Publication Type:

Conference Paper


IEEE International Conference on Bioinformatics and Biomedicine Workshop, 2009. BIBMW 2009., IEEE, Washington, DC (2009)



Accession Number:




Biochemistry, biology computing, cellular biophysics, cellular pathways, data analysis, data manipulation, data query, data storage, Data visualization, Enzymes, expression profiles, gene expression, Genes, genetics, genomics, graphical pathway representation, high-throughput expression data, homology relationship, Humans, integrative approach, Metabolic Network, metabolic pathways, Mice, molecular biophysics, MYSQL database, oncogene pathway identification, Organisms, PathMapper, Pathway Analysis, Protein Engineering, Proteins, regulatory pathways, SQL, Visual databases


Although generation of high-throughput expression data is becoming customary, the fast, easy, and methodical presentation and analysis of these data in a biological context is still an obstruction. To tackle this necessity we have developed PathMapper, a standalone application which maps expression profiles of genes or proteins concurrently onto major, currently available regulatory, metabolic and cellular pathways. PathMapper automatically predicts protein functions directly from genes and can systematically identify differences between metabolic pathways and map genes onto pathways. MYSQL database is used to store, query, and manipulate the large amounts of data that are involved. PathMapper allows its users to (i) upload microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams (iv) visualize gene expression for each pathway. A graphical pathway represe- ntation permits the visualization of the expressed genes in a functional context. Based on publicly available pathway databases, PathMapper can be adapted to any organism and is currently available for human, mouse and rat genome arrays. About 20% of the probe sets of each array have been assigned to EC numbers by homology relationship and linked to its corresponding metabolic pathways. This tool can be downloaded and evaluated using the following Web link : (

Cite this Research Publication

A. Vijayan, Skariah, B. E., Nair, B. G., Lushington, G. H., Subramaniam, S., and Visvanathan, M., “PathMapper-an integrative approach for oncogene pathway identification”, in IEEE International Conference on Bioinformatics and Biomedicine Workshop, 2009. BIBMW 2009., Washington, DC, 2009.