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      Gitools: Analysis and Visualisation of Genomic Data Using Interactive Heat-Maps

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      Journal: PLoS ONE
      Publisher: Public Library of Science

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          Abstract

          Intuitive visualization of data and results is very important in genomics, especially when many conditions are to be analyzed and compared. Heat-maps have proven very useful for the representation of biological data. Here we present Gitools ( http://www.gitools.org), an open-source tool to perform analyses and visualize data and results as interactive heat-maps. Gitools contains data import systems from several sources (i.e. IntOGen, Biomart, KEGG, Gene Ontology), which facilitate the integration of novel data with previous knowledge.

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

          Yoav Benjamini, Yosef Hochberg (1995)
          Article 0 comments Cited 23689 times – based on 0 reviews     Review now
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            Gene Ontology: tool for the unification of biology

            Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
            Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
            Article 0 comments Cited 15236 times – based on 0 reviews     Review now
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              Genesis: cluster analysis of microarray data.

              John Quackenbush, Alexander Sturn, Zlatko Trajanoski (2001)
              A versatile, platform independent and easy to use Java suite for large-scale gene expression analysis was developed. Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines. The results of the clustering are transparent across all implemented methods and enable the analysis of the outcome of different algorithms and parameters. Additionally, mapping of gene expression data onto chromosomal sequences was implemented to enhance promoter analysis and investigation of transcriptional control mechanisms.
              Article 0 comments Cited 376 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                : Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2011
                Publication date (Electronic): 13 May 2011
                Volume: 6
                Issue: 5
                Electronic Location Identifier: e19541
                Affiliations
                [1]Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, University Pompeu Fabra, Barcelona, Spain
                University of Leuven, Belgium
                Author notes

                Wrote the paper: CPL NLB. Conceived the tool: NLB. Designed and developed the software: CPL .

                Article
                Publisher ID: PONE-D-11-00491
                DOI: 10.1371/journal.pone.0019541
                PMC ID: 3094337
                PubMed ID: 21602921
                SO-VID: b06552cb-e148-485e-9dac-cf7a7d4ca42f
                Copyright © Perez-Llamas, Lopez-Bigas. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                Date received : 23 December 2010
                Date accepted : 31 March 2011
                Page count
                Pages: 6
                Categories
                Subject: Research Article
                Subject: Biology
                Subject: Computational Biology
                Subject: Genomics
                Subject: Microarrays
                Subject: Genomics
                Subject: Genome Analysis Tools
                Subject: Gene Ontologies
                Subject: Genome Expression Analysis

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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