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Please use this identifier to cite or link to this item: http://hdl.handle.net/10373/130

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Title: DRASTIC—INSIGHTS: querying information in a plant gene expression database
Authors: Button, Davina K.
Gartland, Kevan M. A.
Ball, Leslie D.
Natanson, Louis
Gartland, Jill S.
Lyon, Gary D.
Affiliation: University of Abertay Dundee. Scottish Informatics, Mathematics, Biology and Statistics Centre
University of Abertay Dundee. School of Computing and Creative Technologies
Keywords: Gene expression
Databases
Up-regulated genes
Down-regulated genes
Issue Date: Jan-2006
Publisher: Oxford University Press
Type: Article
Refereed: peer-reviewed
Rights: Open Access version of this article (c)The authors originally published by Oxford University Press available from http://nar.oxfordjournals.org
Citation: Button, D.K. 2006. DRASTIC—INSIGHTS: querying information in a plant gene expression database. Nucleic Acids Research. 34: D712-D716. DOI: 10.1093/nar/gkj136
Abstract: DRASTIC––Database Resource for the Analysis of Signal Transduction In Cells (http://www.drastic.org.uk/) has been created as a first step towards a data-based approach for constructing signal transduction pathways. DRASTIC is a relational database of plant expressed sequence tags and genes up- or down-regulated in response to various pathogens, chemical exposure or other treatments such as drought, salt and low temperature. More than 17700 records have been obtained from 306 treatments affecting 73 plant species from 512 peer-reviewed publications with most emphasis being placed on data from Arabidopsis thaliana. DRASTIC has been developed by the Scottish Crop Research Institute and the University of Abertay Dundee and allows rapid identification of plant genes that are up- or down-regulated by multiple treatments and those that are regulated by a very limited (or perhaps a single) treatment. The INSIGHTS (INference of cell SIGnaling HypoTheseS) suite of web-based tools allows intelligent data mining and extraction of information from the DRASTIC database. Potential response pathways can be visualized and comparisons made between gene expression patterns in response to various treatments. The knowledge gained informs plant signalling pathways and systems biology investigations.
URI: http://hdl.handle.net/10373/130
Appears in Collections:Computing & Engineering Systems Collection

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