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BiNGO Installation Instructions

Install BiNGO using the Cytoscape App Manager (under Apps menu). BiNGO is now ready to use !

BiNGO Manual

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1. Introduction

BiNGO is a Java-based tool to determine which Gene Ontology (GO) categories are statistically over- or underrepresented in a set of genes or a subgraph of a biological network. BiNGO is implemented as a plugin for Cytoscape, which is a an open source bioinformatics software platform for visualizing and integrating molecular interaction networks. BiNGO maps the predominant functional themes of a given gene set on the GO hierarchy, and outputs this mapping as a Cytoscape graph. Gene sets can either be selected or computed from a Cytoscape network (as subgraphs) or compiled from sources other than Cytoscape (e.g. a list of genes that are significantly upregulated in a microarray experiment). The main advantage of BiNGO over other GO tools is the fact that it can be used directly and interactively on molecular interaction graphs. Another plus is that BiNGO takes full advantage of Cytoscape's versatile visualization environment. This allows you to produce customized high-quality figures.

Features include :

•assessing overrepresentation or underrepresentation of GO categories
•Graph or gene list input
•batch mode : analyze several clusters simultaneously using same settings
•Different GO and GOSlim ontologies
•Evidence code filtering
•Wide range of organisms
•Hypergeometric or binomial test for overrepresentation
•Multiple testing correction using Bonferroni (FWER) or Benjamini&Hochberg (FDR) correction
•Interactive visualization of results mapped on the GO hierarchy.
•more extensive results in tab-delimited text file
•making and using your own annotation files is easy
•open source

This manual aims to explain the inner workings, and potential pitfalls, of BiNGO in more detail. To get a taste of the basic user interface, please take a look at the tutorial section.

2. Statistical tests

BiNGO currently provides two statistical tests for assessing over- or underrepresentation in a set of genes. The basic question answered by these tests is the following :

'When sampling X genes (test set) out of N genes (reference set ; graph or 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 (test without replacement) provides an accurate answer to 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. You should only consider using the binomial test if your test set contains several thousand genes...

3. Multiple testing corrections

Because BiNGO tests 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 individual categories at a significance level (alevel) of say 0.05, you would expect 5 out of each 100 tested categories to be identified as being over-represented just by chance. Suppose you have one truly positive category, this would imply you would have identified 5 times more false positive than true positive categories. Multiple testing corrections are designed to provide better control over the false positive rate at a given significancelevel.

One of the most basic corrections is the Bonferroni correction. The Bonferroni correction provides strong control over the Family-Wise Error Rate (FWER), which is defined as the probability of making at least one type I (false positive) error. E.g., when performing Bonferroni control of the FWER at level a= 0.05, you would be 95% certain that that the over-represented categories that you identified contain no false positives. The Bonferroni correction is generally assumed to be rather conservative, although there have been reports (Boyle et al. 2004) that the Bonferroni correction would actually be rather liberal (at least for FWER control) when used for correcting tests that are not mutually independent, as is the case when testing GO categories (see further).

An alternative to using FWER controlling 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, since we would typically rather 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 & Hochberg correction, which provides strong control over the FDR under positive regression dependency of the null hypotheses.

In fact, it's not sure whether the GO hierarchy fulfills this positive regression dependency requirement. Nevertheless, the Benjamini & Hochberg correction is used widely. Alternatives include the Benjamini & Yekutieli procedure, which controls the FDR under arbitrary dependency, or resampling based procedures to control either the FWER (e.g. Westfall & Young step-down minP procedure) or the FDR (e.g. Storey & Tibshirani ST-q procedure, which calculates adjusted q-values instead of p-values). The latter procedures are rather computationally intensive, which is why they have not yet been implemented in BiNGO. The Benjamini & Yekutieli procedure exhibits severely decreased power compared to the Benjamini & Hochberg correction, which is a large price to pay for allowing arbitrary dependence.

For now, BiNGO only provides only the most widely used (= basic) multiple testing corrections. However, we are planning to provide more correction options (such as those mentioned in the previous paragraph) at some point in the future. In the mean time, the user can add extra tests or multiple testing corrections through implementation of the Java interfaces provided for this purpose.

For a more thorough discussion of this topic, see e.g. Ge et al. (2003).

Boyle, E.I., Weng, S., Gollub, J., Jin, H., Botstein, D., Cherry, J.M., Sherlock, G. (2004) GO::TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes, Bioinformatics20, 3710-3715.

Ge, Y., Dudoit, S. and Speed, T.P. (2003) Resampling-based multiple testing for microarray data an