title: Functional coherence metrics in protein families.
abstract: Biological sequences, such as proteins, have been provided with annotations that assign functional information. These functional annotations are associations of proteins (or other biological sequences) with descriptors characterizing their biological roles. However, not all proteins are fully (or even at all) annotated. This annotation incompleteness limits our ability to make sound assertions about the functional coherence within sets of proteins. Annotation incompleteness is a problematic issue when measuring semantic functional similarity of biological sequences since they can only capture a limited amount of all the semantic aspects the sequences may encompass.
  
  
  
  Instead of relying uniquely on single (reductive) metrics, this work proposes a comprehensive approach for assessing functional coherence within protein sets. The approach entails using visualization and term enrichment techniques anchored in specific domain knowledge, such as a protein family. For that purpose we evaluate two novel functional coherence metrics, mUI and mGIC that combine aspects of semantic similarity measures and term enrichment.
  
  
  
  These metrics were used to effectively capture and measure the local similarity cores within protein sets. Hence, these metrics coupled with visualization tools allow an improved grasp on three important functional annotation aspects: completeness, agreement and coherence.
  
  
  
  Measuring the functional similarity between proteins based on their annotations is a non trivial task. Several metrics exist but due both to characteristics intrinsic to the nature of graphs and extrinsic natures related to the process of annotation each measure can only capture certain functional annotation aspects of proteins. Hence, when trying to measure the functional coherence of a set of proteins a single metric is too reductive. Therefore, it is valuable to be aware of how each employed similarity metric works and what similarity aspects it can best capture. Here we test the behaviour and resilience of some similarity metrics.
authors.affiliation: LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
authors.forename: Hugo P
authors.initials: HP
authors.lastname: Bastos
authors.affiliation: CEAUL, Departamento de Estatística e Investigação Operacional, Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749-016, Portugal.
authors.forename: Lisete
authors.initials: L
authors.lastname: Sousa
authors.affiliation: BioISI - Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749-016, Portugal.
authors.forename: Luka A
authors.initials: LA
authors.lastname: Clarke
authors.affiliation: LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal. fcouto@di.fc.ul.pt.
authors.forename: Francisco M
authors.initials: FM
authors.lastname: Couto
mesh_terms: D030562:Databases, Protein; D058977:Molecular Sequence Annotation / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D011506:Proteins