title: Mining the Arabidopsis thaliana genome for highly-divergent seven transmembrane receptors.
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.
authors.affiliation: School of Biological Sciences and Plant Science Initiative, University of Nebraska-Lincoln, Lincoln, NE 68588-0660, USA. emoriyama2@unl.edu
authors.forename: Etsuko N
authors.initials: EN
authors.lastname: Moriyama
authors.forename: Pooja K
authors.initials: PK
authors.lastname: Strope
authors.forename: Stephen O
authors.initials: SO
authors.lastname: Opiyo
authors.forename: Zhongying
authors.initials: Z
authors.lastname: Chen
authors.forename: Alan M
authors.initials: AM
authors.lastname: Jones
mesh_terms: D017360:Arabidopsis / Q000235:genetics*; D029681:Arabidopsis Proteins / Q000235:genetics; D030562:Databases, Protein; D020869:Gene Expression Profiling; D014644:Genetic Variation; D005822:Genetic Vectors; D018745:Genome, Plant; D008390:Markov Chains; D011956:Receptors, Cell Surface / Q000235:genetics*
chemical_list: D029681:Arabidopsis Proteins; D011956:Receptors, Cell Surface