Improved metagenome binning and assembly using deep variational autoencoders
By Jakob N. Nissen, Joachim Johansen, R. Allesøe, Casper Kaae Sønderby, José Juan Almagro Armenteros, C. Grønbech, Lars Juhl Jensen, Henrik Nielsen, Thomas Nordahl Petersen, Ole Winther and 1 more. Nature Biotechnology, 2021
522 citations recorded by the source corpus.
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.
Subfields: Ecology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/517bc143863a678fbc53180444621dacc2707263
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Citations in the corpus
- Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4 (2023, background): “As metagenomic assembly and binning have improved dramatically in the last few years 31–35 , large-scale MAG catalogs have been compiled and comprise a vast amount of unknown and uncultivated microbial species populating diverse environments 37–46 .”
- Identification of mobile genetic elements with geNomad (2023, unknown): “Furthermore, we found that 98.1% of the scaffolds that binned 48 with RdRP-encoding sequences based on their co-occurrence across multiple samples were also identified as viral by geNomad.”
- CoverM: read alignment statistics for metagenomics (2025, unknown): “For instance, some use the number of aligned reads divided by contig length (Wu et al. 2014, Nissen et al. 2021, Pan et al. 2022, 2023).”