Looking for cancer clues in publicly accessible databases.

What started out as a mere attempt to tentatively identify proteins in experimental cancer-related 2D-PAGE maps developed into VIRTUAL2D, a web-accessible repository for theoretical pI/MW charts for 92 organisms. Using publicly available expression data, we developed a collection of tissue-specific plots based on differential gene expression between normal and diseased states. We use this comparative cancer proteomics knowledge base, known as the tissue molecular anatomy project (TMAP), to uncover threads of cancer markers common to several types of cancer and to relate this information to established biological pathways.

VIRTUAL2D: A web-accessible predictive database for proteomics analysis.

The available archive of sequence databases compiled from whole genome projects and budding proteomics efforts have enabled us to develop VIRTUAL2D, an interactive system for the assembly of virtual protein expression maps computed on the basis of theoretical isoelectric focusing point, molecular weight, tissue specificity and relative abundance for any set of proteins currently catalogued. This tool will assist in the preliminary, albeit putative, prediction of the identity and location of unknown and/or low abundance proteins in experimentally derived two-dimensional polyacrylamide gel electrophoresis maps.

Tissue Molecular Anatomy Project (TMAP): an expression database for comparative cancer proteomics.

By mining publicly accessible databases, we have developed a collection of tissue-specific predictive protein expression maps as a function of cancer histological state. Data analysis is applied to the differential expression of gene products in pooled libraries from the normal to the altered state(s). We wish to report the initial results of our survey across different tissues and explore the extent to which this comparative approach may help uncover panels of potential biomarkers of tumorigenesis which would warrant further examination in the laboratory.