Garnet takes differential gene expression data and epigenetic data and imputes relevant transcription factors that impacted gene expression. It maps epigenetic data to genes and then scans the genome to determine the likelihood of a transcription factor binding the genome near that gene. Then, it uses linear regression to determine if TFs had a high likelihood of binding near genes with large changes in expression.
Forest takes protein data, maps it onto a large interactome network, and then uses the Prize-Collecting Steiner Forest algorithm to identify a high-confidence subnetwork relevant to your data. This can help identify cellular pathways and other proteins that are involved in your experimental system.