title: Homology-based inference sets the bar high for protein function prediction.
abstract: BACKGROUND
  Any method that de novo predicts protein function should do better than random. More challenging, it also ought to outperform simple homology-based inference.
  
  
  METHODS
  Here, we describe a few methods that predict protein function exclusively through homology. Together, they set the bar or lower limit for future improvements.
  
  
  CONCLUSIONS
  During the development of these methods, we faced two surprises. Firstly, our most successful implementation for the baseline ranked very high at CAFA1. In fact, our best combination of homology-based methods fared only slightly worse than the top-of-the-line prediction method from the Jones group. Secondly, although the concept of homology-based inference is simple, this work revealed that the precise details of the implementation are crucial: not only did the methods span from top to bottom performers at CAFA, but also the reasons for these differences were unexpected. In this work, we also propose a new rigorous measure to compare predicted and experimental annotations. It puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users. Clearly, the definition of proper goals remains one major objective for CAFA.
authors.affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.
authors.forename: Tobias
authors.initials: T
authors.lastname: Hamp
authors.forename: Rebecca
authors.initials: R
authors.lastname: Kassner
authors.forename: Stefan
authors.initials: S
authors.lastname: Seemayer
authors.forename: Esmeralda
authors.initials: E
authors.lastname: Vicedo
authors.forename: Christian
authors.initials: C
authors.lastname: Schaefer
authors.forename: Dominik
authors.initials: D
authors.lastname: Achten
authors.forename: Florian
authors.initials: F
authors.lastname: Auer
authors.forename: Ariane
authors.initials: A
authors.lastname: Boehm
authors.forename: Tatjana
authors.initials: T
authors.lastname: Braun
authors.forename: Maximilian
authors.initials: M
authors.lastname: Hecht
authors.forename: Mark
authors.initials: M
authors.lastname: Heron
authors.forename: Peter
authors.initials: P
authors.lastname: Hönigschmid
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Hopf
authors.forename: Stefanie
authors.initials: S
authors.lastname: Kaufmann
authors.forename: Michael
authors.initials: M
authors.lastname: Kiening
authors.forename: Denis
authors.initials: D
authors.lastname: Krompass
authors.forename: Cedric
authors.initials: C
authors.lastname: Landerer
authors.forename: Yannick
authors.initials: Y
authors.lastname: Mahlich
authors.forename: Manfred
authors.initials: M
authors.lastname: Roos
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
mesh_terms: D000465:Algorithms; D011506:Proteins / Q000235:genetics / Q000502:physiology*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins