Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

Narang, Vipin and Ramli, Muhamad Azfar and Singhal, Amit and Kumar, Pavanish and de Libero, Gennaro and Poidinger, Michael and Monterola, Christopher. (2015) Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks. PLoS Computational Biology, 11 (9). e1004504.

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Official URL: https://edoc.unibas.ch/61649/

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Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript.
Faculties and Departments:03 Faculty of Medicine > Departement Biomedizin > Department of Biomedicine, University Hospital Basel > Experimental Immunology (De Libero)
UniBasel Contributors:De Libero, Gennaro
Item Type:Article, refereed
Article Subtype:Research Article
ISSN:1553-7358 (Electronic) 1553-734X (Linking)
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:27 Oct 2018 07:48
Deposited On:27 Oct 2018 07:48

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