PRGdb: a bioinformatics platform for plant resistance gene analysis

Nucleic Acids Res. 2010 Jan;38(Database issue):D814-21. doi: 10.1093/nar/gkp978. Epub 2009 Nov 11.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Computational Biology / trends
  • Databases, Genetic*
  • Databases, Nucleic Acid*
  • Databases, Protein
  • Gene Expression Profiling*
  • Genes, Plant / genetics*
  • Genome, Plant*
  • Information Storage and Retrieval / methods
  • Internet
  • Oxidative Stress
  • Plant Diseases / genetics*
  • Plants / metabolism
  • Protein Structure, Tertiary
  • Software
  • User-Computer Interface