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

Statistical Methods in Medical Research
Michelle CareyHulin Wu

Abstract

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.

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Citations

Oct 2, 2020·Database : the Journal of Biological Databases and Curation·Braja Gopal PatraHulin Wu
Nov 29, 2020·Database : the Journal of Biological Databases and Curation·Braja Gopal PatraAshraf Yaseen

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Datasets Mentioned

BETA
GSE52428
GSE19392
GSE37571
GSE47961
GSE30550

Methods Mentioned

BETA
RNA-Seq
transfection
ubiquitination

Software Mentioned

GCRMA
LASSO
MATLAB
Matlab Workspace3
OneDrive
MATLAB SBEToolbox
GEO
RMA
Pipeline4DGEData
CRYAB

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