Improved metagenome binning and assembly using deep variational autoencoders

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 distrib...

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Bibliographic Details
Published in:Nature biotechnology Vol. 39; no. 5; pp. 555 - 560
Main Authors: 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
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.05.2021
Nature Publishing Group
Subjects:
631/114/2785
631/326/2565/2134
631/61/212/2142
Agriculture
Bacteroides - genetics
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Clustering
Datasets
Deep learning
Distribution patterns
Electronic data processing
Genetic research
Genome, Bacterial - genetics
Genomes
Geographical distribution
Humans
Intestinal microflora
Letter
Life Sciences
Machine learning
Metagenome - genetics
Metagenomics
Methods
Microbiomes
Microbiota - genetics
Microorganisms
Molecular Sequence Annotation
Nucleotides
Software
Technology application
Denmark
ISSN:1087-0156, 1546-1696, 1546-1696
Online Access:Get full text
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Summary: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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ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/s41587-020-00777-4