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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 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

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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 as the files depth.txt.gz and assembly-filtered.fa.gz. The simulated CAMI High and CAMI2 datasets were downloaded from 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. 13, are available on figshare at A CodeOcean capsule of VAMB v.3.0.1, including the six training and test datasets for reproducing benchmarking results, is available from Source data are provided with this paper.

Code availability

All code can be found on GitHub at 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


  1. 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).

    CAS  PubMed  Google Scholar 

  2. 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).

    CAS  PubMed  Google Scholar 

  3. 3.

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

    CAS  PubMed  Google Scholar 

  4. 4.

    Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at (2014).

  5. 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. 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).

    CAS  PubMed  Google Scholar 

  7. 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).

    CAS  PubMed  Google Scholar 

  8. 8.

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

    CAS  PubMed  Google Scholar 

  9. 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).

    CAS  PubMed  Google Scholar 

  10. 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).

    PubMed  PubMed Central  Google Scholar 

  11. 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).

    CAS  PubMed  Google Scholar 

  12. 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).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

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

    PubMed  Google Scholar 

  14. 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. 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. 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).

    CAS  Google Scholar 

  17. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 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).

    Google Scholar 

  20. 20.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

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

    CAS  Google Scholar 

  26. 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).

    CAS  PubMed  Google Scholar 

  27. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

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

    PubMed  PubMed Central  Google Scholar 

  30. 30.

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

    PubMed  PubMed Central  Google Scholar 

  31. 31.

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

    PubMed  Google Scholar 

  32. 32.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 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).

    CAS  PubMed  Google Scholar 

  34. 34.

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

    CAS  PubMed  Google Scholar 

  35. 35.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

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

  38. 38.

    Dilokthanakul, N. et al. Deep unsupervised clustering with Gaussian mixture variational autoencoders. Preprint at (2017).

  39. 39.

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

    Google Scholar 

  40. 40.

    Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Preprint at (2015).

  41. 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 (2012).

  42. 42.

    Maas, A. L., Maas, A. L., Hannun, A. Y. & Ng, A. Y. Rectifier nonlinearities improve neural network acoustic models. Preprint at (2013).

  43. 43.

    Kingma, D. P. & Ba, J. L. Adam: a method for stochastic optimization. Preprint at (2017).

  44. 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. 45.

    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at (2013).

  46. 46.

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

    PubMed  PubMed Central  Google Scholar 

  47. 47.

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

  48. 48.

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

    PubMed  Google Scholar 

  49. 49.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 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. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

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

    Google Scholar 

  56. 56.

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

    PubMed  PubMed Central  Google Scholar 

  57. 57.

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

    Google Scholar 

  58. 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).

    CAS  PubMed  Google Scholar 

  59. 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. 60.

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

  61. 61.

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

    CAS  Google Scholar 

  62. 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).

    PubMed  Google Scholar 

  63. 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  PubMed Central  Google Scholar 

  64. 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).

    CAS  Google Scholar 

  65. 65.

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

    PubMed Central  Google Scholar 

  66. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 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).

    Google Scholar 

  69. 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).

    CAS  PubMed  Google Scholar 

  70. 70.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Oksanen, J. et al. Package ‘vegan’. Community Ecology Package v.2.5-6. R Package version 3.4.0 1–296. (2019).

  72. 72.

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

    Google Scholar 

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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




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.

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Competing interests

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

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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.

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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).

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