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This article is part of the supplement: Proceedings of the Sixth Annual MCBIOS Conference. Transformational Bioinformatics: Delivering Value from Genomes

Open Access Proceedings

Comparing gene annotation enrichment tools for functional modeling of agricultural microarray data

Bart HJ van den Berg13*, Chamali Thanthiriwatte23, Prashanti Manda23 and Susan M Bridges23

Author Affiliations

1 Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS 39762, USA

2 Department of Computer Science and Engineering, Box 9637, Mississippi State University, Starkville, MS 39762, USA

3 Institute for Digital Biology, Mississippi State University, Starkville, MS 39762, USA

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BMC Bioinformatics 2009, 10(Suppl 11):S9  doi:10.1186/1471-2105-10-S11-S9

Published: 8 October 2009

Abstract

The widespread availability of microarray technology has driven functional genomics to the forefront as scientists seek to draw meaningful biological conclusions from their microarray results. Gene annotation enrichment analysis is a functional analysis technique that has gained widespread attention and for which many tools have been developed. Unfortunately, most of these tools have limited support for agricultural species. Here, we evaluate and compare four publicly available computational tools (Onto-Express, EasyGO, GOstat, and DAVID) that support analysis of gene expression datasets in agricultural species. We use AgBase as the functional annotation reference for agricultural species. The selected tools were evaluated based on i) available features, usage and accessibility, ii) implemented statistical computational methods, and iii) annotation and enrichment performance analysis. Annotation was assessed using a randomly selected test gene annotation set and an experimental differentially expressed gene-set – both from chicken. The experimental set was also used to evaluate identification of enriched functional groups.

Comparison of the tools shows that they produce different sets of annotations for the two datasets and different functional groups for the experimental dataset. While DAVID, GOstat and Onto-Express annotate comparable numbers of genes, DAVID provides by far the most annotations per gene. However, many of DAVID's annotations appear to be redundant or are at very high levels in the GO hierarchy. The GOSlim distribution of annotations shows that GOstat, Onto-Express and EasyGO provide similar GO distributions to those found in AgBase while annotations from DAVID show a different GOSlim distribution, again probably due to duplication and many non-specific terms. No consistent trends were found in results of GO term over/under representation analysis applied to the experimental data using different tools. While GOstat, David and Onto-Express could retrieve some significantly enriched terms, EasyGO did not show any significantly enriched terms. There was little agreement about the enriched terms identified by the tools.

Conclusion

Different tools for functionally annotating gene sets and identifying significantly enriched GO categories differ widely in their results when applied to a test annotation gene set and an experimental dataset from chicken. These results emphasize the need for care when interpreting the results of such analysis and the lack of standardization of approaches.