Inferring gene regulatory networks from multiple time course gene expression datasets

2011 ◽  
Author(s):  
Bo-Lin Chen ◽  
Li-Zhi Liu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Course ◽  
Multiple Time ◽  
Gene Regulatory

scholarly journals A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection

2018 ◽  
Vol 27 (7) ◽  
pp. 1930-1955 ◽  
Author(s):  
Michelle Carey ◽  
Juan Camilo Ramírez ◽  
Shuang Wu ◽  
Hulin Wu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Host Response ◽  
Regulatory Networks ◽  
Time Course ◽  
Influenza Infection ◽  
Gene Expression Omnibus ◽  
Expression Data ◽  
Gene Regulatory

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

scholarly journals Detecting shifts in gene regulatory networks during time-course experiments at single-time-point temporal resolution

2015 ◽  
Vol 13 (05) ◽  
pp. 1543002 ◽  
Author(s):  
Yoichi Takenaka ◽  
Shigeto Seno ◽  
Hideo Matsuda
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Course ◽  
Diauxic Shift ◽  
Gene Regulatory ◽  
Gene Regulations ◽  
Single Time Point ◽  
Time Point

Comprehensively understanding the dynamics of biological systems is one of the greatest challenges in biology. Vastly improved biological technologies have provided vast amounts of information that must be understood by bioinformatics and systems biology researchers. Gene regulations have been frequently modeled by ordinary differential equations or graphical models based on time-course gene expression profiles. The state-of-the-art computational approaches for analyzing gene regulations assume that their models are same throughout time-course experiments. However, these approaches cannot easily analyze transient changes at a time point, such as diauxic shift. We propose a score that analyzes the gene regulations at each time point. The score is based on the information gains of information criterion values. The method detects the shifts in gene regulatory networks (GRNs) during time-course experiments with single-time-point resolution. The effectiveness of the method is evaluated on the diauxic shift from glucose to lactose in Escherichia coli. Gene regulation shifts were detected at two time points: the first corresponding to the time at which the growth of E. coli ceased and the second corresponding to the end of the experiment, when the nutrient sources (glucose and lactose) had become exhausted. According to these results, the proposed score and method can appropriately detect the time of gene regulation shifts. The method based on the proposed score provides a new tool for analyzing dynamic biological systems. Because the score value indicates the strength of gene regulation at each time point in a gene expression profile, it can potentially infer hidden GRNs from time-course experiments.

scholarly journals TuxNet: A simple interface to process RNA sequencing data and infer gene regulatory networks

2018 ◽  
Author(s):  
Maria Angels de Luis Balaguer ◽  
Ryan J. Spurney ◽  
Natalie M. Clark ◽  
Adam P. Fisher ◽  
Rosangela Sozzani
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Course ◽  
Expression Profiles ◽  
Expression Data ◽  
Sequencing Data ◽  
Simple Interface ◽  
Gene Regulatory

ABSTRACTPredicting gene regulatory networks (GRNs) from gene expression profiles has become a common approach for identifying important biological regulators. Despite the increase in the use of inference methods, existing computational approaches do not integrate RNA-sequencing data analysis, are often not automated, and are restricted to users with bioinformatics and programming backgrounds. To address these limitations, we have developed TuxNet, an integrated user-friendly platform, which, with just a few selections, allows to process raw RNA-sequencing data (using the Tuxedo pipeline) and infer GRNs from these processed data. TuxNet is implemented as a graphical user interface and, using expression data from any organism with an existing reference genome, can mine the regulations among genes either by applying a dynamic Bayesian network inference algorithm, GENIST, or a regression tree-based pipeline that uses spatiotemporal data, RTP-STAR. To illustrate the use of TuxNet while getting insight into the regulatory cascade downstream of the Arabidopsis root stem cell regulator PERIANTHIA (PAN), we obtained time course gene expression data of a PAN inducible line and inferred a GRN using GENIST. Using RTP-STAR, we then inferred the network of a PAN secondary downstream gene, ATHB13, for which we obtained wildtype and mutant expression profiles. Our case studies feature the versatility of TuxNet to infer networks using different types of gene expression data (i.e time course and steady-state data) as well as how inference networks are used to identify important regulators.SUMMARYTuxNet offers a simple interface for non-computational biologists to infer GRNs from raw RNA-seq data.

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