Dataset Information

REGGAE: a novel approach for the identification of key transcriptional regulators.

ABSTRACT: Motivation:Transcriptional regulators play a major role in most biological processes. Alterations in their activities are associated with a variety of diseases and in particular with tumor development and progression. Hence, it is important to assess the effects of deregulated regulators on pathological processes. Results:Here, we present REGulator-Gene Association Enrichment (REGGAE), a novel method for the identification of key transcriptional regulators that have a significant effect on the expression of a given set of genes, e.g. genes that are differentially expressed between two sample groups. REGGAE uses a Kolmogorov-Smirnov-like test statistic that implicitly combines associations between regulators and their target genes with an enrichment approach to prioritize the influence of transcriptional regulators. We evaluated our method in two different application scenarios, which demonstrate that REGGAE is well suited for uncovering the influence of transcriptional regulators and is a valuable tool for the elucidation of complex regulatory mechanisms. Availability and implementation:REGGAE is freely available at https://regulatortrail.bioinf.uni-sb.de. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Kehl T

PROVIDER: S-EPMC6184769 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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REGGAE: a novel approach for the identification of key transcriptional regulators.

Kehl Tim T Schneider Lara L Kattler Kathrin K Stöckel Daniel D Wegert Jenny J Gerstner Nico N Ludwig Nicole N Distler Ute U Schick Markus M Keller Ulrich U Tenzer Stefan S Gessler Manfred M Walter Jörn J Keller Andreas A Graf Norbert N Meese Eckart E Lenhof Hans-Peter HP

Bioinformatics (Oxford, England) 20181001 20

<h4>Motivation</h4>Transcriptional regulators play a major role in most biological processes. Alterations in their activities are associated with a variety of diseases and in particular with tumor development and progression. Hence, it is important to assess the effects of deregulated regulators on pathological processes.<h4>Results</h4>Here, we present REGulator-Gene Association Enrichment (REGGAE), a novel method for the identification of key transcriptional regulators that have a significant ...[more]

PMID: 29741575

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Project description:BACKGROUND:Identification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance. METHODS:The protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network. RESULTS:The PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest. CONCLUSIONS:The popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.

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Dataset Information

REGGAE: a novel approach for the identification of key transcriptional regulators.

Publications

REGGAE: a novel approach for the identification of key transcriptional regulators.

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