A scalable algorithm for structure identification of complex gene regulatory network from temporal expression data

BMC Bioinformatics

Methodology Article

Hongyu Miao¹  Liang Wu¹  Shupeng Gui²  Ji Liu³  Andrew P. Rice⁴  Rui Chen⁵ 

[1] Department of Biostatistics, University of Texas Health Science Center, 77030, Houston, TX, USA;Department of Computer Science, University of Rochester, 14620, Rochester, NY, USA;Department of Computer Science, University of Rochester, 14620, Rochester, NY, USA;Goergen Institute for Data Science, University of Rochester, 14620, Rochester, NY, USA;Department of Molecular Virology and Microbiology, Baylor College of Medicine, 77030, Houston, TX, USA;Department of Molecular and Human Genetics, Baylor College of Medicine, 77030, Houston, TX, USA;

关键词: Gene regulatory network;    Hub gene structure;    Ultra-high dimensional problem;    Decomposable multi-structure identification;    Influenza infection;   

DOI  :  10.1186/s12859-017-1489-z

 received in 2016-10-22, accepted in 2017-01-20,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundGene regulatory interactions are of fundamental importance to various biological functions and processes. However, only a few previous computational studies have claimed success in revealing genome-wide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover, recent work suggests that these methods still suffer from the curse of dimensionality if a network size increases to 100 or higher.ResultsHere we present a novel scalable algorithm for identifying genome-wide gene regulatory network (GRN) structures, and we have verified the algorithm performances by extensive simulation studies based on the DREAM challenge benchmark data. The highlight of our method is that its superior performance does not degenerate even for a network size on the order of 104, and is thus readily applicable to large-scale complex networks. Such a breakthrough is achieved by considering both prior biological knowledge and multiple topological properties (i.e., sparsity and hub gene structure) of complex networks in the regularized formulation. We also validate and illustrate the application of our algorithm in practice using the time-course gene expression data from a study on human respiratory epithelial cells in response to influenza A virus (IAV) infection, as well as the CHIP-seq data from ENCODE on transcription factor (TF) and target gene interactions. An interesting finding, owing to the proposed algorithm, is that the biggest hub structures (e.g., top ten) in the GRN all center at some transcription factors in the context of epithelial cell infection by IAV.ConclusionsThe proposed algorithm is the first scalable method for large complex network structure identification. The GRN structure identified by our algorithm could reveal possible biological links and help researchers to choose which gene functions to investigate in a biological event. The algorithm described in this article is implemented in MATLAB Ⓡ, and the source code is freely available from https://github.com/Hongyu-Miao/DMI.git.

【 授权许可】

CC BY   
© The Author(s) 2017

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