title: 7TMRmine: a Web server for hierarchical mining of 7TMR proteins.
abstract: BACKGROUND
  Seven-transmembrane region-containing receptors (7TMRs) play central roles in eukaryotic signal transduction. Due to their biomedical importance, thorough mining of 7TMRs from diverse genomes has been an active target of bioinformatics and pharmacogenomics research. The need for new and accurate 7TMR/GPCR prediction tools is paramount with the accelerated rate of acquisition of diverse sequence information. Currently available and often used protein classification methods (e.g., profile hidden Markov Models) are highly accurate for identifying their membership information among already known 7TMR subfamilies. However, these alignment-based methods are less effective for identifying remote similarities, e.g., identifying proteins from highly divergent or possibly new 7TMR families. In this regard, more sensitive (e.g., alignment-free) methods are needed to complement the existing protein classification methods. A better strategy would be to combine different classifiers, from more specific to more sensitive methods, to identify a broader spectrum of 7TMR protein candidates.
  
  
  METHODS
  We developed a Web server, 7TMRmine, by integrating alignment-free and alignment-based classifiers specifically trained to identify candidate 7TMR proteins as well as transmembrane (TM) prediction methods. This new tool enables researchers to easily assess the distribution of GPCR functionality in diverse genomes or individual newly-discovered proteins. 7TMRmine is easily customized and facilitates exploratory analysis of diverse genomes. Users can integrate various alignment-based, alignment-free, and TM-prediction methods in any combination and in any hierarchical order. Sixteen classifiers (including two TM-prediction methods) are available on the 7TMRmine Web server. Not only can the 7TMRmine tool be used for 7TMR mining, but also for general TM-protein analysis. Users can submit protein sequences for analysis, or explore pre-analyzed results for multiple genomes. The server currently includes prediction results and the summary statistics for 68 genomes.
  
  
  CONCLUSIONS
  7TMRmine facilitates the discovery of 7TMR proteins. By combining prediction results from different classifiers in a multi-level filtering process, prioritized sets of 7TMR candidates can be obtained for further investigation. 7TMRmine can be also used as a general TM-protein classifier. Comparisons of TM and 7TMR protein distributions among 68 genomes revealed interesting differences in evolution of these protein families among major eukaryotic phyla.
authors.affiliation: Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182, USA. glu3@mail.unomaha.edu
authors.forename: Guoqing
authors.initials: G
authors.lastname: Lu
authors.forename: Zhifang
authors.initials: Z
authors.lastname: Wang
authors.forename: Alan M
authors.initials: AM
authors.lastname: Jones
authors.forename: Etsuko N
authors.initials: EN
authors.lastname: Moriyama
mesh_terms: D017360:Arabidopsis / Q000235:genetics; D029681:Arabidopsis Proteins / Q000737:chemistry / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D020407:Internet; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000145:classification; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software; D014584:User-Computer Interface
chemical_list: D029681:Arabidopsis Proteins; D043562:Receptors, G-Protein-Coupled