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Database (2009) Vol. 2009:bap005; doi:10.1093/database/bap005 published on May 12, 2009
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© 2009 The Author(s).
This is Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Gramene QTL database: development, content and applications

Junjian Ni1,{dagger}, Anuradha Pujar1,{ddagger}, Ken Youens-Clark2, Immanuel Yap1, Pankaj Jaiswal1,¶, Isaak Tecle1, Chih-Wei Tung1, Liya Ren2, William Spooner2, Xuehong Wei2, Shuly Avraham2, Doreen Ware2, Lincoln Stein2 and Susan McCouch1,*

1Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853-1901, USA and 2Cold Spring Harbor Labs, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA

*Corresponding author: Tel: +1 607-255-0420; Fax: +1 607-255-6683; Email: srm4{at}cornell.edu


  Abstract

Gramene is a comparative information resource for plants that integrates data across diverse data domains. In this article, we describe the development of a quantitative trait loci (QTL) database and illustrate how it can be used to facilitate both the forward and reverse genetics research. The QTL database contains the largest online collection of rice QTL data in the world. Using flanking markers as anchors, QTLs originally reported on individual genetic maps have been systematically aligned to the rice sequence where they can be searched as standard genomic features. Researchers can determine whether a QTL co-localizes with other QTLs detected in independent experiments and can combine data from multiple studies to improve the resolution of a QTL position. Candidate genes falling within a QTL interval can be identified and their relationship to particular phenotypes can be inferred based on functional annotations provided by ontology terms. Mutations identified in functional genomics populations and association mapping panels can be aligned with QTL regions to facilitate fine mapping and validation of gene–phenotype associations. By assembling and integrating diverse types of data and information across species and levels of biological complexity, the QTL database enhances the potential to understand and utilize QTL information in biological research.

Received October 30, 2008; Accepted March 20, 2009

{dagger}Present address: Junjian Ni, Syngenta Biotechnology Inc., 3054 Cornwallis Road, Research Triangle Park, NC 27709, USA.

{ddagger}Present address: Anuradha Pujar, Boyce Thompson Institute, Cornell University, Ithaca, NY 14853-1801, USA.

Present address: Pankaj Jaiswal, Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331-2902, USA.


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