INsPeCT: INtegrative Platform for Cancer Transcriptomics

Madhamshettiwar, Piyush B., Maetschke, Stefan R., Davis, Melissa J., Reverter, Antonio and Ragan, Mark A. (2014) INsPeCT: INtegrative Platform for Cancer Transcriptomics. Cancer Informatics, 13 59-66. doi:10.4137/CIN.S13630

Author Madhamshettiwar, Piyush B.
Maetschke, Stefan R.
Davis, Melissa J.
Reverter, Antonio
Ragan, Mark A.
Title INsPeCT: INtegrative Platform for Cancer Transcriptomics
Journal name Cancer Informatics   Check publisher's open access policy
ISSN 1176-9351
Publication date 2014-03-12
Year available 2014
Sub-type Article (original research)
DOI 10.4137/CIN.S13630
Open Access Status DOI
Volume 13
Start page 59
End page 66
Total pages 8
Place of publication Auckland, New Zealand
Publisher Libertas Academica
Collection year 2015
Abstract The emergence of transcriptomics, fuelled by high-throughput sequencing technologies, has changed the nature of cancer research and resulted in a massive accumulation of data. Computational analysis, integration, and data visualization are now major bottlenecks in cancer biology and translational research. Although many tools have been brought to bear on these problems, their use remains unnecessarily restricted to computational biologists, as many tools require scripting skills, data infrastructure, and powerful computational facilities. New user-friendly, integrative, and automated analytical approaches are required to make computational methods more generally useful to the research community. Here we present INsPeCT (INtegra-tive Platform for Cancer Transcriptomics), which allows users with basic computer skills to perform comprehensive in-silico analyses of microarray, ChIP-seq, and RNA-seq data. INsPeCT supports the selection of interesting genes for advanced functional analysis. Included in its automated workflows are (i) a novel analytical framework, RMaNI (regulatory module network inference), which supports the inference of cancer subtype-specific transcriptional module networks and the analysis of modules; and (ii) WGCNA (weighted gene co-expression network analysis), which infers modules of highly correlated genes across microarray samples, associated with sample traits, eg survival time. INsPeCT is available free of cost from Bioinformatics Resource Australia-EMBL and can be accessed at
Keyword Cancer
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
Institute for Molecular Bioscience - Publications
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