Detecting Cooperativity between Transcription Factors Based on Functional Coherence and Similarity of Their Target Gene Sets

Abstract

In eukaryotic cells, transcriptional regulation of gene expression is usually achieved by cooperative transcription factors (TFs). Therefore, knowing cooperative TFs is the first step toward uncovering the molecular mechanisms of gene expression regulation. Many algorithms based on different rationales have been proposed to predict cooperative TF pairs in yeast. Although various types of rationales have been used in the existing algorithms, functional coherence is not yet used. This prompts us to develop a new algorithm based on functional coherence and similarity of the target gene sets to identify cooperative TF pairs in yeast. The proposed algorithm predicted 40 cooperative TF pairs. Among them, three (Pdc2-Thi2, Hot1-Msn1 and Leu3-Met28) are novel predictions, which have not been predicted by any existing algorithms. Strikingly, two (Pdc2-Thi2 and Hot1-Msn1) of the three novel predictions have been experimentally validated, demonstrating the power of the proposed algorithm. Moreover, we show that the predictions of the proposed algorithm are more biologically meaningful than the predictions of 17 existing algorithms under four evaluation indices. In summary, our study suggests that new algorithms based on novel rationales are worthy of developing for detecting previously unidentifiable cooperative TF pairs.

Thermodynamic modeling of genome-wide nucleosome depleted regions in yeast

“…These results may indicate that cooperativity is not a general property of yeast TFs, and most NDRs are generated by TFs independently, or even redundantly. This is consistent with previous analysis that only 0.1–0.2% of yeast TF pairs show cooperative binding [ 56 ]. Besides direct binding cooperativity, previous study also proposed a nucleosome-mediated cooperativity [ 57 ].…”

Removing Background Co-occurrences of Transcription Factor Binding Sites Greatly Improves the Prediction of Specific Transcription Factor Cooperations

“…In the last decade, a various number of computational methods for the identification of cooperating TFs has been proposed (Hu et al, 2007 ; Van Loo and Marynen, 2009 ; Girgis and Ovcharenko, 2012 ; Ha et al, 2012 ; Sun et al, 2012 ; Deyneko et al, 2013 ; Nandi et al, 2013 ; Jankowski et al, 2014 ; Navarro et al, 2014 ; Meckbach et al, 2015 ; Wu and Lai, 2016 ; Spadafore et al, 2017 ). Among these methods, predicting the putative TFBSs in the sequences under study and building a meaningful quantification measure of the cooperation between two TFs are two essential steps to make the predictions successful.…”

Patterns of transcription factor binding and epigenome at promoters allow interpretable predictability of multiple functions of non-coding and coding genes

“…For example, for cluster-47, the top predictors are MYC, PARP-1, CTCF, and SMARCA4, which are involved in the cell cycle [28] , [29] , [30] , [31] . There are few indirect studies on the coregulation of functions by the combinatorics of TF and cofactor binding [66] . However our study could be unique due to analysis of common top predictive TFs that show the interdependence between molecular or biological processes and may also explain the perturbation effect on a key regulator that can potentially affect a myriad of functions.…”