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  • Brief Communication
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Metabuli: sensitive and specific metagenomic classification via joint analysis of amino acid and DNA


Metagenomic taxonomic classifiers analyze either DNA or amino acid (AA) sequences. Metabuli (, however, jointly analyzes both DNA and AA to leverage AA conservation for sensitive homology detection and DNA mutations for specific differentiation of closely related taxa. In the Critical Assessment of Metagenome Interpretation 2 plant-associated dataset, Metabuli covered 99% and 98% of classifications of state-of-the-art DNA- and AA-based classifiers, respectively.

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Fig. 1: Metabuli’s workflow.
Fig. 2: Benchmark results.

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Data availability

The raw data for Fig. 2 and Extended Data Figs. 14 are provided as Source data. Performance measures at different ranks of benchmarks of Fig. 2a,b and Extended Data Fig. 1 are available in Supplementary Tables 13. The assemblies used for read simulation and database creation in synthetic benchmarks are listed in Supplementary Table 4, and the simulated reads are available via Zenodo at (ref. 28). More detailed results and utilized accessions of Fig. 2c,d are provided in Supplementary Tables 5 and 6. The databases used in Fig. 2c,d were built using viral genomes (release 212) and a human genome (GCF_009914755.1) downloaded from NCBI RefSeq, and accessions of genomes of analyzed SARS-CoV-2 variants were denoted in ‘Pathogen detection tests’ section in Methods. Performance measures at different ranks of Fig. 2e and Extended Data Fig. 2 are provided in Supplementary Tables 79. Precision and recall of Extended Data Fig. 4 are available in Supplementary Table 10. The accessions of real data analyzed in Fig. 2g,h and Extended Data Fig. 3 are denoted in ‘Benchmarks with real metagenomes’ section in Methods. CAMI2-provided datasets and taxonomy used in Fig. 2e,f and Extended Data Fig. 2 can be downloaded from Source data are provided with this paper.

Code availability

Metabuli is GPLv3-licensed free open-source software. The source code and ready-to-use binaries, as well as precomputed databases (Supplementary Table 11), can be downloaded at The scripts used for benchmarks and plots are available at and


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The authors thank E. Levy Karin for the valuable scientific feedback and the careful review and revision of the paper; J. Söding for the discussions on metamer encoding; M. Mirdita for the usability improvements of the software; H. Kim for the improvement of figures; S. Jaenicke for the voluntary examination of the software; and M. Kim for the feedback on the paper. M.S. acknowledges support by the National Research Foundation of Korea grants (2020M3-A9G7-103933, 2021-R1C1-C102065 and 2021-M3A9-I4021220), the Samsung DS research fund, and the Creative-Pioneering Researchers Program and AI-Bio Research Grant through Seoul National University.

Author information

Authors and Affiliations



J.K. and M.S. designed the research, developed the software, performed analysis and wrote the paper.

Corresponding author

Correspondence to Martin Steinegger.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks André Soares and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Lei Tang, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Synthetic benchmark results.

Simulated short (Illumina) and long (PacBio HiFi, ONT, and PacBio Sequel II) reads were used for performance evaluation based on GTDB genomes and taxonomy. Hybrid = (x, y) is the result of applying the DNA-based tool x, followed by the AA-based tool y, where both are the best-performing. ad Subspecies-level classification tests. Reads were simulated from subspecies present in databases, and precision and recall were measured at subspecies rank. a) Hybrid = (KrakenUniq, Kraken2x). b-d) Hybrid = (MetaMaps, Kraken2X). Raw data for performance measurements at subspecies, species, genus, and family ranks are available in Supplementary Table 1. eh Species-level classification tests. Not the queried subspecies but their sibling subspecies were contained in databases to measure species-level classification. Hybrid = (KrakenUniq, Kraken2X). Raw data for performance measurements at species, genus, family, and order ranks are available in Supplementary Table 2. il Genus-level classification tests. Not the queried species but their sibling species were contained in databases, so how well each tool can detect homology within the same genus was measured. i) Hybrid = (Kraken2, MMseqs2). j-l) Hybrid = (Kraken2, Kraken2X). Raw data for performance measurements at genus, family, order, and class ranks are available in Supplementary Table 3.

Source data

Extended Data Fig. 2 Benchmarks using CAMI2’s strain-madness, marine, and plant-associated datasets.

GTDB genomes and the CAMI2-provided taxonomy were used for the database creation. CAMI2-provided short reads of strain-madness (a), marine (b), and plant-associated (c) datasets were classified by each tool, and the average values of the metrics that were measured at the species and genus rank for each sample were plotted. Raw data and metrics for each sample are available in Supplementary Tables 79.

Source data

Extended Data Fig. 3 Comparison of Metabuli to best performing AA- and DNA-based tools on real long-read metagenomic samples.

In contrast to Fig. 2g–h, Kraken2X instead of Kaiju is utilized due to its superior performance on long reads. The databases were built using GTDB genomes and a human genome (T2T-CHM13v2.0) based on GTDB taxonomy edited to include a human taxon. Real nanopore sequencing data from human gut (a) and marine (b) environments, as well as PacBio HiFi reads from human gut (c) and marine (d) environments, were classified by each tool. The area is proportional to the number of reads within each panel. The proportion of reads classified by each tool is denoted in parentheses.

Source data

Extended Data Fig. 4 Subspecies-level classification performance by clade size.

All 2,382 query subspecies used in Extended Data Fig. 1a were divided into groups according to the number of subspecies siblings they had in the reference database, that is, by their species clade size. The average F1 score for queries in each group decreases as the clade’s size increases, indicating that more sibling subspecies pose a harder classification challenge to all tools. Precision and recall are available in Supplementary Table 10.

Source data

Extended Data Table 1 Resource measurements in subspecies inclusion test

Supplementary information

Supplementary Information

Supplementary Figs. 1–7.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables 1–10. Raw data and utilized accessions of Fig. 2a–e and Extended Data Figs. 1, 2 and 4. Supplementary Table 11. A list of provided prebuilt databases.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

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Kim, J., Steinegger, M. Metabuli: sensitive and specific metagenomic classification via joint analysis of amino acid and DNA. Nat Methods 21, 971–973 (2024).

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