Global Microbial Gene Catalog (GMGC)

The Global Microbial Gene Catalog (GMGC, currently version 1.0) is an integrated, consistently-processed, gene catalog of the microbial world, combining metagenomics and high-quality sequenced isolates (from the ProGenomes2 database, Mende et al., 2019).

A total of 2.3 Billion genes were used to build the catalog. After 100%-identity redundancy removal, we obtained a 100% non-redundant catalog with 966 million sequences. Further, species-level (95% nucleotide identity) resulted in the main GMGC catalog, which includes 302 million unigenes. See the Downloads page for more details and various habitat-specific subcatalogs that may be more convenient for your usage.

Available subcatalogs

In addition to the stratification by habitat, we also provide versions of the catalog that exclude fragmentary ORFs. Fragmentary ORFs arise when due to the incomplete assemblies so that either the start or the stop codons may be missing (or both). They represent real DNA and should be accounted for when considering all the extant sequences (_e.g._, when considering whether and where a particular sequence has been sequenced before). However, they are not appropriate to use for all analyses. For example, multiple sequence alignments in protein families which include fragmentary ORFs need to be interpreted with care as missing portions of a gene may be missing solely due to the incompleteness of the assembly.

Thus, to cater to users who wish for a higher standard of quality of each individual sequence (albeit at potential the cost of a loss of coverage), we also make available a version of the catalog that only includes complete ORFs (both start and stop codon present).

Similarly, our protein families were built using _e-value_ thresholds to ensure that they are grouping together sequences which share an evolutionary history, even at the so-called _twilight zone_ (<30% amino acid identity, see Rost 1999). However, for users who want a stricter dataset, we also provide families with an additional identity threshold of 30% (thus avoiding the twilight zone) as well as 50% (analogously to what is provided by RefSeq50). While, as we also show in the manuscript, many analyses will be robust to these technical choices (and we default to the broader 20% amino acid threshold for general usage), we offer the higher-threshold versions for users who need stricter assurances with respect to their analyses.


Genes in the main catalog and subcatalogs are identified with the scheme GMGC10.###_###_###.NAME. The initial GMGC10 indicates the version of the catalog (Global Microbial Gene Catalog 1.0). The numeric ID uniquely identifies the unigene (the underscores are for readability only). Finally, the NAME is the predicted gene name, obtained from eggnog-mapper (Huerta-Cepas et al., 2017).


Search uses a kmer-based index. Briefly, the query sequence is broken up into consecutive 7-mers and the 100 sequences in the database which share the highest number of kmers with the query are retrieved. These are aligned against the query using a fast implementation of Smith-Waterman (Zhao et al., 2013) and sorted by alignment score. This method is very efficient at recovering sequence that are similar to ones in the database, but is less sensitive than BLAST.


  1. Burkhard Rost Twilight zone of protein sequence alignments in Protein Engineering, Design and Selection, 1999, doi:10.1093/protein/12.2.85
  2. Daniel R Mende, Ivica Letunic, Oleksandr M Maistrenko, Thomas S B Schmidt, Alessio Milanese, Lucas Paoli, Ana Hernández-Plaza, Askarbek N Orakov, Sofia K Forslund, Shinichi Sunagawa, Georg Zeller, Jaime Huerta-Ceps, Luis Pedro Coelho and Peer Bork proGenomes2: an improved database for accurate and consistent habitat, taxonomic and functional annotations of prokaryotic genomes in Nucleic Acid Research, 2019 doi:10.1093/nar/gkz1002
  3. Jaime Huerta-Cepas, Kristoffer Forslund, Luis Pedro Coelho, Damian Szklarczyk, Lars Juhl Jensen, Christian von Mering and Peer Bork Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper in Mol Biol Evol, 2017 doi:10.1093/molbev/msx148
  4. Mengyao Zhao, Wan-Ping Lee, Erik P. Garrison, and Gabor T. Marth SSW Library: An SIMD Smith-Waterman C/C++ Library for Use in Genomic Applications in PloS One, 2013 doi:10.1371/journal.pone.0082138


Detailed description and more information about the GMGC v1.0 API is available here.