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Cited by 649

The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.

Improved metagenome binning and assembly using deep variational autoencoders

Open access
Nissen, Jakob Nybo, Johansen, Joachim, Allesøe, Rosa Lundbye, Sønderby, Casper Kaae, Armenteros, Jose Juan Almagro, Grønbech, Christopher Heje, Jensen, Lars Juhl, Nielsen, Henrik Bjørn, Petersen, Thomas Nordahl, Winther, Ole, Rasmussen, Simon·2021·Nature Biotechnology

Abstract

Despite recent advances in metagenomic binning, reconstruction of microbial species from metagenomics data remains challenging. Here we develop variational autoencoders for metagenomic binning (VAMB), a program that uses deep variational autoencoders to encode sequence coabundance and k-mer distribution information before clustering. We show that a variational autoencoder is able to integrate these two distinct data types without any previous knowledge of the datasets. VAMB outperforms existing state-of-the-art binners, reconstructing 29–98% and 45% more near-complete (NC) genomes on simulated and real data, respectively. Furthermore, VAMB is able to separate closely related strains up to 99.5% average nucleotide identity (ANI), and reconstructed 255 and 91 NC Bacteroides vulgatus and Bacteroides dorei sample-specific genomes as two distinct clusters from a dataset of 1,000 human gut microbiome samples. We use 2,606 NC bins from this dataset to show that species of the human gut microbiome have different geographical distribution patterns. VAMB can be run on standard hardware and is freely available at https://github.com/RasmussenLab/vamb. Metagenomics data are resolved into their constituent genomes using a new deep learning method.

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Metadata

Authors
Nissen, J. N., Johansen, J., Allesøe, R. L., Sønderby, C. K., Armenteros, J. J. A., Grønbech, C. H., Jensen, L. J., Nielsen, H. B., Petersen, T. N., Winther, O., & Rasmussen, S.
Venue
Nature Biotechnology
Published
2021-01-04
Volume
39
Issue
5
Pages
555-560
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
OpenAlex
W3120084484
Cited by
649
References
90
FWCI
25.90
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Improved metagenome binning and assembly using deep variational autoencoders. https://app.cochat.ai/papers/10.1038/s41587-020-00777-4
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Nissen, J. N., Johansen, J., Allesøe, R. L., Sønderby, C. K., Armenteros, J. J. A., Grønbech, C. H., Jensen, L. J., Nielsen, H. B., Petersen, T. N., Winther, O., & Rasmussen, S. (2021). Improved metagenome binning and assembly using deep variational autoencoders. Nature Biotechnology. https://doi.org/10.1038/s41587-020-00777-4

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