1887

Microbial Genomics logo

Volume 10, Issue 2

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling Open Access

Abstract

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): 16S copy number normalization , 16S rRNA gene sequencing , metagenome shotgun sequencing , microbial functions , PanFP and PICRUSt2
Funding
This study was supported by the:
  • German Federal Ministry of Education and Research (Award 01IS21079)
    • Principle Award Recipient: JanBaumbach
  • VILLUM Young Investigator Grant (Award 13154)
    • Principle Award Recipient: JanBaumbach
  • Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (Award 422216132)
    • Principle Award Recipient: JanBaumbach
  • Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (Award 395357507)
    • Principle Award Recipient: MarkusList
  • Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (Award 395357507)
    • Principle Award Recipient: DirkHaller
© 2024 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.001203
2024-02-29
2024-04-28
Loading full text...

Full text loading...

/deliver/fulltext/mgen/10/2/mgen001203.html?itemId=/content/journal/mgen/10.1099/mgen.0.001203&mimeType=html&fmt=ahah

References

  1. Malla MA, Dubey A, Kumar A, Yadav S, Hashem A et al. Exploring the human microbiome: the potential future role of next-generation sequencing in disease diagnosis and treatment. Front Immunol 2018; 9:2868 [View Article] [PubMed]
     [Google Scholar]
  2. Do T, Dame-Teixeira N, Deng D. Editorial: applications of next generation sequencing (NGS) technologies to decipher the oral microbiome in systemic health and disease. Front Cell Infect Microbiol 2021; 11:801122 [View Article] [PubMed]
     [Google Scholar]
  3. Gao B, Chi L, Zhu Y, Shi X, Tu P et al. An introduction to next generation sequencing bioinformatic analysis in gut microbiome studies. Biomolecules 2021; 11:530 [View Article] [PubMed]
     [Google Scholar]
  4. Yang B, Wang Y, Qian P-Y. Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC Bioinformatics 2016; 17:135 [View Article] [PubMed]
     [Google Scholar]
  5. Hamady M, Knight R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res 2009; 19:1141–1152 [View Article] [PubMed]
     [Google Scholar]
  6. Quince C, Lanzen A, Davenport RJ, Turnbaugh PJ. Removing noise from pyrosequenced amplicons. BMC Bioinformatics 2011; 12:38 [View Article] [PubMed]
     [Google Scholar]
  7. Abellan-Schneyder I, Matchado MS, Reitmeier S, Sommer A, Sewald Z et al. Primer, pipelines, parameters: Issues in 16S rRNA gene sequencing. mSphere 2021; 6:e01202-20 [View Article] [PubMed]
     [Google Scholar]
  8. Bukin YuS, Galachyants YuP, Morozov IV, Bukin SV, Zakharenko AS et al. The effect of 16S rRNA region choice on bacterial community metabarcoding results. Sci Data 2019; 6:190007 [View Article]
     [Google Scholar]
  9. Bavykin SG, Lysov YP, Zakhariev V, Kelly JJ, Jackman J et al. Use of 16S rRNA, 23S rRNA, and gyrB gene sequence analysis to determine phylogenetic relationships of Bacillus cereus group microorganisms. J Clin Microbiol 2004; 42:3711–3730 [View Article] [PubMed]
     [Google Scholar]
  10. Huse SM, Welch DM, Morrison HG, Sogin ML. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ Microbiol 2010; 12:1889–1898 [View Article] [PubMed]
     [Google Scholar]
  11. Rosselló-Mora R, Amann R. The species concept for prokaryotes. FEMS Microbiol Rev 2001; 25:39–67 [View Article] [PubMed]
     [Google Scholar]
  12. Peterson D, Bonham KS, Rowland S, Pattanayak CW, Klepac-Ceraj V et al. Comparative analysis of 16S rRNA gene and metagenome sequencing in pediatric gut microbiomes. Front Microbiol 2021; 12:670336 [View Article] [PubMed]
     [Google Scholar]
  13. Durazzi F, Sala C, Castellani G, Manfreda G, Remondini D et al. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci Rep 2021; 11:3030 [View Article] [PubMed]
     [Google Scholar]
  14. Jovel J, Patterson J, Wang W, Hotte N, O’Keefe S et al. Characterization of the gut microbiome using 16S or shotgun metagenomics. Front Microbiol 2016; 7:459 [View Article] [PubMed]
     [Google Scholar]
  15. Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 2013; 31:814–821 [View Article] [PubMed]
     [Google Scholar]
  16. Qiao Y, Sun J, Xia S, Li L, Li Y et al. Effects of different Lactobacillus reuteri on inflammatory and fat storage in high-fat diet-induced obesity mice model. J Funct Foods 2015; 14:424–434 [View Article]
     [Google Scholar]
  17. Wu G, Zhang C, Wu H, Wang R, Shen J et al. Genomic microdiversity of Bifidobacterium pseudocatenulatum underlying differential strain-level responses to dietary carbohydrate intervention. mBio 2017; 8:e02348-16 [View Article] [PubMed]
     [Google Scholar]
  18. Börnigen D, Morgan XC, Franzosa EA, Ren B, Xavier RJ et al. Functional profiling of the gut microbiome in disease-associated inflammation. Genome Med 2013; 5:65 [View Article] [PubMed]
     [Google Scholar]
  19. Beghini F, McIver LJ, Blanco-Míguez A, Dubois L, Asnicar F et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife 2021; 10:e65088 [View Article] [PubMed]
     [Google Scholar]
  20. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
     [Google Scholar]
  21. Brown SM, Chen H, Hao Y, Laungani BP, Ali TA et al. MGS-Fast: metagenomic shotgun data fast annotation using microbial gene catalogs. Gigascience 2019; 8:giz020 [View Article] [PubMed]
     [Google Scholar]
  22. van der Walt AJ, van Goethem MW, Ramond J-B, Makhalanyane TP, Reva O et al. Assembling metagenomes, one community at a time. BMC Genomics 2017; 18:521 [View Article] [PubMed]
     [Google Scholar]
  23. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 2013; 14:365–376 [View Article] [PubMed]
     [Google Scholar]
  24. Zhang L, Chen F, Zeng Z, Xu M, Sun F et al. Advances in metagenomics and its application in environmental microorganisms. Front Microbiol 2021; 12:766364 [View Article] [PubMed]
     [Google Scholar]
  25. Jervis-Bardy J, Leong LEX, Marri S, Smith RJ, Choo JM et al. Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data. Microbiome 2015; 3:19 [View Article] [PubMed]
     [Google Scholar]
  26. Minich JJ, Zhu Q, Janssen S, Hendrickson R, Amir A et al. KatharoSeq enables high-throughput microbiome analysis from low-biomass samples. mSystems 2018; 3:e00218-17 [View Article] [PubMed]
     [Google Scholar]
  27. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol 2020; 38:685–688 [View Article] [PubMed]
     [Google Scholar]
  28. Wemheuer F, Taylor JA, Daniel R, Johnston E, Meinicke P et al. Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environ Microbiome 2020; 15:11 [View Article] [PubMed]
     [Google Scholar]
  29. Jun S-R, Robeson MS, Hauser LJ, Schadt CW, Gorin AA. PanFP: pangenome-based functional profiles for microbial communities. BMC Res Notes 2015; 8:479 [View Article] [PubMed]
     [Google Scholar]
  30. Narayan NR, Weinmaier T, Laserna-Mendieta EJ, Claesson MJ, Shanahan F et al. Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences. BMC Genomics 2020; 21:56 [View Article] [PubMed]
     [Google Scholar]
  31. Wilkinson TJ, Huws SA, Edwards JE, Kingston-Smith AH, Siu-Ting K et al. CowPI: a rumen microbiome focussed version of the PICRUSt functional inference software. Front Microbiol 2018; 9:1095 [View Article] [PubMed]
     [Google Scholar]
  32. Patumcharoenpol P, Nakphaichit M, Panagiotou G, Senavonge A, Suratannon N et al. MetGEMs toolbox: metagenome-scale models as integrative toolbox for uncovering metabolic functions and routes of human gut microbiome. PLoS Comput Biol 2021; 17:e1008487 [View Article] [PubMed]
     [Google Scholar]
  33. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28:27–30 [View Article] [PubMed]
     [Google Scholar]
  34. Magnúsdóttir S, Heinken A, Kutt L, Ravcheev DA, Bauer E et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol 2017; 35:81–89 [View Article] [PubMed]
     [Google Scholar]
  35. Heinken A, Ravcheev DA, Baldini F, Heirendt L, Fleming RMT et al. Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease. Microbiome 2019; 7:75 [View Article] [PubMed]
     [Google Scholar]
  36. Tramontano M, Andrejev S, Pruteanu M, Klünemann M, Kuhn M et al. Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies. Nat Microbiol 2018; 3:514–522 [View Article] [PubMed]
     [Google Scholar]
  37. Human Microbiome Project Consortium Structure, function and diversity of the healthy human microbiome. Nature 2012; 486:207–214 [View Article]
     [Google Scholar]
  38. Kembel SW, Wu M, Eisen JA, Green JL. Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS Comput Biol 2012; 8:e1002743 [View Article] [PubMed]
     [Google Scholar]
  39. Louca S, Doebeli M, Parfrey LW. Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem. Microbiome 2018; 6:41 [View Article] [PubMed]
     [Google Scholar]
  40. Bowman JS, Ducklow HW. Microbial communities can be described by metabolic structure: a general framework and application to a seasonally variable, depth-stratified microbial community from the Coastal West Antarctic Peninsula. PLoS One 2015; 10:e0135868 [View Article] [PubMed]
     [Google Scholar]
  41. Stoddard SF, Smith BJ, Hein R, Roller BRK, Schmidt TM. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res 2015; 43:D593–8 [View Article] [PubMed]
     [Google Scholar]
  42. Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 2014; 42:D633–42 [View Article] [PubMed]
     [Google Scholar]
  43. Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ et al. GenBank. Nucleic Acids Res 2013; 41:D36–42 [View Article] [PubMed]
     [Google Scholar]
  44. Ortiz‐Estrada ÁM, Gollas‐Galván T, Martínez‐Córdova LR, Martínez‐Porchas M. Predictive functional profiles using metagenomic 16S rRNA data: a novel approach to understanding the microbial ecology of aquaculture systems. Rev Aquac 2019; 11:234–245 [View Article]
     [Google Scholar]
  45. Djemiel C, Maron P-A, Terrat S, Dequiedt S, Cottin A et al. Inferring microbiota functions from taxonomic genes: a review. Gigascience 2022; 11:giab090 [View Article] [PubMed]
     [Google Scholar]
  46. Sun S, Jones RB, Fodor AA. Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories. Microbiome 2020; 8:46 [View Article] [PubMed]
     [Google Scholar]
  47. Toole DR, Zhao J, Martens-Habbena W, Strauss SL. Bacterial functional prediction tools detect but underestimate metabolic diversity compared to shotgun metagenomics in southwest Florida soils. Appl Soil Ecol 2021; 168:104129 [View Article]
     [Google Scholar]
  48. He Y, Wu W, Zheng H-M, Li P, McDonald D et al. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat Med 2018; 24:1532–1535 [View Article] [PubMed]
     [Google Scholar]
  49. Holle R, Happich M, Löwel H, Wichmann HE. MONICA/KORA Study Group KORA--a research platform for population based health research. Gesundheitswesen 2005; 67 Suppl 1:S19–25 [View Article] [PubMed]
     [Google Scholar]
  50. Rühlemann MC, Hermes BM, Bang C, Doms S, Moitinho-Silva L et al. Genome-wide association study in 8,956 German individuals identifies influence of ABO histo-blood groups on gut microbiome. Nat Genet 2021; 53:147–155 [View Article] [PubMed]
     [Google Scholar]
  51. Krawczak M, Nikolaus S, von Eberstein H, Croucher PJP, El Mokhtari NE et al. PopGen: population-based recruitment of patients and controls for the analysis of complex genotype-phenotype relationships. Community Genet 2006; 9:55–61 [View Article] [PubMed]
     [Google Scholar]
  52. Zeller G, Tap J, Voigt AY, Sunagawa S, Kultima JR et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol Syst Biol 2014; 10:766 [View Article] [PubMed]
     [Google Scholar]
  53. Větrovský T, Baldrian P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS One 2013; 8:e57923 [View Article] [PubMed]
     [Google Scholar]
  54. Fritz A, Hofmann P, Majda S, Dahms E, Dröge J et al. CAMISIM: simulating metagenomes and microbial communities. Microbiome 2019; 7:17 [View Article] [PubMed]
     [Google Scholar]
  55. Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S et al. Critical assessment of metagenome interpretation-a benchmark of metagenomics software. Nat Methods 2017; 14:1063–1071 [View Article] [PubMed]
     [Google Scholar]
  56. Katz K, Shutov O, Lapoint R, Kimelman M, Brister JR et al. The sequence read archive: a decade more of explosive growth. Nucleic Acids Res 2022; 50:D387–D390 [View Article] [PubMed]
     [Google Scholar]
  57. McIver LJ, Abu-Ali G, Franzosa EA, Schwager R, Morgan XC et al. bioBakery: a meta’omic analysis environment. Bioinformatics 2018; 34:1235–1237 [View Article] [PubMed]
     [Google Scholar]
  58. Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods 2018; 15:962–968 [View Article] [PubMed]
     [Google Scholar]
  59. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 2013; 41:D590–6 [View Article] [PubMed]
     [Google Scholar]
  60. Greenfield P, Tran-Dinh N, Midgley D. Kelpie: generating full-length “amplicons” from whole-metagenome datasets. PeerJ 2019; 6:e6174 [View Article] [PubMed]
     [Google Scholar]
  61. Chen Y, Li J, Zhang Y, Zhang M, Sun Z et al. Parallel‐meta suite: interactive and rapid microbiome data analysis on multiple platforms. iMeta 2022; 1: [View Article] [PubMed]
     [Google Scholar]
  62. Jing G, Zhang Y, Cui W, Liu L, Xu J et al. Meta-Apo improves accuracy of 16S-amplicon-based prediction of microbiome function. BMC Genomics 2021; 22:9 [View Article] [PubMed]
     [Google Scholar]
  63. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 2016; 13:581–583 [View Article] [PubMed]
     [Google Scholar]
  64. Cochrane G, Karsch-Mizrachi I, Takagi T. International nucleotide sequence database collaboration. The International nucleotide sequence database collaboration. Nucleic Acids Res 2016; 44:D48–50 [View Article]
     [Google Scholar]
  65. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 2016; 44:D733–D745 [View Article]
     [Google Scholar]
  66. Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B et al. UniProt: the universal protein knowledgebase. Nucleic Acids Res 2004; 32:D115–9 [View Article] [PubMed]
     [Google Scholar]
  67. Dray S, Dufour A-B. The ade4 package: implementing the duality diagram for ecologists. J Stat Soft 2007; 22:1–20 [View Article]
     [Google Scholar]
  68. Walker AW, Martin JC, Scott P, Parkhill J, Flint HJ et al. 16S rRNA gene-based profiling of the human infant gut microbiota is strongly influenced by sample processing and PCR primer choice. Microbiome 2015; 3:26 [View Article] [PubMed]
     [Google Scholar]
  69. Huang R, He K, Duan X, Xiao J, Wang H et al. Changes of intestinal microflora in colorectal cancer patients after surgical resection and chemotherapy. Comput Math Methods Med 2022; 2022:1940846 [View Article] [PubMed]
     [Google Scholar]
  70. Fang C-Y, Chen J-S, Hsu B-M, Hussain B, Rathod J et al. Colorectal cancer stage-specific fecal bacterial community fingerprinting of the Taiwanese population and underpinning of potential taxonomic biomarkers. Microorganisms 2021; 9:1548 [View Article] [PubMed]
     [Google Scholar]
  71. Dai Z, Coker OO, Nakatsu G, Wu WKK, Zhao L et al. Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial markers. Microbiome 2018; 6:70 [View Article] [PubMed]
     [Google Scholar]
  72. Coleman O, Ecker M, Haller D. Dysregulated lipid metabolism in colorectal cancer. Curr Opin Gastroenterol 2022; 38:162–167 [View Article] [PubMed]
     [Google Scholar]
  73. Liu N-N, Jiao N, Tan J-C, Wang Z, Wu D et al. Multi-kingdom microbiota analyses identify bacterial-fungal interactions and biomarkers of colorectal cancer across cohorts. Nat Microbiol 2022; 7:238–250 [View Article] [PubMed]
     [Google Scholar]
  74. Foss MC, Vlachokosta FV, Aoki TT. Carbohydrate, lipid and amino acid metabolism of insulin-dependent diabetic patients regulated by an artificial beta-cell unit. Diabetes Res 1989; 11:1–8 [PubMed]
     [Google Scholar]
  75. Zhu H, Bai M, Xie X, Wang J, Weng C et al. Impaired amino acid metabolism and its correlation with diabetic kidney disease progression in type 2 diabetes mellitus. Nutrients 2022; 14:3345 [View Article] [PubMed]
     [Google Scholar]
  76. Vanweert F, Schrauwen P, Phielix E. Role of branched-chain amino acid metabolism in the pathogenesis of obesity and type 2 diabetes-related metabolic disturbances BCAA metabolism in type 2 diabetes. Nutr Diabetes 2022; 12:35 [View Article] [PubMed]
     [Google Scholar]
  77. Mills H, Acquah R, Tang N, Cheung L, Klenk S et al. Type 2 diabetes mellitus (T2DM) and carbohydrate metabolism in relation to T2DM from endocrinology, neurophysiology, molecular biology, and biochemistry perspectives. Evid Based Complement Alternat Med 2022; 2022:1708769 [View Article] [PubMed]
     [Google Scholar]
  78. Bassil MS. Protein and glucose metabolism in response to hyperinsulinemia with and without hyperaminoacidemia in men with type 2 diabetes mellitus. McGill University Library; 2011 https://play.google.com/store/books/details?id=xk95nQAACAAJ
  79. Newsholme P, Bender K, Kiely A, Brennan L. Amino acid metabolism, insulin secretion and diabetes. Biochem Soc Trans 2007; 35:1180–1186 [View Article] [PubMed]
     [Google Scholar]
  80. Duan M, Wang Y, Zhang Q, Zou R, Guo M et al. Characteristics of gut microbiota in people with obesity. PLoS One 2021; 16:e0255446 [View Article] [PubMed]
     [Google Scholar]
  81. Bombin A, Yan S, Bombin S, Mosley JD, Ferguson JF. Obesity influences composition of salivary and fecal microbiota and impacts the interactions between bacterial taxa. Physiol Rep 2022; 10:e15254 [View Article] [PubMed]
     [Google Scholar]
  82. Langille MGI. Exploring linkages between taxonomic and functional profiles of the human microbiome. mSystems 2018; 3:e00163-17 [View Article] [PubMed]
     [Google Scholar]
  83. Doolittle WF, Booth A. It’s the song, not the singer: an exploration of holobiosis and evolutionary theory. Biol Philos 2017; 32:5–24 [View Article]
     [Google Scholar]
  84. Knight R, Vrbanac A, Taylor BC, Aksenov A, Callewaert C et al. Best practices for analysing microbiomes. Nat Rev Microbiol 2018; 16:410–422 [View Article] [PubMed]
     [Google Scholar]
  85. Morgan XC, Huttenhower C. Chapter 12: human microbiome analysis. PLoS Comput Biol 2012; 8:e1002808 [View Article] [PubMed]
     [Google Scholar]
  86. Su X, Jing G, Zhang Y, Wu S. Method development for cross-study microbiome data mining: challenges and opportunities. Comput Struct Biotechnol J 2020; 18:2075–2080 [View Article] [PubMed]
     [Google Scholar]
  87. Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI et al. Bacterial community variation in human body habitats across space and time. Science 2009; 326:1694–1697 [View Article] [PubMed]
     [Google Scholar]
  88. Song M, Chan AT, Sun J. Influence of the gut microbiome, diet, and environment on risk of colorectal cancer. Gastroenterology 2020; 158:322–340 [View Article] [PubMed]
     [Google Scholar]
  89. Lavelle A, Sokol H. Gut microbiota-derived metabolites as key actors in inflammatory bowel disease. Nat Rev Gastroenterol Hepatol 2020; 17:223–237 [View Article] [PubMed]
     [Google Scholar]
  90. Ternes D, Karta J, Tsenkova M, Wilmes P, Haan S et al. Microbiome in colorectal cancer: how to get from meta-omics to mechanism?. Trends Microbiol 2020; 28:401–423 [View Article] [PubMed]
     [Google Scholar]
  91. Beydag-Tasöz BS, Yennek S, Grapin-Botton A. Towards a better understanding of diabetes mellitus using organoid models. Nat Rev Endocrinol 2023; 19:232–248 [View Article] [PubMed]
     [Google Scholar]
  92. Skyler JS, Bakris GL, Bonifacio E, Darsow T, Eckel RH et al. Differentiation of diabetes by pathophysiology, natural history, and prognosis. Diabetes 2017; 66:241–255 [View Article] [PubMed]
     [Google Scholar]
  93. Chuvochina M, Rinke C, Parks DH, Rappé MS, Tyson GW et al. The importance of designating type material for uncultured taxa. Syst Appl Microbiol 2019; 42:15–21 [View Article] [PubMed]
     [Google Scholar]
  94. Mukherjee S, Seshadri R, Varghese NJ, Eloe-Fadrosh EA, Meier-Kolthoff JP et al. 1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life. Nat Biotechnol 2017; 35:676–683 [View Article] [PubMed]
     [Google Scholar]
  95. Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol 2018; 36:996–1004 [View Article] [PubMed]
     [Google Scholar]
  96. Xu W, Chen T, Pei Y, Guo H, Li Z et al. Characterization of shallow whole-metagenome shotgun sequencing as a high-accuracy and low-cost method by complicated mock microbiomes. Front Microbiol 2021; 12:678319 [View Article] [PubMed]
     [Google Scholar]
  97. Lugli GA, Ventura M. A breath of fresh air in microbiome science: shallow shotgun metagenomics for a reliable disentangling of microbial ecosystems. Microbiome Res Rep 2022; 1:8 [View Article] [PubMed]
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.001203
Loading
On the limits of 16S rRNA gene-based metagenome prediction and functional profiling
M Gen 10, 001203 (2024); https://doi.org/10.1099/mgen.0.001203
/content/journal/mgen/10.1099/mgen.0.001203
/content/journal/mgen/10.1099/mgen.0.001203
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Most cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error