Mining the Arabidopsis thalianagenome for highly-divergent seven transmembrane receptors Public Deposited

Downloadable Content

Download PDF
Creator
  • Strope, Pooja K
    • Other Affiliation: School of Biological Sciences and Plant Science Initiative, University of Nebraska-Lincoln, Lincoln, NE 68588-0660, USA
  • Chen, Zhongying
    • Affiliation: College of Arts and Sciences, Department of Biology, School of Medicine, Department of Pharmacology
  • Jones, Alan M.
    • Affiliation: College of Arts and Sciences, Department of Biology, School of Medicine, Department of Pharmacology
  • Moriyama, Etsuko N
    • Other Affiliation: School of Biological Sciences and Plant Science Initiative, University of Nebraska-Lincoln, Lincoln, NE 68588-0660, USA
  • Opiyo, Stephen O
    • Other Affiliation: Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588-0660, USA
Abstract
  • Abstract To identify divergent seven-transmembrane receptor (7TMR) candidates from the Arabidopsis thaliana genome, multiple protein classification methods were combined, including both alignment-based and alignment-free classifiers. This resolved problems in optimally training individual classifiers using limited and divergent samples, and increased stringency for candidate proteins. We identified 394 proteins as 7TMR candidates and highlighted 54 with corresponding expression patterns for further investigation.
Date of publication
Identifier
  • doi:10.1186/gb-2006-7-10-r96
  • 17064408
Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • Etsuko N Moriyama et al.; licensee BioMed Central Ltd.
License
Journal title
  • Genome Biology
Journal volume
  • 7
Journal issue
  • 10
Page start
  • R96
Language
  • English
Is the article or chapter peer-reviewed?
  • Yes
ISSN
  • 1465-6906
Bibliographic citation
  • Genome Biology. 2006 Oct 25;7(10):R96
Access
  • Open Access
Publisher
  • BioMed Central Ltd
Parents:

This work has no parents.

Items