Gene expression connectivity mapping software tools | Drug discovery data analysis
Here, we surveyed bioinformatics software tools for exploring gene expression connectivity mapping. Interaction of a drug or chemical with a biological system can result in a gene-expression profile or signature characteristic of the event. Connectivity mapping is a process to recognize novel pharmacological and toxicological properties in small molecules by comparing their gene expression signatures with others in a database.
An open source chemistry toolbox. Open Babel is a chemical toolbox designed to speak the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. It's an open, collaborative project allowing anyone to search, convert, analyze, or store data from molecular modeling, chemistry, solid-state materials, biochemistry, or related areas.
A Java application designed to undertake connectivity mapping tasks. sscMap is bundled with a default collection of reference gene-expression profiles based on the publicly available dataset from the Broad Institute Connectivity Map 02, which includes data from over 7000 Affymetrix microarrays, for over 1000 small-molecule compounds, and 6100 treatment instances in 5 human cell lines. In addition, the application allows users to add their custom collections of reference profiles and is applicable to a wide range of other 'omics technologies.
A fully configurable framework for co-expressed gene set enrichment analysis. By combining pathway analysis and drug repositioning analysis, Cogena provides a unique approach to imply the drug mode of action in a disease context, which is important to the translational development of computationally repositioned drugs. Cogena is a powerful tool for co-expressed gene set enrichment analysis, including pathway analysis and drug repositioning.
A network biology-based computational platform designed to integrate transcriptomes, interactomes and gene ontologies to identify phenotype-specific subnetworks. NetDecoder is based on network flow algorithm and formulated as a minimum-cost flow optimization problem to identify and prioritize paths and key regulators within disease specific subnetworks. NetDecoder is designed to capture molecular switches and infer disease-specific networks to better understand pathways and identify key regulators that contribute to a disease phenotype.
Combines toxicity and efficacy of candidate drugs, expansibility of data and algorithms, and customizes reference gene profiles. It is able to connect query gene expression profile signatures with a selected reference database. The tool permits the user to perform a drug efficacy analysis to identify some known mechanisms of drug action or generate new predictions.
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