I am working on a project to build a transcriptome (more of a sequence resource) for a cell line derived from a particular species. In order to capture as many genes as possible, we ran RNA-Seq for untreated cells and cells treated with PMA/Ionomycin (a means of activating transcription in a mostly non-specific fashion). We have the raw pile of transcripts as well as differential expression between untreated and treated cells.
Our interest in this species is determining what possible differences may exist that allow it to support very different biology than what is found in species of primata (e.g. humans) and rodentia (e.g. mice). As far as what to do with the transcript data I am fairly sure (BLAST annotation, protein sequence prediction, etc).
However, what to do with the fold change data is less obvious. What I would like to do is compare PMA/Ionomycin data from other species to see if there are differences in the regulatory networks of our species of interest. There are two obvious choices: a simple gene-wise comparison, and functional enrichment, but these I feel would be fairly weak due to the broad action of PMA/Ionomycin.
Are there any tools that can take gene expression or transcriptome data and extract putative regulatory networks from them?