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Fast functional annotation of novel sequences using eggNOG orthology assignments.

Project description

Build Status European Galaxy server


EggNOG-mapper is a tool for fast functional annotation of novel sequences. It uses precomputed orthologous groups and phylogenies from the eggNOG database ( to transfer functional information from fine-grained orthologs only.

Common uses of eggNOG-mapper include the annotation of novel genomes, transcriptomes or even metagenomic gene catalogs.

The use of orthology predictions for functional annotation permits a higher precision than traditional homology searches (i.e. BLAST searches), as it avoids transferring annotations from close paralogs (duplicate genes with a higher chance of being involved in functional divergence).

Benchmarks comparing different eggNOG-mapper options against BLAST and InterProScan can be found here.

EggNOG-mapper is also available as a public online resource:



If you use this software, please cite:

[1] eggNOG-mapper v2: functional annotation, orthology assignments, and domain 
    prediction at the metagenomic scale. Carlos P. Cantalapiedra, 
    Ana Hernandez-Plaza, Ivica Letunic, Peer Bork, Jaime Huerta-Cepas. 2021.
    Molecular Biology and Evolution, msab293,

[2] eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated
    orthology resource based on 5090 organisms and 2502 viruses. Jaime
    Huerta-Cepas, Damian Szklarczyk, Davide Heller, Ana Hernández-Plaza, Sofia
    K Forslund, Helen Cook, Daniel R Mende, Ivica Letunic, Thomas Rattei, Lars
    J Jensen, Christian von Mering, Peer Bork Nucleic Acids Res. 2019 Jan 8;
    47(Database issue): D309–D314. doi: 10.1093/nar/gky1085 

Please, cite also the underlying algorithm used for the search step of eggNOG-mapper, and Prodigal if it was used for gene prediction:

[HMMER] Accelerated Profile HMM Searches. 
        Eddy SR. 2011. PLoS Comput. Biol. 7:e1002195.

[DIAMOND] Sensitive protein alignments at tree-of-life scale using DIAMOND.
          Buchfink B, Reuter K, Drost HG. 2021.
          Nature Methods 18, 366–368 (2021).

[MMSEQS2] MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.
          Steinegger M & Söding J. 2017. Nat. Biotech. 35, 1026–1028.

[PRODIGAL] Prodigal: prokaryotic gene recognition and translation initiation site identification.
           Hyatt et al. 2010. BMC Bioinformatics 11, 119.

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