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      CancerMA: a web-based tool for automatic meta-analysis of public cancer microarray data

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          Abstract

          The identification of novel candidate markers is a key challenge in the development of cancer therapies. This can be facilitated by putting accessible and automated approaches analysing the current wealth of ‘omic’-scale data in the hands of researchers who are directly addressing biological questions. Data integration techniques and standardized, automated, high-throughput analyses are needed to manage the data available as well as to help narrow down the excessive number of target gene possibilities presented by modern databases and system-level resources. Here we present CancerMA, an online, integrated bioinformatic pipeline for automated identification of novel candidate cancer markers/targets; it operates by means of meta-analysing expression profiles of user-defined sets of biologically significant and related genes across a manually curated database of 80 publicly available cancer microarray datasets covering 13 cancer types. A simple-to-use web interface allows bioinformaticians and non-bioinformaticians alike to initiate new analyses as well as to view and retrieve the meta-analysis results. The functionality of CancerMA is shown by means of two validation datasets.

          Database URL: http://www.cancerma.org.uk

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          Most cited references52

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Cancer genes and the pathways they control.

            The revolution in cancer research can be summed up in a single sentence: cancer is, in essence, a genetic disease. In the last decade, many important genes responsible for the genesis of various cancers have been discovered, their mutations precisely identified, and the pathways through which they act characterized. The purposes of this review are to highlight examples of progress in these areas, indicate where knowledge is scarce and point out fertile grounds for future investigation.
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              Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles.

              DNA microarrays have been widely applied to cancer transcriptome analysis; however, the majority of such data are not easily accessible or comparable. Furthermore, several important analytic approaches have been applied to microarray analysis; however, their application is often limited. To overcome these limitations, we have developed Oncomine, a bioinformatics initiative aimed at collecting, standardizing, analyzing, and delivering cancer transcriptome data to the biomedical research community. Our analysis has identified the genes, pathways, and networks deregulated across 18,000 cancer gene expression microarrays, spanning the majority of cancer types and subtypes. Here, we provide an update on the initiative, describe the database and analysis modules, and highlight several notable observations. Results from this comprehensive analysis are available at http://www.oncomine.org.
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                Author and article information

                Journal
                Database (Oxford)
                Database (Oxford)
                databa
                databa
                Database: The Journal of Biological Databases and Curation
                Oxford University Press
                1758-0463
                2012
                15 December 2012
                15 December 2012
                : 2012
                : bas055
                Affiliations
                1North West Cancer Research Fund Institute, Bangor University, Bangor, Gwynedd LL57 2UW, UK, 2Institute for Genomics and Bioinformatics, Graz University of Technology, Graz, Petersgasse 14, 8010, Austria, 3NISCHR Cancer Genetics Biomedical Research Unit, Bangor University, Bangor, Gwynedd LL57 2UW, UK and 4Cranfield Health, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK
                Author notes
                *Corresponding author: Tel: +44 1248 382360; Fax: +44 1248 370731; Email: r.macfarlane@ 123456bangor.ac.uk
                Correspondence may also be addressed to Julia Feichtinger. Email: julia.feichtinger@ 123456gmail.com

                Citation details: Julia Feichtinger, Ramsay J. McFarlane and Lee D. Larcombe. CancerMA: a web-based tool for automatic meta-analysis of public cancer microarray data. Database (2012) Vol. 2012: article ID bas055; doi:10.1093/database/bas055.

                Article
                bas055
                10.1093/database/bas055
                3522872
                23241162
                ed20faa0-17d1-4784-b575-cb310636c13d
                © The Author(s) 2012. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 8 September 2012
                : 22 November 2012
                : 25 November 2012
                Page count
                Pages: 8
                Categories
                Database Tool

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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