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This article is part of the supplement: Validation methods for functional genome annotation

Open Access Research

Synergistic use of plant-prokaryote comparative genomics for functional annotations

Svetlana Gerdes12, Basma El Yacoubi2, Marc Bailly2, Ian K Blaby2, Crysten E Blaby-Haas2, Linda Jeanguenin3, Aurora Lara-Núñez3, Anne Pribat3, Jeffrey C Waller3, Andreas Wilke4, Ross Overbeek1, Andrew D Hanson3* and Valérie de Crécy-Lagard2*

Author Affiliations

1 Fellowship for Interpretation of Genomes, Burr Ridge, IL, USA

2 Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, USA

3 Department of Horticultural Sciences, University of Florida, Gainesville, FL, USA

4 Computation Institute, University of Chicago, Chicago, IL, USA

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BMC Genomics 2011, 12(Suppl 1):S2  doi:10.1186/1471-2164-12-S1-S2

Published: 15 June 2011

Abstract

Background

Identifying functions for all gene products in all sequenced organisms is a central challenge of the post-genomic era. However, at least 30-50% of the proteins encoded by any given genome are of unknown or vaguely known function, and a large number are wrongly annotated. Many of these ‘unknown’ proteins are common to prokaryotes and plants. We set out to predict and experimentally test the functions of such proteins. Our approach to functional prediction integrates comparative genomics based mainly on microbial genomes with functional genomic data from model microorganisms and post-genomic data from plants. This approach bridges the gap between automated homology-based annotations and the classical gene discovery efforts of experimentalists, and is more powerful than purely computational approaches to identifying gene-function associations.

Results

Among Arabidopsis genes, we focused on those (2,325 in total) that (i) are unique or belong to families with no more than three members, (ii) occur in prokaryotes, and (iii) have unknown or poorly known functions. Computer-assisted selection of promising targets for deeper analysis was based on homology-independent characteristics associated in the SEED database with the prokaryotic members of each family. In-depth comparative genomic analysis was performed for 360 top candidate families. From this pool, 78 families were connected to general areas of metabolism and, of these families, specific functional predictions were made for 41. Twenty-one predicted functions have been experimentally tested or are currently under investigation by our group in at least one prokaryotic organism (nine of them have been validated, four invalidated, and eight are in progress). Ten additional predictions have been independently validated by other groups. Discovering the function of very widespread but hitherto enigmatic proteins such as the YrdC or YgfZ families illustrates the power of our approach.

Conclusions

Our approach correctly predicted functions for 19 uncharacterized protein families from plants and prokaryotes; none of these functions had previously been correctly predicted by computational methods. The resulting annotations could be propagated with confidence to over six thousand homologous proteins encoded in over 900 bacterial, archaeal, and eukaryotic genomes currently available in public databases.