Inferring transcription factor collaborations in gene regulatory networks

Abstract

BackgroundLiving cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the expression of target genes is a challenging task, especially when multiple TFs collaboratively participate in the transcriptional regulation.ResultsWe model the underlying regulatory interactions in terms of the directions (activation or repression) and their logical roles (necessary and/or sufficient) with a modified association rule mining approach, called mTRIM. The experiment on Yeast discovered 670 regulatory interactions, in which multiple TFs express their functions on common target genes collaboratively. The evaluation on yeast genetic interactions, TF knockouts and a synthetic dataset shows that our algorithm is significantly better than the existing ones.ConclusionsmTRIM is a novel method to infer TF collaborations in transcriptional regulation networks. mTRIM is available at http://www.msu.edu/~jinchen/mTRIM.

Data integration in the era of omics: current and future challenges

“…Data integration is both a challenge and an opportunity and most certainly an increasing reality in genome research. Scientists have acknowledged that biological systems cannot be understood by the analysis of single-type datasets as the regulation of the system certainly occurs at many levels (see [ 29 , 44 ] and in this supplement [ 45 ]). Therefore projects have appeared aiming to investigate biological systems at several levels and create large heterogeneous data-sets .…”

Scalable Nonparametric Prescreening Method for Searching Higher-Order Genetic Interactions Underlying Quantitative Traits

“…The study by Mak et al (2014) demonstrated that apoptosis repressor with caspase recruitment domain (ARC), encoded by the NOL3 gene [MIM: 605235], is repeatedly overexpressed in diagnosed AML samples, and is involved in the regulation of apoptosis of cardiac cells. However, the precise molecular mechanism regulating this antiapoptotic protein still remains unknown due to the overwhelming number of possible combinations in high-throughput datasets (Awad and Chen 2014). Here, the PH-E dimension reduction method was applied to identify two-and three-way regulatory interactions controlling the variation of ARC/NOL3 (1 of the remaining 16,976 genes).…”

“…This implies that literature for the known three-way interactions is fairly sparse at the present time due to the small number of scalable methods; thus, all findings are likely to be novel (cf. Awad and Chen 2014).…”

Characterization of transcription factor response kinetics in parallel

“…They interact with specific sites on genomic DNA, often recruiting other co-factors to the location, resulting in activation or repression of the corresponding genes. Analysis of cell signaling responses to stimuli is complicated by the fact that TFs generally control several genes, most genes are controlled by multiple TFs, and any given stimulus can result in the activation of multiple TFs [ 10 – 12 ]. Moreover, TF levels must change in response to a stimulus but, in most cases, must then return to baseline levels to avoid long-term perturbation of cellular function [ 13 , 14 ].…”