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      A survey of visualization tools for biological network analysis

      review-article
      1 , , 1 , 1
      BioData Mining
      BioMed Central

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          Abstract

          The analysis and interpretation of relationships between biological molecules, networks and concepts is becoming a major bottleneck in systems biology. Very often the pure amount of data and their heterogeneity provides a challenge for the visualization of the data. There are a wide variety of graph representations available, which most often map the data on 2D graphs to visualize biological interactions. These methods are applicable to a wide range of problems, nevertheless many of them reach a limit in terms of user friendliness when thousands of nodes and connections have to be analyzed and visualized. In this study we are reviewing visualization tools that are currently available for visualization of biological networks mainly invented in the latest past years. We comment on the functionality, the limitations and the specific strengths of these tools, and how these tools could be further developed in the direction of data integration and information sharing.

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          Most cited references61

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          Gene Ontology: tool for the unification of biology

          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.
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            TRANSFAC: transcriptional regulation, from patterns to profiles.

            The TRANSFAC database on eukaryotic transcriptional regulation, comprising data on transcription factors, their target genes and regulatory binding sites, has been extended and further developed, both in number of entries and in the scope and structure of the collected data. Structured fields for expression patterns have been introduced for transcription factors from human and mouse, using the CYTOMER database on anatomical structures and developmental stages. The functionality of Match, a tool for matrix-based search of transcription factor binding sites, has been enhanced. For instance, the program now comes along with a number of tissue-(or state-)specific profiles and new profiles can be created and modified with Match Profiler. The GENE table was extended and gained in importance, containing amongst others links to LocusLink, RefSeq and OMIM now. Further, (direct) links between factor and target gene on one hand and between gene and encoded factor on the other hand were introduced. The TRANSFAC public release is available at http://www.gene-regulation.com. For yeast an additional release including the latest data was made available separately as TRANSFAC Saccharomyces Module (TSM) at http://transfac.gbf.de. For CYTOMER free download versions are available at http://www.biobase.de:8080/index.html.
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              The ENZYME database in 2000.

              A Bairoch (2000)
              The ENZYME database is a repository of information related to the nomenclature of enzymes. In recent years it has became an indispensable resource for the development of metabolic databases. The current version contains information on 3705 enzymes. It is available through the ExPASy WWW server (http://www.expasy.ch/enzyme/ ).
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                Author and article information

                Journal
                BioData Min
                BioData Mining
                BioMed Central
                1756-0381
                2008
                28 November 2008
                : 1
                : 12
                Affiliations
                [1 ]Structural and Computational Biology Unit, EMBL, Meyerhofstrasse 1, Heidelberg, Germany
                Article
                1756-0381-1-12
                10.1186/1756-0381-1-12
                2636684
                19040716
                2abd2c0d-c6ba-4f90-8bf7-aa39862f9e18
                Copyright © 2008 Pavlopoulos et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 June 2008
                : 28 November 2008
                Categories
                Review

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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