Expressed sequence tags (ESTs) and simple sequence repeat (SSR) markers from octoploid strawberry (Fragaria × ananassa)

Kevin M Folta^*¹ , Margaret Staton^*² , Philip J Stewart¹ , Sook Jung² , Dawn H Bies¹ , Christopher Jesdurai² and Dorrie Main²

¹Plant Molecular and Cellular Biology Program and Horticultural Sciences Department, University of Florida, Gainesville, FL, USA

²Genetics, Biochemistry & Life Science Studies, Clemson University, Clemson, SC, USA

author email corresponding author email* Contributed equally

BMC Plant Biology 2005, 5:12doi:10.1186/1471-2229-5-12


Published:	28 June 2005

Abstract

Background

Cultivated strawberry (Fragaria × ananassa) represents one of the most valued fruit crops in the United States. Despite its economic importance, the octoploid genome presents a formidable barrier to efficient study of genome structure and molecular mechanisms that underlie agriculturally-relevant traits. Many potentially fruitful research avenues, especially large-scale gene expression surveys and development of molecular genetic markers have been limited by a lack of sequence information in public databases. As a first step to remedy this discrepancy a cDNA library has been developed from salicylate-treated, whole-plant tissues and over 1800 expressed sequence tags (EST's) have been sequenced and analyzed.

Results

A putative unigene set of 1304 sequences – 133 contigs and 1171 singlets – has been developed, and the transcripts have been functionally annotated. Homology searches indicate that 89.5% of sequences share significant similarity to known/putative proteins or Rosaceae ESTs. The ESTs have been functionally characterized and genes relevant to specific physiological processes of economic importance have been identified. A set of tools useful for SSR development and mapping is presented.

Conclusion

Sequences derived from this effort may be used to speed gene discovery efforts in Fragaria and the Rosaceae in general and also open avenues of comparative mapping. This report represents a first step in expanding molecular-genetic analyses in strawberry and demonstrates how computational tools can be used to optimally mine a large body of useful information from a relatively small data set.