Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Proceedings of the 2009 AMIA Summit on Translational Bioinformatics

Open Access Proceedings

Evaluating the accuracy of a functional SNP annotation system

Terry H Shen1*, Christopher S Carlson25 and Peter Tarczy-Hornoch134

Author Affiliations

1 Departments of Biomedical & Health Informatics, University of Washington, Seattle, WA, USA

2 Genome Sciences, University of Washington, Seattle, WA, USA

3 Computer Science and Engineering, University of Washington, Seattle, WA, USA

4 Pediatrics, University of Washington, Seattle, WA, USA

5 Fred Hutchinson Cancer Research Center, Seattle, WA, USA

For all author emails, please log on.

BMC Bioinformatics 2009, 10(Suppl 9):S11  doi:10.1186/1471-2105-10-S9-S11

Published: 17 September 2009


Many common and chronic diseases are influenced at some level by genetic variation. Research done in population genetics, specifically in the area of single nucleotide polymorphisms (SNPs) is critical to understanding human genetic variation. A key element in assessing role of a given SNP is determining if the variation is likely to result in change in function. The SNP Integration Tool (SNPit) is a comprehensive tool that integrates diverse, existing predictors of SNP functionality, providing the user with information for improved association study analysis. To evaluate the SNPit system, we developed an alternative gold standard to measure accuracy using sensitivity and specificity. The results of our evaluation demonstrated that our alternative gold standard produced encouraging results.