INsPeCT: INtegrative Platform for Cancer Transcriptomics
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 re...
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doaj-0d23e13cda0c43dda666e1d7a2f3341c2020-11-25T03:06:42ZengSAGE PublishingCancer Informatics1176-93512014-01-011310.4137/CIN.S13630INsPeCT: INtegrative Platform for Cancer TranscriptomicsPiyush B. Madhamshettiwar0Stefan R. Maetschke1Melissa J. Davis2Antonio Reverter3Mark A. Ragan4Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia.Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia.Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia.CSIRO Animal, Food and Health Sciences, St. Lucia, Brisbane, Queensland, Australia.Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia.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 (INtegrative Platform for Cancer Transcriptomics), which allows users with basic computer skills to perform comprehensive in-silico analyses of microarray, ChlPseq, 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 http://inspect.braembl.org.au .https://doi.org/10.4137/CIN.S13630 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Piyush B. Madhamshettiwar Stefan R. Maetschke Melissa J. Davis Antonio Reverter Mark A. Ragan |
spellingShingle |
Piyush B. Madhamshettiwar Stefan R. Maetschke Melissa J. Davis Antonio Reverter Mark A. Ragan INsPeCT: INtegrative Platform for Cancer Transcriptomics Cancer Informatics |
author_facet |
Piyush B. Madhamshettiwar Stefan R. Maetschke Melissa J. Davis Antonio Reverter Mark A. Ragan |
author_sort |
Piyush B. Madhamshettiwar |
title |
INsPeCT: INtegrative Platform for Cancer Transcriptomics |
title_short |
INsPeCT: INtegrative Platform for Cancer Transcriptomics |
title_full |
INsPeCT: INtegrative Platform for Cancer Transcriptomics |
title_fullStr |
INsPeCT: INtegrative Platform for Cancer Transcriptomics |
title_full_unstemmed |
INsPeCT: INtegrative Platform for Cancer Transcriptomics |
title_sort |
inspect: integrative platform for cancer transcriptomics |
publisher |
SAGE Publishing |
series |
Cancer Informatics |
issn |
1176-9351 |
publishDate |
2014-01-01 |
description |
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 (INtegrative Platform for Cancer Transcriptomics), which allows users with basic computer skills to perform comprehensive in-silico analyses of microarray, ChlPseq, 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 http://inspect.braembl.org.au . |
url |
https://doi.org/10.4137/CIN.S13630 |
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