TIGERi: modeling and visualizing the responses to perturbation of a transcription factor network

BMC Bioinformatics

Research

Namshik Han¹  Harry A. Noyes²  Andy Brass³ 

[1] Gurdon Institute, University of Cambridge, Cambridge, UK;School of Computer Science and School of Health Sciences, University of Manchester, Manchester, UK;School of Biological Sciences, University of Liverpool, Liverpool, UK;School of Computer Science and School of Health Sciences, University of Manchester, Manchester, UK;

关键词: Machine Learning;    Transcriptional regulatory network;    Transcription factor binding site;    Gene expression;   

DOI  :  10.1186/s12859-017-1636-6

来源: Springer

【 摘 要 】

BackgroundTranscription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimental and theoretical methodologies. Less work has focused on using these models to examine how TF networks respond to changes in the cellular environment.MethodsIn this paper, we have developed a simple, pragmatic methodology, TIGERi (Transcription-factor-activity Illustrator for Global Explanation of Regulatory interaction), to model the response of an inferred TF network to changes in cellular environment. The methodology was tested using publicly available data comparing gene expression profiles of a mouse p38α (Mapk14) knock-out line to the original wild-type.ResultsUsing the model, we have examined changes in the TF network resulting from the presence or absence of p38α. A part of this network was confirmed by experimental work in the original paper. Additional relationships were identified by our analysis, for example between p38α and HNF3, and between p38α and SOX9, and these are strongly supported by published evidence. FXR and MYC were also discovered in our analysis as two novel links of p38α. To provide a computational methodology to the biomedical communities that has more user-friendly interface, we also developed a standalone GUI (graphical user interface) software for TIGERi and it is freely available at https://github.com/namshik/tigeri/.ConclusionsWe therefore believe that our computational approach can identify new members of networks and new interactions between members that are supported by published data but have not been integrated into the existing network models. Moreover, ones who want to analyze their own data with TIGERi could use the software without any command line experience. This work could therefore accelerate researches in transcriptional gene regulation in higher eukaryotes.

【 授权许可】

CC BY   
© The Author(s). 2017

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