Bioinformatics Advance Access originally published online on June 21, 2005
Bioinformatics 2005 21(16):3448-3449; doi:10.1093/bioinformatics/bti551
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BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks
Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Ghent University Technologiepark 927, B-9052, Ghent, Belgium
*To whom correspondence should be addressed.
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Abstract |
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 TOP Abstract 1 INTRODUCTION2 METHODS AND IMPLEMENTATIONREFERENCES |
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Summary: The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape's versatile visualization environment to produce an intuitive and customizable visual representation of the results.
Availability: http://www.psb.ugent.be/cbd/papers/BiNGO/
Contact: martin.kuiper{at}psb.ugent.be
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1 INTRODUCTION |
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TOPAbstract1 INTRODUCTION2 METHODS AND IMPLEMENTATIONREFERENCES |
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Over the last decade, the development of high-throughput technologies, such as microarray-based transcript profiling, has led to an exponential increase in the volume of functional genomics data. Interpretation of these data is greatly facilitated by a structured description of known biological information at different levels of granularity. The Gene Ontology (GO) project (Ashburner et al., 2000), initiated in the late 1990's, aims at capturing the increasing knowledge on gene function in a controlled vocabulary applicable to all organisms. GO consists of three hierarchically structured vocabularies that describe gene products in terms of their associated biological processes, molecular functions and cellular components. Gene products may be annotated to one or several nodes in each hierarchy.
The increasing complexity of functional genomics data also drives the development of methods and tools for data integration and visualization. Cytoscape (Shannon et al., 2003) is an open-source software platform for visualizing molecular interaction networks and integrating these interactions with gene expression profiles and other functional genomics data. The Cytoscape platform actively supports the development of plugin tools that extend the core functionality. We developed the Biological Networks Gene Ontology tool (BiNGO) as a plugin for Cytoscape. BiNGO assesses the overrepresentation of GO categories in a subgraph of a biological network, or any other set of genes. Several tools exist that analyze GO term enrichment in a given gene set (Berriz et al., 2003; Hosack et al., 2003; Zeeberg et al., 2003; Al-Shahrour et al., 2004; Beißbarth and Speed, 2004; Boyle et al., 2004; Masseroli et al., 2004; Zhang et al., 2004). A comprehensive list can be found at http://www.geneontology.org/GO.tools.shtml. The main advantage of BiNGO over these tools is its interactive use on molecular interaction networks, e.g. protein interaction networks or transcriptional coregulation networks, visualized in Cytoscape. Furthermore, BiNGO offers great flexibility in the use of ontologies and annotations. Besides the traditional GO ontologies, BiNGO also supports the use of GOSlim ontologies, as well as custom ontologies and annotations. Finally, the Cytoscape graphs produced by BiNGO can be viewed, laid out, modified and saved in various manners.
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2 METHODS AND IMPLEMENTATION |
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TOPAbstract1 INTRODUCTION2 METHODS AND IMPLEMENTATIONREFERENCES |
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There are two modes for selecting the set of genes to be functionally profiled. In the default mode, a set of nodes can be selected from a Cytoscape network, either manually or using other plugins such as MCODE (Bader and Hogue, 2003). Alternatively, a test set can be compiled from other sources, for instance a set of genes that are up-regulated in a microarray experiment, and pasted in a text input box. BiNGO retrieves the relevant GO annotations and propagates them upwards through the GO hierarchy, i.e. any gene annotated to a certain GO category is also explicitly included in all parental categories. BiNGO currently provides two statistical tests for assessing the enrichment of a GO term in the test set. The basic question answered by these tests is as follows: when sampling X genes (test set) out of N genes (reference set, either a graph or an annotation), what is the probability that x or more of these genes belong to a functional category C shared by n of the N genes in the reference set? The hypergeometric test, in which sampling occurs without replacement, answers this question in the form of a P-value. Its counterpart with replacement, the binomial test, provides only an approximate P-value, but requires less calculation time.
Because BiNGO tests the significance of all GO labels present in the test set, the number of statistical tests performed in a single analysis may amount to several hundreds. When testing multiple hypotheses, the obtained P-values have to be corrected in order to control the type I error (false positive) rate (Ge et al., 2003). One of the most basic multiple testing corrections is the Bonferroni correction, which strongly controls the family-wise error rate (FWER), i.e. the probability of making at least one type I error. The Bonferroni correction is generally assumed to be conservative, although it might actually be rather liberal (Boyle et al., 2004), at least for FWER control, when used for correcting tests that are not mutually independent, as is the case when testing GO categories (see below). An alternative to FWER corrections is to control the false discovery rate (FDR), i.e. the expected proportion of false positives among the positively identified tests. Generally, this type of correction is more appropriate for our purposes, because we would typically prefer to have more power (less false negatives) at the cost of a few more false positives. One of the most popular FDR corrections is the Benjamini and Hochberg correction, which provides strong control over the FDR under positive regression dependency of the test statistics (Benjamini and Yekutieli, 2001). In fact, it is unclear whether the GO hierarchy fulfils the requirement of positive regression dependency. Nevertheless, the Benjamini and Hochberg correction is used widely. BiNGO is an open-source Java package, distributed under the GNU General Public Licence (http://www.gnu.org/). Extra statistical tests or more refined multiple testing corrections can be added to BiNGO through the implementation of interfaces provided for this purpose. However, the safest way to minimize the impact of multiple testing issues is to test fewer categories. To this end, we provide several GOSlim ontologies in BiNGO that are organism-specific slimmed-down versions of the full GO hierarchy. When using these GOSlims in combination with a standard or custom annotation, the organism's full GO annotation is remapped onto the chosen GOSlim, with the full GO ontology as a remapping guide, thereby eliminating the need for separate GOSlim annotation files.
BiNGO provides annotations for a wide range of organisms. These default annotations are parsed from the GO information available from NCBI (http://www.ncbi.nlm.nih.gov/Ftp/). Several gene identifiers are supported. Probably the most stable one is the Entrez GeneID, which is the unique identifier for a gene in NCBI's Entrez Gene (formerly LocusLink) database, and to a lesser extent the LocusTag identifier, which is unique to a particular locus (e.g. ORF names for baker's yeast and AGI codes for Arabidopsis). Official Gene Symbols and Unigene IDs are generally less stable, or at least more prone to misinterpretation. We deliberately chose not to support the use of synonyms or other commonly used names. These alternative names are frequently non-unique and may lead to confusion, so we do not want to encourage their use.
Although BiNGO is primarily designed for use with GO ontologies, other classification systems [e.g. the MIPS Functional Catalogue (Ruepp et al., 2004)] can be used as well, provided that the classification information is parsed into the right format. More information about the required formats is available at our website (http://www.psb.ugent.be/cbd/papers/BiNGO/).
BiNGO assesses the functional themes that are present in a set of genes. Eventhough a P-value gives a good indication about the prominence of a certain functional category, it is risky to draw conclusions solely based on P-values. The P-values returned by BiNGO should be regarded as suggestions, and interpreted in the light of other evidence. Due to the interdependency of functional categories in the GO hierarchy, it is very likely that not one category, but a whole branch of the GO hierarchy is highlighted as being significantly overrepresented (Fig. 1). In such cases, interpretation can be more difficult. The most intensely colored nodes that are farthest down the hierarchy are probably the most relevant ones. For example, let us suppose that a branch of ‘catalytic activity’ subcategories is highlighted (Fig. 1), then we cannot conclude that genes involved in ‘catalytic activity’ as a whole are significantly overrepresented in the test set. In fact, if ‘kinase activity’ is the relevant category, the overrepresentation of the ‘transferase activity’ and ‘catalytic activity’ categories merely results from the presence of ‘kinase activity’ genes. Would there be a substantial contribution of genes in the ‘catalytic activity’ category other than ‘kinase activity’ genes, then the ‘catalytic activity’ node would be bigger in size, which is not the case. Next to the visual representation, BiNGO produces a tab-delimited text file containing more detailed results. Apart from a listing of the analysis options, the results file contains the (adjusted) P-value for each significantly overrepresented GO class, the number of genes in the test set annotated to that class and their identity, and the number of genes annotated to that class in the reference set.
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BiNGO is a flexible, extendable tool used to analyze GO term overrepresentation in biological networks. We believe that embedding BiNGO in Cytoscape will further contribute to the establishment of Cytoscape as an integrated suite of tools for the analysis of biological networks. As Cytoscape continues to evolve, BiNGO will evolve alongside it. Comments and feature requests will be considered thoroughly.
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Acknowledgments |
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The authors would like to thank Benno Schwikowski, Iliana Avila, Gary Bader, Rowan Christmas, Andrew Markiel and the whole Cytoscape development team for their technical support and useful comments. S.M. is a research fellow at the Fund for Scientific Research (Flanders, Belgium).
Conflict of Interest: none declared.
Received on May 18, 2005; revised on June 13, 2005; accepted on June 17, 2005
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REFERENCES |
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TOPAbstract1 INTRODUCTION2 METHODS AND IMPLEMENTATIONREFERENCES |
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Al-Shahrour, F., et al. (2004) FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics, 20, 578–580
Ashburner, M., et al. (2000) Gene Ontology: tool for the unification of biology. Nat. Genet., 25, 25–29[CrossRef][Web of Science][Medline].
Bader, G.D. and Hogue, C.W.V. (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4, 2[CrossRef][Medline].
Beißbarth, T. and Speed, T.P. (2004) GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics, 20, 1464–1465
Benjamini, Y. and Yekutieli, D. (2001) The control of the false discovery rate in multiple testing under dependency. Ann. Statist., 29, 1165–1188[CrossRef].
Berriz, G.F., et al. (2003) Characterizing gene sets with FuncAssociate. Bioinformatics, 19, 2502–2504
Boyle, E.I., et al. (2004) GO::TermFinder—open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics, 20, 3710–3715
Ge, Y., Dudoit, S., Speed, T.P. (2003) Resampling-based multiple testing for microarray data analysis. Technical Report #633, , UC Berkeley Dept. of Statistics.
Hosack, D.A., et al. (2003) Identifying biological themes within lists of genes with EASE. Genome Biol., 4, R70[CrossRef][Medline].
Masseroli, M., et al. (2004) GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysis, and mining. Nucleic Acids Res., 32, W293–W300
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J. Adachi, C. Kumar, Y. Zhang, and M. Mann In-depth Analysis of the Adipocyte Proteome by Mass Spectrometry and Bioinformatics Mol. Cell. Proteomics, July 1, 2007; 6(7): 1257 - 1273. [Abstract] [Full Text] [PDF]
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J. Van Leene, H. Stals, D. Eeckhout, G. Persiau, E. Van De Slijke, G. Van Isterdael, A. De Clercq, E. Bonnet, K. Laukens, N. Remmerie, et al. A Tandem Affinity Purification-based Technology Platform to Study the Cell Cycle Interactome in Arabidopsis thaliana Mol. Cell. Proteomics, July 1, 2007; 6(7): 1226 - 1238. [Abstract] [Full Text] [PDF]
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B. Macek, I. Mijakovic, J. V. Olsen, F. Gnad, C. Kumar, P. R. Jensen, and M. Mann The Serine/Threonine/Tyrosine Phosphoproteome of the Model Bacterium Bacillus subtilis Mol. Cell. Proteomics, April 1, 2007; 6(4): 697 - 707. [Abstract] [Full Text] [PDF]
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I. Rivals, L. Personnaz, L. Taing, and M.-C. Potier Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics, February 15, 2007; 23(4): 401 - 407. [Abstract] [Full Text] [PDF]
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R. Jasavala, H. Martinez, J. Thumar, A. Andaya, A.-C. Gingras, J. K. Eng, R. Aebersold, D. K. Han, and M. E. Wright Identification of Putative Androgen Receptor Interaction Protein Modules: Cytoskeleton and Endosomes Modulate Androgen Receptor Signaling in Prostate Cancer Cells Mol. Cell. Proteomics, February 1, 2007; 6(2): 252 - 271. [Abstract] [Full Text] [PDF]
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O. Garcia, C. Saveanu, M. Cline, M. Fromont-Racine, A. Jacquier, B. Schwikowski, and T. Aittokallio GOlorize: a Cytoscape plug-in for network visualization with Gene Ontology-based layout and coloring Bioinformatics, February 1, 2007; 23(3): 394 - 396. [Abstract] [Full Text] [PDF]
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D. Fleury, K. Himanen, G. Cnops, H. Nelissen, T. M. Boccardi, S. Maere, G. T.S. Beemster, P. Neyt, S. Anami, P. Robles, et al. The Arabidopsis thaliana Homolog of Yeast BRE1 Has a Function in Cell Cycle Regulation during Early Leaf and Root Growth PLANT CELL, February 1, 2007; 19(2): 417 - 432. [Abstract] [Full Text] [PDF]
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F. Hermans and E. Tsiporkova Merging microarray cell synchronization experiments through curve alignment Bioinformatics, January 15, 2007; 23(2): e64 - e70. [Abstract] [Full Text] [PDF]
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N. Salamat-Miller, J. Fang, C. W. Seidel, A. M. Smalter, Y. Assenov, M. Albrecht, and C. R. Middaugh A Network-based Analysis of Polyanion-binding Proteins Utilizing Yeast Protein Arrays Mol. Cell. Proteomics, December 1, 2006; 5(12): 2263 - 2278. [Abstract] [Full Text] [PDF]
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J.-G. Joung, D. Shin, R. H. Seong, and B.-T. Zhang Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation Bioinformatics, August 15, 2006; 22(16): 2005 - 2011. [Abstract] [Full Text] [PDF]
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P. D. Thomas, A. Kejariwal, N. Guo, H. Mi, M. J. Campbell, A. Muruganujan, and B. Lazareva-Ulitsky Applications for protein sequence-function evolution data: mRNA/protein expression analysis and coding SNP scoring tools. Nucleic Acids Res., July 1, 2006; 34(Web Server issue): W645 - W650. [Abstract] [Full Text] [PDF]
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J. S. Verducci, V. F. Melfi, S. Lin, Z. Wang, S. Roy, and C. K. Sen Microarray analysis of gene expression: considerations in data mining and statistical treatment Physiol Genomics, May 16, 2006; 25(3): 355 - 363. [Abstract] [Full Text] [PDF]
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