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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Libertas Academica
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3956744/ https://www.ncbi.nlm.nih.gov/pubmed/24653643 http://dx.doi.org/10.4137/CIN.S13630 |
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author | Madhamshettiwar, Piyush B. Maetschke, Stefan R. Davis, Melissa J. Reverter, Antonio Ragan, Mark A. |
author_facet | Madhamshettiwar, Piyush B. Maetschke, Stefan R. Davis, Melissa J. Reverter, Antonio Ragan, Mark A. |
author_sort | Madhamshettiwar, Piyush B. |
collection | PubMed |
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, 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 http://inspect.braembl.org.au. |
format | Online Article Text |
id | pubmed-3956744 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Libertas Academica |
record_format | MEDLINE/PubMed |
spelling | pubmed-39567442014-03-20 INsPeCT: INtegrative Platform for Cancer Transcriptomics Madhamshettiwar, Piyush B. Maetschke, Stefan R. Davis, Melissa J. Reverter, Antonio Ragan, Mark A. Cancer Inform Original Research 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, 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 http://inspect.braembl.org.au. Libertas Academica 2014-03-12 /pmc/articles/PMC3956744/ /pubmed/24653643 http://dx.doi.org/10.4137/CIN.S13630 Text en © 2014 the author(s), publisher and licensee Libertas Academica Ltd. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License. |
spellingShingle | Original Research Madhamshettiwar, Piyush B. Maetschke, Stefan R. Davis, Melissa J. Reverter, Antonio Ragan, Mark A. INsPeCT: INtegrative Platform for Cancer Transcriptomics |
title | 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_short | INsPeCT: INtegrative Platform for Cancer Transcriptomics |
title_sort | inspect: integrative platform for cancer transcriptomics |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3956744/ https://www.ncbi.nlm.nih.gov/pubmed/24653643 http://dx.doi.org/10.4137/CIN.S13630 |
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