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BMC Genomics. 2006 Sep 28;7:245.

Survey and analysis of microsatellites from transcript sequences in Phytophthora species: frequency, distribution, and potential as markers for the genus.

Author information

1
Laboratorio de Micología y Fitopatología Uniandes (LAMFU), Universidad de Los Andes, Bogotá, Colombia. d-garnic@uniandes.edu.co

Abstract

BACKGROUND:

Members of the genus Phytophthora are notorious pathogens with world-wide distribution. The most devastating species include P. infestans, P. ramorum and P. sojae. In order to develop molecular methods for routinely characterizing their populations and to gain a better insight into the organization and evolution of their genomes, we used an in silico approach to survey and compare simple sequence repeats (SSRs) in transcript sequences from these three species. We compared the occurrence, relative abundance, relative density and cross-species transferability of the SSRs in these oomycetes.

RESULTS:

The number of SSRs in oomycetes transcribed sequences is low and long SSRs are rare. The in silico transferability of SSRs among the Phytophthora species was analyzed for all sets generated, and primers were selected on the basis of similarity as possible candidates for transferability to other Phytophthora species. Sequences encoding putative pathogenicity factors from all three Phytophthora species were also surveyed for presence of SSRs. However, no correlation between gene function and SSR abundance was observed. The SSR survey results, and the primer pairs designed for all SSRs from the three species, were deposited in a public database.

CONCLUSION:

In all cases the most common SSRs were trinucleotide repeat units with low repeat numbers. A proportion (7.5%) of primers could be transferred with 90% similarity between at least two species of Phytophthora. This information represents a valuable source of molecular markers for use in population genetics, genetic mapping and strain fingerprinting studies of oomycetes, and illustrates how genomic databases can be exploited to generate data-mining filters for SSRs before experimental validation.

PMID:
17007642
PMCID:
PMC1594578
DOI:
10.1186/1471-2164-7-245
[Indexed for MEDLINE]
Free PMC Article
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