Improving metagenome binning by integrating intrinsic features and taxonomy

A common procedure for studying the microbiome is binning the sequenced contigs into metagenome-assembled genomes. State-of-the-art binning methods use coabundance and sequence-based motifs such as tetranucleotide frequencies, whereas taxonomic labels derived from alignment based classification have not been widely used. Here we propose TaxVAMB, a metagenome binning tool based on semisupervised bimodal variational autoencoders, combining tetranucleotide frequencies and contig coabundances with taxonomic information. TaxVAMB outperformed all other binners on CAMI2 human microbiome datasets, returning on average 29% more high-quality assemblies than the next best binner, and performed on par with the best binners on short-read datasets. On a human gut long-read dataset, TaxVAMB recovered 29% more high-quality bins. In a typical single-sample setup, TaxVAMB on average returns 83% more high-quality bins compared to VAMB. Lastly, TaxVAMB binned incomplete genomes better than any other tool, returning on average 300% more high-quality bins of incomplete genomes than the next best binner.

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Publication Details

Journal
Nature Biotechnology
Published
2026-04-27
DOI
https://doi.org/10.1038/s41587-026-03098-0
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Improving metagenome binning by integrating intrinsic features and taxonomy

L N Hansen, Peter Erdmann Dougherty, Pau Piera Líndez, Slt Christensen et al.
Nature Biotechnology
Genomics and Phylogenetic Studies
article

Improving metagenome binning by integrating intrinsic features and taxonomy

L N Hansen, Peter Erdmann Dougherty, Pau Piera Líndez, Slt Christensen, Patrick Denis Browne, Svetlana Kutuzova, Mads Nielsen, Alex Gobbi, Jesper Cairo Westergaard, Simon Rasmussen, Nikoline S. Olsen, Leise Riber, Lasse Schnell Danielsen, Knud Nor Nielsen, Jakob Nybo Andersen, Laura Milena Forero-Junco
article en

Abstract

A common procedure for studying the microbiome is binning the sequenced contigs into metagenome-assembled genomes. State-of-the-art binning methods use coabundance and sequence-based motifs such as tetranucleotide frequencies, whereas taxonomic labels derived from alignment based classification have not been widely used. Here we propose TaxVAMB, a metagenome binning tool based on semisupervised bimodal variational autoencoders, combining tetranucleotide frequencies and contig coabundances with taxonomic information. TaxVAMB outperformed all other binners on CAMI2 human microbiome datasets, returning on average 29% more high-quality assemblies than the next best binner, and performed on par with the best binners on short-read datasets. On a human gut long-read dataset, TaxVAMB recovered 29% more high-quality bins. In a typical single-sample setup, TaxVAMB on average returns 83% more high-quality bins compared to VAMB. Lastly, TaxVAMB binned incomplete genomes better than any other tool, returning on average 300% more high-quality bins of incomplete genomes than the next best binner.

Nature Biotechnology
University of Copenhagen (DK), Novo Nordisk Foundation (DK), Cereal Research Centre (IT), Research Centre for Cereal and Industrial Crops (IT), Technical University of Denmark (DK)
Innovationsfonden, National Research Foundation, Danmarks Grundforskningsfond, Novo Nordisk, Novo Nordisk Fonden
Openalex Percentile: Top 8%
Genomics and Phylogenetic Studies
DOIOpen Access PDFOpenAlex
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