This web application provides a supplement to the following paper submitted to OMICS: A Journal of Integrative Biology.
Inference of new and useful hypotheses from heterogeneous sources of genome-scale experimental data requires new computational methods that can integrate different types of data. Gene expression and genetic interaction data are two most informative data types, each allowing the identification of genes at different levels of cellular regulatory network hierarchy. We present an integrative data analysis approach which, rather than correlating the findings from the two data sets, uses each type of data independently to identify the components of molecular pathways and combines them into a single directed network. Our computational genomics approach is based on a set of inference rules traditionally used for reasoning on genetic experiments, which we have formalized and implemented in a software tool. The approach uses chemogenetic interaction and expression data to infer the type of relation between the chemical substance (perturber) and a transcription factor by using previous knowledge on the set of genes whose expression the transcription factor in question regulates. We have used the proposed approach to successfully infer the models for the action of the drug rapamycin and of a DNA damaging agent on their molecular targets and pathways in yeast cells.
Use this program to infer a model for molecular mechanisms of action of a perturbagen.
The user can provide data on effects of a pertubagen on the transcriptome and its genetic interactome. The program then uses these data together with literature data on effects of regulators that are in genetic interaction with differentially expressed genes and infers a model of molecular mechanisms of the perturbagen. The model is presented as a set of relations between genes and as a "wiring diagram."
Here are some already analysed examples (you can try to upload the associated data and rerun the analyses):