Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper.

Autor:	Huerta-Cepas J; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany., Forslund K; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany., Coelho LP; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany., Szklarczyk D; Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.; Bioinformatics/Systems Biology Group, Swiss Institute of Bioinformatics (SIB), Zurich, Switzerland., Jensen LJ; The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark., von Mering C; Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.; Bioinformatics/Systems Biology Group, Swiss Institute of Bioinformatics (SIB), Zurich, Switzerland., Bork P; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.; Germany Molecular Medicine Partnership Unit (MMPU), University Hospital Heidelberg and European Molecular Biology Laboratory, Heidelberg, Germany.; Max Delbrück Centre for Molecular Medicine, Berlin, Germany.; Department of Bioinformatics, Biocenter University of Würzburg, Würzburg, Germany.
Jazyk:	angličtina
Zdroj:	Molecular biology and evolution [Mol Biol Evol] 2017 Aug 01; Vol. 34 (8), pp. 2115-2122.
DOI:	10.1093/molbev/msx148
Abstrakt:	Orthology assignment is ideally suited for functional inference. However, because predicting orthology is computationally intensive at large scale, and most pipelines are relatively inaccessible (e.g., new assignments only available through database updates), less precise homology-based functional transfer is still the default for (meta-)genome annotation. We, therefore, developed eggNOG-mapper, a tool for functional annotation of large sets of sequences based on fast orthology assignments using precomputed clusters and phylogenies from the eggNOG database. To validate our method, we benchmarked Gene Ontology (GO) predictions against two widely used homology-based approaches: BLAST and InterProScan. Orthology filters applied to BLAST results reduced the rate of false positive assignments by 11%, and increased the ratio of experimentally validated terms recovered over all terms assigned per protein by 15%. Compared with InterProScan, eggNOG-mapper achieved similar proteome coverage and precision while predicting, on average, 41 more terms per protein and increasing the rate of experimentally validated terms recovered over total term assignments per protein by 35%. EggNOG-mapper predictions scored within the top-5 methods in the three GO categories using the CAFA2 NK-partial benchmark. Finally, we evaluated eggNOG-mapper for functional annotation of metagenomics data, yielding better performance than interProScan. eggNOG-mapper runs ∼15× faster than BLAST and at least 2.5× faster than InterProScan. The tool is available standalone and as an online service at http://eggnog-mapper.embl.de. (© The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)
Databáze:	MEDLINE
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