Project Coordinators: M Rudd & RS Houlston

We have classified and catalogued the predicted impact on protein function of non-synonymous single nucleotide polymorphisms (nsSNPs) in genes relevant to the biology of cancer using in silico computational tools. The data is supplementary to that published in: Matthew F. Rudd, Richard D. Williams, Emily L. Webb, Steffen Schmidt, Gabrielle S. Sellick, Richard S. Houlston. The Predicted Impact of Coding Single Nucleotide Polymorphisms Database. Published in: Cancer Epidemiology, Biomarkers and Prevention, 2005.

Supplementary Table 1 details 9,537 validated bi-allelic nsSNPs retrieved from NCBI dbSNP Build 123 located within one of 21,506 annotated genes. The data is available in Excel (.xls) and plain text (.txt) formats.

Supplementary Table 2 details the 7,080 genes curated on the basis of their potential biological relevance to cancer. The data is available in Excel (.xls) format.

Supplementary Table 3 details 3,009 nsSNPs located within one of 7,080 candidate cancer genes, with minor allele frequencies (MAF) >= 0.01 validated in Caucasian populations. The predicted impact on wild-type protein structure and function was computed for each entry using three freely available algorithms: Grantham matrix¹, PolyPhen², and SIFT³. The data is available in Excel (.xls) and plain text (.txt) formats.

Header descriptions for Tables 1 & 3 are found in the Readme (.doc) file.

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