Nucleic Acids Research Advance Access originally published online on November 16, 2006
Nucleic Acids Research 2007 35(Database issue):D700-D706; doi:10.1093/nar/gkl826
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Nucleic Acids Research, 2007, Vol. 35, Database issue D700-D706
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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PolyDoms: a whole genome database for the identification of non-synonymous coding SNPs with the potential to impact disease
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center Cincinnati, OH 45229, USA 1 Department of Pediatrics, College of Medicine Cincinnati, OH 45229, USA 2 Department of Biomedical Engineering, University of Cincinnati Cincinnati, OH 45229, USA
*To whom correspondence should be addressed at Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH 45229-3039, USA. Tel: +1 513 636 4865; Fax: +1 513 636 2056; Email: bruce.aronow{at}cchmc.org
Received August 15, 2006. Revised September 29, 2006. Accepted October 2, 2006.
As knowledge of human genetic polymorphisms grows, so does the opportunity and challenge of identifying those polymorphisms that may impact the health or disease risk of an individual person. A critical need is to organize large-scale polymorphism analyses and to prioritize candidate non-synonymous coding SNPs (nsSNPs) that should be tested in experimental and epidemiological studies to establish their context-specific impacts on protein function. In addition, with emerging high-resolution clinical genetics testing, new polymorphisms must be analyzed in the context of all available protein feature knowledge including other known mutations and polymorphisms. To approach this, we developed PolyDoms (http://polydoms.cchmc.org/) as a database to integrate the results of multiple algorithmic procedures and functional criteria applied to the entire Entrez dbSNP dataset. In addition to predicting structural and functional impacts of all nsSNPs, filtering functions enable group-based identification of potentially harmful nsSNPs among multiple genes associated with specific diseases, anatomies, mammalian phenotypes, gene ontologies, pathways or protein domains. PolyDoms, thus, provides a means to derive a list of candidate SNPs to be evaluated in experimental or epidemiological studies for impact on protein functions and disease risk associations. PolyDoms will continue to be curated to improve its usefulness.
The authors wish it to be known that, the first three authors should be regarded as joint First Authors
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