Bioinformatics Advance Access published online on September 12, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm437
Accurate Prediction of Deleterious Protein Kinase Polymorphisms
1 Graduate Program in Biomedical Sciences; Department of Medicine; and Center for Human Genetics and Genomics, University of California at San Diego, La Jolla, CA 92093 and 2Scripps Genomic Medicine and Department of Molecular and Experimental Medicine, The Scripps Research Institute; Center for Human Genetics and Genomics, University of California at San Diego, La Jolla, CA 9209
*To whom correspondence should be addressed. Mr. Nicholas J. Schork, E-mail: nschork{at}scripps.edu
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Abstract |
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Motivation: Contemporary, high-throughput sequencing efforts have identified a rich source of naturally occurring single nucleotide polymorphisms (SNPs), a subset of which occur in the coding region of genes and result in a change in the encoded amino acid sequence (nonsynonymous coding SNPs or ‘nsSNPs’). It is hypothesized that a subset of these nsSNPs may underlie common human disease. Testing all these polymorphisms for disease association would be time consuming and expensive. Thus, computational methods have been developed to both prioritize candidate nsSNPs and make sense of their likely molecular physiologic impact.
Results: We have developed a method to prioritize nsSNPs and have applied it to the human protein kinase gene family. The results of our analyses provide high quality predictions and outperform available whole genome prediction methods (74% vs. 83% prediction accuracy). Our analyses and methods consider both DNA sequence conservation, which most traditional methods are based on, as well unique structural and functional features of kinases. We provide a ranked list of common kinase nsSNPs that have a higher probability of impacting human disease based on our analyses.
Contact: nschork{at}scripps.edu
Associate Editor: Prof. Martin Bishop
Received on July 20, 2007; revised on August 2, 2007; accepted on August 19, 2007
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