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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Molecular Plant Pathology Laboratory » Research » Publications at this Location » Publication #299191
Title: In silico identification of transcription factors in medicago sativa using available transcriptomic resources

Author
item POSTNIKOVA, OLGA - Russian Academy Of Sciences
item Shao, Jonathan
item Nemchinov, Lev

Submitted to: Molecular Genetics and Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/30/2014
Publication Date: 2/21/2014
Citation: Postnikova, O., Shao, J.Y., Nemchinov, L.G. 2014. In silico identification of transcription factors in medicago sativa using available transcriptomic resources. Molecular Genetics and Genomics. 289:457-468.

Interpretive Summary: Fundamental research having practical applications is critical for improvement of alfalfa. Transcription factors (TFs), proteins that regulate gene expression, are at the center of the complex system controlling plant’s growth, development, responses to the environment and evolution. Currently, information on TFs in alfalfa is very limited: for instance, the most comprehensive plant database of transcription factors, PlantTFDB, does not carry any data on alfalfa’s TFs. In this work we computationally predicted alfalfa transcription factors, classified them into families, made their annotations and combined all the data into a simple database called AlfalfaTFDB. We expect that this study will contribute toward understanding of the expression and regulation of genes associated with alfalfa development and stress tolerance and will be of interest to a wide range of researchers in academia and government organizations.

Technical Abstract: Functional genomics of alfalfa, the most extensively cultivated forage legume in the world, is in the developing stage. Although alfalfa genome sequence is not yet completed, several large transcriptomic resources that can be used to identify genes and determine the amount of their activity are freely available online. In this study, we have performed a comprehensive in silico analysis of transcriptrome data generated in our lab and publicly available from other sources to predict and systematize alfalfa transcription factors (TFs), proteins that bind to DNA and regulate gene expression. Transcriptome-wide mining enabled prediction of 1244 TFs along with their sequence features, putative phylogenies and GO annotations. Integrated TF repertoires of Medicago sativa will provide an important tool for studying regulation of gene expression in alfalfa development and stress tolerance.