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This article is part of the supplement: Proceedings of the 21st International Conference on Genome Informatics (GIW2010)

Open Access Research

TF-centered downstream gene set enrichment analysis: Inference of causal regulators by integrating TF-DNA interactions and protein post-translational modifications information

Qi Liu12*, Yejun Tan3, Tao Huang2, Guohui Ding2, Zhidong Tu4, Lei Liu2, Yixue Li12, Hongyue Dai4* and Lu Xie2*

Author Affiliations

1 School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China

2 Shanghai Center for Bioinformation Technology, Shanghai, 200235, China

3 Merck Research Laboratory, Rahway, NJ 07065, USA

4 Merck Research Laboratory, Boston, MA 02115, USA

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BMC Bioinformatics 2010, 11(Suppl 11):S5  doi:10.1186/1471-2105-11-S11-S5

Published: 14 December 2010

Abstract

Background

Inference of causal regulators responsible for gene expression changes under different conditions is of great importance but remains rather challenging. To date, most approaches use direct binding targets of transcription factors (TFs) to associate TFs with expression profiles. However, the low overlap between binding targets of a TF and the affected genes of the TF knockout limits the power of those methods.

Results

We developed a TF-centered downstream gene set enrichment analysis approach to identify potential causal regulators responsible for expression changes. We constructed hierarchical and multi-layer regulation models to derive possible downstream gene sets of a TF using not only TF-DNA interactions, but also, for the first time, post-translational modifications (PTM) information. We verified our method in one expression dataset of large-scale TF knockout and another dataset involving both TF knockout and TF overexpression. Compared with the flat model using TF-DNA interactions alone, our method correctly identified five more actual perturbed TFs in large-scale TF knockout data and six more perturbed TFs in overexpression data. Potential regulatory pathways downstream of three perturbed regulators— SNF1, AFT1 and SUT1 —were given to demonstrate the power of multilayer regulation models integrating TF-DNA interactions and PTM information. Additionally, our method successfully identified known important TFs and inferred some novel potential TFs involved in the transition from fermentative to glycerol-based respiratory growth and in the pheromone response. Downstream regulation pathways of SUT1 and AFT1 were also supported by the mRNA and/or phosphorylation changes of their mediating TFs and/or “modulator” proteins.

Conclusions

The results suggest that in addition to direct transcription, indirect transcription and post-translational regulation are also responsible for the effects of TFs perturbation, especially for TFs overexpression. Many TFs inferred by our method are supported by literature. Multiple TF regulation models could lead to new hypotheses for future experiments. Our method provides a valuable framework for analyzing gene expression data to identify causal regulators in the context of TF-DNA interactions and PTM information.