Format

Send to

Choose Destination
Bioinformatics. 2016 Feb 15;32(4):635-7. doi: 10.1093/bioinformatics/btv598. Epub 2015 Oct 21.

Web-based Gene Pathogenicity Analysis (WGPA): a web platform to interpret gene pathogenicity from personal genome data.

Author information

1
School of Engineering, Pablo de Olavide University, Seville, 41013 Spain and.
2
Duke-NUS Graduate Medical School Singapore, Singapore, 169857 Singapore.

Abstract

As the volume of patient-specific genome sequences increases the focus of biomedical research is switching from the detection of disease-mutations to their interpretation. To this end a number of techniques have been developed that use mutation data collected within a population to predict whether individual genes are likely to be disease-causing or not. As both sequence data and associated analysis tools proliferate, it becomes increasingly difficult for the community to make sense of these data and their implications. Moreover, no single analysis tool is likely to capture all relevant genomic features that contribute to the gene's pathogenicity. Here, we introduce Web-based Gene Pathogenicity Analysis (WGPA), a web-based tool to analyze genes impacted by mutations and rank them through the integration of existing prioritization tools, which assess different aspects of gene pathogenicity using population-level sequence data. Additionally, to explore the polygenic contribution of mutations to disease, WGPA implements gene set enrichment analysis to prioritize disease-causing genes and gene interaction networks, therefore providing a comprehensive annotation of personal genomes data in disease.

AVAILABILITY AND IMPLEMENTATION:

wgpa.systems-genetics.net.

PMID:
26490503
PMCID:
PMC4743624
DOI:
10.1093/bioinformatics/btv598
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center