title: TFRank: network-based prioritization of regulatory associations underlying transcriptional responses.
abstract: BACKGROUND
  Uncovering mechanisms underlying gene expression control is crucial to understand complex cellular responses. Studies in gene regulation often aim to identify regulatory players involved in a biological process of interest, either transcription factors coregulating a set of target genes or genes eventually controlled by a set of regulators. These are frequently prioritized with respect to a context-specific relevance score. Current approaches rely on relevance measures accounting exclusively for direct transcription factor-target interactions, namely overrepresentation of binding sites or target ratios. Gene regulation has, however, intricate behavior with overlapping, indirect effect that should not be neglected. In addition, the rapid accumulation of regulatory data already enables the prediction of large-scale networks suitable for higher level exploration by methods based on graph theory. A paradigm shift is thus emerging, where isolated and constrained analyses will likely be replaced by whole-network, systemic-aware strategies.
  
  
  RESULTS
  We present TFRank, a graph-based framework to prioritize regulatory players involved in transcriptional responses within the regulatory network of an organism, whereby every regulatory path containing genes of interest is explored and incorporated into the analysis. TFRank selected important regulators of yeast adaptation to stress induced by quinine and acetic acid, which were missed by a direct effect approach. Notably, they reportedly confer resistance toward the chemicals. In a preliminary study in human, TFRank unveiled regulators involved in breast tumor growth and metastasis when applied to genes whose expression signatures correlated with short interval to metastasis.
authors.affiliation: Knowledge Discovery and Bioinformatics group, INESC-ID, Portugal. jpg@kdbio.inesc-id.pt
authors.forename: Joana P
authors.initials: JP
authors.lastname: Gonçalves
authors.forename: Alexandre P
authors.initials: AP
authors.lastname: Francisco
authors.forename: Nuno P
authors.initials: NP
authors.lastname: Mira
authors.forename: Miguel C
authors.initials: MC
authors.lastname: Teixeira
authors.forename: Isabel
authors.initials: I
authors.lastname: Sá-Correia
authors.forename: Arlindo L
authors.initials: AL
authors.lastname: Oliveira
authors.forename: Sara C
authors.initials: SC
authors.lastname: Madeira
mesh_terms: D019342:Acetic Acid / Q000494:pharmacology; D001665:Binding Sites; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D009362:Neoplasm Metastasis; D011803:Quinine / Q000494:pharmacology; D012441:Saccharomyces cerevisiae / Q000187:drug effects / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism*; D014158:Transcription, Genetic / Q000187:drug effects
chemical_list: D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors; D011803:Quinine; D019342:Acetic Acid