title: Detection of significant protein coevolution.
abstract: BACKGROUND
  The evolution of proteins cannot be fully understood without taking into account the coevolutionary linkages entangling them. From a practical point of view, coevolution between protein families has been used as a way of detecting protein interactions and functional relationships from genomic information. The most common approach to inferring protein coevolution involves the quantification of phylogenetic tree similarity using a family of methodologies termed mirrortree. In spite of their success, a fundamental problem of these approaches is the lack of an adequate statistical framework to assess the significance of a given coevolutionary score (tree similarity). As a consequence, a number of ad hoc filters and arbitrary thresholds are required in an attempt to obtain a final set of confident coevolutionary signals.
  
  
  RESULTS
  In this work, we developed a method for associating confidence estimators (P values) to the tree-similarity scores, using a null model specifically designed for the tree comparison problem. We show how this approach largely improves the quality and coverage (number of pairs that can be evaluated) of the detected coevolution in all the stages of the mirrortree workflow, independently of the starting genomic information. This not only leads to a better understanding of protein coevolution and its biological implications, but also to obtain a highly reliable and comprehensive network of predicted interactions, as well as information on the substructure of macromolecular complexes using only genomic information.
  
  
  METHODS
  The software and datasets used in this work are freely available at: http://csbg.cnb.csic.es/pMT/.
  
  
  BACKGROUND
  pazos@cnb.csic.es
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid and Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO), C/ Melchor Fernández Almagro 3, 28029 Madrid, Spain.
authors.forename: David
authors.initials: D
authors.lastname: Ochoa
authors.affiliation: Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid and Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO), C/ Melchor Fernández Almagro 3, 28029 Madrid, Spain.
authors.forename: David
authors.initials: D
authors.lastname: Juan
authors.affiliation: Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid and Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO), C/ Melchor Fernández Almagro 3, 28029 Madrid, Spain.
authors.forename: Alfonso
authors.initials: A
authors.lastname: Valencia
authors.affiliation: Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid and Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO), C/ Melchor Fernández Almagro 3, 28029 Madrid, Spain.
authors.forename: Florencio
authors.initials: F
authors.lastname: Pazos
mesh_terms: D030562:Databases, Protein; D019143:Evolution, Molecular; D015894:Genome, Human; D006801:Humans; D020407:Internet; D046911:Macromolecular Substances / Q000737:chemistry*; D010802:Phylogeny; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D046911:Macromolecular Substances; D011506:Proteins