Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Improved metagenome binning and assembly using deep variational autoencoders

Nature Biotechnology volume 39, pages 555–560 (2021)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Performance of VAMB.
Fig. 2: Performance of clustering different inputs.
Fig. 3: Phylogeny of bins across 1,000 human gut microbiome samples.

Data availability

The sequence data used in this study are publicly available from either the respective studies or ENA. The semisynthetic MetaHIT dataset was downloaded from https://portal.nersc.gov/dna/RD/Metagenome_RD/MetaBAT/Files/ as the files depth.txt.gz and assembly-filtered.fa.gz. The simulated CAMI High and CAMI2 datasets were downloaded from https://data.cami-challenge.org/participate from ‘Toy Test Dataset High_Complexity’ and ‘2nd CAMI Toy Human Microbiome Project Dataset’, respectively. The de novo assemblies of the Almeida dataset were obtained through personal communication with A. Almeida and R. D. Finn, and the reads downloaded from ENA as specified in their publication. The data and results of binning the MetaHIT, CAMI2 and Almeida datasets, as well as the source data for Figs. 1–3, are available on figshare at https://figshare.com/projects/VAMB/72677. A CodeOcean capsule of VAMB v.3.0.1, including the six training and test datasets for reproducing benchmarking results, is available from https://doi.org/10.24433/CO.2518623.v1. Source data are provided with this paper.

Code availability

All code can be found on GitHub at https://github.com/RasmussenLab/vamb and is freely available under the permissive MIT license. All analyses were performed using VAMB v.3.0.1. Additionally, code are available as a CodeOcean capsule at https://doi.org/10.24433/CO.2518623.v1.

References

  1. Turaev, D. & Rattei, T. High definition for systems biology of microbial communities: metagenomics gets genome-centric and strain-resolved. Curr. Opin. Biotechnol. 39, 174–181 (2016).

    Article  CAS  PubMed  Google Scholar 

  2. Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J. & Segata, N. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35, 833–844 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. Wang, J. & Jia, H. Metagenome-wide association studies: fine-mining the microbiome. Nat. Rev. Microbiol. 14, 508–522 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at https://arxiv.org/abs/1312.6114 (2014).

  5. Rezende, D. J., Mohamed, S. & Wierstra, D. Stochastic backpropagation and approximate inference in deep generative models. Proc. Mach. Learn. Res. 32, 1278–1286 (2014).

    Google Scholar 

  6. Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Teeling, H., Meyerdierks, A., Bauer, M., Amann, R. & Glöckner, F. O. Application of tetranucleotide frequencies for the assignment of genomic fragments. Environ. Microbiol. 6, 938–947 (2004).

    Article  CAS  PubMed  Google Scholar 

  8. Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Albertsen, M. et al. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat. Biotechnol. 31, 533–538 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 3, e1165 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).

    Article  CAS  PubMed  Google Scholar 

  12. Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 7, e7359 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Plaza Oñate, F. et al. MSPminer: abundance-based reconstitution of microbial pan-genomes from shotgun metagenomic data. Bioinformatics 35, 1544–1552 (2019).

    Article  PubMed  Google Scholar 

  14. Lin, H. H. & Liao, Y. C. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci. Rep. 6, 24175 (2016).

  15. Chatterji, S., Yamazaki, I., Bai, Z. & Eisen, J. A. CompostBin: A DNA Composition-Based Algorithm for Binning Environmental Shotgun Reads. in Research in Computational Molecular Biology (eds. Vingron, M. & Wong, L.) 17–28 (Springer, 2008).

  16. Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649–662 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499–504 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Brooks, B. et al. Strain-resolved analysis of hospital rooms and infants reveals overlap between the human and room microbiome. Nat. Commun. 8, 1–7 (2017).

    Article  Google Scholar 

  20. Sczyrba, A. et al. Critical Assessment of Metagenome Interpretation – a benchmark of metagenomics software. Nat. Methods 14, 1063–1071 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cleary, B. et al. Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning. Nat. Biotechnol. 33, 1053–1060 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    Article  CAS  Google Scholar 

  26. Saeed, I., Tang, S.-L. & Halgamuge, S. K. Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition. Nucleic Acids Res. 40, e34 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. Pride, D. T., Meinersmann, R. J., Wassenaar, T. M. & Blaser, M. J. Evolutionary implications of microbial genome tetranucleotide frequency biases. Genome Res. 13, 145–156 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chen, L.-X., Anantharaman, K., Shaiber, A., Eren, A. M. & Banfield, J. F. Accurate and complete genomes from metagenomes. Genome Res. 30, 315–333 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Daubin, V., Lerat, E. & Perrière, G. The source of laterally transferred genes in bacterial genomes. Genome Biol. 4, R57 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Schloissnig, S. et al. Genomic variation landscape of the human gut microbiome. Nature 493, 45–50 (2013).

    Article  PubMed  Google Scholar 

  32. Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Deschasaux, M. et al. Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography. Nat. Med. 24, 1526–1531 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. He, Y. et al. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat. Med. 24, 1532–1535 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Asnicar, F. et al. Studying vertical microbiome transmission from mothers to infants by strain-level metagenomic profiling. mSystems 2, e00164–16 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ferretti, P. et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe 24, 133–145 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Grønbech, C. H. et al. scVAE: variational auto-encoders for single-cell gene expression data. Bioinformatics 36, 4415–4422 (2020).

  38. Dilokthanakul, N. et al. Deep unsupervised clustering with Gaussian mixture variational autoencoders. Preprint at https://arxiv.org/abs/1611.02648 (2017).

  39. Kislyuk, A., Bhatnagar, S., Dushoff, J. & Weitz, J. S. Unsupervised statistical clustering of environmental shotgun sequences. BMC Bioinform. 10, 316 (2009).

    Article  Google Scholar 

  40. Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Preprint at https://arxiv.org/abs/1502.03167 (2015).

  41. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. R. Improving neural networks by preventing co-adaptation of feature detectors. Preprint at https://arxiv.org/pdf/1207.0580.pdf (2012).

  42. Maas, A. L., Maas, A. L., Hannun, A. Y. & Ng, A. Y. Rectifier nonlinearities improve neural network acoustic models. Preprint at https://arxiv.org/pdf/1207.0580.pdf (2013).

  43. Kingma, D. P. & Ba, J. L. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2017).

  44. Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8026–8037 (2019).

    Google Scholar 

  45. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997?upload=1 (2013).

  46. Li, H. et al. The sequence alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Sculley, D. Web-Scale k-Means Clustering. in Proc. 19th International Conference on World Wide Web 1177–1178 (ACM Press, 2010).

  48. Huang, W., Li, L., Myers, J. R. & Marth, G. T. ART: a next-generation sequencing read simulator. Bioinformatics 28, 593–594 (2012).

    Article  PubMed  Google Scholar 

  49. Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. MetaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kent, W. J. BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).

  54. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009).

    Article  Google Scholar 

  56. Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).

    Article  Google Scholar 

  58. Mitchell, A. L. et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 47, D351–D360 (2019).

    Article  CAS  PubMed  Google Scholar 

  59. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B 57, 289–300 (1995).

    Google Scholar 

  60. Nayfach, S., Pedro Camargo, A., Eloe-Fadrosh, E. & Roux, S. CheckV: assessing the quality of metagenome-assembled viral genomes. Preprint at bioRxiv https://doi.org/10.1101/2020.05.06.081778 (2020).

  61. Ren, J. et al. Identifying viruses from metagenomic data using deep learning. Quant. Biol. 8, 64–77 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).

    Article  PubMed  Google Scholar 

  63. Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).

    PubMed Central  Google Scholar 

  64. Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).

    Article  CAS  PubMed  Google Scholar 

  65. Cosentino, S. & Iwasaki, W. SonicParanoid: fast, accurate and easy orthology inference. Bioinformatics 35, 149–151 (2018).

    Article  PubMed Central  Google Scholar 

  66. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., Von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Zhang, C., Rabiee, M., Sayyari, E. & Mirarab, S. ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinform. 19, 153 (2018).

    Article  Google Scholar 

  69. Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation.Mol. Biol. Evol. 35, 518–522 (2018).

    Article  CAS  PubMed  Google Scholar 

  70. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Oksanen, J. et al. Package ‘vegan’. Community Ecology Package v.2.5-6. R Package version 3.4.0 1–296. https://cran.r-project.org/src/contrib/Archive/vegan/vegan_2.5-6.tar.gz (2019).

  72. Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

We thank A. Almeida and R. D. Finn for sharing de novo assemblies of the 1,000 gut microbiome samples that we used for benchmarking VAMB. We thank C. Titus Brown for his source code contribution to the VAMB software package. J.N.N., J.J., R.L.A., L.J.J. and S.R. were supported by the Novo Nordisk Foundation (grant NNF14CC0001). S.R. was supported by the Jorck Foundation Research Award.

Author information

Authors and Affiliations

  1. Department of Health Technology, Technical University of Denmark, Lyngby, Denmark

    Jakob Nybo Nissen & Jose Juan Almagro Armenteros

  2. Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Jakob Nybo Nissen, Joachim Johansen, Rosa Lundbye Allesøe, Lars Juhl Jensen & Simon Rasmussen

  3. Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark

    Casper Kaae Sønderby, Christopher Heje Grønbech & Ole Winther

  4. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark

    Christopher Heje Grønbech & Ole Winther

  5. Clinical-Microbiomics A/S, Copenhagen, Denmark

    Henrik Bjørn Nielsen

  6. National Food Institute, Technical University of Denmark, Lyngby, Denmark

    Thomas Nordahl Petersen

  7. Center for Genomic Medicine, Copenhagen University Hospital, Copenhagen, Denmark

    Ole Winther

Authors
  1. Jakob Nybo Nissen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joachim Johansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rosa Lundbye Allesøe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Casper Kaae Sønderby
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jose Juan Almagro Armenteros
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christopher Heje Grønbech
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lars Juhl Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Henrik Bjørn Nielsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Thomas Nordahl Petersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ole Winther
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Simon Rasmussen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.R. conceived the study and guided the analysis. J.N.N., S.R., J.J. and R.L.A. performed the analyses. J.N.N. wrote the software. C.K.S., J.J.A.A., C.H.G., T.N.P., L.J.J., H.B.N. and O.W. provided guidance and input for the analysis. J.N.N., L.J.J. and S.R. wrote the manuscript with contributions from all coauthors. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Simon Rasmussen.

Ethics declarations

Competing interests

H.B.N. is employed at Clinical-Microbiomics A/S. The remaining authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–33 and Tables 1–10.

Reporting Summary

Supplementary Data 1

Overview of Salmonella spike-in with one to three Salmonella strains in a background of 50 HMP samples.

Supplementary Data 2

Results of Salmonella spike-in with one to three genomes in a background of 50 HMP samples.

Supplementary Data 3

Results of comparison between multisplit and single-sample binning.

Supplementary Data 4

Information on the 1,000 human gut microbiome samples (benchmark set from Almeida et al.18).

Supplementary Data 5

GTDB annotation of VAMB NC bins from the dataset of Almeida et al.18.

Supplementary Data 6

Number of NC bins generated by VAMB and MetaBAT2 that are annotated by GTDB to a particular species.

Supplementary Data 7

Overview of BLAST hits for alignment of VAMB clusters versus NCBI nonredundant nucleotides.

Supplementary Data 8

PERMANOVA analysis of phylogenetic placements and geography.

Supplementary Data 9

Results of hyperparameter optimizations of the VAE in VAMB.

Supplementary Data 10

CheckM results for all bins produced by VAMB.

Source data

Source Data Fig. 1

Statistical source data Fig. 1a–d,f.

Source Data Fig. 2

Statistical source data Fig. 2a–f.

Source Data Fig. 3

Statistical source data Fig. 3c,d.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nissen, J.N., Johansen, J., Allesøe, R.L. et al. Improved metagenome binning and assembly using deep variational autoencoders. Nat Biotechnol 39, 555–560 (2021). https://doi.org/10.1038/s41587-020-00777-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-020-00777-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing