A novel statistical approach for identification of the master regulator transcription factor. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Sikdar, Sinjini; Datta, Susmita
• Is Part Of: BMC bioinformatics. Volume 18:Issue 1(2017)
• Page Start: 1
• Page End: 11
• Publication Date: 2017-12
• Subjects: Master regulator -- Transcription factor -- Differential connectivity -- Regulation -- Concordance; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-017-1499-x
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Transcription factors are known to play key roles in carcinogenesis and therefore, are gaining popularity as potential therapeutic targets in drug development. A 'master regulator' transcription factor often appears to control most of the regulatory activities of the other transcription factors and the associated genes. This 'master regulator' transcription factor is at the top of the hierarchy of the transcriptomic regulation. Therefore, it is important to identify and target the master regulator transcription factor for proper understanding of the associated disease process and identifying the best therapeutic option. Methods We present a novel two-step computational approach for identification of master regulator transcription factor in a genome. At the first step of our method we test whether there exists any master regulator transcription factor in the system. We evaluate the concordance of two ranked lists of transcription factors using a statistical measure. In case the concordance measure is statistically significant, we conclude that there is a master regulator. At the second step, our method identifies the master regulator transcription factor, if there exists one. Results In the simulation scenario, our method performs reasonably well in validating the existence of a master regulator when the number of subjects in each treatment group is reasonably large. In application to two real datasets, our method ensures the existence of master regulators and identifies biologically meaningful master regulators. An R code for implementing our method in a sample test data can be found inhttp://www.somnathdatta.org/software . Conclusion We have developed a screening method of identifying the 'master regulator' transcription factor just using only the gene expression data. Understanding the regulatory structure and finding the master regulator help narrowing the search space for identifying biomarkers for complex diseases such as cancer. In addition to identifying the master regulator our method provides an overview of the regulatory structure of the transcription factors which control the global gene expression profiles and consequently the cell functioning.