Evidence map›Paper›PMID 33398153›Full record

ArticleNature biotechnology2021

Improved metagenome binning and assembly using deep variational autoencoders.

Jakob Nybo Nissen, Joachim Johansen, Rosa Lundbye Allesøe, Casper Kaae Sønderby, Jose Juan Almagro Armenteros, Christopher Heje Grønbech, Lars Juhl Jensen, Henrik Bjørn Nielsen, Thomas Nordahl Petersen, Ole Winther and 1 more

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In one paragraph

Article in Nature biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 336 papers.

0numbers the graph read from it
0cells of the map it votes in
336citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

336 citing papers in PubMed.

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276 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jakob Nybo NissenDepartment of Health Technology, Technical University of Denmark, Lyngby, Denmark.
Joachim JohansenNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.ORCID http://orcid.org/0000-0001-7052-1870
Rosa Lundbye AllesøeNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Casper Kaae SønderbyBioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Jose Juan Almagro ArmenterosDepartment of Health Technology, Technical University of Denmark, Lyngby, Denmark.ORCID http://orcid.org/0000-0003-0111-1362
Christopher Heje GrønbechBioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Lars Juhl JensenNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.ORCID http://orcid.org/0000-0001-7885-715X
Henrik Bjørn NielsenClinical-Microbiomics A/S, Copenhagen, Denmark.ORCID http://orcid.org/0000-0003-2281-5713
Thomas Nordahl PetersenNational Food Institute, Technical University of Denmark, Lyngby, Denmark.
Ole WintherBioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Simon RasmussenNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. simon.rasmussen@cpr.ku.dk.ORCID http://orcid.org/0000-0001-6323-9041

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 .

Indexed as

Molecular Sequence AnnotationSoftwareBacteroidesGenome, BacterialHumansMetagenomeMetagenomicsMicrobiota

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.