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 distribu...

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Bibliographic Details
Published in:Nature biotechnology 2021-05, Vol.39 (5), p.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: Article
Language:English
Subjects:
Bacteroides - genetics
Clustering
Datasets
Deep learning
Distribution patterns
Electronic data processing
Genetic research
Genome, Bacterial - genetics
Genomes
Geographical distribution
Humans
Intestinal microflora
Machine learning
Metagenome - genetics
Metagenomics
Methods
Microbiomes
Microbiota - genetics
Microorganisms
Molecular Sequence Annotation
Nucleotides
Software
Technology application
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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 .
ISSN:1087-0156
1546-1696
DOI:10.1038/s41587-020-00777-4