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      Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles

      research-article
      1 , 1 , 2 , , 1 , 1 , 1 , 2
      BMC Bioinformatics
      BioMed Central
      The International Conference on Bioinformatics of Genome Regulation and Structure\Systems Biology (BGRS\SB-2016)
      29 August-2 September 2016
      Cytoscape plugin, Ortholog, Paralog, Metabolic pathway, Gene regulatory network, Evolution, Phylostratigraphy, Evolution

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          Abstract

          Background

          There are many available software tools for visualization and analysis of biological networks. Among them, Cytoscape ( http://cytoscape.org/) is one of the most comprehensive packages, with many plugins and applications which extends its functionality by providing analysis of protein-protein interaction, gene regulatory and gene co-expression networks, metabolic, signaling, neural as well as ecological-type networks including food webs, communities networks etc. Nevertheless, only three plugins tagged ‘network evolution’ found in Cytoscape official app store and in literature. We have developed a new Cytoscape 3.0 application Orthoscape aimed to facilitate evolutionary analysis of gene networks and visualize the results.

          Results

          Orthoscape aids in analysis of evolutionary information available for gene sets and networks by highlighting: (1) the orthology relationships between genes; (2) the evolutionary origin of gene network components; (3) the evolutionary pressure mode (diversifying or stabilizing, negative or positive selection) of orthologous groups in general and/or branch-oriented mode. The distinctive feature of Orthoscape is the ability to control all data analysis steps via user-friendly interface.

          Conclusion

          Orthoscape allows its users to analyze gene networks or separated gene sets in the context of evolution. At each step of data analysis, Orthoscape also provides for convenient visualization and data manipulation.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12859-016-1427-5) contains supplementary material, which is available to authorized users.

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          Most cited references30

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          Identification of common molecular subsequences.

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            The Ka/Ks ratio: diagnosing the form of sequence evolution

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              Statistical methods for detecting molecular adaptation.

              The past few years have seen the development of powerful statistical methods for detecting adaptive molecular evolution. These methods compare synonymous and nonsynonymous substitution rates in protein-coding genes, and regard a nonsynonymous rate elevated above the synonymous rate as evidence for darwinian selection. Numerous cases of molecular adaptation are being identified in various systems from viruses to humans. Although previous analyses averaging rates over sites and time have little power, recent methods designed to detect positive selection at individual sites and lineages have been successful. Here, we summarize recent statistical methods for detecting molecular adaptation, and discuss their limitations and possible improvements.
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                Author and article information

                Contributors
                mustafinzs@bionet.nsc.ru
                lashin@bionet.nsc.ru
                mat@bionet.nsc.ru
                genkvg@bionet.nsc.ru
                ada@bionet.nsc.ru
                Conference
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                27 January 2017
                27 January 2017
                2017
                : 18
                Issue : Suppl 1 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
                : 1-9
                Affiliations
                [1 ]GRID grid.418953.2, , Institute of Cytology and Genetics SB RAS, ; Lavrentiev Avenue 10, Novosibirsk, 630090 Russia
                [2 ]ISNI 0000000121896553, GRID grid.4605.7, , Novosibirsk State University, ; Pirogova st. 2, Novosibirsk, 630090 Russia
                Article
                1427
                10.1186/s12859-016-1427-5
                5333177
                0a6d2bae-7809-41b0-ad4c-1ad1749a5f5d
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                The International Conference on Bioinformatics of Genome Regulation and Structure\Systems Biology (BGRS\SB-2016)
                Novosibirsk, Russia
                29 August-2 September 2016
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                Research
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                © The Author(s) 2017

                Bioinformatics & Computational biology
                cytoscape plugin,ortholog,paralog,metabolic pathway,gene regulatory network,evolution,phylostratigraphy

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