GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists
Eran Eden,Roy Navon,Roy Navon,Israel Steinfeld,Israel Steinfeld,Doron Lipson,Zohar Yakhini,Zohar Yakhini +7 more
TL;DR: GOrilla is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets, and its unique features and advantages over other threshold free enrichment tools include rigorous statistics, fast running time and an effective graphical representation.
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Abstract: Since the inception of the GO annotation project, a variety of tools have been developed that support exploring and searching the GO database In particular, a variety of tools that perform GO enrichment analysis are currently available Most of these tools require as input a target set of genes and a background set and seek enrichment in the target set compared to the background set A few tools also exist that support analyzing ranked lists The latter typically rely on simulations or on union-bound correction for assigning statistical significance to the results GOrilla is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets This is particularly useful in many typical cases where genomic data may be naturally represented as a ranked list of genes (eg by level of expression or of differential expression) GOrilla employs a flexible threshold statistical approach to discover GO terms that are significantly enriched at the top of a ranked gene list Building on a complete theoretical characterization of the underlying distribution, called mHG, GOrilla computes an exact p-value for the observed enrichment, taking threshold multiple testing into account without the need for simulations This enables rigorous statistical analysis of thousand of genes and thousands of GO terms in order of seconds The output of the enrichment analysis is visualized as a hierarchical structure, providing a clear view of the relations between enriched GO terms GOrilla is an efficient GO analysis tool with unique features that make a useful addition to the existing repertoire of GO enrichment tools GOrilla's unique features and advantages over other threshold free enrichment tools include rigorous statistics, fast running time and an effective graphical representation GOrilla is publicly available at: http://cbl-gorillacstechnionacil
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Figure 3 certainly support this hypothesis. These results suggest that 14 bits of information in a 33% GC-rich genome ought to be enough to provide a search efficiency roughly similar to that of CRP in E. coli. Nevertheless, H. influenzae CRP displays 17.83 bits, indicating that additional information is being used to provide it with an adequate operating range. 
Figure 2 and Figure 3 show the ROC curves for information theory-based methods attempting to locate, respectively, CRP and Fur binding sites against equiprobable, 66% GC- and 66% AT-skewed randomly generated backgrounds, with their RE profile plots shown as insets in the bottom-right corner. The curves show the mean and standard deviation of three independent experiments and thus reveal that the differences between the observed methods are statistically significant. As it can be seen, all methods substantially improve their results on equiprobable random backgrounds when compared to those obtained on the E. coli genome. Even though the E. coli genome is nearly equiprobable, this is to be expected, since naive random sequences are not very good approximations of genome sequences, in which certain word fre- 
Table 1: Summary of relative method performances.
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