1932

Abstract

Shotgun metagenomic sequencing has revolutionized our ability to detect and characterize the diversity and function of complex microbial communities. In this review, we highlight the benefits of using metagenomics as well as the breadth of conclusions that can be made using currently available analytical tools, such as greater resolution of species and strains across phyla and functional content, while highlighting challenges of metagenomic data analysis. Major challenges remain in annotating function, given the dearth of functional databases for environmental bacteria compared to model organisms, and the technical difficulties of metagenome assembly and phasing in heterogeneous environmental samples. In the future, improvements and innovation in technology and methodology will lead to lowered costs. Data integration using multiple technological platforms will lead to a better understanding of how to harness metagenomes. Subsequently, we will be able not only to characterize complex microbiomes but also to manipulate communities to achieve prosperous outcomes for health, agriculture, and environmental sustainability.

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2020-09-08
2024-04-18
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Literature Cited

  1. 1. 
    Albanese D, Donati C. 2017. Strain profiling and epidemiology of bacterial species from metagenomic sequencing. Nat. Commun. 8:12260
    [Google Scholar]
  2. 2. 
    Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J et al. 2014. Binning metagenomic contigs by coverage and composition. Nat. Methods 11:111144–46
    [Google Scholar]
  3. 3. 
    Arevalo P, VanInsberghe D, Elsherbini J, Gore J, Polz MF 2019. A reverse ecology approach based on a biological definition of microbial populations. Cell 178:4820–34.e14
    [Google Scholar]
  4. 4. 
    Ayling M, Clark MD, Leggett RM 2019. New approaches for metagenome assembly with short reads. Brief. Bioinform. 21:2584–94
    [Google Scholar]
  5. 5. 
    Beaulaurier J, Zhu S, Deikus G, Mogno I, Zhang X-S et al. 2018. Metagenomic binning and association of plasmids with bacterial host genomes using DNA methylation. Nat. Biotechnol. 36:161–69
    [Google Scholar]
  6. 6. 
    Berger B, Peng J, Singh M 2013. Computational solutions for omics data. Nat. Rev. Genet. 14:5333–46
    [Google Scholar]
  7. 7. 
    Bersanelli M, Mosca E, Remondini D, Giampieri E, Sala C et al. 2016. Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinform 17:Suppl. 215
    [Google Scholar]
  8. 8. 
    Bertrand D, Shaw J, Kalathiyappan M, Ng AHQ, Kumar MS et al. 2019. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. Nat. Biotechnol. 37:8937–44
    [Google Scholar]
  9. 9. 
    Bishara A, Moss EL, Kolmogorov M, Parada AE, Weng Z et al. 2018. High-quality genome sequences of uncultured microbes by assembly of read clouds. Nat. Biotechnol. 36:111067–80
    [Google Scholar]
  10. 10. 
    Blin K, Shaw S, Steinke K, Villebro R, Ziemert N et al. 2019. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res 47:W1W81–87
    [Google Scholar]
  11. 11. 
    Boisvert S, Raymond F, Godzaridis É, Laviolette F, Corbeil J 2012. Ray Meta: scalable de novo metagenome assembly and profiling. Genome Biol 13:12R122
    [Google Scholar]
  12. 12. 
    Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC et al. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37:8852–57
    [Google Scholar]
  13. 13. 
    Bonder MJ, Kurilshikov A, Tigchelaar EF, Mujagic Z, Imhann F et al. 2016. The effect of host genetics on the gut microbiome. Nat. Genet. 48:111407–12
    [Google Scholar]
  14. 14. 
    Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D et al. 2017. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35:8725–31
    [Google Scholar]
  15. 15. 
    Brady SF. 2007. Construction of soil environmental DNA cosmid libraries and screening for clones that produce biologically active small molecules. Nat. Protoc. 2:51297–305
    [Google Scholar]
  16. 16. 
    Breitbart M, Hewson I, Felts B, Mahaffy JM, Nulton J et al. 2003. Metagenomic analyses of an uncultured viral community from human feces. J. Bacteriol. 185:206220–23
    [Google Scholar]
  17. 17. 
    Breitbart M, Salamon P, Andresen B, Mahaffy JM, Segall AM et al. 2002. Genomic analysis of uncultured marine viral communities. PNAS 99:2214250–55
    [Google Scholar]
  18. 18. 
    Brito IL, Gurry T, Zhao S, Huang K, Young SK et al. 2019. Transmission of human-associated microbiota along family and social networks. Nat. Microbiol. 4:6964–71
    [Google Scholar]
  19. 19. 
    Brito IL, Yilmaz S, Huang K, Xu L, Jupiter SD et al. 2016. Mobile genes in the human microbiome are structured from global to individual scales. Nature 535:7612435–39
    [Google Scholar]
  20. 20. 
    Brown CT, Olm MR, Thomas BC, Banfield JF 2016. Measurement of bacterial replication rates in microbial communities. Nat. Biotechnol. 34:121256–63
    [Google Scholar]
  21. 21. 
    Browne HP, Forster SC, Anonye BO, Kumar N, Neville BA et al. 2016. Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature 533:7604543–46
    [Google Scholar]
  22. 22. 
    Burstein D, Harrington LB, Strutt SC, Probst AJ, Anantharaman K et al. 2017. New CRISPR-Cas systems from uncultivated microbes. Nature 542:7640237–41
    [Google Scholar]
  23. 23. 
    Callahan BJ, McMurdie PJ, Holmes SP 2017. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J 11:122639–43
    [Google Scholar]
  24. 24. 
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13:7581–83
    [Google Scholar]
  25. 25. 
    Castillo Villamizar GA, Nacke H, Boehning M, Herz K, Daniel R 2019. Functional metagenomics reveals an overlooked diversity and novel features of soil-derived bacterial phosphatases and phytases. mBio 10:1e01966–18
    [Google Scholar]
  26. 26. 
    Chang F-Y, Brady SF. 2013. Discovery of indolotryptoline antiproliferative agents by homology-guided metagenomic screening. PNAS 110:72478
    [Google Scholar]
  27. 27. 
    Chaudhary N, Gupta A, Gupta S, Sharma VK 2017. BioFuelDB: a database and prediction server of enzymes involved in biofuels production. PeerJ 5:e3497
    [Google Scholar]
  28. 28. 
    Cleary B, Brito IL, Huang K, Gevers D, Shea T et al. 2015. Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning. Nat. Biotechnol. 33:101053–60
    [Google Scholar]
  29. 29. 
    Coutinho FH, Silveira CB, Gregoracci GB, Thompson CC, Edwards RA et al. 2017. Marine viruses discovered via metagenomics shed light on viral strategies throughout the oceans. Nat. Commun. 8:115955
    [Google Scholar]
  30. 30. 
    Devoto AE, Santini JM, Olm MR, Anantharaman K, Munk P et al. 2019. Megaphages infect Prevotella and variants are widespread in gut microbiomes. Nat. Microbiol. 4:4693–700
    [Google Scholar]
  31. 31. 
    Duerkop BA, Kleiner M, Paez-Espino D, Zhu W, Bushnell B et al. 2018. Murine colitis reveals a disease-associated bacteriophage community. Nat. Microbiol. 3:91023–31
    [Google Scholar]
  32. 32. 
    Dunphy CM, Gouhier TC, Chu ND, Vollmer SV 2019. Structure and stability of the coral microbiome in space and time. Sci. Rep. 9:16785
    [Google Scholar]
  33. 33. 
    Durrant MG, Li MM, Siranosian BA, Montgomery SB, Bhatt AS 2020. A bioinformatic analysis of integrative mobile genetic elements highlights their role in bacterial adaptation. Cell Host Microbe 27:1140–53.e9
    [Google Scholar]
  34. 34. 
    Dutilh BE, Cassman N, McNair K, Sanchez SE, Silva GGZ et al. 2014. A highly abundant bacteriophage discovered in the unknown sequences of human faecal metagenomes. Nat. Commun. 5:14498
    [Google Scholar]
  35. 35. 
    Edgar RC. 2016. UCHIME2: improved chimera prediction for amplicon sequencing. bioRxiv 074252
  36. 36. 
    Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG et al. 2015. Anvi'o: an advanced analysis and visualization platform for ’omics data. PeerJ 3:e1319
    [Google Scholar]
  37. 37. 
    Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D et al. 2012. Microbial co-occurrence relationships in the human microbiome. PLOS Comput. Biol. 8:7e1002606
    [Google Scholar]
  38. 38. 
    Ferretti P, Farina S, Cristofolini M, Girolomoni G, Tett A, Segata N 2017. Experimental metagenomics and ribosomal profiling of the human skin microbiome. Exp. Dermatol. 26:3211–19
    [Google Scholar]
  39. 39. 
    Ferretti P, Pasolli E, Tett A, Asnicar F, Gorfer V et al. 2018. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe 24:1133–145.e5
    [Google Scholar]
  40. 40. 
    Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY et al. 2014. Pfam: the protein families database. Nucleic Acids Res 42:D1D222–30
    [Google Scholar]
  41. 41. 
    Fouts DE. 2006. Phage_Finder: automated identification and classification of prophage regions in complete bacterial genome sequences. Nucleic Acids Res 34:205839–51
    [Google Scholar]
  42. 42. 
    Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M et al. 2018. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15:11962–68
    [Google Scholar]
  43. 43. 
    Friedman J, Alm EJ. 2012. Inferring correlation networks from genomic survey data. PLOS Comput. Biol. 8:9e1002687
    [Google Scholar]
  44. 44. 
    Garud NR, Good BH, Hallatschek O, Pollard KS 2019. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLOS Biol 17:1e3000102
    [Google Scholar]
  45. 45. 
    Goltsman DSA, Sun CL, Proctor DM, DiGiulio DB, Robaczewska A et al. 2018. Metagenomic analysis with strain-level resolution reveals fine-scale variation in the human pregnancy microbiome. Genome Res 28:101467–80
    [Google Scholar]
  46. 46. 
    Greenblum S, Carr R, Borenstein E 2015. Extensive strain-level copy-number variation across human gut microbiome species. Cell 160:4583–94
    [Google Scholar]
  47. 47. 
    Grottoli AG, Dalcin Martins P, Wilkins MJ, Johnston MD, Warner ME et al. 2018. Coral physiology and microbiome dynamics under combined warming and ocean acidification. PLOS ONE 13:1e0191156
    [Google Scholar]
  48. 48. 
    Guerin E, Shkoporov A, Stockdale SR, Gonzalez-Tortuero E, Ross RP, Hill C 2018. Biology and taxonomy of crAss-like bacteriophages, the most abundant virus in the human gut. Cell Host Microbe 24:653–64
    [Google Scholar]
  49. 49. 
    Haft DH, Selengut JD, White O 2003. The TIGRFAMs database of protein families. Nucleic Acids Res 31:1371–73
    [Google Scholar]
  50. 50. 
    Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Gohl DM et al. 2018. Evaluating the information content of shallow shotgun metagenomics. mSystems 3:6e00069–18
    [Google Scholar]
  51. 51. 
    Hum. Microbiome Proj. Consort. Huttenhower C, Gevers D, Knight R, Abubucker S et al. 2012. Structure, function and diversity of the healthy human microbiome. Nature 486:7402207–14
    [Google Scholar]
  52. 52. 
    Huson DH, Auch AF, Qi J, Schuster SC 2007. MEGAN analysis of metagenomic data. Genome Res 17:3377–86
    [Google Scholar]
  53. 53. 
    Imelfort M, Parks D, Woodcroft BJ, Dennis P, Hugenholtz P, Tyson GW 2014. GroopM: an automated tool for the recovery of population genomes from related metagenomes. PeerJ 2:e603
    [Google Scholar]
  54. 54. 
    Joice R, Yasuda K, Shafquat A, Morgan XC, Huttenhower C 2014. Determining microbial products and identifying molecular targets in the human microbiome. Cell Metab 20:5731–41
    [Google Scholar]
  55. 55. 
    Jump. Consort. Hum. Microbiome Proj. Data Gener. Work. Group 2012. Evaluation of 16S rDNA-based community profiling for human microbiome research. PLOS ONE 7:6e39315
    [Google Scholar]
  56. 56. 
    Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M 2016. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44:D1D457–62
    [Google Scholar]
  57. 57. 
    Kang DD, Li F, Kirton E, Thomas A, Egan R et al. 2019. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7:e7359
    [Google Scholar]
  58. 58. 
    Karp PD, Riley M, Paley SM, Pellegrini-Toole A 2002. The MetaCyc database. Nucleic Acids Res 30:159–61
    [Google Scholar]
  59. 59. 
    Kim D, Song L, Breitwieser FP, Salzberg SL 2016. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res 26:121721–29
    [Google Scholar]
  60. 60. 
    Korem T, Zeevi D, Suez J, Weinberger A, Avnit-Sagi T et al. 2015. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349:62521101–6
    [Google Scholar]
  61. 61. 
    Kundu P, Manna B, Majumder S, Ghosh A 2019. Species-wide metabolic interaction network for understanding natural lignocellulose digestion in termite gut microbiota. Sci. Rep. 9:116329
    [Google Scholar]
  62. 62. 
    Kurokawa K, Itoh T, Kuwahara T, Oshima K, Toh H et al. 2007. Comparative metagenomics revealed commonly enriched gene sets in human gut microbiomes. DNA Res 14:4169–81
    [Google Scholar]
  63. 63. 
    Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA 2015. Sparse and compositionally robust inference of microbial ecological networks. PLOS Comput. Biol. 11:5e1004226
    [Google Scholar]
  64. 64. 
    Labonté JM, Field EK, Lau M, Chivian D, Van Heerden E et al. 2015. Single cell genomics indicates horizontal gene transfer and viral infections in a deep subsurface Firmicutes population. Front. Microbiol. 6:349
    [Google Scholar]
  65. 65. 
    Lagier J-C, Dubourg G, Million M, Cadoret F, Bilen M et al. 2018. Culturing the human microbiota and culturomics. Nat. Rev. Microbiol. 16:9540–50
    [Google Scholar]
  66. 66. 
    Li D, Liu C-M, Luo R, Sadakane K, Lam T-W 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31:101674–76
    [Google Scholar]
  67. 67. 
    Li SS, Zhu A, Benes V, Costea PI, Hercog R et al. 2016. Durable coexistence of donor and recipient strains after fecal microbiota transplantation. Science 352:6285586–89
    [Google Scholar]
  68. 68. 
    Lim ES, Zhou Y, Zhao G, Bauer IK, Droit L et al. 2015. Early life dynamics of the human gut virome and bacterial microbiome in infants. Nat. Med. 21:101228–34
    [Google Scholar]
  69. 69. 
    Lloyd-Price J, Arze C, Ananthakrishnan AN, Schirmer M, Avila-Pacheco J et al. 2019. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569:7758655–62
    [Google Scholar]
  70. 70. 
    Lloyd-Price J, Mahurkar A, Rahnavard G, Crabtree J, Orvis J et al. 2017. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 550:767461–66
    [Google Scholar]
  71. 71. 
    Magnúsdóttir S, Heinken A, Kutt L, Ravcheev DA, Bauer E et al. 2017. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35:181–89
    [Google Scholar]
  72. 72. 
    Maini Rekdal V, Bess EN, Bisanz JE, Turnbaugh PJ, Balskus EP 2019. Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism. Science 364:6445eaau6323
    [Google Scholar]
  73. 73. 
    Mallick H, Franzosa EA, Mclver LJ, Banerjee S, Sirota-Madi A et al. 2019. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat. Commun. 10:13136
    [Google Scholar]
  74. 74. 
    Marotz CA, Sanders JG, Zuniga C, Zaramela LS, Knight R, Zengler K 2018. Improving saliva shotgun metagenomics by chemical host DNA depletion. Microbiome 6:142
    [Google Scholar]
  75. 75. 
    Mikheenko A, Saveliev V, Gurevich A 2016. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics 32:71088–90
    [Google Scholar]
  76. 76. 
    Mizuno CM, Rodriguez-Valera F, Kimes NE, Ghai R 2013. Expanding the marine virosphere using metagenomics. PLOS Genet 9:12e1003987
    [Google Scholar]
  77. 77. 
    Nayfach S, Shi ZJ, Seshadri R, Pollard KS, Kyrpides NC 2019. New insights from uncultivated genomes of the global human gut microbiome. Nature 568:7753505–10
    [Google Scholar]
  78. 78. 
    Nurk S, Meleshko D, Korobeynikov A, Pevzner PA 2017. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27:5824–34
    [Google Scholar]
  79. 79. 
    Oh J, Byrd AL, Deming C, Conlan S, NISC Comp. Seq. Program, et al. 2014. Biogeography and individuality shape function in the human skin metagenome. Nature 514:752059–64
    [Google Scholar]
  80. 80. 
    Olm MR, West PT, Brooks B, Firek BA, Baker R et al. 2019. Genome-resolved metagenomics of eukaryotic populations during early colonization of premature infants and in hospital rooms. Microbiome 7:126
    [Google Scholar]
  81. 81. 
    Orellana LH, Chee-Sanford JC, Sanford RA, Löffler FE, Konstantinidis KT 2018. Year-round shotgun metagenomes reveal stable microbial communities in agricultural soils and novel ammonia oxidizers responding to fertilization. Appl. Environ. Microbiol. 84:2e01646–17
    [Google Scholar]
  82. 82. 
    Pachiadaki MG, Brown JM, Brown J, Bezuidt O, Berube PM et al. 2019. Charting the complexity of the marine microbiome through single-cell genomics. Cell 179:71623–35.e11
    [Google Scholar]
  83. 83. 
    Parasar B, Zhou H, Xiao X, Shi Q, Brito IL, Chang PV 2019. Chemoproteomic profiling of gut microbiota-associated bile salt hydrolase activity. ACS Cent. Sci. 5:5867–73
    [Google Scholar]
  84. 84. 
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25:71043–55
    [Google Scholar]
  85. 85. 
    Pasolli E, Asnicar F, Manara S, Zolfo M, Karcher N et al. 2019. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176:3649–62.e20
    [Google Scholar]
  86. 86. 
    Peng Y, Leung HCM, Yiu SM, Chin FYL 2012. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28:111420–28
    [Google Scholar]
  87. 87. 
    Pepe-Ranney C, Campbell AN, Koechli CN, Berthrong S, Buckley DH 2016. Unearthing the ecology of soil microorganisms using a high resolution DNA-SIP approach to explore cellulose and xylose metabolism in soil. Front. Microbiol. 7:703
    [Google Scholar]
  88. 88. 
    Porras AM, Brito IL. 2019. The internationalization of human microbiome research. Curr. Opin. Microbiol. 50:50–55
    [Google Scholar]
  89. 89. 
    Poyet M, Groussin M, Gibbons SM, Avila-Pacheco J, Jiang X et al. 2019. A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research. Nat. Med. 25:91442–52
    [Google Scholar]
  90. 90. 
    Qin J, Li Y, Cai Z, Li S, Zhu J et al. 2012. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490:741855–60
    [Google Scholar]
  91. 91. 
    Riva A, Kuzyk O, Forsberg E, Siuzdak G, Pfann C et al. 2019. A fiber-deprived diet disturbs the fine-scale spatial architecture of the murine colon microbiome. Nat. Commun. 10:14366
    [Google Scholar]
  92. 92. 
    Roux S, Enault F, Hurwitz BL, Sullivan MB 2015. VirSorter: mining viral signal from microbial genomic data. PeerJ 3:e985
    [Google Scholar]
  93. 93. 
    Sarhan MS, Hamza MA, Youssef HH, Patz S, Becker M et al. 2019. Culturomics of the plant prokaryotic microbiome and the dawn of plant-based culture media—a review. J. Adv. Res. 19:15–27
    [Google Scholar]
  94. 94. 
    Sberro H, Fremin BJ, Zlitni S, Edfors F, Greenfield N et al. 2019. Large-scale analyses of human microbiomes reveal thousands of small, novel genes. Cell 178:51245–59.e14
    [Google Scholar]
  95. 95. 
    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M et al. 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75:237537–41
    [Google Scholar]
  96. 96. 
    Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S et al. 2017. Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software. Nat. Methods 14:111063–71
    [Google Scholar]
  97. 97. 
    Sheth RU, Li M, Jiang W, Sims PA, Leong KW, Wang HH 2019. Spatial metagenomic characterization of microbial biogeography in the gut. Nat. Biotechnol. 37:8877–83
    [Google Scholar]
  98. 98. 
    Shi H, Zipfel W, Brito I, De Vlaminck I 2019. Highly multiplexed spatial mapping of microbial communities. bioRxiv 678672
  99. 99. 
    Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M et al. 2018. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3:7836–43
    [Google Scholar]
  100. 100. 
    Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM 2015. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31:193210–12
    [Google Scholar]
  101. 101. 
    Simpson JT, Wong K, Jackman SD, Schein JE, Jones SJM, Birol I 2009. ABySS: a parallel assembler for short read sequence data. Genome Res 19:61117–23
    [Google Scholar]
  102. 102. 
    Smillie CS, Sauk J, Gevers D, Friedman J, Sung J et al. 2018. Strain tracking reveals the determinants of bacterial engraftment in the human gut following fecal microbiota transplantation. Cell Host Microbe 23:2229–40.e5
    [Google Scholar]
  103. 103. 
    Smillie CS, Smith MB, Friedman J, Cordero OX, David LA, Alm EJ 2011. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480:7376241–44
    [Google Scholar]
  104. 104. 
    Söllinger A, Tveit AT, Poulsen M, Noel SJ, Bengtsson M et al. 2018. Holistic assessment of rumen microbiome dynamics through quantitative metatranscriptomics reveals multifunctional redundancy during key steps of anaerobic feed degradation. mSystems 3:4e00038–18
    [Google Scholar]
  105. 105. 
    Sommer MOA, Dantas G, Church GM 2009. Functional characterization of the antibiotic resistance reservoir in the human microflora. Science 325:59441128–31
    [Google Scholar]
  106. 106. 
    Song W, Wemheuer B, Zhang S, Steensen K, Thomas T MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches. Microbiome 7:136
    [Google Scholar]
  107. 107. 
    Song W-Z, Thomas T. 2017. Binning_refiner: improving genome bins through the combination of different binning programs. Bioinformatics 33:121873–75
    [Google Scholar]
  108. 108. 
    Stalder T, Press MO, Sullivan S, Liachko I, Top EM 2019. Linking the resistome and plasmidome to the microbiome. ISME J 13:102437–46
    [Google Scholar]
  109. 109. 
    Su J-Q, An X-L, Li B, Chen Q-L, Gillings MR et al. 2017. Metagenomics of urban sewage identifies an extensively shared antibiotic resistome in China. Microbiome 5:184
    [Google Scholar]
  110. 110. 
    Sugimoto Y, Camacho FR, Wang S, Chankhamjon P, Odabas A et al. 2019. A metagenomic strategy for harnessing the chemical repertoire of the human microbiome. Science 366:6471eaax9176
    [Google Scholar]
  111. 111. 
    Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K et al. 2015. Structure and function of the global ocean microbiome. Science 348:62371261359
    [Google Scholar]
  112. 112. 
    Sunagawa S, Mende DR, Zeller G, Izquierdo-Carrasco F, Berger SA et al. 2013. Metagenomic species profiling using universal phylogenetic marker genes. Nat. Methods 10:121196–99
    [Google Scholar]
  113. 113. 
    Tatusov RL, Galperin MY, Natale DA, Koonin EV 2000. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res 28:133–36
    [Google Scholar]
  114. 114. 
    Thomas AM, Manghi P, Asnicar F, Pasolli E, Armanini F et al. 2019. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 25:4667–78
    [Google Scholar]
  115. 115. 
    Truong DT, Tett A, Pasolli E, Huttenhower C, Segata N 2017. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res 27:4626–38
    [Google Scholar]
  116. 116. 
    Uritskiy GV, DiRuggiero J, Taylor J 2018. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6:1158
    [Google Scholar]
  117. 117. 
    Vandeputte D, Kathagen G, D'hoe K, Vieira-Silva S, Valles-Colomer M et al. 2017. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551:7681507–11
    [Google Scholar]
  118. 118. 
    Vatanen T, Franzosa EA, Schwager R, Tripathi S, Arthur TD et al. 2018. The human gut microbiome in early-onset type 1 diabetes from the TEDDY study. Nature 562:7728589–94
    [Google Scholar]
  119. 119. 
    Visconti A, Le Roy CI, Rosa F, Rossi N, Martin TC et al. 2019. Interplay between the human gut microbiome and host metabolism. Nat. Commun. 10:14505
    [Google Scholar]
  120. 120. 
    Weissbrod O, Rothschild D, Barkan E, Segal E 2018. Host genetics and microbiome associations through the lens of genome wide association studies. Curr. Opin. Microbiol. 44:9–19
    [Google Scholar]
  121. 121. 
    Welch JLM, Hasegawa Y, McNulty NP, Gordon JI, Borisy GG 2017. Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. PNAS 114:43E9105–14
    [Google Scholar]
  122. 122. 
    Welch JLM, Rossetti BJ, Rieken CW, Dewhirst FE, Borisy GG 2016. Biogeography of a human oral microbiome at the micron scale. PNAS 113:6E791–800
    [Google Scholar]
  123. 123. 
    West PT, Probst AJ, Grigoriev IV, Thomas BC, Banfield JF 2018. Genome-reconstruction for eukaryotes from complex natural microbial communities. Genome Res 28:4569–80
    [Google Scholar]
  124. 124. 
    Wilhelm RC, Singh R, Eltis LD, Mohn WW 2019. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J 13:2413–29
    [Google Scholar]
  125. 125. 
    Wirbel J, Pyl PT, Kartal E, Zych K, Kashani A et al. 2019. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 25:4679–89
    [Google Scholar]
  126. 126. 
    Wood DE, Lu J, Langmead B 2019. Improved metagenomic analysis with Kraken 2. Genome Biol 20:1257
    [Google Scholar]
  127. 127. 
    Wu Y-W, Simmons BA, Singer SW 2016. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32:4605–7
    [Google Scholar]
  128. 128. 
    Xie H, Guo R, Zhong H, Feng Q, Lan Z et al. 2016. Shotgun metagenomics of 250 adult twins reveals genetic and environmental impacts on the gut microbiome. Cell Syst 3:6572–84.e3
    [Google Scholar]
  129. 129. 
    Yaffe E, Relman DA. 2019. Tracking microbial evolution in the human gut using Hi-C reveals extensive horizontal gene transfer, persistence and adaptation. Nat. Microbiol. 5:2343–53
    [Google Scholar]
  130. 130. 
    Yassour M, Jason E, Hogstrom LJ, Arthur TD, Tripathi S et al. 2018. Strain-level analysis of mother-to-child bacterial transmission during the first few months of life. Cell Host Microbe 24:1146–54.e4
    [Google Scholar]
  131. 131. 
    Ye SH, Siddle KJ, Park DJ, Sabeti PC 2019. Benchmarking metagenomics tools for taxonomic classification. Cell 178:4779–94
    [Google Scholar]
  132. 132. 
    Zeevi D, Korem T, Godneva A, Bar N, Kurilshikov A et al. 2019. Structural variation in the gut microbiome associates with host health. Nature 568:775043–48
    [Google Scholar]
  133. 133. 
    Zerbino DR, Birney E. 2008. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18:821–29
    [Google Scholar]
  134. 134. 
    Zhou W, Sailani MR, Contrepois K, Zhou Y, Ahadi S et al. 2019. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature 569:7758663–71
    [Google Scholar]
  135. 135. 
    Zlitni S, Bishara A, Moss EL, Tkachenko E, Kang JB et al. 2020. Strain-resolved microbiome sequencing reveals mobile elements that drive bacterial competition on a clinical timescale. Genome Med 12:50
    [Google Scholar]
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