A new framework for identifying combinatorial regulation of transcription factors: a case study of the yeast cell cycle

TL;DR

This paper introduces a novel computational framework that integrates diverse genomic data to identify combinatorial transcription factor regulation, demonstrated through yeast cell cycle analysis, revealing new regulatory mechanisms.

Contribution

The framework combines SVD-based TF activity estimation with graphical models to detect cooperative regulation, offering a new approach for analyzing gene regulation.

Findings

Successfully inferred TF-TF and TF-gene interactions

Identified negative correlations in protein interactions

Revealed low affinity protein-DNA interactions

Abstract

By integrating heterogeneous functional genomic datasets, we have developed a new framework for detecting combinatorial control of gene expression, which includes estimating transcription factor activities using a singular value decomposition method and reducing high-dimensional input gene space by considering genomic properties of gene clusters. The prediction of cooperative gene regulation is accomplished by either Gaussian Graphical Models or Pairwise Mixed Graphical Models. The proposed framework was tested on yeast cell cycle datasets: (1) 54 known yeast cell cycle genes with 9 cell cycle regulators and (2) 676 putative yeast cell cycle genes with 9 cell cycle regulators. The new framework gave promising results on inferring TF-TF and TF-gene interactions. It also revealed several interesting mechanisms such as negatively correlated protein-protein interactions and low affinity protein-DNA interactions that may be important during the yeast cell cycle. The new framework may easily be extended to study other higher eukaryotes.