In silico multi-species comparisons of publicly available EST data to identify genes putatively involved in response of tomato to stress.

Large-scale sequencing and analysis of Expressed Sequence Tags (ESTs) remains a fundamental part of genomics and post-genomics research to enable gene discovery and annotation. EST data are particularly important for organisms whose genomes are not yet sequenced and for which other expression profiling techniques have not yet been extensively applied. At the moment, millions of ESTs, collectively representing a variety of biochemical and functional states, are available in public databases such as dbEST-NCBI (http://www.ncbi.nlm.nih.gov/dbEST/) and TGI (http://compbio.dfci.harvard.edu/tgi/). This impressive wealth of information constitutes a valuable resource for comparative transcriptomic analysis, but has so far been exploited only in part. Here we present an EST data mining strategy based on comparative meta-analysis. The method was applied to the Solanaceae family in an attempt to identify genes involved in the response of tomato to stress. This is possible due to the large number of ESTs available for at least some members of the family. The method consists in ranking tomato ESTs co-expressed with a gene of interest according to the level of expression pattern conservation in related plants (potato, pepper and tobacco), to obtain a list of genes putatively functionally related to that gene. The candidate genes are then analyzed for Gene Ontology keyword overrepresentation and related to available information from the literature.