14. Arumugam, M., et al., Enterotypes of the human gut microbiome. Nature, 2011. 473(7346): p. 174-80.
15. Nielsen, H.B., et al., Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat Biotechnol, 2014. 32(8): p. 822-8.
16. Affeldt, S., et al., Spectral consensus strategy for accurate reconstruction of large biological networks. BMC Bioinformatics, 2016. 17(Suppl 16): p. 493.
17. Prifti, E., et al., Interpretable and accurate prediction models for metagenomics data. Gigascience, 2020. 9(3).
18. Palm, N.W., et al., Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease. Cell, 2014. 158(5): p. 1000-1010.
19. Arthur, J.C., et al., Intestinal inflammation targets cancer-inducing activity of the microbiota. Science, 2012. 338(6103): p. 120-3.
20. Blanco-Miguez, A., et al., Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat Biotechnol, 2023.
21. Almeida, A., et al., A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat Biotechnol, 2021. 39(1): p. 105-114.
22. Ayling, M., M.D. Clark, and R.M. Leggett, New approaches for metagenome assembly with short reads. Brief Bioinform, 2020. 21(2): p. 584-594.
23. Lapidus, A.L. and A.I. Korobeynikov, Metagenomic Data Assembly - The Way of Decoding Unknown Microorganisms. Front Microbiol, 2021. 12: p. 613791.
24. Pan, S., et al., A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments. Nat Commun, 2022. 13(1): p. 2326.
25. Nissen, J.N., et al., Improved metagenome binning and assembly using deep variational autoencoders. Nat Biotechnol, 2021. 39(5): p. 555-560.
26. Gurbich, T.A., et al., MGnify Genomes: A Resource for Biome-specific Microbial Genome Catalogues. J Mol Biol, 2023: p. 168016.
27. Pasolli, E., et al., Accessible, curated metagenomic data through ExperimentHub. Nat Methods, 2017. 14(11): p. 1023-1024.