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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...

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Autores principales: Renaux, Alexandre, Papadimitriou, Sofia, Versbraegen, Nassim, Nachtegael, Charlotte, Boutry, Simon, Nowé, Ann, Smits, Guillaume, Lenaerts, Tom
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
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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.
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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
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