Reconstructing context-specific gene regulatory network and identifying modules and network rewiring through data integration

Reconstructing context-specific transcriptional regulatory network is crucial for deciphering principles of regulatory mechanisms underlying various conditions. Recently studies that reconstructed transcriptional networks have focused on individual organisms or cell types and relied on data reposito...

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Bibliographic Details
Published in:Methods (San Diego, Calif.) Calif.), 2017-07, Vol.124, p.36-45
Main Authors: Ma, Tianle, Zhang, Aidong
Format: Article
Language:English
Subjects:
Algorithms
Autistic Disorder - genetics
Autistic Disorder - metabolism
Autistic Disorder - physiopathology
Collaborative clustering
Context-specific network
Data integration
Databases, Genetic
Gene Expression Profiling
Gene Expression Regulation
Gene regulatory network
Gene Regulatory Networks
High-Throughput Nucleotide Sequencing
Humans
Multigene Family
Network rewiring
Principal Component Analysis
Regulatory module
Sequence Analysis, RNA
Signal Transduction
Transcription Factors - genetics
Transcription Factors - metabolism
Transcription, Genetic
Transcriptional profiling
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Summary:Reconstructing context-specific transcriptional regulatory network is crucial for deciphering principles of regulatory mechanisms underlying various conditions. Recently studies that reconstructed transcriptional networks have focused on individual organisms or cell types and relied on data repositories of context-free regulatory relationships. Here we present a comprehensive framework to systematically derive putative regulator-target pairs in any given context by integrating context-specific transcriptional profiling and public data repositories of gene regulatory networks. Moreover, our framework can identify core regulatory modules and signature genes underlying global regulatory circuitry, and detect network rewiring and core rewired modules in different contexts by considering gene modules and edge (gene interaction) modules collaboratively. We applied our methods to analyzing Autism RNA-seq experiment data and produced biologically meaningful results. In particular, all 11 hub genes in a predicted rewired autistic regulatory subnetwork have been linked to autism based on literature review. The predicted rewired autistic regulatory network may shed some new insight into disease mechanism.
ISSN:1046-2023
1095-9130
DOI:10.1016/j.ymeth.2017.05.002