In silico identification of transcription factors in Medicago sativa using available transcriptomic resources

Transcription factors (TFs) are proteins that govern organismal development and response to the environment by regulating gene expression. Information on the amount and diversity of TFs within individual plant species is critical for understanding of their biological roles and evolutionary history across the plant kingdom. Currently, only scattered information on separate TFs is available for alfalfa, the most extensively cultivated forage legume in the world. In the meantime, several large transcriptomic resources that can be used to identify and characterize alfalfa TF genes are freely accessible online. In this study, we have performed an in silico analysis of transcriptome data generated in our laboratory and publicly acquirable from other sources to reveal and systematize alfalfa transcription factors. Transcriptome-wide mining enabled prediction of 983 TFs along with their sequence features and putative phylogenies of the largest families. All data were assembled into a simple open-access database named AlfalfaTFDB (http://plantpathology.ba.ars.usda.gov/alfalfatfdb.html). Transcriptomic analysis used in this work represents an effective approach for the identification of TF genes in plants with incomplete genomes, such as alfalfa. Integrated TF repertoires of Medicago sativa will provide an important tool for studying regulation of gene expression in other complex non-model species of agricultural significance.