Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper

Abstract: 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 fu...

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Main Authors: Huerta-Cepas, Jaime (Author) , Bork, Peer (Author)
Format: Article (Journal)
Language:English
Published: 2017
In: Molecular biology and evolution
Year: 2017, Volume: 34, Issue: 8, Pages: 2115-2122
ISSN:1537-1719
DOI:10.1093/molbev/msx148
Online Access:Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1093/molbev/msx148
Verlag, kostenfrei, Volltext: https://academic.oup.com/mbe/article/34/8/2115/3782716
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Author Notes:Jaime Huerta-Cepas, Kristoffer Forslund, Luis Pedro Coelho, Damian Szklarczyk, Lars Juhl Jensen, Christian von Mering, Peer Bork

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520 |a Abstract: 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. 
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