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. 2015 Dec 1;31(23):3822-9.
doi: 10.1093/bioinformatics/btv473. Epub 2015 Aug 12.

OVA: integrating molecular and physical phenotype data from multiple biomedical domain ontologies with variant filtering for enhanced variant prioritization

Agne Antanaviciute  1 , Christopher M Watson  2 , Sally M Harrison  1 , Carolina Lascelles  1 , Laura Crinnion  2 , Alexander F Markham  1 , David T Bonthron  1 , Ian M Carr  1
Affiliations

OVA: integrating molecular and physical phenotype data from multiple biomedical domain ontologies with variant filtering for enhanced variant prioritization

Agne Antanaviciute et al. Bioinformatics. .

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.

Availability and implementation: OVA is freely accessible at http://dna2.leeds.ac.uk:8080/OVA/index.jsp.

Supplementary information: Supplementary data are available at Bioinformatics online.

Contact: umaan@leeds.ac.uk.

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Figures

Fig. 1.
Fig. 1.
Overview of OVA workflow
Fig. 2
Fig. 2
ROC curves showing the performance of OVA while using Dataset 1 (A) and Dataset 2 (B). Each dataset was prioritized using three methods—the average (green), the weighted average (red) and a classifier (blue) built using a supervised learning approach
Fig. 3.
Fig. 3.
Performance comparison between OVA and ExomeWalker using 150 exomes
See this image and copyright information in PMC

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