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.jspSupplementary information: Supplementary data are available at Bioinformatics online.Contact: umaan@leeds.ac.uk
Highlights
The application of next-generation sequencing for disease gene discovery or clinical diagnostics can generate large volumes of data, often resulting in identification of thousands of candidate disease genes or variants
We rank each test gene with respect to disease together with 200 randomly selected genes from the pool of all human genes which have at least minimal Gene Ontology annotations in order to avoid any bias, as known disease genes are rarely entirely unannotated
There is a notable difference in performance between the three methods that is consistent across the datasets used
Summary
The application of next-generation sequencing for disease gene discovery or clinical diagnostics can generate large volumes of data, often resulting in identification of thousands of candidate disease genes or variants. A healthy individual genome can harbor more than a hundred genuine loss-of-function mutations (MacArthur et al, 2012), making the identification of mutations responsible for a given phenotype a non-trivial task. As systematic experimental verification of each variant is infeasible, several computational prioritization methods have emerged in recent years that attempt to tackle this problem.
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