Cargando…
ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations
A tremendous amount of DNA sequencing data is being produced around the world with the ambition to capture in more detail the mechanisms underlying human diseases. While numerous bioinformatics tools exist that allow the discovery of causal variants in Mendelian diseases, little to no support is pro...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602484/ https://www.ncbi.nlm.nih.gov/pubmed/31147699 http://dx.doi.org/10.1093/nar/gkz437 |
_version_ | 1783431386765459456 |
---|---|
author | Renaux, Alexandre Papadimitriou, Sofia Versbraegen, Nassim Nachtegael, Charlotte Boutry, Simon Nowé, Ann Smits, Guillaume Lenaerts, Tom |
author_facet | Renaux, Alexandre Papadimitriou, Sofia Versbraegen, Nassim Nachtegael, Charlotte Boutry, Simon Nowé, Ann Smits, Guillaume Lenaerts, Tom |
author_sort | Renaux, Alexandre |
collection | PubMed |
description | A tremendous amount of DNA sequencing data is being produced around the world with the ambition to capture in more detail the mechanisms underlying human diseases. While numerous bioinformatics tools exist that allow the discovery of causal variants in Mendelian diseases, little to no support is provided to do the same for variant combinations, an essential task for the discovery of the causes of oligogenic diseases. ORVAL (the Oligogenic Resource for Variant AnaLysis), which is presented here, provides an answer to this problem by focusing on generating networks of candidate pathogenic variant combinations in gene pairs, as opposed to isolated variants in unique genes. This online platform integrates innovative machine learning methods for combinatorial variant pathogenicity prediction with visualization techniques, offering several interactive and exploratory tools, such as pathogenic gene and protein interaction networks, a ranking of pathogenic gene pairs, as well as visual mappings of the cellular location and pathway information. ORVAL is the first web-based exploration platform dedicated to identifying networks of candidate pathogenic variant combinations with the sole ambition to help in uncovering oligogenic causes for patients that cannot rely on the classical disease analysis tools. ORVAL is available at https://orval.ibsquare.be. |
format | Online Article Text |
id | pubmed-6602484 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-66024842019-07-05 ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations Renaux, Alexandre Papadimitriou, Sofia Versbraegen, Nassim Nachtegael, Charlotte Boutry, Simon Nowé, Ann Smits, Guillaume Lenaerts, Tom Nucleic Acids Res Web Server Issue A tremendous amount of DNA sequencing data is being produced around the world with the ambition to capture in more detail the mechanisms underlying human diseases. While numerous bioinformatics tools exist that allow the discovery of causal variants in Mendelian diseases, little to no support is provided to do the same for variant combinations, an essential task for the discovery of the causes of oligogenic diseases. ORVAL (the Oligogenic Resource for Variant AnaLysis), which is presented here, provides an answer to this problem by focusing on generating networks of candidate pathogenic variant combinations in gene pairs, as opposed to isolated variants in unique genes. This online platform integrates innovative machine learning methods for combinatorial variant pathogenicity prediction with visualization techniques, offering several interactive and exploratory tools, such as pathogenic gene and protein interaction networks, a ranking of pathogenic gene pairs, as well as visual mappings of the cellular location and pathway information. ORVAL is the first web-based exploration platform dedicated to identifying networks of candidate pathogenic variant combinations with the sole ambition to help in uncovering oligogenic causes for patients that cannot rely on the classical disease analysis tools. ORVAL is available at https://orval.ibsquare.be. Oxford University Press 2019-07-02 2019-05-31 /pmc/articles/PMC6602484/ /pubmed/31147699 http://dx.doi.org/10.1093/nar/gkz437 Text en © The Author(s) 2019. 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 Renaux, Alexandre Papadimitriou, Sofia Versbraegen, Nassim Nachtegael, Charlotte Boutry, Simon Nowé, Ann Smits, Guillaume Lenaerts, Tom ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations |
title | ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations |
title_full | ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations |
title_fullStr | ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations |
title_full_unstemmed | ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations |
title_short | ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations |
title_sort | orval: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602484/ https://www.ncbi.nlm.nih.gov/pubmed/31147699 http://dx.doi.org/10.1093/nar/gkz437 |
work_keys_str_mv | AT renauxalexandre orvalanovelplatformforthepredictionandexplorationofdiseasecausingoligogenicvariantcombinations AT papadimitriousofia orvalanovelplatformforthepredictionandexplorationofdiseasecausingoligogenicvariantcombinations AT versbraegennassim orvalanovelplatformforthepredictionandexplorationofdiseasecausingoligogenicvariantcombinations AT nachtegaelcharlotte orvalanovelplatformforthepredictionandexplorationofdiseasecausingoligogenicvariantcombinations AT boutrysimon orvalanovelplatformforthepredictionandexplorationofdiseasecausingoligogenicvariantcombinations AT noweann orvalanovelplatformforthepredictionandexplorationofdiseasecausingoligogenicvariantcombinations AT smitsguillaume orvalanovelplatformforthepredictionandexplorationofdiseasecausingoligogenicvariantcombinations AT lenaertstom orvalanovelplatformforthepredictionandexplorationofdiseasecausingoligogenicvariantcombinations |