GECO: gene expression correlation analysis after genetic algorithm-driven deconvolution.

Motivation: Large-scale gene expression analysis is a valuable asset for data-driven hypothesis generation. However, the convoluted nature of large expression datasets often hinders extraction of meaningful biological information. Results: To this end, we developed GECO, a gene expression correlation analysis software that uses a genetic algorithm-driven approach to deconvolute complex expression datasets into two subpopulations that display positive and negative correlations between a pair of queried genes. GECO's mutational enrichment and pairwise drug sensitivity analyses functions that follow the deconvolution step may help to identify the mutational factors that drive the gene expression correlation in the generated subpopulations and their differential drug vulnerabilities. Finally, GECO's drug sensitivity screen function can be used to identify drugs that differentially affect the subpopulations. Availability and implementation: http://www.proteinguru.com/geco/ and http://www.proteinguru.com/geco/codes/. Supplementary information: Supplementary data are available at Bioinformatics online.

CrossCheck: an open-source web tool for high-throughput screen data analysis.

Modern high-throughput screening methods allow researchers to generate large datasets that potentially contain important biological information. However, oftentimes, picking relevant hits from such screens and generating testable hypotheses requires training in bioinformatics and the skills to efficiently perform database mining. There are currently no tools available to general public that allow users to cross-reference their screen datasets with published screen datasets. To this end, we developed CrossCheck, an online platform for high-throughput screen data analysis. CrossCheck is a centralized database that allows effortless comparison of the user-entered list of gene symbols with 16,231 published datasets. These datasets include published data from genome-wide RNAi and CRISPR screens, interactome proteomics and phosphoproteomics screens, cancer mutation databases, low-throughput studies of major cell signaling mediators, such as kinases, E3 ubiquitin ligases and phosphatases, and gene ontological information. Moreover, CrossCheck includes a novel database of predicted protein kinase substrates, which was developed using proteome-wide consensus motif searches. CrossCheck dramatically simplifies high-throughput screen data analysis and enables researchers to dig deep into the published literature and streamline data-driven hypothesis generation. CrossCheck is freely accessible as a web-based application at http://proteinguru.com/crosscheck.