Standardized benchmarking in the quest for orthologs

Adrian Altenhoff 1 Brigitte Boeckmann 1 Salvador Capella-Gutiérrez 2 Daniel Dalquen 3 Todd Deluca 4 Kristoffer Forslund 5 Jaime Huerta-Cepas 5 Benjamin Linard 6 Cécile Pereira 7 Leszek Pryszcz 8 Fabian Schreiber 9 Alan Sousa da Silva 9 Damian Szklarczyk 10 Clément-Marie Train 11 Peer Bork 12 Odile Lecompte 13 Christian Von Mering 1 Ioannis Xenarios 14 Kimmen Sjölander 15 Lars Juhl Jensen 16 Maria J Martin 9 Matthieu Muffato 9 Toni Gabaldon 8 Suzanna E Lewis 17 Paul D Thomas 18 Erik Sonnhammer 19 Christophe Dessimoz 20
1 Swiss Institute of Bioinformatics (SIB)
2 Universitat Pompeu Fabra [Barcelona]
3 Department of Genetics, Evolution and Environment
4 HMS - Harvard Medical School [Boston]
5 EMBL - European Molecular Biology Laboratory [Heidelberg]
6 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
7 LRI - Laboratoire de Recherche en Informatique
8 BIST - Barcelona Institute of Science and Technology
9 EMBL-EBI - European Bioinformatics Institute [Hinxton]
10 UZH - Universität Zürich [Zürich]
11 D-INFK - Department of Computer Science [ETH Zürich]
12 Max Delbrück Centre for Molecular Medicine
13 IGBMC - Institut de Génétique et de Biologie Moléculaire et Cellulaire
14 SIB - Swiss Institute of Bioinformatics [Genève]
15 University of California [Berkeley]
16 Novo Nordisk Foundation Center for Protein Research
17 LBNL - Lawrence Berkeley National Laboratory [Berkeley]
18 SRI International, Evolutionary System Biolology
19 USC - University of Southern California
20 Department of Computer Science
Abstract : Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision–recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. Here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. Using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. Standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods.
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IGBMC | LIRMM | INRIA | CENTRALESUPELEC | MIPS | UNIV-MONTPELLIER | UMR8623 | UNIV-PARIS-SACLAY | MAB | LRI-BIOINFO | SITE-ALSACE

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Adrian Altenhoff, Brigitte Boeckmann, Salvador Capella-Gutiérrez, Daniel Dalquen, Todd Deluca, et al.. Standardized benchmarking in the quest for orthologs. Nature Methods, Nature Publishing Group, 2016, 13 (5), pp.425-430. ⟨10.1038/nMeth.3830⟩. ⟨hal-01636892⟩

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