Accurate prediction of deleterious protein kinase polymorphisms Export

@article{citeulike:2548548, abstract = {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 (non-synonymous 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\% versus 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@scripps.edu Supplementary information: Supplementary data are available on Bioinformatics online. 10.1093/bioinformatics/btm437}, address = {Department of Medicine, Center for Human Genetics and Genomics, The Scripps Research Institute, University of California, San Diego, La Jolla, CA 92093, USA.}, author = {Torkamani, Ali and Schork, Nicholas J.}, citeulike-article-id = {2548548}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btm437}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/content/23/21/2918.abstract}, citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/23/21/2918.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/17855419}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=17855419}, day = {1}, doi = {10.1093/bioinformatics/btm437}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {evolution, statistics}, month = {November}, number = {21}, pages = {2918--2925}, posted-at = {2009-11-06 18:51:58}, priority = {2}, title = {Accurate prediction of deleterious protein kinase polymorphisms}, url = {http://dx.doi.org/10.1093/bioinformatics/btm437}, volume = {23}, year = {2007} }

TY - JOUR ID - citeulike:2548548 L3 - citeulike-article-id:2548548 N2 - 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 (non-synonymous 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% versus 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@scripps.edu Supplementary information: Supplementary data are available on Bioinformatics online. 10.1093/bioinformatics/btm437 IS - 21 JF - Bioinformatics SN - 1460-2059 AD - Department of Medicine, Center for Human Genetics and Genomics, The Scripps Research Institute, University of California, San Diego, La Jolla, CA 92093, USA. EP - 2925 TI - Accurate prediction of deleterious protein kinase polymorphisms VL - 23 SP - 2918 KW - evolution KW - statistics AU - Torkamani, Ali AU - Schork, Nicholas PY - 2007/11/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btm437 ER -