Antanaviciute, A, Watson, CM orcid.org/0000-0003-2371-1844, Harrison, SM et al. (5 more authors) (2015) OVA: Integrating molecular and physical phenotype data from multiple biomedical domain ontologies with variant filtering for enhanced variant prioritization. Bioinformatics, 31 (23). pp. 3822-3829. ISSN 1367-4803
Abstract
Motivation: Exome sequencing has become a de facto standard method for Mendelian disease gene discovery in recent years, yet identifying disease-causing mutations among thousands of candidate variants remains a non-trivial task. Results: Here we describe a new variant prioritization tool, OVA (ontology variant analysis), in which user-provided phenotypic information is exploited to infer deeper biological context. OVA combines a knowledge-based approach with a variant-filtering framework. It reduces the number of candidate variants by considering genotype and predicted effect on protein sequence, and scores the remainder on biological relevance to the query phenotype. We take advantage of several ontologies in order to bridge knowledge across multiple biomedical domains and facilitate computational analysis of annotations pertaining to genes, diseases, phenotypes, tissues and pathways. In this way, OVA combines information regarding molecular and physical phenotypes and integrates both human and model organism data to effectively prioritize variants. By assessing performance on both known and novel disease mutations, we show that OVA performs biologically meaningful candidate variant prioritization and can be more accurate than another recently published candidate variant prioritization tool.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author 2015. Published by Oxford University Press. 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. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > Institute of Molecular Medicine (LIMM) (Leeds) > Section of Opthalmology and Neurosciences (Leeds) |
Funding Information: | Funder Grant number MRC (Medical Research Council) MR/L01629X/1 MRC (Medical Research Council) MR/M009084/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jan 2016 11:12 |
Last Modified: | 09 Apr 2021 14:13 |
Published Version: | http://dx.doi.org/10.1093/bioinformatics/btv473 |
Status: | Published |
Publisher: | Oxford University Press |
Identification Number: | 10.1093/bioinformatics/btv473 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:93118 |