This article is part of the supplement: Selected articles from the 9th Annual Biotechnology and Bioinformatics Symposium (BIOT 2012)
VarRanker: rapid prioritization of sequence variations associated with human disease
ARUP Institute for Clinical and Experimental Pathology, 500 Chipeta Way, Salt Lake City, UT, USA
BMC Bioinformatics 2013, 14(Suppl 13):S1 doi:10.1186/1471-2105-14-S13-S1Published: 1 October 2013
Identification of the genetic alterations responsible for human disease is a central challenge facing medical genetics. While many algorithms have been developed to predict the degree of damage caused by a given sequence alteration, few tools are able to incorporate information about a given phenotype of interest.
Here, we describe an algorithm and web-based application which take into account both the probability that a variant damages the function of a gene as well as the relevance of the gene to a given phenotype. Phenotypes are described by a list of scored terms supplied by the user. These terms are then used to search a variety of public databases including NCBI gene summaries, PubMed abstracts, and Gene Ontology terms, and protein-protein interactions in String-DB to determine a relevance score. The overall ranking is determined by the product of the functional damage score and the relevance score, such that highly ranked variants are likely to be damaging and in genes of interest.
We demonstrate the method on several test cases including samples with Hereditary Hemorrhagic Telangiectasia (HHT) and Diamond-Blackfan Anemia (DBA). We have also implemented a web-based application which allows public access to the VarRanker algorithm.
Automated searching of public literature and online databases may substantially decrease the amount of time required to identify the mutations underlying human disease. However, several ad-hoc and subjective decisions must be made, and the results of such analyses are likely to depend on the researcher and the state of the literature and databases involved.