MtDB: A database for personalized data mining of the model legume Medicago truncatula transcriptome

Anne Françoise J. Lamblin, John A. Crow, James E Johnson, Kevin A Silverstein, Timothy M. Kunau, Alan Kilian, Diane Benz, Martina Stromvik, Gabriella Endré, Kathryn A. VandenBosch, Douglas R. Cook, Nevin D Young, Ernest F. Retzel

Research output: Contribution to journalReview articlepeer-review

47 Scopus citations


In order to identify the genes and gene functions that underlie key aspects of legume biology, researchers have selected the cool season legume Medicago truncatula (Mt) as a model system for legume research. A set of >170 000 Mt ESTs has been assembled based on in-depth sampling from various developmental stages and pathogen-challenged tissues. MtDB is a relational database that integrates Mt transcriptome data and provides a wide range of user-defined data mining options. The database is interrogated through a series of interfaces with 58 options grouped into two filters. In addition, the user can select and compare unigene sets generated by different assemblers: Phrap, Cap3 and Cap4. Sequence identifiers from all public Mt sites (e.g. IDs from GenBank, CCGB, TIGR, NCGR, INRA) are fully cross-referenced to facilitate comparisons between different sites, and hypertext links to the appropriate database records are provided for all queries' results. MtDB's goal is to provide researchers with the means to quickly and independently identify sequences that match specific research interests based on user-defined criteria. The underlying database and query software have been designed for ease of updates and portability to other model organisms. Public access to the database is at

Original languageEnglish (US)
Pages (from-to)196-201
Number of pages6
JournalNucleic acids research
Issue number1
StatePublished - Jan 1 2003

Bibliographical note

Funding Information:
Over the last three years, more than 170 000 expressed sequence tags (ESTs) have been generated world-wide from these groups: the Mt consortium funded by the National Science Foundation, The Samuel Roberts Noble Foundation and a consortium of researchers in Europe with primary funding from the EU programme. In general, EST projects offer a quick source of information for micro-and macroarray based functional genomics, for metabolic reconstructions, a point of reference for proteomics and a quick assessment of the number and diversity of genes being expressed. Mt EST data is accessible from different public repositories worldwide, including NCBI-dbEST database (http://www.ncbi.nlm.nih. gov/cgi-bin/Entrez/map00?taxid=3880), The Institute for Genomic Research (TIGR,, the National Center for Genome Research (NCGR, https://xgi/ ncgr/org/mgi/), the Institut National de la Recherche Agro-nomique et le Centre National de la Recherche Scientifique (INRA–CNRS, Mtruncatula.html) and, the Center for Computational

Funding Information:
This work supported in part by the National Science Foundation awards DBI-0196197, DBI-0110206, DBI-9975806 and DBI-9872565; by the USDA SCA 58-3625-8-117 funded by the North Central Soybean Research board and the United Soybean Board, and the USDA SCA 58-1907-0-030. The authors sincerely thank their colleagues who kindly shared data and comments: Jérôme Gouzy, Pascal Gamas and Jean Dénarié (INRA–CNRS-Genoscope Mt project), Gregory May and Angela Scott (Samuel R. Noble Foundation), Christopher Town and Foo Cheung (TIGR), Mark Waugh and William Beavis (NCGR). Special thanks to Suzanne Grindle, Rodney Staggs, Shalini Raghavan and Charles Paule for their help in sequence processing and Chris Dwan for improvement and maintenance on processing software pipeline implementation.


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