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About |
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Gramene SAB |
Copyright Statement
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The rice genome is more than a resource for understanding the biology of a single species. It is a window into the structure and function of genes in other crop grasses as well. Using rice as the sequenced reference genome, researchers can identify and understand the relationships among genes, pathways and phenotypes in a wide range of grass species.Extensive work over the past two decades has shown remarkably consistent conservation of gene order within large segments of linkage groups in rice, maize, sorghum, barley, wheat, rye, sugarcane and other agriculturally important grasses. A substantial body of data supports the notion that the rice genome is substantially colinear at both large and short scales with other crop grasses, opening the possibility of using rice synteny relationships to rapidly isolate and characterize homologues in maize, wheat, barley and sorghum.
As an information resource, Gramene's purpose is to provide added value to data sets available within the public sector, which will facilitate researchers' ability to understand the rice genome and leverage the rice genomic sequence for identifying and understanding corresponding genes, pathways and phenotypes in other crop grasses. This is achieved by building automated and curated relationships between rice and other cereals for both sequence and biology. The automated and curated relationships are queried and displayed using controlled vocabularies and web-based displays. The controlled vocabularies (Ontologies), currently being utilized include Gene ontology, Plant ontology, Trait ontology, Environment ontology and Gramene Taxonomy ontology. The web-based displays for phenotypes include the Genes and Quantitative Trait Loci (QTL) modules. Sequence based relationships are displayed in the Genomes module using the genome browser adapted from Ensembl, in the Maps module using the comparative map viewer (CMap) from GMOD, and in the Proteins module displays. BLAST is used to search for similar sequences. Literature supporting all the above data is organized in the Literature database.
The technological core of Gramene is the MySQL database management system. We have developed a rational schema to represent the various biological entities of Gramene, and a middleware layer to dynamically translate this information into Web pages.
Gramene project is supported by an IFAFS grant from the USDA Cooperative State Research and Education Service (CSREES), PGI grant from the National Science Foundation, and was previously supported by a Cooperative Agreement through the USDA Agricultural Research Service.
The name Gramene is a play on the name of the Grameen Bank which specializes in small loans to the poor (mostly women) in emerging economies.
- Lincoln Stein (PI)
- Cold Spring Harbor Laboratory
- lstein@cshl.edu
- Susan McCouch (Co-PI)
- Cornell University
- srm4@cornell.edu
- Edward Buckler (Co-PI)
- Institute for Genomic Diversity, Cornell University
- esb33@cornell.edu
- Doreen Ware (Co-PI)
- Cold Spring Harbor Laboratory
- ware@cshl.edu
- Pankaj Jaiswal (Co-PI)
- Cornell University
- pj37@cornell.edu
- Other Personnel
The following employment opportunities are available at Gramene:
Cold Spring Harbor laboratory is seeking talented and motivated individuals for current and future job opening as postdoctoral fellows and software developers in the expanding bioinformatics program at Cold Spring Harbor Laboratory. An ideal candidate will have an interdisciplinary training with strong computational science background and biology knowledge. The successful candidates will develop a strong bioinformatics research program, collaborate with a diverse research community and contribute to an emerging community resource (www.gramene.org). Applicants should have a Ph.D. (or equivalent degree) in biology, bioinformatics, molecular biology, biochemistry, genetics, evolutionary or systematic biology, or related fields, and demonstrated experience in computer science and expertise in at least one scientific programming language and relational data management system. Experience in network analysis methods, and in the analysis of signaling processes and networks using engineering or applied mathematics approaches are strongly desired. Will consider M.S. with very good relevant experience. For more information on the projects please see the following links. .
https://www.fastlane.nsf.gov/servlet/showaward?award=0333074,
https://www.fastlane.nsf.gov/servlet/showaward?award=0321666,
https://www.fastlane.nsf.gov/servlet/showaward?award=0321467Interested individuals should send or email curriculum vitae with names of three references and a brief description of previous experience and accomplishments to:
Doreen Ware, Ph.D., USDA ARS Research Scientist, Cold Spring Harbor Laboratory, 1 Bungtown Rd., Cold Spring Harbor, NY 11724, E-mail: ware@cshl.edu
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.