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EVA: Exome Variation Analyzer, an efficient and versatile tool for filtering strategies in medical genomics
- 作者:Sophie Coutant (1) (2) (3)
Chloé Cabot (2) (3) Arnaud Lefebvre (2) (3) Martine Léonard (2) (3) Elise Prieur-Gaston (2) (3) Dominique Campion (1) (3) Thierry Lecroq (2) (3) Hélène Dauchel (2) (3)
- 刊名:BMC Bioinformatics
- 出版年:2012
- 出版时间:September 2012
- 年:2012
- 卷:13
- 期:14-supp
- 全文大小:3855KB
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- 作者单位:Sophie Coutant (1) (2) (3)
Chloé Cabot (2) (3) Arnaud Lefebvre (2) (3) Martine Léonard (2) (3) Elise Prieur-Gaston (2) (3) Dominique Campion (1) (3) Thierry Lecroq (2) (3) Hélène Dauchel (2) (3)
1. University of Rouen, INSERM U1079 Molecular genetics of cancer and neuropsychiatric diseases, 76183, Rouen cedex, France 2. University of Rouen, LITIS EA 4108 Computer science, information processing and systems laboratory, 76821, Mont-Saint-Aignan cedex, France 3. Institute of Research and Biomedical Innovation (IRIB), Haute-Normandie, France
- ISSN:1471-2105
文摘
Background 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. Results We describe EVA (Exome Variation Analyzer), a user-friendly web-interfaced software dedicated to the filtering strategies for medical WES. Thanks to different modules, EVA (i) integrates and stores annotated exome variation data as strictly confidential to the project owner, (ii) allows to combine the main filters dealing with common variations, molecular types, inheritance mode and multiple samples, (iii) offers the browsing of annotated data and filtered results in various interactive tables, graphical visualizations and statistical charts, (iv) and finally offers export files and cross-links to external useful databases and softwares for further prioritization of the small subset of sorted candidate variations and genes. We report a demonstrative case study that allowed to identify a new candidate gene related to a rare form of Alzheimer disease. Conclusions EVA is developed to be a user-friendly, versatile, and efficient-filtering assisting software for WES. It constitutes a platform for data storage and for drastic screening of clinical relevant genetics variations by non-programmer geneticists. Thereby, it provides a response to new needs at the expanding era of medical genomics investigated by WES for both fundamental research and clinical diagnostics.
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