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AN INTEGRATED APPROACH TO RECONSTRUCTING GENOME SCALE TRANSCRIPTIONAL REGULATORY NETWORKS SAHEED IMAM ET.AL. A paper pitch by Ivan Valdes, Samar Tareen, Chibuike Ugwuoke, Maryam Suleimani, Gianluca Mazzoni Transcriptional Regulatory Networks • Transcriptionally regulatory networks (TRNs) dynamically alter gene expression in response to stimuli • Many approaches generate TRNs based on the assumption: expression is directly related to cognate transcription factors (TFs). • Drawback: Compromised by indirect effects such as co-expressed but not co-regulated genes • A novel workflow based on: • Integration of comparative genomics data • Global gene expression • Intrinsic properties of TFs The Workflow Phylogenetic Footprinting • Incorporates comparative genomics and phylogenetics • Selection of appropriate organisms • Very closely related organisms: might be uninformative • Very far: might not be conserved Author’s analysis: as few as 6 appropriately selected organisms were sufficient for a robust analysis Identification of Orthologs • Orthologs are genes between species sharing common ancestry • OrthoMCL to detect orthologs • Builds upon the bidirectional best BLAST using Markov Cluster algorithm (MCL) Integration of Gene Expression Data • Matching clusters to expression profiles • Used DISTILLER • Bi-clustering algorithm • Presence/absence of motifs via binary classification • Gives sub-conditions for shared significant coexpression pattern Linking TFs to Clusters • Uses correlation expression profiles to map TFs to targets • Proximity of location • Similarity in DNA binding motif • Phylogenetic correlation QUESTIONS?