous combining themselves different filtering criteria and conduct a personal strategy depending on their assumptions and study design. |
X Demographics
Geographical breakdown
Country | Count | As % |
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China | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 2 | 2% |
France | 1 | 1% |
Italy | 1 | 1% |
Australia | 1 | 1% |
Netherlands | 1 | 1% |
Sweden | 1 | 1% |
Brazil | 1 | 1% |
Spain | 1 | 1% |
United Kingdom | 1 | 1% |
Other | 0 | 0% |
Unknown | 74 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 25 | 30% |
Student > Ph. D. Student | 17 | 20% |
Student > Master | 12 | 14% |
Student > Bachelor | 6 | 7% |
Student > Doctoral Student | 4 | 5% |
Other | 11 | 13% |
Unknown | 9 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 37 | 44% |
Biochemistry, Genetics and Molecular Biology | 9 | 11% |
Medicine and Dentistry | 8 | 10% |
Computer Science | 5 | 6% |
Neuroscience | 3 | 4% |
Other | 7 | 8% |
Unknown | 15 | 18% |