Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets.Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets.Availability: The causal analytics tools ‘Upstream Regulator Analysis', ‘Mechanistic Networks', ‘Causal Network Analysis' and ‘Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com).Supplementary information:
Supplementary material is available at Bioinformatics online.
Azotobacter vinelandii is an obligately aerobic bacterium in which aerotolerant nitrogen fixation requires cytochrome bd. Regulation of cytochrome bd expression is achieved by CydR (an Fnr homologue), which represses transcription of the oxidase genes cydAB. cydAB mRNA was mapped by primer extension; the transcriptional start site was determined, and putative ؊10 and ؊35 regions were deduced. Two "CydR boxes," one at the ؉1 site and one upstream of the ؊35 region, were identified. Transcriptionally inactive, purified CydR was converted, by adding NifS, cysteine, and
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