eggNOG-mapper v3

Warning: v3 is currently under heavy testing and has not been officially released. For production use, install the stable v2 release: pip install eggnog-mapper==2.1.15 (see v2 branch).

eggNOG-mapper is a tool for fast functional annotation of novel sequences using precomputed orthologous groups and phylogenies from the eggNOG database. Functional information is transferred exclusively from fine-grained orthologs, yielding higher precision than homology-based approaches (e.g. BLAST) by avoiding annotation transfer from close paralogs.

Common uses include annotation of novel genomes, transcriptomes, and metagenomic gene catalogs.

eggNOG-mapper is also available as a public web server: http://mapper.eggnogdb.org

What's new in v3

v3 is a major release targeting the eggNOG v7 database and a completely redesigned annotation engine.

• eggNOG v7 database with integer-encoded orthology, phylogeny-aware speciation events, and ~12M proteins across ~10k taxa. eggNOG v5 databases are no longer supported.
• Curated-only functional donors: only manually curated functional terms (from SwissProt and equivalent curated sources) are used as annotation donors. This stops the propagation of misannotations inherited from automated pipelines. Despite the stricter source requirements, v3 achieves better annotation coverage than v2.
• Per-seed taxonomic ceiling replaces the old --tax_scope predefined scope lists. Each query seed gets its own ev_lca-based ceiling automatically narrowed to the most informative phylogenetic level (--tax_scope auto, default). Fixed clades (Metazoa, 33208, etc.) are still accepted.
• Cascade annotation engine: for each functional source (GO, KEGG, Pfam, EC, ...) donors are walked from closest and best-typed first, with the seed's own curated annotation as the strongest tier-0 donor.
• No bundled binaries — DIAMOND, HMMER, MMseqs2, and Prodigal must be installed externally (see Requirements below). The wheel shrinks from ~150 MB to ~5 MB and cross-platform installs (macOS, Windows) now work.
• Compressed input — gzip and bzip2 FASTA inputs are autodetected by magic bytes.
• Parallel annotation — --cpu N parallelises both search and annotation.
• Cython-accelerated inner loops — _codec and _collect_inner extensions give ~2–3× speedup on the annotation phase.
• --resume — safely resumes an interrupted run, reusing the existing hits file.
• Apptainer/Singularity image — a self-contained HPC image is provided via apptainer/build.sh.

Requirements

• Python ≥ 3.9
• At least one search backend:

Tool	Install
DIAMOND	conda install -c bioconda diamond
HMMER	conda install -c bioconda hmmer
MMseqs2	conda install -c bioconda mmseqs2
Prodigal	conda install -c bioconda prodigal (gene prediction only)

Installation

pip install eggnog-mapper

Or from source:

git clone https://github.com/eggnogdb/eggnog-mapper.git
cd eggnog-mapper
pip install .

Download the eggNOG v7 database

download_eggnog_data.py --data_dir /path/to/eggnog-data

Quick start

# Protein sequences against eggNOG v7 using DIAMOND
emapper.py -m diamond -i proteins.fa --itype proteins \
    --data_dir /path/to/eggnog-data \
    -o my_annotation --output_dir results/ --cpu 20

# Two-step: search first, annotate later
emapper.py -m diamond -i proteins.fa --itype proteins \
    --data_dir /path/to/eggnog-data \
    -o my_annotation --output_dir results/ --no_annot --cpu 20

emapper.py -m no_search --annotate_hits_table results/my_annotation.emapper.seed_orthologs \
    --data_dir /path/to/eggnog-data \
    -o my_annotation --output_dir results/

Documentation

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

Citation

If you use eggNOG-mapper, 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 v7: phylogeny-based orthology predictions and functional annotations.
    Ana Hernández-Plaza, Ziqi Deng, Fabian Robledo-Yagüe, Damian Szklarczyk,
    Christian von Mering, Peer Bork, Jaime Huerta-Cepas. Nucleic Acids Research,
    Volume 54, Issue D1, 6 January 2026, Pages D402-D408.
    https://doi.org/10.1093/nar/gkaf1249

Please also cite the search tool used:

[DIAMOND] Sensitive protein alignments at tree-of-life scale using DIAMOND.
          Buchfink B, Reuter K, Drost HG. 2021.
          Nature Methods 18, 366–368. https://doi.org/10.1038/s41592-021-01101-x

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

[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

Legacy v2 (eggNOG v5)

If you are working with eggNOG v5 databases, use the v2 branch or install the last v2 release from PyPI:

pip install eggnog-mapper==2.1.15

v2 and v3 databases are not interchangeable. v3 only works with eggNOG v7.