Literature DB >> 16284384

The predicted impact of coding single nucleotide polymorphisms database.

Matthew F Rudd1, Richard D Williams, Emily L Webb, Steffen Schmidt, Gabrielle S Sellick, Richard S Houlston.   

Abstract

Nonsynonymous single nucleotide polymorphisms (nsSNP) have the potential to affect the structure or function of expressed proteins and are, therefore, likely to represent modifiers of inherited susceptibility. We have classified and catalogued the predicted functionality of nsSNPs in genes relevant to the biology of cancer to facilitate sequence-based association studies. Candidate genes were identified using targeted search terms and pathways to interrogate the Gene Ontology Consortium database, Kyoto Encyclopedia of Genes and Genomes database, Iobion's Interaction Explorer PathwayAssist Program, National Center for Biotechnology Information Entrez Gene database, and CancerGene database. A total of 9,537 validated nsSNPs located within annotated genes were retrieved from National Center for Biotechnology Information dbSNP Build 123. Filtering this list and linking it to 7,080 candidate genes yielded 3,666 validated nsSNPs with minor allele frequencies > or =0.01 in Caucasian populations. The functional effect of nsSNPs in genes with a single mRNA transcript was predicted using three computational tools-Grantham matrix, Polymorphism Phenotyping, and Sorting Intolerant from Tolerant algorithms. The resultant pool of 3,009 fully annotated nsSNPs is accessible from the Predicted Impact of Coding SNPs database at http://www.icr.ac.uk/cancgen/molgen/MolPopGen_PICS_database.htm. Predicted Impact of Coding SNPs is an ongoing project that will continue to curate and release data on the putative functionality of coding SNPs.

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Year:  2005        PMID: 16284384     DOI: 10.1158/1055-9965.EPI-05-0469

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  14 in total

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Authors:  Matthew F Rudd; Emily L Webb; Athena Matakidou; Gabrielle S Sellick; Richard D Williams; Helen Bridle; Tim Eisen; Richard S Houlston
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10.  Exploring functional variant discovery in non-coding regions with SInBaD.

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