title: A meta-approach for improving the prediction and the functional annotation of ortholog groups.
abstract: BACKGROUND
  In comparative genomics, orthologs are used to transfer annotation from genes already characterized to newly sequenced genomes. Many methods have been developed for finding orthologs in sets of genomes. However, the application of different methods on the same proteome set can lead to distinct orthology predictions.
  
  
  METHODS
  We developed a method based on a meta-approach that is able to combine the results of several methods for orthologous group prediction. The purpose of this method is to produce better quality results by using the overlapping results obtained from several individual orthologous gene prediction procedures. Our method proceeds in two steps. The first aims to construct seeds for groups of orthologous genes; these seeds correspond to the exact overlaps between the results of all or several methods. In the second step, these seed groups are expanded by using HMM profiles.
  
  
  RESULTS
  We evaluated our method on two standard reference benchmarks, OrthoBench and Orthology Benchmark Service. Our method presents a higher level of accurately predicted groups than the individual input methods of orthologous group prediction. Moreover, our method increases the number of annotated orthologous pairs without decreasing the annotation quality compared to twelve state-of-the-art methods.
  
  
  CONCLUSIONS
  The meta-approach based method appears to be a reliable procedure for predicting orthologous groups. Since a large number of methods for predicting groups of orthologous genes exist, it is quite conceivable to apply this meta-approach to several combinations of different methods.
authors.forename: Cécile
authors.initials: C
authors.lastname: Pereira
authors.forename: Alain
authors.initials: A
authors.lastname: Denise
authors.forename: Olivier
authors.initials: O
authors.lastname: Lespinet
mesh_terms: D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular; D023281:Genomics / Q000379:methods*; D058977:Molecular Sequence Annotation / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software