Article

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

Center for Computational Genomics and Bioinformatics, University of Minnesota, MMC43, 420 Delaware Street S.E., Minneapolis, MN 55455, USA.
Nucleic Acids Research (impact factor: 8.03). 01/2003; 31(1):196-201. pp.196-201
Source: PubMed

ABSTRACT 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 http://www.medicago.org/MtDB.

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Keywords

appropriate database records
 
cool season legume Medicago truncatula
 
different assemblers
 
different sites
 
gene functions
 
hypertext links
 
in-depth sampling
 
integrates Mt transcriptome data
 
legume biology
 
match specific research interests
 
model organisms
 
pathogen-challenged tissues
 
Public access
 
query software
 
Sequence identifiers
 
underlie key aspects
 
unigene sets
 
user-defined criteria
 
user-defined data mining options
 
various developmental stages