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      funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data

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          Abstract

          Background

          A widely applied approach to extract knowledge from high-throughput genomic data is clustering of gene expression profiles followed by functional enrichment analysis. This type of analysis, when done manually, is highly subjective and has limited reproducibility. Moreover, this pipeline can be very time-consuming and resource-demanding as enrichment analysis is done for tens to hundreds of clusters at a time. Thus, the task often needs programming skills to form a pipeline of different software tools or R packages to enable an automated approach. Furthermore, visualising the results can be challenging.

          Results

          We developed a web tool, funcExplorer, which automatically combines hierarchical clustering and enrichment analysis to detect functionally related gene clusters. The functional characterisation is achieved using structured knowledge from data sources such as Gene Ontology, KEGG and Reactome pathways, Human Protein Atlas, and Human Phenotype Ontology. funcExplorer includes various measures for finding biologically meaningful clusters, provides a modern graphical user interface, and has wide-ranging data export and sharing options as well as software transparency by open-source code. The results are presented in a visually compact and interactive format, enabling users to explore the biological essence of the data. We compared our results with previously published gene clusters to demonstrate that funcExplorer can perform the data characterisation equally well, but without requiring labour-intensive manual interference.

          Conclusions

          The open-source web tool funcExplorer enables scientists with high-throughput genomic data to obtain a preliminary interactive overview of the expression patterns, gene names, and shared functionalities in their dataset in a visually pleasing format. funcExplorer is publicly available at https://biit.cs.ut.ee/funcexplorer

          Electronic supplementary material

          The online version of this article (10.1186/s12864-018-5176-x) contains supplementary material, which is available to authorized users.

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          Most cited references 23

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          D³: Data-Driven Documents.

          Data-Driven Documents (D3) is a novel representation-transparent approach to visualization for the web. Rather than hide the underlying scenegraph within a toolkit-specific abstraction, D3 enables direct inspection and manipulation of a native representation: the standard document object model (DOM). With D3, designers selectively bind input data to arbitrary document elements, applying dynamic transforms to both generate and modify content. We show how representational transparency improves expressiveness and better integrates with developer tools than prior approaches, while offering comparable notational efficiency and retaining powerful declarative components. Immediate evaluation of operators further simplifies debugging and allows iterative development. Additionally, we demonstrate how D3 transforms naturally enable animation and interaction with dramatic performance improvements over intermediate representations. © 2010 IEEE
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            Is Open Access

            CORUM: the comprehensive resource of mammalian protein complexes—2009

            CORUM is a database that provides a manually curated repository of experimentally characterized protein complexes from mammalian organisms, mainly human (64%), mouse (16%) and rat (12%). Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. The new CORUM 2.0 release encompasses 2837 protein complexes offering the largest and most comprehensive publicly available dataset of mammalian protein complexes. The CORUM dataset is built from 3198 different genes, representing ∼16% of the protein coding genes in humans. Each protein complex is described by a protein complex name, subunit composition, function as well as the literature reference that characterizes the respective protein complex. Recent developments include mapping of functional annotation to Gene Ontology terms as well as cross-references to Entrez Gene identifiers. In addition, a ‘Phylogenetic Conservation’ analysis tool was implemented that analyses the potential occurrence of orthologous protein complex subunits in mammals and other selected groups of organisms. This allows one to predict the occurrence of protein complexes in different phylogenetic groups. CORUM is freely accessible at (http://mips.helmholtz-muenchen.de/genre/proj/corum/index.html).
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              Computational analysis of microarray data.

              Microarray experiments are providing unprecedented quantities of genome-wide data on gene-expression patterns. Although this technique has been enthusiastically developed and applied in many biological contexts, the management and analysis of the millions of data points that result from these experiments has received less attention. Sophisticated computational tools are available, but the methods that are used to analyse the data can have a profound influence on the interpretation of the results. A basic understanding of these computational tools is therefore required for optimal experimental design and meaningful data analysis.
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                Author and article information

                Contributors
                liis.kolberg@ut.ee
                ivan.kuzmin@ut.ee
                priit.adler@ut.ee
                jaak.vilo@ut.ee
                hedi.peterson@ut.ee
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                14 November 2018
                14 November 2018
                2018
                : 19
                Affiliations
                [1 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, Institute of Computer Science, University of Tartu, ; Juhan Liivi 2, Tartu, Estonia
                [2 ]GRID grid.436973.c, Quretec Ltd, ; Ülikooli 6a, Tartu, Estonia
                Article
                5176
                10.1186/s12864-018-5176-x
                6236982
                30428831
                © The Author(s) 2018

                Open Access This 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.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002301, Eesti Teadusagentuur;
                Award ID: PSG59
                Funded by: Eesti Teadusagentuur (EE)
                Award ID: IUT34-4
                Funded by: FundRef http://dx.doi.org/10.13039/501100008530, European Regional Development Fund;
                Award ID: EXCITE
                Funded by: European Union through the Structural Fund
                Award ID: No 2014-2020.4.01.16-0271, ELIXIR)
                Categories
                Software
                Custom metadata
                © The Author(s) 2018

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