MOTIVATION: High-throughput 'ChIP-chip' and 'ChIP-seq' methodologies generate sufficiently large data sets that analysis poses significant informatics challenges, particularly for research groups with modest computational support. To address this challenge, we devised a software platform for storing, analyzing and visualizing high resolution genome-wide binding data. GeneTrack automates several steps of a typical data processing pipeline, including smoothing and peak detection, and facilitates dissemination of the results via the web. Our software is freely available via the Google Project Hosting environment at http://genetrack.googlecode.com

MOTIVATION: Global protein interaction network (interactome) analysis provides an effective way to understand the relationships between genes. Through this approach, it was demonstrated that the essential genes in yeast tend to be highly connected as well as connected to other highly connected genes. This is in contrast to the genes that are not essential, which share neither of these properties. Using a similar interactome-transcriptome approach, the topological features in the interactome of differentially expressed genes in lung squamous cancer tissues are assessed. RESULTS: This analysis reveals that the genes that are differentially elevated, as obtained from the microarray gene profiling data, in cancer are well connected, whereas the suppressed genes and randomly selected ones are less so. These results support the notion that a topological analysis of cancer genes using protein interaction data will allow the placement of the list of genes, often of the disparate nature, into the global, systematic context of the cell. The result of this type of analysis may provide the rationale for therapeutic targets in cancer treatment.