Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms

Rutgers University, United States of America
PLoS ONE (Impact Factor: 3.23). 09/2011; 6(9):e24289. DOI: 10.1371/journal.pone.0024289
Source: PubMed


Functional analysis of large sets of genes and proteins is becoming more and more necessary with the increase of experimental biomolecular data at omic-scale. Enrichment analysis is by far the most popular available methodology to derive functional implications of sets of cooperating genes. The problem with these techniques relies in the redundancy of resulting information, that in most cases generate lots of trivial results with high risk to mask the reality of key biological events. We present and describe a computational method, called GeneTerm Linker, that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. The algorithm is tested with a small set of well known interacting proteins from yeast and with a large collection of reference sets from three heterogeneous resources: multiprotein complexes (CORUM), cellular pathways (SGD) and human diseases (OMIM). Statistical Precision, Recall and balanced F-score are calculated showing robust results, even when different levels of random noise are included in the test sets. Although we could not find an equivalent method, we present a comparative analysis with a widely used method that combines enrichment and functional annotation clustering. A web application to use the method here proposed is provided at

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Available from: Alberto Pascual-Montano, Sep 29, 2015
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    • "However, in most cases the results of these analyses are very long lists of biological terms associated to genes that are difficult to digest and interpret. Some tools cluster the FEA results, like DAVID-FAC (Huang et al., 2009) and GeneTerm Linker (Fontanillo et al., 2011), but their output is provided as large tables and there are not many tools to integrate and visualize these results . Here we present Functional Gene Networks (FGNet), an R/ Bioconductor package that uses FEA results to perform networkbased analyses and visualization. "
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    ABSTRACT: Functional Gene Networks (FGNet) is an R/Bioconductor package that generates gene networks derived from the results of functional enrichment analysis (FEA) and annotation clustering. The sets of genes enriched with specific biological terms -obtained from a FEA platform- are transformed into a network by establishing links between genes based on common functional annotations. The network provides a new view of FEA results revealing gene modules with similar functions and genes that are related to multiple functions. In addition to building the functional network, FGNet analyzes the similarity between the groups of genes and provides a distance heatmap and a bipartite network of functionally overlapping genes. The application includes an interface to directly perform FEA queries using different external tools: DAVID, GeneTerm Linker, TopGO or GAGE; and a graphical interface (GUI) to facilitate the use. Availability: FGNet is available in Bioconductor, including a tutorial. URL: CONTACT:; © The Author(s) 2015. Published by Oxford University Press.
    Bioinformatics 05/2015; 31(10):1686-1688. DOI:10.1093/bioinformatics/btu864 · 4.98 Impact Factor
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    • "A recently proposed computational method, called GeneTerm Linker or GTLinker for short (20), summarizes the enrichment results by finding significant and coherent collections of genes and terms. This approach executes several filtering and clustering steps to eliminate redundant and non-informative terms and produces genes and annotations grouped in modules (metagroups). "
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    ABSTRACT: Since its first release in 2007, GeneCodis has become a valuable tool to functionally interpret results from experimental techniques in genomics. This web-based application integrates different sources of information to finding groups of genes with similar biological meaning. This process, known as enrichment analysis, is essential in the interpretation of high-throughput experiments. The frequent feedbacks and the natural evolution of genomics and bioinformatics have allowed the growth of the tool and the development of this third release. In this version, a special effort has been made to remove noisy and redundant output from the enrichment results with the inclusion of a recently reported algorithm that summarizes significantly enriched terms and generates functionally coherent modules of genes and terms. A new comparative analysis has been added to allow the differential analysis of gene sets. To expand the scope of the application, new sources of biological information have been included, such as genetic diseases, drugs-genes interactions and Pubmed information among others. Finally, the graphic section has been renewed with the inclusion of new interactive graphics and filtering options. The application is freely available at
    Nucleic Acids Research 05/2012; 40(Web Server issue):W478-83. DOI:10.1093/nar/gks402 · 9.11 Impact Factor
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