title: Domain similarity based orthology detection.
abstract: BACKGROUND
  Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationally feasible in a reasonable amount of time. We propose to speed up the detection of orthologous proteins by using strings of domains to characterize the proteins.
  
  
  RESULTS
  We present two new protein similarity measures, a cosine and a maximal weight matching score based on domain content similarity, and new software, named porthoDom. The qualities of the cosine and the maximal weight matching similarity measures are compared against curated datasets. The measures show that domain content similarities are able to correctly group proteins into their families. Accordingly, the cosine similarity measure is used inside porthoDom, the wrapper developed for proteinortho. porthoDom makes use of domain content similarity measures to group proteins together before searching for orthologs. By using domains instead of amino acid sequences, the reduction of the search space decreases the computational complexity of an all-against-all sequence comparison.
  
  
  CONCLUSIONS
  We demonstrate that representing and comparing proteins as strings of discrete domains, i.e. as a concatenation of their unique identifiers, allows a drastic simplification of search space. porthoDom has the advantage of speeding up orthology detection while maintaining a degree of accuracy similar to proteinortho. The implementation of porthoDom is released using python and C++ languages and is available under the GNU GPL licence 3 at http://www.bornberglab.org/pages/porthoda .
authors.affiliation: Institute for Evolution and Biodiversity, University of Münster, Hüfferstr. 1, Münster, Germany. t.bitard.feildel@uni-muenster.de.
authors.forename: Tristan
authors.initials: T
authors.lastname: Bitard-Feildel
authors.affiliation: Institute for Evolution and Biodiversity, University of Münster, Hüfferstr. 1, Münster, Germany. c.kemena@uni-muenster.de.
authors.forename: Carsten
authors.initials: C
authors.lastname: Kemena
authors.affiliation: Institute for Evolution and Biodiversity, University of Münster, Hüfferstr. 1, Münster, Germany. j.greenwood@uni-muenster.de.
authors.forename: Jenny M
authors.initials: JM
authors.lastname: Greenwood
authors.affiliation: Institute for Evolution and Biodiversity, University of Münster, Hüfferstr. 1, Münster, Germany. ebb@uni-muenster.de.
authors.forename: Erich
authors.initials: E
authors.lastname: Bornberg-Bauer
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006801:Humans; D054730:Protein Interaction Domains and Motifs; D011506:Proteins / Q000737:chemistry*; D017386:Sequence Homology, Amino Acid; D012984:Software
chemical_list: D011506:Proteins