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Nat Methods. 2016 May;13(5):425-30. doi: 10.1038/nmeth.3830. Epub 2016 Apr 4.

Standardized benchmarking in the quest for orthologs.

Author information

1
Department of Computer Science, ETH Zurich, Zurich, Switzerland.
2
Computational Biochemistry Research Group, Swiss Institute of Bioinformatics, Zurich, Switzerland.
3
Swiss-Prot Group, Swiss Institute of Bioinformatics, Geneva, Switzerland.
4
Bioinformatics and Genomics Programme, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.
5
Universitat Pompeu Fabra, Barcelona, Spain.
6
Yeast and Basidiomycete Research Group, CBS Fungal Biodiversity Centre, Utrecht, the Netherlands.
7
Department of Genetics, Evolution, and Environment, University College London, London, UK.
8
Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
9
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
10
Department of Life Sciences, Natural History Museum, London, UK.
11
Université Paris-Sud, Laboratoire de Recherche en Informatique, Orsay, France.
12
Université Paris-Sud, Institute for Integrative Biology of the Cell, Orsay, France.
13
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK.
14
Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
15
Bioinformatics Systems Biology Group, Swiss Institute of Bioinformatics, Zurich, Switzerland.
16
Germany Molecular Medicine Partnership Unit, University Hospital Heidelberg and European Molecular Biology Laboratory, Heidelberg, Germany.
17
Max Delbrück Centre for Molecular Medicine, Berlin, Germany.
18
LBGI, Computer Science Department, ICube, University of Strasbourg, Strasbourg, France.
19
Vital-IT, Swiss Institute of Bioinformatics, Lausanne, Switzerland.
20
Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.
21
Department of Bioengineering, University of California, Berkeley, California, USA.
22
The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
23
Institució Catalana de Recerca I Estudis Avançats, Barcelona, Spain.
24
Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
25
Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA.
26
Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Solna, Sweden.
27
Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.
28
Department of Computer Science, University College London, London, UK.
29
Swiss Institute of Bioinformatics, Biophore Building, Lausanne, Switzerland.

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.

PMID:
27043882
PMCID:
PMC4827703
DOI:
10.1038/nmeth.3830
[Indexed for MEDLINE]
Free PMC Article

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