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Nucleic Acids Research Advance Access published online on November 11, 2009
Nucleic Acids Research, doi:10.1093/nar/gkp978
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Database Issue

PRGdb: a bioinformatics platform for plant resistance gene analysis

Walter Sanseverino1, Guglielmo Roma2, Marco De Simone3, Luigi Faino1, Sara Melito1, Elia Stupka4,5, Luigi Frusciante1 and Maria Raffaella Ercolano1,*

1Department of Soil, Plant, Environmental and Animal Production Sciences, University of Naples ‘Federico II’, Via Università 100, 80055 Portici, Italy, 2Center for Genomic Regulation, Dr Aiguader 88, 08003 Barcelona, Spain, 3CBM S.c.r.l., Area Science Park, 34012 Trieste, Italy, 4UCL Cancer Institute, Paul O’Gorman Building, University College London, Gower Street, London, WC1E 6BT and 5The Blizard Institute, Barts and The London School of Medicine and Dentistry, 4 Newark Street, London, E1 2AT, UK

*To whom correspondence should be addressed. Tel: +39 81 253 9431; Fax: +39 81 775 7935; Email: ercolano{at}unina.it

Received August 13, 2009. Revised October 2, 2009. Accepted October 15, 2009.

PRGdb is a web accessible open-source (http://www.prgdb.org) database that represents the first bioinformatic resource providing a comprehensive overview of resistance genes (R-genes) in plants. PRGdb holds more than 16 000 known and putative R-genes belonging to 192 plant species challenged by 115 different pathogens and linked with useful biological information. The complete database includes a set of 73 manually curated reference R-genes, 6308 putative R-genes collected from NCBI and 10463 computationally predicted putative R-genes. Thanks to a user-friendly interface, data can be examined using different query tools. A home-made prediction pipeline called Disease Resistance Analysis and Gene Orthology (DRAGO), based on reference R-gene sequence data, was developed to search for plant resistance genes in public datasets such as Unigene and Genbank. New putative R-gene classes containing unknown domain combinations were discovered and characterized. The development of the PRG platform represents an important starting point to conduct various experimental tasks. The inferred cross-link between genomic and phenotypic information allows access to a large body of information to find answers to several biological questions. The database structure also permits easy integration with other data types and opens up prospects for future implementations.

The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.


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