Skip to main content
SpringerLink
Log in
Book cover

Statistical Human Genetics pp 649–677Cite as

Processing and Analyzing Human Microbiome Data

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1666))

Abstract

The human microbiome is associated with complex disorders such as diabetes, cancer, obesity and cardiovascular disorders. Recent technological developments have allowed researchers to fully quantify the composition of the microbiome using culture-independent approaches, resulting in a large amount of microbiome data, which provide invaluable opportunities to assess the important contributions of the microbiome to human health and disease. In this chapter, we discuss and evaluate multiple statistical approaches for processing, summarizing, and analyzing microbiome data. Specifically, we provide programming scripts for processing microbiome data using QIIME and calculating alpha and beta diversities, assessing the association between diversities and outcomes of interest using R programs, as well as interpretation of results. We illustrate the methods in the context of analyzing the foregut microbiome in esophageal adenocarcinoma.

The original version of this chapter was revised. A correction to this chapter can be found at https://doi.org/10.1007/978-1-4939-7274-6_32

This is a preview of subscription content, log in via an institution.

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

Change history

  • 05 September 2018

    The original version of this chapter was inadvertently published without including the dbGaP acknowledgment. The updated chapter now contains that information.

References

  1. Ursell LK, Metcalf JL, Parfrey LW, Knight R (2012) Defining the human microbiome. Nutr Rev 70(Suppl 1):S38–S44

    Article  PubMed  PubMed Central  Google Scholar 

  2. Li H (2015) Microbiome, metagenomics, and high-dimensional compositional data analysis. Annu Rev Stat Appl 2:73–94

    Article  Google Scholar 

  3. Backhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI (2005) Host-bacterial mutualism in the human intestine. Science 307:1915–1920

    Article  CAS  PubMed  Google Scholar 

  4. Human Microbiome Project (2016) About HMP metagenomic sequencing & analysis. http://hmpdacc.org/micro_analysis/microbiome_ analyses.php

  5. Claus SP, Guillou H, Ellero-Simatos S (2016) The gut microbiota: a major player in the toxicity of environmental pollutants? NPJ Biofilms Microbiomes 2:16003

    Article  PubMed  PubMed Central  Google Scholar 

  6. National Institutes of Health (2016) NIH Human Microbiome Project defines normal bacterial makeup of the body. https://www.nih.gov/news-events/news-releases/nih-human-microbiome-project-defines-normal-bacterial-makeup-body

  7. Hartstra AV, Bouter KEC, Backhed F, Nieuwdorp M (2015) Insights into the role of the microbiome in obesity and type 2 diabetes. Diabetes Care 38:159–165

    Article  CAS  PubMed  Google Scholar 

  8. Tang WHW, Hazen SL (2014) The contributory role of gut microbiota in cardiovascular disease. J Clin Invest 124:4204–4211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Dulal S, Keku TO (2014) Gut microbiome and colorectal adenomas. Cancer J 20:225–231

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Illumina (2016) Introduction to human microbiome analysis, Survey the genomes of entire communities. http://www.illumina.com/areas-of-interest/microbiology/human-microbiome-analysis.html

  11. Woo PCY, Lau SKP, Teng JLL, Tse H, Yuen KY (2008) Then and now: use of 16S rDNA gene sequencing for bacterial identification and discovery of novel bacteria in clinical microbiology laboratories. Clin Microbiol Infect 14:908–934

    Article  CAS  PubMed  Google Scholar 

  12. Clarridge JE (2004) Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clin Microbiol Rev 17:840–862

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hamady M, Knight R (2009) Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res 19:1141–1152

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fiona Stewart EY (2014) Addressing challenges in microbiome DNA analysis, NEB UK Expressions

    Google Scholar 

  15. Brooks JP (2016) Challenges for case-control studies with microbiome data. Ann Epidemiol 26:336–341

    Google Scholar 

  16. Yang L, Chaudhary N, Baghdadi J, Pei Z (2014) Microbiome in reflux disorders and esophageal adenocarcinoma. Cancer J 20:207–210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Gilles A, Meglecz E, Pech N, Ferreira S, Malausa T, Martin JF (2011) Accuracy and quality assessment of 454 GS-FLX Titanium pyrosequencing. BMC Genomics 12:245

    Article  PubMed  PubMed Central  Google Scholar 

  18. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R (2012) Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6:1621–1624

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Erica Plummer JT, Bulach DM, Garland SM, Tabrizi SN (2015) A comparison of three bioinformatics pipelines for the analysis of preterm gut microbiota using 16S rRNA gene sequencing data. J Proteomics Bioinform 8:283–291

    Google Scholar 

  22. Navas-Molina JA, Peralta-Sanchez JM, Gonzalez A, McMurdie PJ, Vazquez-Baeza Y, Xu ZJ, Ursell LK, Lauber C, Zhou HW, Song SJ, Huntley J, Ackermann GL, Berg-Lyons D, Holmes S, Caporaso JG, Knight R (2013) Advancing our understanding of the human microbiome using QIIME. Methods Enzymol 531:371–444

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Mir K, Neuhaus K, Bossert M, Schober S (2013) Short barcodes for next generation sequencing. PLoS One 8:e82933

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Edgar RC (2013) UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 10:996–998

    Article  CAS  Google Scholar 

  25. Scitable by nature education (2016) Primer. http://www.nature.com/scitable/definition/primer-305

  26. De Beuf K, De Schrijver J, Thas O, Van Criekinge W, Irizarry RA, Clement L (2012) Improved base-calling and quality scores for 454 sequencing based on a Hurdle Poisson model. BMC Bioinformatics 13:303

    Article  PubMed  PubMed Central  Google Scholar 

  27. Si XF, Baselga A, Leprieur F, Song X, Ding P (2016) Selective extinction drives taxonomic and functional alpha and beta diversities in island bird assemblages. J Anim Ecol 85:409–418

    Article  PubMed  Google Scholar 

  28. McMurdie PJ, Holmes S (2013) Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hill MO (1973) Diversity and evenness: a unifying notation and its consequences. Ecology 54:427–432

    Article  Google Scholar 

  30. Li K, Bihan M, Yooseph S, Methe BA (2012) Analyses of the microbial diversity across the human microbiome. PLoS One 7:e32118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lande R (1996) Statistics and partitioning of species diversity, and similarity among multiple communities. Oikos 76:5–13

    Article  Google Scholar 

  32. Basualdo CV (2011) Choosing the best non-parametric richness estimator for benthic macroinvertebrates databases. Rev Soc Entomol Argent 70(1–2):27–38

    Google Scholar 

  33. Sandra D, Williamson KB (2013) Species richness and diversity of a terrestrial insular environment: serpentine of the Barberton Greenstone Belt, South Africa. Int J Biodivers Conserv 5(5):296–310

    Google Scholar 

  34. Morris EK, Caruso T, Buscot F, Fischer M, Hancock C, Maier TS, Meiners T, Muller C, Obermaier E, Prati D, Socher SA, Sonnemann I, Waschke N, Wubet T, Wurst S, Rillig MC (2014) Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories. Ecol Evol 4:3514–3524

    Article  PubMed  PubMed Central  Google Scholar 

  35. Nagendra H (2002) Opposite trends in response for the Shannon and Simpson indices of landscape diversity. Appl Geogr 22:175–186

    Article  Google Scholar 

  36. Saucedo-Garcia A, Anaya AL, Espinosa-Garcia FJ, Gonzalez MC (2014) Diversity and communities of foliar endophytic fungi from different agroecosystems of Coffea arabica L. in two regions of Veracruz, Mexico. PLoS One 9:e98454

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Williams VL, Witkowski ETF, Balkwill K (2005) Application of diversity indices to appraise plant availability in the traditional medicinal markets of Johannesburg, South Africa. Biodivers Conserv 14:2971–3001

    Article  Google Scholar 

  38. Colwell RK (2009) Biodiversity: concepts, patterns, and measurement. In: Levin SA (ed) The Princeton guide to ecology. Princeton University Press, Princeton, NJ, pp 257–263

    Google Scholar 

  39. Fisher RA, Corbet AS, Williams CB (1943) The relation between the number of species and the number of individuals in a random sample of an animal population. J Anim Ecol 12:42–58

    Article  Google Scholar 

  40. Magurran AE (2004) Measuring biological diversity. Blackwell Publishing, Oxford, UK

    Google Scholar 

  41. Hughes JB, Hellmann JJ, Ricketts TH, Bohannan BJ (2001) Counting the uncountable: statistical approaches to estimating microbial diversity. Appl Environ Microbiol 67:4399–4406

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Chao A, Ma MC, Yang MCK (1993) Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika 80:193–201

    Article  Google Scholar 

  43. Gotelli NJ, Colwell RK (2010) Estimating species richness. In: Magurran AE, McGill BJ (eds) Frontiers in measuring biodiversity. Oxford University, New York, pp 39–54

    Google Scholar 

  44. Chao A, Chazdon RL, Colwell RK, Shen TJ (2005) A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecol Lett 8:148–159

    Article  Google Scholar 

  45. Soininen J (2010) Species turnover along abiotic and biotic gradients: patterns in space equal patterns in time? Bioscience 60:433–439

    Article  Google Scholar 

  46. Koleff P, Gaston KJ, Lennon JJ (2003) Measuring beta diversity for presence-absence data. J Anim Ecol 72:367–382

    Article  Google Scholar 

  47. Biology-forums (2016) Species turnover. http://biology-forums.com/definitions/index.php/Species_turnover

  48. Shirkhorshidi AS, Aghabozorgi S, Wah TY (2015) A comparison study on similarity and dissimilarity measures in clustering continuous data. PLoS One 10:e0144059

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Emran SM, Ye N (2002) Robustness of Chi-square and Canberra distance metrics for computer intrusion detection. Qual Reliab Eng Int 18:19–28

    Article  Google Scholar 

  50. Giuseppe Jurman SR, Visintainer R, Furlanello C (2009) Canberra distance on ranked lists. Advances in ranking–NIPS 09 workshop, pp 22–27

    Google Scholar 

  51. Hennig C, Hausdorf B (2006) Design of dissimilarity measures: a new dissimilarity between species distribution areas. In: Batagelj V, Bock H-H, Ferligoj A, Žiberna A (eds) Stud class data anal. Springer, Berlin, Heidelberg, pp 29–37

    Google Scholar 

  52. Anderson MJ, Millar RB (2004) Spatial variation and effects of habitat on temperate reef fish assemblages in northeastern New Zealand. J Exp Mar Biol Ecol 305:191–221

    Article  Google Scholar 

  53. Horn HS (1966) Measurement of overlap in comparative ecological studies. Am Nat 100:419

    Article  Google Scholar 

  54. Anderson MJ, Ellingsen KE, McArdle BH (2006) Multivariate dispersion as a measure of beta diversity. Ecol Lett 9:683–693

    Article  Google Scholar 

  55. Cao Y, Williams WP, Bark AW (1997) Similarity measure bias in river benthic Aufwuchs community analysis. Water Environ Res 69:95–106

    Article  CAS  Google Scholar 

  56. Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71:8228–8235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Clarke KR, Somerfield PJ, Chapman MG (2006) On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray-Curtis coefficient for denuded assemblages. J Exp Mar Biol Ecol 330:55–80

    Article  Google Scholar 

  58. Fukuyama J, McMurdie PJ, Dethlefsen L, Relman DA, Holmes S (2012) Comparisons of distance methods for combining covariates and abundances in microbiome studies. Pac Symp Biocomput:213–224

    Google Scholar 

  59. Lozupone CA, Knight R (2007) Global patterns in bacterial diversity. Proc Natl Acad Sci U S A 104:11436–11440

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Schloss PD (2008) Evaluating different approaches that test whether microbial communities have the same structure. ISME J 2:265–275

    Article  PubMed  Google Scholar 

  61. Ives AR, Helmus MR (2010) Phylogenetic metrics of community similarity. Am Nat 176:E128–E142

    Article  PubMed  Google Scholar 

  62. McArdle BH, Anderson MJ (2001) Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82:290–297

    Article  Google Scholar 

  63. Zhao N, Chen J, Carroll IM, Ringel-Kulka T, Epstein MP, Zhou H, Zhou JJ, Ringel Y, Li HZ, Wu MC (2015) Testing in microbiome-profiling studies with MiRKAT, the Microbiome regression-based kernel association test. Am J Hum Genet 96:797–807

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Zhu X, Wang J, Peng B, Shete S (2016) Empirical estimation of sequencing error rates using smoothing splines. BMC Bioinformatics 17:177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Scealy JL, Welsh AH (2011) Regression for compositional data by using distributions defined on the hypersphere. J R Stat Soc B 73:351–375

    Article  Google Scholar 

  66. Kent JT (1982) The Fisher-Bingham distribution on the sphere. J R Stat Soc B 44:71–80

    Google Scholar 

  67. Aitchison J (1982) The statistical-analysis of compositional data. J R Stat Soc B 44:139–177

    Google Scholar 

  68. Shi PX, Zhang AR, Li HZ (2016) Regression analysis for microbiome compositional data. Ann Appl Stat 10:1019–1040

    Article  Google Scholar 

  69. Fisher CK, Mehta P (2014) Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS One 9:e102451

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Chen EZ, Li HZ (2016) A two-part mixed-effects model for analyzing longitudinal microbiome compositional data. Bioinformatics 32:2611–2617

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Gevers D, Knight R, Petrosino JF, Huang K, McGuire AL, Birren BW, Nelson KE, White O, Methe BA, Huttenhower C (2012) The Human Microbiome Project: a community resource for the healthy human microbiome. PLoS Biol 10:e1001377

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Edgar RC (2016) UNCROSS: filtering of high-frequency cross-talk in 16S amplicon reads. doi:10.1101/088666

  73. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P (2012) An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 6:610–618

    Article  CAS  PubMed  Google Scholar 

  74. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. R Core Team (2016) R: a language and environment for statistical computing. R foundation for statistical computing. https://www.R-project.org/

  76. van den Boogaart KG, Tolosana R, Bren M (2014) compositions: Compositional Data Analysis. R Package Version 1:40–1. http://CRAN.R-project.org/package=compositions

  77. Oksanen J, Guillaume Blanchet F, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Henry M, Stevens H (2016) vegan: Community Ecology Package. R package version 2.3-5. http://CRAN.R-project.org/package=vegan

  78. Paradis E, Claude J, Strimmer K (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289–290

    Article  CAS  Google Scholar 

  79. Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, Mills DA, Caporaso JG (2013) Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods 10:57–59

    Google Scholar 

  80. Walters WA, Caporaso JG, Lauber CL, Berg-Lyons D, Fierer N, Knight R (2011) PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers. Bioinformatics 27:1159–1161

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

Funding support for the Study of Foregut Microbiome in Development of Esophageal Adenocarcinoma was provided by the National Cancer Institute (UH3CA140233) through the Human Microbiome Project of the NIH Roadmap Initiative. Data for the Foregut Microbiome study were provided by Zhiheng Pei, MD, PhD, on behalf of his collaborators at New York University School of Medicine, the J. Craig Venter Institute, and Lawrence Berkeley National Laboratory.

Author information

Authors and Affiliations

  1. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA

    Xuan Zhu, Jian Wang & Sanjay Shete

  2. Department of Emergency Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA

    Cielito Reyes-Gibby

  3. Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA

    Sanjay Shete

Authors
  1. Xuan Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cielito Reyes-Gibby
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanjay Shete
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanjay Shete .

Editor information

Editors and Affiliations

  1. Case Western Reserve University, Cleveland, OH, USA

    Robert C. Elston

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Zhu, X., Wang, J., Reyes-Gibby, C., Shete, S. (2017). Processing and Analyzing Human Microbiome Data. In: Elston, R. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 1666. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7274-6_31

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7274-6_31

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7273-9

  • Online ISBN: 978-1-4939-7274-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation