Overview

EggNOG-mapper is a tool for fast functional annotation of novel sequences. It uses precomputed orthologous groups and phylogenies from the eggNOG database (http://eggnog5.embl.de) 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: http://eggnog-mapper.embl.de

Documentation

https://github.com/jhcepas/eggnog-mapper/wiki

Citation

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, https://doi.org/10.1093/molbev/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). https://doi.org/10.1038/s41592-021-01101-x

[MMSEQS2] MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.
          Steinegger M & Söding J. 2017. Nat. Biotech. 35, 1026–1028. https://doi.org/10.1038/nbt.3988

[PRODIGAL] Prodigal: prokaryotic gene recognition and translation initiation site identification.
           Hyatt et al. 2010. BMC Bioinformatics 11, 119. https://doi.org/10.1186/1471-2105-11-119.