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Published online 18 December 2007
Published in Crop Sci 47:S-32-S-43 (2007)
© 2007 Crop Science Society of America
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Translational Bioinformatics: At the Interface of Genomics and Quantitative Genetics

William D. Beavis*, Faye D. Schilkey and Susan M. Baxter

National Center for Genome Resources, 2935 Rodeo Park Dr. East, Santa Fe, NM 87505. Funded, in part, by NIH-NIAID HHS200400064C, NSF BDI-0516487, and USDA-ARS SCA 58-3625-2-109

* Corresponding author (wdbeavis{at}agron.iastate.edu).

Genomics and bioinformatics are expected to revolutionize crop improvement. We have to admit, however, that while information from the various "omics" is being used by developmental and evolutionary biologists, the information is not being used routinely by translational plant biologists or applied plant breeders. This is due to a failure to provide information from omics technologies in formats that can be used to develop more effective and efficient assays that the breeder can use for selection. A similar situation exists in biomedical research where more efficacious therapies will be realized if omics information is translated into biomarker-based diagnostics. Because data are still lacking in plant science, herein we describe the development of an integrated web-based system that supports translational research using a biomedical example that can serve as a model for translational plant research. We also discuss how the bioinformatic system is agnostic with respect to data content and is capable of accepting omics data that eventually will be generated by plant biologists for use by plant breeders to develop diagnostic biomarkers for use in selection.

Abbreviations: CGL, candidate genetic loci • CMTV, Comparative Map and Trait Viewer • GEYSIR, Genome Exploration and Survey of Immune Response • KEGG, Kyoto Encyclopedia of Genes and Genomes • LD, linkage disequilibrium • LIS, Legume Information System • NCBI, National Center for Biotechnology Information • NCGR, National Center for Genome Resources • NIAID, National Institute of Allergy and Infectious Disease • NIH, National Institutes of Health • OMIM, Online Mendelian Inheritance in Man • PI, principal investigator • QTL, quantitative trait loci • SNP, single nucleotide polymorphism • SSWAP, Simple Semantic Web Architecture and Protocol • TEAM, A Tool for the Integration of Expression and Linkage in Association Maps • XML, extensible markup language


We wish to express our gratitude to the many members of the immune response population genetics project team and to an anonymous reviewer who provided a number of helpful suggestions.

Received for publication August 7, 2006.





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