1887

Abstract

16S rRNA gene profiling is currently the most widely used technique in microbiome research and allows the study of microbial diversity, taxonomic profiling, phylogenetics, functional and network analysis. While a plethora of tools have been developed for the analysis of 16S rRNA gene data, only a few platforms offer a user-friendly interface and none comprehensively covers the whole analysis pipeline from raw data processing down to complex analysis. We introduce Namco, an R shiny application that offers a streamlined interface and serves as a one-stop solution for microbiome analysis. We demonstrate Namco’s capabilities by studying the association between a rich fibre diet and the gut microbiota composition. Namco helped to prove the hypothesis that butyrate-producing bacteria are prompted by fibre-enriched intervention. Namco provides a broad range of features from raw data processing and basic statistics down to machine learning and network analysis, thus covering complex data analysis tasks that are not comprehensively covered elsewhere. Namco is freely available at https://exbio.wzw.tum.de/namco/.

Funding
This study was supported by the:
  • Bundesministerium für Bildung und Forschung (Award 01EA1409C)
    • Principle Award Recipient: JanBaumbach
  • Villum Fonden (Award 13154)
    • Principle Award Recipient: JanBaumbach
  • Horizon 2020 Framework Programme (Award 777111)
    • Principle Award Recipient: JanBaumbach
  • Deutsche Forschungsgemeinschaft (Award 395357507)
    • Principle Award Recipient: DirkHaller
  • 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.
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2022-08-02
2024-03-19
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References

  1. Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nat Rev Genet 2012; 13:260–270 [View Article] [PubMed]
    [Google Scholar]
  2. Devaraj S, Hemarajata P, Versalovic J. The human gut microbiome and body metabolism: implications for obesity and diabetes. Clin Chem 2013; 59:617–628 [View Article] [PubMed]
    [Google Scholar]
  3. Sepich-Poore GD, Zitvogel L, Straussman R, Hasty J, Wargo JA et al. The microbiome and human cancer. Science 2021; 371:eabc4552 [View Article] [PubMed]
    [Google Scholar]
  4. Glassner KL, Abraham BP, Quigley EMM. The microbiome and inflammatory bowel disease. J Allergy Clin Immunol 2020; 145:16–27 [View Article] [PubMed]
    [Google Scholar]
  5. Morais LH, Schreiber HL, Mazmanian SK. The gut microbiota-brain axis in behaviour and brain disorders. Nat Rev Microbiol 2021; 19:241–255 [View Article] [PubMed]
    [Google Scholar]
  6. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010; 26:2460–2461 [View Article] [PubMed]
    [Google Scholar]
  7. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J 2017; 11:2639–2643 [View Article] [PubMed]
    [Google Scholar]
  8. 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]
  9. Eren AM, Morrison HG, Lescault PJ, Reveillaud J, Vineis JH et al. Minimum entropy decomposition: unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. ISME J 2015; 9:968–979 [View Article] [PubMed]
    [Google Scholar]
  10. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 2009; 75:7537–7541 [View Article] [PubMed]
    [Google Scholar]
  11. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019; 37:852–857 [View Article] [PubMed]
    [Google Scholar]
  12. Bokulich NA, Dillon MR, Bolyen E, Kaehler BD, Huttley GA et al. q2-sample-classifier: machine-learning tools for microbiome classification and regression. J Open Res Softw 2018; 3:934 [View Article] [PubMed]
    [Google Scholar]
  13. Bokulich NA, Dillon MR, Zhang Y, Rideout JR, Bolyen E et al. q2-longitudinal: Longitudinal and Paired-Sample Analyses of Microbiome Data. mSystems 2018; 3:e00219-18 [View Article] [PubMed]
    [Google Scholar]
  14. Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis 2015; 26:27663 [View Article] [PubMed]
    [Google Scholar]
  15. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 2013; 8:e61217 [View Article] [PubMed]
    [Google Scholar]
  16. McMurdie PJ, Holmes S. Shiny-phyloseq: Web application for interactive microbiome analysis with provenance tracking. Bioinformatics 2015; 31:282–283 [View Article] [PubMed]
    [Google Scholar]
  17. Woloszynek S, Mell JC, Zhao Z, Simpson G, O’Connor MP et al. Themetagenomics: exploring thematic structure and predicted functionality of 16s rRNA amplicon data. Bioinformatics 2019678110 [View Article]
    [Google Scholar]
  18. Dhariwal A, Chong J, Habib S, King IL, Agellon LB et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res 2017; 45:W180–W188 [View Article] [PubMed]
    [Google Scholar]
  19. Lagkouvardos I, Joseph D, Kapfhammer M, Giritli S, Horn M et al. IMNGS: a comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Sci Rep 2016; 6:33721 [View Article] [PubMed]
    [Google Scholar]
  20. Buza TM, Tonui T, Stomeo F, Tiambo C, Katani R et al. iMAP: an integrated bioinformatics and visualization pipeline for microbiome data analysis. BMC Bioinformatics 2019; 20:374 [View Article]
    [Google Scholar]
  21. Wilke A, Bischof J, Gerlach W, Glass E, Harrison T et al. The MG-RAST metagenomics database and portal in 2015. Nucleic Acids Res 2016; 44:D590–4 [View Article]
    [Google Scholar]
  22. Su S-C, Galvin JE, Yang S-F, Chung W-H, Chang L-C et al. wiSDOM: a visual and statistical analytics for interrogating microbiome. Bioinformatics 2021; 37:2795–2797 [View Article]
    [Google Scholar]
  23. Huse SM, Mark Welch DB, Voorhis A, Shipunova A, Morrison HG et al. VAMPS: a website for visualization and analysis of microbial population structures. BMC Bioinformatics 2014; 15: [View Article]
    [Google Scholar]
  24. Aßhauer KP, Wemheuer B, Daniel R, Meinicke P. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics 2015; 31:2882–2884 [View Article]
    [Google Scholar]
  25. 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]
  26. 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]
  27. Özkurt E, Fritscher J, Soranzo N, Ng DYK, Davey RP et al. LotuS2: an ultrafast and highly accurate tool for amplicon sequencing analysis. [Internet]. bioRxiv; 2021 https://www.biorxiv.org/content/biorxiv/early/2021/12/24/2021.12.24.474111
  28. Unoise ERC. Improved error-correction for illumina 16S and ITS amplicon reads. bioRxiv 2016
    [Google Scholar]
  29. 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]
  30. Cao Q, Sun X, Rajesh K, Chalasani N, Gelow K et al. Effects of rare microbiome taxa filtering on statistical analysis. Front Microbiol 2020; 11:607325 [View Article] [PubMed]
    [Google Scholar]
  31. McKnight DT, Huerlimann R, Bower DS, Schwarzkopf L, Alford RA et al. Methods for normalizing microbiome data: an ecological perspective. Methods Ecol Evol 2019; 10:389–400 [View Article]
    [Google Scholar]
  32. Reitmeier S, Hitch TCA, Treichel N, Fikas N, Hausmann B et al. Handling of spurious sequences affects the outcome of high-throughput 16S rRNA gene amplicon profiling. ISME COMMUN 2021; 1: [View Article]
    [Google Scholar]
  33. Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this Is not optional. Front Microbiol 2017; 8:2224 [View Article] [PubMed]
    [Google Scholar]
  34. Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 2018; 6:226 [View Article] [PubMed]
    [Google Scholar]
  35. Lauder AP, Roche AM, Sherrill-Mix S, Bailey A, Laughlin AL et al. Comparison of placenta samples with contamination controls does not provide evidence for a distinct placenta microbiota. Microbiome 2016; 4:29 [View Article] [PubMed]
    [Google Scholar]
  36. Callahan BJ, DiGiulio DB, Goltsman DSA, Sun CL, Costello EK et al. Replication and refinement of a vaginal microbial signature of preterm birth in two racially distinct cohorts of US women. Proc Natl Acad Sci U S A 2017; 114:9966–9971 [View Article] [PubMed]
    [Google Scholar]
  37. Lagkouvardos I, Fischer S, Kumar N, Clavel T. Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons. PeerJ 2017; 5:e2836 [View Article] [PubMed]
    [Google Scholar]
  38. Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948; 27:379–423 [View Article]
    [Google Scholar]
  39. Simpson EH. Measurement of diversity. Nature 1949; 163:688 [View Article]
    [Google Scholar]
  40. Chao A, Chiu C-H, Jost L. Phylogenetic diversity measures based on Hill numbers. Philos Trans R Soc Lond B Biol Sci 2010; 365:3599–3609 [View Article] [PubMed]
    [Google Scholar]
  41. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 2005; 71:8228–8235 [View Article] [PubMed]
    [Google Scholar]
  42. Bray JR, Curtis JT. An Ordination of the upland forest communities of Southern Wisconsin. Ecol Monogr 1957; 27:325–349 [View Article]
    [Google Scholar]
  43. Zuur AF, Ieno EN, Smith GM. editors Principal coordinate analysis and non-metric multidimensional scaling. In Analysing Ecological Data New York, NY: Springer New York; 2007 pp 259–264
    [Google Scholar]
  44. Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH et al. The vegan package. Community ecology package; 2007;10(631637):719 https://www.researchgate.net/profile/Gavin_Simpson/publication/228339454_The_vegan_Package/links/0912f50be86bc29a7f000000/The-vegan-Package.pdf
  45. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 1995; 57:289–300 [View Article]
    [Google Scholar]
  46. Wirbel J, Zych K, Essex M, Karcher N, Kartal E et al. Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox. Genome Biol 2021; 22:93 [View Article] [PubMed]
    [Google Scholar]
  47. Hawinkel S, Mattiello F, Bijnens L, Thas O. A broken promise: microbiome differential abundance methods do not control the false discovery rate. Brief Bioinform 2019; 20:210–221 [View Article] [PubMed]
    [Google Scholar]
  48. Wirbel J, Pyl PT, Kartal E, Zych K, Kashani A et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat Med 2019; 25:679–689 [View Article] [PubMed]
    [Google Scholar]
  49. Blei DM, Lafferty JD. A correlated topic model of Science. Ann Appl Stat 2007; 1: [View Article]
    [Google Scholar]
  50. Woloszynek S, Mell JC, Zhao Z, Simpson G, O’Connor MP et al. Exploring thematic structure and predicted functionality of 16S rRNA amplicon data. PLoS One 2019; 14:e0219235 [View Article] [PubMed]
    [Google Scholar]
  51. Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS One 2013; 8:e67019 [View Article] [PubMed]
    [Google Scholar]
  52. Nearing JT, Douglas GM, Hayes M, MacDonald J, Desai D et al. Microbiome differential abundance methods produce disturbingly different results across 38 datasets. Bioinformatics 20212021 [View Article]
    [Google Scholar]
  53. Hooper LV, Littman DR, Macpherson AJ. Interactions between the microbiota and the immune system. Science 2012; 336:1268–1273 [View Article] [PubMed]
    [Google Scholar]
  54. Matchado MS, Lauber M, Reitmeier S, Kacprowski T, Baumbach J et al. Network analysis methods for studying microbial communities: a mini review. Comput Struct Biotechnol J 2021; 19:2687–2698 [View Article] [PubMed]
    [Google Scholar]
  55. Peschel S, Müller CL, von Mutius E, Boulesteix A-L, Depner M. NetCoMi: network construction and comparison for microbiome data in R. Bioinformatics 2020 [View Article]
    [Google Scholar]
  56. Clayden A. Causal relationships in medicine: a practical system for critical appraisal. Ann Intern Med 1991; 114:916 [View Article]
    [Google Scholar]
  57. Vujkovic-Cvijin I, Sklar J, Jiang L, Natarajan L, Knight R et al. Host variables confound gut microbiota studies of human disease. Nature 2020; 587:448–454 [View Article] [PubMed]
    [Google Scholar]
  58. Wright MN, Ziegler A. Ranger: a fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw Articles 2017; 77:1–17
    [Google Scholar]
  59. Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat Med 2021; 27:321–332 [View Article] [PubMed]
    [Google Scholar]
  60. Polster SP, Sharma A, Tanes C, Tang AT, Mericko P et al. Permissive microbiome characterizes human subjects with a neurovascular disease cavernous angioma. Nat Commun 2020; 11:2659 [View Article] [PubMed]
    [Google Scholar]
  61. Brandl B, Skurk T, Rennekamp R, Hannink A, Kiesswetter E et al. A phenotyping platform to characterize healthy individuals across four stages of life - The Enable Study. Front Nutr 2020; 7:582387 [View Article] [PubMed]
    [Google Scholar]
  62. Rennekamp R, Brandl B, Giesbertz P, Skurk T, Hauner H. Metabolic and satiating effects and consumer acceptance of a fibre-enriched Leberkas meal: a randomized cross-over trial. Eur J Nutr 2021; 60:3203–3210 [View Article] [PubMed]
    [Google Scholar]
  63. Reitmeier S, Kiessling S, Clavel T, List M, Almeida EL et al. Arrhythmic gut microbiome signatures predict risk of type 2 diabetes. Cell Host Microbe 2020; 28:258–272 [View Article] [PubMed]
    [Google Scholar]
  64. McRae MP. Dietary fiber is beneficial for the prevention of cardiovascular disease: an umbrella review of meta-analyses. J Chiropr Med 2017; 16:289–299 [View Article] [PubMed]
    [Google Scholar]
  65. Anderson JW, Baird P, Davis RH, Ferreri S, Knudtson M et al. Health benefits of dietary fiber. Nutr Rev 2009; 67:188–205 [View Article] [PubMed]
    [Google Scholar]
  66. Aune D, Chan DSM, Lau R, Vieira R, Greenwood DC et al. Dietary fibre, whole grains, and risk of colorectal cancer: systematic review and dose-response meta-analysis of prospective studies. BMJ 2011; 343:d6617 [View Article] [PubMed]
    [Google Scholar]
  67. Xu X, Zhu Y, Li J, Wang S. Dietary fiber, glycemic index, glycemic load and renal cell carcinoma risk. Carcinogenesis 2019; 40:441–447 [View Article] [PubMed]
    [Google Scholar]
  68. da Cruz AG, Senaka Ranadheera C, Nazzaro F, Mortazavian A. Probiotics and Prebiotics in Foods: Challenges, Innovations, and Advances. Academic Press; 2021. P. 346 https://play.google.com/store/books/details?id=6WIFEAAAQBAJ
  69. Bailén M, Bressa C, Martínez-López S, González-Soltero R, Montalvo Lominchar MG et al. Microbiota features associated with a high-fat/low-fiber diet in healthy adults. Front Nutr 2020; 7:583608 [View Article] [PubMed]
    [Google Scholar]
  70. Aoe S, Nakamura F, Fujiwara S. Effect of wheat bran on fecal butyrate-producing bacteria and wheat bran combined with barley on Bacteroides abundance in Japanese healthy adults. Nutrients 2018; 10:E1980 [View Article] [PubMed]
    [Google Scholar]
  71. Van den Abbeele P, Belzer C, Goossens M, Kleerebezem M, De Vos WM et al. Butyrate-producing clostridium cluster XIVa species specifically colonize mucins in an in vitro gut model. ISME J 2013; 7:949–961 [View Article] [PubMed]
    [Google Scholar]
  72. Menni C, Jackson MA, Pallister T, Steves CJ, Spector TD et al. Gut microbiome diversity and high-fibre intake are related to lower long-term weight gain. Int J Obes 2017; 41:1099–1105 [View Article]
    [Google Scholar]
  73. Higgins JA, Higbee DR, Donahoo WT, Brown IL, Bell ML et al. Resistant starch consumption promotes lipid oxidation. Nutr Metab 2004; 1:8 [View Article]
    [Google Scholar]
  74. Kverka M, Zakostelska Z, Klimesova K, Sokol D, Hudcovic T et al. Oral administration of Parabacteroides distasonis antigens attenuates experimental murine colitis through modulation of immunity and microbiota composition. Clin Exp Immunol 2011; 163:250–259 [View Article] [PubMed]
    [Google Scholar]
  75. Wang K, Liao M, Zhou N, Bao L, Ma K et al. Parabacteroides distasonis alleviates obesity and metabolic dysfunctions via production of succinate and secondary bile acids. Cell Rep 2019; 26:222–235 [View Article] [PubMed]
    [Google Scholar]
  76. Qin J, Li Y, Cai Z, Li S, Zhu J et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012; 490:55–60 [View Article] [PubMed]
    [Google Scholar]
  77. Cuffaro B, Assohoun ALW, Boutillier D, Súkeníková L, Desramaut J et al. In vitro characterization of gut microbiota-derived commensal strains: selection of Parabacteroides distasonis strains alleviating TNBS-induced colitis in mice. Cells 2020; 9:E2104 [View Article] [PubMed]
    [Google Scholar]
  78. Falony G, Joossens M, Vieira-Silva S, Wang J, Darzi Y et al. Population-level analysis of gut microbiome variation. Science 2016; 352:560–564 [View Article] [PubMed]
    [Google Scholar]
  79. Ezeji JC, Sarikonda DK, Hopperton A, Erkkila HL, Cohen DE et al. Parabacteroides distasonis: intriguing aerotolerant gut anaerobe with emerging antimicrobial resistance and pathogenic and probiotic roles in human health. Gut Microbes 2021; 13:1922241 [View Article] [PubMed]
    [Google Scholar]
  80. Tian T, Zhang X, Luo T, Wang D, Sun Y et al. Effects of short-term dietary fiber intervention on gut microbiota in young healthy people. Diabetes Metab Syndr Obes 2021; 14:3507–3516 [View Article] [PubMed]
    [Google Scholar]
  81. Shortt C, Hasselwander O, Meynier A, Nauta A, Fernández EN et al. Systematic review of the effects of the intestinal microbiota on selected nutrients and non-nutrients. Eur J Nutr 2018; 57:25–49 [View Article] [PubMed]
    [Google Scholar]
  82. Sun B, Hou L, Yang Y. Effects of altered dietary fiber on the gut microbiota, short-chain fatty acids and cecum of chickens during different growth periods. Biology (Basel) 2020 [View Article]
    [Google Scholar]
  83. Andersen V, Svenningsen K, Knudsen LA, Hansen AK, Holmskov U et al. Novel understanding of ABC transporters ABCB1/MDR/P-glycoprotein, ABCC2/MRP2, and ABCG2/BCRP in colorectal pathophysiology. World J Gastroenterol 2015; 21:11862–11876 [View Article] [PubMed]
    [Google Scholar]
  84. Abazarfard Z, Eslamian G, Salehi M, Keshavarzi S. A randomized controlled trial of the effects of an almond-enriched, hypocaloric diet on liver function tests in overweight/obese women. Iran Red Crescent Med J 2016; 18:e23628 [View Article] [PubMed]
    [Google Scholar]
  85. Yoon G, Gaynanova I, Müller CL. Microbial networks in SPRING - Semi-parametric rank-based correlation and partial correlation estimation for quantitative microbiome data. Front Genet 2019; 10:516 [View Article] [PubMed]
    [Google Scholar]
  86. Corrêa-Oliveira R, Fachi JL, Vieira A, Sato FT, Vinolo MAR. Regulation of immune cell function by short-chain fatty acids. Clin Transl Immunology 2016; 5:e73 [View Article] [PubMed]
    [Google Scholar]
  87. Donohoe DR, Garge N, Zhang X, Sun W, O’Connell TM et al. The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab 2011; 13:517–526 [View Article] [PubMed]
    [Google Scholar]
  88. Wang G. Human antimicrobial peptides and proteins. Pharmaceuticals 2014; 7:545–594 [View Article]
    [Google Scholar]
  89. Fukagawa NK, Anderson JW, Hageman G, Young VR, Minaker KL. High-carbohydrate, high-fiber diets increase peripheral insulin sensitivity in healthy young and old adults. Am J Clin Nutr 1990; 52:524–528 [View Article] [PubMed]
    [Google Scholar]
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