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Candidate gene prioritization with Endeavour
Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates a...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4987917/ https://www.ncbi.nlm.nih.gov/pubmed/27131783 http://dx.doi.org/10.1093/nar/gkw365 |
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author | Tranchevent, Léon-Charles Ardeshirdavani, Amin ElShal, Sarah Alcaide, Daniel Aerts, Jan Auboeuf, Didier Moreau, Yves |
author_facet | Tranchevent, Léon-Charles Ardeshirdavani, Amin ElShal, Sarah Alcaide, Daniel Aerts, Jan Auboeuf, Didier Moreau, Yves |
author_sort | Tranchevent, Léon-Charles |
collection | PubMed |
description | Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and integrating this heterogeneous information into a global ranking. We describe an extended version of our gene prioritization method, Endeavour, now available for six species and integrating 75 data sources. The performance (Area Under the Curve) of Endeavour on cross-validation benchmarks using ‘gold standard’ gene sets varies from 88% (for human phenotypes) to 95% (for worm gene function). In addition, we have also validated our approach using a time-stamped benchmark derived from the Human Phenotype Ontology, which provides a setting close to prospective validation. With this benchmark, using 3854 novel gene–phenotype associations, we observe a performance of 82%. Altogether, our results indicate that this extended version of Endeavour efficiently prioritizes candidate genes. The Endeavour web server is freely available at https://endeavour.esat.kuleuven.be/. |
format | Online Article Text |
id | pubmed-4987917 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-49879172016-08-22 Candidate gene prioritization with Endeavour Tranchevent, Léon-Charles Ardeshirdavani, Amin ElShal, Sarah Alcaide, Daniel Aerts, Jan Auboeuf, Didier Moreau, Yves Nucleic Acids Res Web Server issue Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and integrating this heterogeneous information into a global ranking. We describe an extended version of our gene prioritization method, Endeavour, now available for six species and integrating 75 data sources. The performance (Area Under the Curve) of Endeavour on cross-validation benchmarks using ‘gold standard’ gene sets varies from 88% (for human phenotypes) to 95% (for worm gene function). In addition, we have also validated our approach using a time-stamped benchmark derived from the Human Phenotype Ontology, which provides a setting close to prospective validation. With this benchmark, using 3854 novel gene–phenotype associations, we observe a performance of 82%. Altogether, our results indicate that this extended version of Endeavour efficiently prioritizes candidate genes. The Endeavour web server is freely available at https://endeavour.esat.kuleuven.be/. Oxford University Press 2016-07-08 2016-04-30 /pmc/articles/PMC4987917/ /pubmed/27131783 http://dx.doi.org/10.1093/nar/gkw365 Text en © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Web Server issue Tranchevent, Léon-Charles Ardeshirdavani, Amin ElShal, Sarah Alcaide, Daniel Aerts, Jan Auboeuf, Didier Moreau, Yves Candidate gene prioritization with Endeavour |
title | Candidate gene prioritization with Endeavour |
title_full | Candidate gene prioritization with Endeavour |
title_fullStr | Candidate gene prioritization with Endeavour |
title_full_unstemmed | Candidate gene prioritization with Endeavour |
title_short | Candidate gene prioritization with Endeavour |
title_sort | candidate gene prioritization with endeavour |
topic | Web Server issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4987917/ https://www.ncbi.nlm.nih.gov/pubmed/27131783 http://dx.doi.org/10.1093/nar/gkw365 |
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