RMaNI: Regulatory module network inference framework

Madhamshettiwar, Piyush B., Maetschke, Stefan R., Davis, Melissa J. and Ragan, Mark A. (2013) RMaNI: Regulatory module network inference framework. BMC Bioinformatics, 14 Suppl. 16: S14.1-S14.10. doi:10.1186/1471-2105-14-S16-S14

Author Madhamshettiwar, Piyush B.
Maetschke, Stefan R.
Davis, Melissa J.
Ragan, Mark A.
Title RMaNI: Regulatory module network inference framework
Journal name BMC Bioinformatics   Check publisher's open access policy
ISSN 1471-2105
Publication date 2013-10-22
Year available 2013
Sub-type Article (original research)
DOI 10.1186/1471-2105-14-S16-S14
Open Access Status DOI
Volume 14
Issue Suppl. 16
Start page S14.1
End page S14.10
Total pages 11
Place of publication London, United Kingdom
Publisher BioMed Central Ltd
Language eng
Subject 1303 Specialist Studies in Education
1312 Molecular Biology
1706 Computer Science Applications
2604 Applied Mathematics
1315 Structural Biology
Abstract Background: Cell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology. In recent years, module-based approaches for GRN inference have been proposed to address this challenge. Despite the demonstrated success of module-based approaches in uncovering biologically meaningful regulatory interactions, their application remains limited a single condition, without supporting the comparison of multiple disease subtypes/conditions. Also, their use remains unnecessarily restricted to computational biologists, as accurate inference of modules and their regulators requires integration of diverse tools and heterogeneous data sources, which in turn requires scripting skills, data infrastructure and powerful computational facilities. New analytical frameworks are required to make module-based GRN inference approach more generally useful to the research community.Results: We present the RMaNI (Regulatory Module Network Inference) framework, which supports cancer subtype-specific or condition specific GRN inference and differential network analysis. It combines both transcriptomic as well as genomic data sources, and integrates heterogeneous knowledge resources and a set of complementary bioinformatic methods for automated inference of modules, their condition specific regulators and facilitates downstream network analyses and data visualization. To demonstrate its utility, we applied RMaNI to a hepatocellular microarray data containing normal and three disease conditions. We demonstrate that how RMaNI can be employed to understand the genetic architecture underlying three disease conditions. RMaNI is freely available at http://inspect.braembl.org.au/bi/inspect/rmani. Conclusion: RMaNI makes available a workflow with comprehensive set of tools that would otherwise be challenging for non-expert users to install and apply. The framework presented in this paper is flexible and can be easily extended to analyse any dataset with multiple disease conditions.
Keyword Cancer
Gene Regulatory Network
Systems biology
Transcriptional Module Networks
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID LE098933
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Chemistry and Molecular Biosciences
Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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Created: Fri, 29 Nov 2013, 07:14:00 EST by System User on behalf of Institute for Molecular Bioscience