PrimerSNP: a web tool for whole-genome selection of allele-specific and common primers of phylogenetically-related bacterial genomic sequences
1 Citrus Research Board, 323 W. Oak Street, Visalia, CA 93291, USA
2 USDA-ARS, San Joaquin Valley Agricultural Science Center, Parlier, CA 93648, USA
3 Seed Biotechnology Center, University of California, One Shields Ave, Davis, CA 95616, USA
4 University of California Davis, Department of Viticulture and Enology, Davis, CA 95616, USA
5 University of California, Department of Plant Pathology, CA 95616, USA
6 Departamento de Tecnologia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Câmpus Jaboticabal, Via de Acesso Prof. Paulo Donato Castellane, s/n, km 5, 14884-900 Jaboticabal, São Paulo, Brasil
BMC Microbiology 2008, 8:185 doi:10.1186/1471-2180-8-185Published: 20 October 2008
The increasing number of genomic sequences of bacteria makes it possible to select unique SNPs of a particular strain/species at the whole genome level and thus design specific primers based on the SNPs. The high similarity of genomic sequences among phylogenetically-related bacteria requires the identification of the few loci in the genome that can serve as unique markers for strain differentiation. PrimerSNP attempts to identify reliable strain-specific markers, on which specific primers are designed for pathogen detection purpose.
PrimerSNP is an online tool to design primers based on strain specific SNPs for multiple strains/species of microorganisms at the whole genome level. The allele-specific primers could distinguish query sequences of one strain from other homologous sequences by standard PCR reaction. Additionally, PrimerSNP provides a feature for designing common primers that can amplify all the homologous sequences of multiple strains/species of microorganisms. PrimerSNP is freely available at http://cropdisease.ars.usda.gov/~primer webcite.
PrimerSNP is a high-throughput specific primer generation tool for the differentiation of phylogenetically-related strains/species. Experimental validation showed that this software had a successful prediction rate of 80.4 – 100% for strain specific primer design.