Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data

Bioinformatics
Huai Li, Ming Zhan

Abstract

Unraveling the transcriptional regulatory program mediated by transcription factors (TFs) is a fundamental objective of computational biology, yet still remains a challenge. Here, we present a new methodology that integrates microarray and TF binding data for unraveling transcriptional regulatory networks. The algorithm is based on a two-stage constrained matrix decomposition model. The model takes into account the non-linear structure in gene expression data, particularly in the TF-target gene interactions and the combinatorial nature of gene regulation by TFs. The gene expression profile is modeled as a linear weighted combination of the activity profiles of a set of TFs. The TF activity profiles are deduced from the expression levels of TF target genes, instead directly from TFs themselves. The TF-target gene relationships are derived from ChIP-chip and other TF binding data. The proposed algorithm can not only identify transcriptional modules, but also reveal regulatory programs of which TFs control which target genes in which specific ways (either activating or inhibiting). In comparison with other methods, our algorithm identifies biologically more meaningful transcriptional modules relating to specific TFs. We applied th...Continue Reading

Associated Proceedings Papers

Aug 24, 2018·Teresa W-M. FanYi Zhang

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Related Concepts

In Silico
Microarray Analysis
Biological Adaptation to Stress
Transcription, Genetic
Candidate Disease Gene
Gene Expression
Protein Interaction Mapping
Systems Integration
Transcription Factor Binding
Cell Cycle

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