Discovery of Functional Genes for Systemic Acquired Resistance in Arabidopsis Thaliana through Integrated Data Mining

Download
  1. (PDF, 673 KB)
  2. Get@NRC: Discovery of Functional Genes for Systemic Acquired Resistance in Arabidopsis Thaliana through Integrated Data Mining (Opens in a new window)
AuthorSearch for: ; Search for: ; Search for: ; Search for: ; Search for:
TypeArticle
Journal titleJournal of Bioinformatics and Computational Biology
Volume2
Issue4
Subjectintegrated data mining; motif identification; classification; systemic acquired resistance; microarray; motif identification; classification; systemic acquired resistance; microarray
AbstractVarious data mining techniques combined with sequence motif information in the promoter region of genes were applied to discover functional genes that are involved in the defense mechanism of systemic acquired resistance (SAR) in Arabidopsis thaliana. A series of K-Means clustering with difference-in-shape as distance measure was initially applied. A stability measure was used to validate this clustering process. A decision tree algorithm with the discover-and-mask technique was used to identify a group of most informative genes. Appearance and abundance of various transcription factor binding sites in the promoter region of the genes were studied. Through the combination of these techniques, we were able to identify 24 candidate genes involved in the SAR defense mechanism. The candidate genes fell into 2 highly resolved categories, each category showing significantly unique profiles of regulatory elements in their promoter regions. This study demonstrates the strength of such integration methods and suggests a broader application of this approach.
Publication date
LanguageEnglish
AffiliationNRC Plant Biotechnology Institute; NRC Institute for Information Technology; National Research Council Canada
Peer reviewedYes
NRC number46550
NPARC number10260257
Export citationExport as RIS
Report a correctionReport a correction
Record identifierc0821bca-4238-474d-9091-9a1d6a9c44a7
Record created2009-07-22
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)