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Nucleic Acids Research 2005 33(Database Issue):D660-D665; doi:10.1093/nar/gki128
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Nucleic Acids Research, 2005, Vol. 33, Database issue D660-D665
© 2005, the authors
Nucleic Acids Research, Vol. 33, Database issue © Oxford University Press 2005; all rights reserved

The Legume Information System (LIS): an integrated information resource for comparative legume biology

Michael D. Gonzales, Eric Archuleta, Andrew Farmer, Kamal Gajendran, David Grant1, Randy Shoemaker1, William D. Beavis* and Mark E. Waugh2

National Center for Genome Resources, Santa Fe, NM 87505, USA, 1 USDA-ARS-CICGR and Department of Agronomy, Iowa State University, Ames, IA 50011, USA and 2 Bio5, University of Arizona, Tucson, AR 85721, USA

* To whom correspondence should be addressed. Tel: +1 505 995 4412; Fax: +1 505 995 4412; Email: wdb{at}ncgr.org

Received August 15, 2004; Revised and Accepted October 24, 2004

The Legume Information System (LIS) (http://www.comparative-legumes.org), developed by the National Center for Genome Resources in cooperation with the USDA Agricultural Research Service (ARS), is a comparative legume resource that integrates genetic and molecular data from multiple legume species enabling cross-species genomic and transcript comparisons. The LIS virtual plant interface allows simplified and intuitive navigation of transcript data from Medicago truncatula, Lotus japonicus, Glycine max and Arabidopsis thaliana. Transcript libraries are represented as images of plant organs in different developmental stages, which are selected to query the analyzed and annotated data. Complex queries can be accomplished by adding modifiers, keywords and sequence names. The LIS also contains annotated genomic data featuring transcript alignments to validate gene predictions as well as motif and similarity analyses. The genomic browser supports comparative analysis via novel dynamic functional annotation comparisons. CMap, developed as part of the GMOD project (http://www.gmod.org/cmap/index.shtml), has been incorporated to support comparative analyses of community linkage and physical map data. LIS is being expanded to incorporate gene expression and biochemical pathways which will be seamlessly integrated forming a knowledge discovery framework.


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