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Published in: Digestive Diseases and Sciences 3/2020

01-03-2020 | Review

Tools for Analysis of the Microbiome

Authors: Jessica Galloway-Peña, Blake Hanson

Published in: Digestive Diseases and Sciences | Issue 3/2020

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Abstract

Over the past decade, it has become exceedingly clear that the microbiome is a critical factor in human health and disease and thus should be investigated to develop innovative treatment strategies. The field of metagenomics has come a long way in leveraging the advances of next-generation sequencing technologies resulting in the capability to identify and quantify all microorganisms present in human specimens. However, the field of metagenomics is still in its infancy, specifically in regard to the limitations in computational analysis, statistical assessments, standardization, and validation due to vast variability in the cohorts themselves, experimental design, and bioinformatic workflows. This review summarizes the methods, technologies, computational tools, and model systems for characterizing and studying the microbiome. We also discuss important considerations investigators must make when interrogating the involvement of the microbiome in health and disease in order to establish robust results and mechanistic insights before moving into therapeutic design and intervention.
Literature
1.
go back to reference Song EJ, Lee ES, Nam YD. Progress of analytical tools and techniques for human gut microbiome research. J Microbiol. 2018;56:693–705.PubMed Song EJ, Lee ES, Nam YD. Progress of analytical tools and techniques for human gut microbiome research. J Microbiol. 2018;56:693–705.PubMed
2.
go back to reference Thursby E, Juge N. Introduction to the human gut microbiota. Biochem J. 2017;474:1823–1836.PubMed Thursby E, Juge N. Introduction to the human gut microbiota. Biochem J. 2017;474:1823–1836.PubMed
3.
go back to reference Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI. The human microbiome project. Nature. 2007;449:804–810.PubMedPubMedCentral Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI. The human microbiome project. Nature. 2007;449:804–810.PubMedPubMedCentral
4.
go back to reference Tringe SG, Rubin EM. Metagenomics: DNA sequencing of environmental samples. Nat Rev Genet. 2005;6:805–814.PubMed Tringe SG, Rubin EM. Metagenomics: DNA sequencing of environmental samples. Nat Rev Genet. 2005;6:805–814.PubMed
5.
go back to reference Riesenfeld CS, Schloss PD, Handelsman J. Metagenomics: genomic analysis of microbial communities. Annu Rev Genet. 2004;38:525–552.PubMed Riesenfeld CS, Schloss PD, Handelsman J. Metagenomics: genomic analysis of microbial communities. Annu Rev Genet. 2004;38:525–552.PubMed
6.
go back to reference Qin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65.PubMedPubMedCentral Qin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65.PubMedPubMedCentral
7.
go back to reference Integrative HMPRNC. The integrative human microbiome project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe. 2014;16:276–289. Integrative HMPRNC. The integrative human microbiome project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe. 2014;16:276–289.
8.
go back to reference Bantock GG. The modern doctrine of bacteriology, or the germ theory of disease. Br Med J. 1997;1899:846–848. Bantock GG. The modern doctrine of bacteriology, or the germ theory of disease. Br Med J. 1997;1899:846–848.
10.
go back to reference Maruvada P, Leone V, Kaplan LM, Chang EB. The human microbiome and obesity: moving beyond associations. Cell Host Microbe. 2017;22:589–599.PubMed Maruvada P, Leone V, Kaplan LM, Chang EB. The human microbiome and obesity: moving beyond associations. Cell Host Microbe. 2017;22:589–599.PubMed
11.
go back to reference Jamshidi P, Hasanzadeh S, Tahvildari A, et al. Is there any association between gut microbiota and type 1 diabetes? A systematic review. Gut Pathog. 2019;11:49.PubMedPubMedCentral Jamshidi P, Hasanzadeh S, Tahvildari A, et al. Is there any association between gut microbiota and type 1 diabetes? A systematic review. Gut Pathog. 2019;11:49.PubMedPubMedCentral
12.
go back to reference Ahmadmehrabi S, Tang WHW. Gut microbiome and its role in cardiovascular diseases. Curr Opin Cardiol. 2017;32:761–766.PubMedPubMedCentral Ahmadmehrabi S, Tang WHW. Gut microbiome and its role in cardiovascular diseases. Curr Opin Cardiol. 2017;32:761–766.PubMedPubMedCentral
13.
go back to reference Scott AJ, Alexander JL, Merrifield CA, et al. International cancer microbiome consortium consensus statement on the role of the human microbiome in carcinogenesis. Gut. 2019;68:1624–1632.PubMed Scott AJ, Alexander JL, Merrifield CA, et al. International cancer microbiome consortium consensus statement on the role of the human microbiome in carcinogenesis. Gut. 2019;68:1624–1632.PubMed
14.
go back to reference Gopalakrishnan V, Helmink BA, Spencer CN, Reuben A, Wargo JA. The influence of the gut microbiome on cancer, immunity, and cancer immunotherapy. Cancer Cell. 2018;33:570–580.PubMedPubMedCentral Gopalakrishnan V, Helmink BA, Spencer CN, Reuben A, Wargo JA. The influence of the gut microbiome on cancer, immunity, and cancer immunotherapy. Cancer Cell. 2018;33:570–580.PubMedPubMedCentral
15.
go back to reference Shen L, Ji HF. Associations between gut microbiota and Alzheimer’s disease: current evidences and future therapeutic and diagnostic perspectives. J Alzheimers Dis. 2019;68:25–31.PubMed Shen L, Ji HF. Associations between gut microbiota and Alzheimer’s disease: current evidences and future therapeutic and diagnostic perspectives. J Alzheimers Dis. 2019;68:25–31.PubMed
16.
go back to reference Rieder R, Wisniewski PJ, Alderman BL, Campbell SC. Microbes and mental health: a review. Brain Behav Immun. 2017;66:9–17.PubMed Rieder R, Wisniewski PJ, Alderman BL, Campbell SC. Microbes and mental health: a review. Brain Behav Immun. 2017;66:9–17.PubMed
17.
go back to reference Vetrovsky 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.PubMedPubMedCentral Vetrovsky 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.PubMedPubMedCentral
18.
go back to reference Xue Z, Kable ME, Marco ML. Impact of DNA sequencing and analysis methods on 16S rRNA gene bacterial community analysis of dairy products. mSphere. 2018;3:e00410–e00418.PubMedPubMedCentral Xue Z, Kable ME, Marco ML. Impact of DNA sequencing and analysis methods on 16S rRNA gene bacterial community analysis of dairy products. mSphere. 2018;3:e00410–e00418.PubMedPubMedCentral
19.
go back to reference Stackebrandt E, Goebel BM. Taxonomic note: a place for DNA-DNA reassociation and 16S rRNA sequence analysis in the present species definition in bacteriology. Int J Syst Evol Microbiol. 1994;44:846–849. Stackebrandt E, Goebel BM. Taxonomic note: a place for DNA-DNA reassociation and 16S rRNA sequence analysis in the present species definition in bacteriology. Int J Syst Evol Microbiol. 1994;44:846–849.
20.
go back to reference Yarza P, Yilmaz P, Pruesse E, et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol. 2014;12:635–645.PubMed Yarza P, Yilmaz P, Pruesse E, et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol. 2014;12:635–645.PubMed
21.
go back to reference Johnson JS, Spakowicz DJ, Hong BY, et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun. 2019;10:5029.PubMedPubMedCentral Johnson JS, Spakowicz DJ, Hong BY, et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun. 2019;10:5029.PubMedPubMedCentral
22.
go back to reference Edgar RC. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics. 2018;34:2371–2375.PubMed Edgar RC. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics. 2018;34:2371–2375.PubMed
23.
go back to reference Hamady M, Knight R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res. 2009;19:1141–1152.PubMedPubMedCentral Hamady M, Knight R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res. 2009;19:1141–1152.PubMedPubMedCentral
24.
go back to reference Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–5267.PubMedPubMedCentral Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–5267.PubMedPubMedCentral
25.
go back to reference McDonald D, Price MN, Goodrich J, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–618.PubMed McDonald D, Price MN, Goodrich J, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–618.PubMed
26.
go back to reference Yilmaz P, Parfrey LW, Yarza P, et al. The SILVA and “all-species living tree project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42:D643–D648.PubMed Yilmaz P, Parfrey LW, Yarza P, et al. The SILVA and “all-species living tree project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42:D643–D648.PubMed
27.
go back to reference Schloss PD, Westcott SL, Ryabin T, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–7541.PubMedPubMedCentral Schloss PD, Westcott SL, Ryabin T, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–7541.PubMedPubMedCentral
28.
go back to reference Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–336.PubMedPubMedCentral Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–336.PubMedPubMedCentral
29.
go back to reference Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–583.PubMedPubMedCentral Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–583.PubMedPubMedCentral
30.
go back to reference Walker JN, Hanson BM, Pinkner CL, et al. Insights into the microbiome of breast implants and periprosthetic tissue in breast implant-associated anaplastic large cell lymphoma. Sci Rep. 2019;9:10393.PubMedPubMedCentral Walker JN, Hanson BM, Pinkner CL, et al. Insights into the microbiome of breast implants and periprosthetic tissue in breast implant-associated anaplastic large cell lymphoma. Sci Rep. 2019;9:10393.PubMedPubMedCentral
31.
go back to reference Zhou W, Sailani MR, Contrepois K, et al. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature. 2019;569:663–671.PubMedPubMedCentral Zhou W, Sailani MR, Contrepois K, et al. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature. 2019;569:663–671.PubMedPubMedCentral
32.
go back to reference Callahan BJ, Wong J, Heiner C, et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res. 2019;47:e103.PubMedPubMedCentral Callahan BJ, Wong J, Heiner C, et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res. 2019;47:e103.PubMedPubMedCentral
33.
go back to reference Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 2017;35:833–844.PubMed Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 2017;35:833–844.PubMed
36.
go back to reference Sam QH, Chang MW, Chai LY. The fungal mycobiome and its interaction with gut bacteria in the host. Int J Mol Sci. 2017;18(2):E330.PubMed Sam QH, Chang MW, Chai LY. The fungal mycobiome and its interaction with gut bacteria in the host. Int J Mol Sci. 2017;18(2):E330.PubMed
37.
go back to reference Nash AK, Auchtung TA, Wong MC, et al. The gut mycobiome of the Human Microbiome Project healthy cohort. Microbiome. 2017;5:153.PubMedPubMedCentral Nash AK, Auchtung TA, Wong MC, et al. The gut mycobiome of the Human Microbiome Project healthy cohort. Microbiome. 2017;5:153.PubMedPubMedCentral
38.
go back to reference Stern J, Miller G, Li X, Saxena D. Virome and bacteriome: two sides of the same coin. Curr Opin Virol. 2019;37:37–43.PubMedPubMedCentral Stern J, Miller G, Li X, Saxena D. Virome and bacteriome: two sides of the same coin. Curr Opin Virol. 2019;37:37–43.PubMedPubMedCentral
39.
go back to reference Mukhopadhya I, Segal JP, Carding SR, Hart AL, Hold GL. The gut virome: the ‘missing link’ between gut bacteria and host immunity? Therap Adv Gastroenterol. 2019;12:1756284819836620.PubMedPubMedCentral Mukhopadhya I, Segal JP, Carding SR, Hart AL, Hold GL. The gut virome: the ‘missing link’ between gut bacteria and host immunity? Therap Adv Gastroenterol. 2019;12:1756284819836620.PubMedPubMedCentral
40.
go back to reference Moreno-Gallego JL, Chou SP, Di Rienzi SC, et al. Virome diversity correlates with intestinal microbiome diversity in adult monozygotic twins. Cell Host Microbe. 2019;25:261.e5–272.e5. Moreno-Gallego JL, Chou SP, Di Rienzi SC, et al. Virome diversity correlates with intestinal microbiome diversity in adult monozygotic twins. Cell Host Microbe. 2019;25:261.e5–272.e5.
41.
go back to reference Xia LC, Cram JA, Chen T, Fuhrman JA, Sun F. Accurate genome relative abundance estimation based on shotgun metagenomic reads. PLoS ONE. 2011;6:e27992.PubMedPubMedCentral Xia LC, Cram JA, Chen T, Fuhrman JA, Sun F. Accurate genome relative abundance estimation based on shotgun metagenomic reads. PLoS ONE. 2011;6:e27992.PubMedPubMedCentral
42.
44.
45.
go back to reference Namiki T, Hachiya T, Tanaka H, Sakakibara Y. MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res. 2012;40:e155.PubMedPubMedCentral Namiki T, Hachiya T, Tanaka H, Sakakibara Y. MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res. 2012;40:e155.PubMedPubMedCentral
46.
go back to reference Peng Y, Leung HC, Yiu SM, FY Chin. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28:1420–1428.PubMed Peng Y, Leung HC, Yiu SM, FY Chin. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28:1420–1428.PubMed
47.
go back to reference Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–834.PubMedPubMedCentral Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–834.PubMedPubMedCentral
48.
go back to reference Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–1676.PubMed Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–1676.PubMed
49.
go back to reference Claesson MJ, Clooney AG, O’Toole PW. A clinician’s guide to microbiome analysis. Nat Rev Gastroenterol Hepatol. 2017;14:585–595.PubMed Claesson MJ, Clooney AG, O’Toole PW. A clinician’s guide to microbiome analysis. Nat Rev Gastroenterol Hepatol. 2017;14:585–595.PubMed
50.
go back to reference Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 2014;15:R46.PubMedPubMedCentral Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 2014;15:R46.PubMedPubMedCentral
51.
go back to reference Truong DT, Franzosa EA, Tickle TL, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015;12:902–903.PubMed Truong DT, Franzosa EA, Tickle TL, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015;12:902–903.PubMed
52.
go back to reference Bashiardes S, Zilberman-Schapira G, Elinav E. Use of metatranscriptomics in microbiome research. Bioinform Biol Insights. 2016;10:19–25.PubMedPubMedCentral Bashiardes S, Zilberman-Schapira G, Elinav E. Use of metatranscriptomics in microbiome research. Bioinform Biol Insights. 2016;10:19–25.PubMedPubMedCentral
53.
go back to reference Bikel S, Valdez-Lara A, Cornejo-Granados F, et al. Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome. Comput Struct Biotechnol J. 2015;13:390–401.PubMedPubMedCentral Bikel S, Valdez-Lara A, Cornejo-Granados F, et al. Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome. Comput Struct Biotechnol J. 2015;13:390–401.PubMedPubMedCentral
55.
go back to reference Xie Y, Wu G, Tang J, et al. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics. 2014;30:1660–1666.PubMed Xie Y, Wu G, Tang J, et al. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics. 2014;30:1660–1666.PubMed
56.
go back to reference Jorth P, Turner KH, Gumus P, Nizam N, Buduneli N, Whiteley M. Metatranscriptomics of the human oral microbiome during health and disease. MBio. 2014;5:e01012–e01014.PubMedPubMedCentral Jorth P, Turner KH, Gumus P, Nizam N, Buduneli N, Whiteley M. Metatranscriptomics of the human oral microbiome during health and disease. MBio. 2014;5:e01012–e01014.PubMedPubMedCentral
57.
go back to reference Lamichhane S, Sen P, Dickens AM, Orešič M, Bertram HC. Gut metabolome meets microbiome: a methodological perspective to understand the relationship between host and microbe. Methods. 2018;149:3–12.PubMed Lamichhane S, Sen P, Dickens AM, Orešič M, Bertram HC. Gut metabolome meets microbiome: a methodological perspective to understand the relationship between host and microbe. Methods. 2018;149:3–12.PubMed
58.
go back to reference Zierer J, Jackson MA, Kastenmüller G, et al. The fecal metabolome as a functional readout of the gut microbiome. Nat Genet. 2018;50:790–795.PubMedPubMedCentral Zierer J, Jackson MA, Kastenmüller G, et al. The fecal metabolome as a functional readout of the gut microbiome. Nat Genet. 2018;50:790–795.PubMedPubMedCentral
59.
go back to reference Lai LA, Tong Z, Chen R, S Pan. Metaproteomics study of the gut microbiome. Methods Mol Biol. 2019;1871:123–132.PubMed Lai LA, Tong Z, Chen R, S Pan. Metaproteomics study of the gut microbiome. Methods Mol Biol. 2019;1871:123–132.PubMed
60.
go back to reference Blakeley-Ruiz JA, Erickson AR, Cantarel BL, et al. Metaproteomics reveals persistent and phylum-redundant metabolic functional stability in adult human gut microbiomes of Crohn’s remission patients despite temporal variations in microbial taxa, genomes, and proteomes. Microbiome. 2019;7:18.PubMedPubMedCentral Blakeley-Ruiz JA, Erickson AR, Cantarel BL, et al. Metaproteomics reveals persistent and phylum-redundant metabolic functional stability in adult human gut microbiomes of Crohn’s remission patients despite temporal variations in microbial taxa, genomes, and proteomes. Microbiome. 2019;7:18.PubMedPubMedCentral
61.
go back to reference Kuczynski J, Lauber CL, Walters WA, et al. Experimental and analytical tools for studying the human microbiome. Nat Rev Genet. 2011;13:47–58.PubMedPubMedCentral Kuczynski J, Lauber CL, Walters WA, et al. Experimental and analytical tools for studying the human microbiome. Nat Rev Genet. 2011;13:47–58.PubMedPubMedCentral
62.
go back to reference Chao A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics. 1987;43:783–791.PubMed Chao A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics. 1987;43:783–791.PubMed
63.
go back to reference Kim BR, Shin J, Guevarra R, et al. Deciphering diversity indices for a better understanding of microbial communities. J Microbiol Biotechnol. 2017;27:2089–2093.PubMed Kim BR, Shin J, Guevarra R, et al. Deciphering diversity indices for a better understanding of microbial communities. J Microbiol Biotechnol. 2017;27:2089–2093.PubMed
64.
go back to reference Knight R, Vrbanac A, Taylor BC, et al. Best practices for analysing microbiomes. Nat Rev Microbiol. 2018;16:410–422.PubMed Knight R, Vrbanac A, Taylor BC, et al. Best practices for analysing microbiomes. Nat Rev Microbiol. 2018;16:410–422.PubMed
65.
go back to reference Bent SJ, Forney LJ. The tragedy of the uncommon: understanding limitations in the analysis of microbial diversity. ISME J. 2008;2:689–695.PubMed Bent SJ, Forney LJ. The tragedy of the uncommon: understanding limitations in the analysis of microbial diversity. ISME J. 2008;2:689–695.PubMed
66.
go back to reference Barwell LJ, Isaac NJ, Kunin WE. Measuring beta-diversity with species abundance data. J Anim Ecol. 2015;84:1112–1122.PubMedPubMedCentral Barwell LJ, Isaac NJ, Kunin WE. Measuring beta-diversity with species abundance data. J Anim Ecol. 2015;84:1112–1122.PubMedPubMedCentral
68.
go back to reference Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–8235.PubMedPubMedCentral Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–8235.PubMedPubMedCentral
69.
go back to reference McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.PubMedPubMedCentral McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.PubMedPubMedCentral
70.
go back to reference Langille MG, Zaneveld J, Caporaso JG, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31:814–821.PubMedPubMedCentral Langille MG, Zaneveld J, Caporaso JG, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31:814–821.PubMedPubMedCentral
71.
go back to reference Asshauer KP, et al. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics. 2015;31:2882–2884.PubMedPubMedCentral Asshauer KP, et al. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics. 2015;31:2882–2884.PubMedPubMedCentral
72.
go back to reference Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–2461.PubMed Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–2461.PubMed
73.
go back to reference Huerta-Cepas J, Szklarczyk D, Forslund K, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–D293.PubMed Huerta-Cepas J, Szklarczyk D, Forslund K, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–D293.PubMed
74.
go back to reference Tatusov RL, et al. The COG database: an updated version includes eukaryotes. BMC Bioinf. 2003;4:41. Tatusov RL, et al. The COG database: an updated version includes eukaryotes. BMC Bioinf. 2003;4:41.
75.
go back to reference Finn RD, Bateman A, Clements P, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42:D222–D230.PubMed Finn RD, Bateman A, Clements P, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42:D222–D230.PubMed
76.
go back to reference Selengut JD, et al. TIGRFAMs and Genome Properties: tools for the assignment of molecular function and biological process in prokaryotic genomes. Nucleic Acids Res. 2007;35:D260–D264.PubMed Selengut JD, et al. TIGRFAMs and Genome Properties: tools for the assignment of molecular function and biological process in prokaryotic genomes. Nucleic Acids Res. 2007;35:D260–D264.PubMed
77.
go back to reference Hunter S, Apweilwer R, Attwood TK, et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 2009;37:D211–D215.PubMed Hunter S, Apweilwer R, Attwood TK, et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 2009;37:D211–D215.PubMed
78.
go back to reference Ulgen E, Ozisik O, Sezerman OU. pathfindR: an R package for comprehensive identification of enriched pathways in omics data through active subnetworks. Front Genet. 2019;10:858.PubMedPubMedCentral Ulgen E, Ozisik O, Sezerman OU. pathfindR: an R package for comprehensive identification of enriched pathways in omics data through active subnetworks. Front Genet. 2019;10:858.PubMedPubMedCentral
79.
go back to reference Nishida K, Ono K, Kanaya S, Takahashi K. KEGGscape: a Cytoscape app for pathway data integration. F1000Res. 2014;3:144.PubMedPubMedCentral Nishida K, Ono K, Kanaya S, Takahashi K. KEGGscape: a Cytoscape app for pathway data integration. F1000Res. 2014;3:144.PubMedPubMedCentral
80.
go back to reference Keegan KP, Glass EM, Meyer F. MG-RAST, a metagenomics service for analysis of microbial community structure and function. Methods Mol Biol. 2016;1399:207–233.PubMed Keegan KP, Glass EM, Meyer F. MG-RAST, a metagenomics service for analysis of microbial community structure and function. Methods Mol Biol. 2016;1399:207–233.PubMed
81.
go back to reference Huson DH, Beier S, Flade I, et al. MEGAN community edition—interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput Biol. 2016;12:e1004957.PubMedPubMedCentral Huson DH, Beier S, Flade I, et al. MEGAN community edition—interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput Biol. 2016;12:e1004957.PubMedPubMedCentral
82.
go back to reference Abubucker S, Segata N, Goll J, et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput Biol. 2012;8:e1002358.PubMedPubMedCentral Abubucker S, Segata N, Goll J, et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput Biol. 2012;8:e1002358.PubMedPubMedCentral
83.
go back to reference Peters DL, Wang W, Zhang X, Ning Z, Mayne J, Figeys D. Metaproteomic and metabolomic approaches for characterizing the gut microbiome. Proteomics. 2019;19:e1800363.PubMed Peters DL, Wang W, Zhang X, Ning Z, Mayne J, Figeys D. Metaproteomic and metabolomic approaches for characterizing the gut microbiome. Proteomics. 2019;19:e1800363.PubMed
84.
go back to reference Verberkmoes NC, Russell Rl, Shah M, et al. Shotgun metaproteomics of the human distal gut microbiota. ISME J. 2009;3:179–189.PubMed Verberkmoes NC, Russell Rl, Shah M, et al. Shotgun metaproteomics of the human distal gut microbiota. ISME J. 2009;3:179–189.PubMed
85.
go back to reference Galloway-Pena J, Guindani M. Editorial: novel approaches in microbiome analyses and data visualization. Front Microbiol. 2018;9:2274.PubMedPubMedCentral Galloway-Pena J, Guindani M. Editorial: novel approaches in microbiome analyses and data visualization. Front Microbiol. 2018;9:2274.PubMedPubMedCentral
86.
go back to reference Sudarikov K, Tyakht A, Alexeev D. Methods for the metagenomic data visualization and analysis. Curr Issues Mol Biol. 2017;24:37–58.PubMed Sudarikov K, Tyakht A, Alexeev D. Methods for the metagenomic data visualization and analysis. Curr Issues Mol Biol. 2017;24:37–58.PubMed
88.
go back to reference Kelly BJ, Gross R, Bittinger K, et al. Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA. Bioinformatics. 2015;31:2461–2468.PubMedPubMedCentral Kelly BJ, Gross R, Bittinger K, et al. Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA. Bioinformatics. 2015;31:2461–2468.PubMedPubMedCentral
89.
go back to reference Tang ZZ, Chen G, Alekseyenko AV. PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances. Bioinformatics. 2016;32:2618–2625.PubMedPubMedCentral Tang ZZ, Chen G, Alekseyenko AV. PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances. Bioinformatics. 2016;32:2618–2625.PubMedPubMedCentral
90.
go back to reference Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015;26:27663.PubMed Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015;26:27663.PubMed
91.
go back to reference Staley C, Sadowsky MJ. Practical considerations for sampling and data analysis in contemporary metagenomics-based environmental studies. J Microbiol Methods. 2018;154:14–18.PubMed Staley C, Sadowsky MJ. Practical considerations for sampling and data analysis in contemporary metagenomics-based environmental studies. J Microbiol Methods. 2018;154:14–18.PubMed
92.
93.
94.
go back to reference Fang H, Huang C, Zhao H, Deng M. CCLasso: correlation inference for compositional data through Lasso. Bioinformatics. 2015;31:3172–3180.PubMedPubMedCentral Fang H, Huang C, Zhao H, Deng M. CCLasso: correlation inference for compositional data through Lasso. Bioinformatics. 2015;31:3172–3180.PubMedPubMedCentral
95.
go back to reference Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015;11:e1004226.PubMedPubMedCentral Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015;11:e1004226.PubMedPubMedCentral
96.
go back to reference Faust K, Sathirapongsasuti JF, Izard J, et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol. 2012;8:e1002606.PubMedPubMedCentral Faust K, Sathirapongsasuti JF, Izard J, et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol. 2012;8:e1002606.PubMedPubMedCentral
97.
go back to reference La Rosa PS, Brooks JP, Deych E, et al. Hypothesis testing and power calculations for taxonomic-based human microbiome data. PLoS ONE. 2012;7:e52078.PubMedPubMedCentral La Rosa PS, Brooks JP, Deych E, et al. Hypothesis testing and power calculations for taxonomic-based human microbiome data. PLoS ONE. 2012;7:e52078.PubMedPubMedCentral
98.
go back to reference Karpinets TV, Park BH, Uberbacher EC. Analyzing large biological datasets with association networks. Nucleic Acids Res. 2012;40:e131.PubMedPubMedCentral Karpinets TV, Park BH, Uberbacher EC. Analyzing large biological datasets with association networks. Nucleic Acids Res. 2012;40:e131.PubMedPubMedCentral
99.
go back to reference Knights D, Costello EK, Knight R. Supervised classification of human microbiota. FEMS Microbiol Rev. 2011;35:343–359.PubMed Knights D, Costello EK, Knight R. Supervised classification of human microbiota. FEMS Microbiol Rev. 2011;35:343–359.PubMed
100.
go back to reference Zhang Q, Abel H, Wells A, et al. Selection of models for the analysis of risk-factor trees: leveraging biological knowledge to mine large sets of risk factors with application to microbiome data. Bioinformatics. 2015;31:1607–1613.PubMedPubMedCentral Zhang Q, Abel H, Wells A, et al. Selection of models for the analysis of risk-factor trees: leveraging biological knowledge to mine large sets of risk factors with application to microbiome data. Bioinformatics. 2015;31:1607–1613.PubMedPubMedCentral
101.
go back to reference Kennedy EA, King KY, Baldridge MT. Mouse microbiota models: comparing germ-free mice and antibiotics treatment as tools for modifying gut bacteria. Front Physiol. 2018;9:1534.PubMedPubMedCentral Kennedy EA, King KY, Baldridge MT. Mouse microbiota models: comparing germ-free mice and antibiotics treatment as tools for modifying gut bacteria. Front Physiol. 2018;9:1534.PubMedPubMedCentral
102.
go back to reference Gootenberg DB, Turnbaugh PJ. Companion animals symposium: humanized animal models of the microbiome. J Anim Sci. 2011;89:1531–1537.PubMed Gootenberg DB, Turnbaugh PJ. Companion animals symposium: humanized animal models of the microbiome. J Anim Sci. 2011;89:1531–1537.PubMed
103.
go back to reference Douglas, A.E., Simple animal models for microbiome research. Nat Rev Microbiol 2019;17(12):764–775.PubMed Douglas, A.E., Simple animal models for microbiome research. Nat Rev Microbiol 2019;17(12):764–775.PubMed
104.
go back to reference Hacquard S, Garrido-Oter R, González A, et al. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe. 2015;17:603–616.PubMed Hacquard S, Garrido-Oter R, González A, et al. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe. 2015;17:603–616.PubMed
105.
go back to reference Pearce SC, Coia HG, Karl JP, et al. Intestinal in vitro and ex vivo models to study host-microbiome interactions and acute stressors. Front Physiol. 2018;9:1584.PubMedPubMedCentral Pearce SC, Coia HG, Karl JP, et al. Intestinal in vitro and ex vivo models to study host-microbiome interactions and acute stressors. Front Physiol. 2018;9:1584.PubMedPubMedCentral
106.
go back to reference Dutton JS, Hinman SS, Kim R, Wang Y, Allbritton NL. Primary cell-derived intestinal models: recapitulating physiology. Trends Biotechnol. 2019;37:744–760.PubMed Dutton JS, Hinman SS, Kim R, Wang Y, Allbritton NL. Primary cell-derived intestinal models: recapitulating physiology. Trends Biotechnol. 2019;37:744–760.PubMed
107.
go back to reference McDonald JA, Fuentes S, Schroeter K, et al. Simulating distal gut mucosal and luminal communities using packed-column biofilm reactors and an in vitro chemostat model. J Microbiol Methods. 2015;108:36–44.PubMed McDonald JA, Fuentes S, Schroeter K, et al. Simulating distal gut mucosal and luminal communities using packed-column biofilm reactors and an in vitro chemostat model. J Microbiol Methods. 2015;108:36–44.PubMed
108.
go back to reference Van den Abbeele P, roos S, Eeckhaut V, et al. Incorporating a mucosal environment in a dynamic gut model results in a more representative colonization by lactobacilli. Microb Biotechnol. 2012;5:106–115.PubMed Van den Abbeele P, roos S, Eeckhaut V, et al. Incorporating a mucosal environment in a dynamic gut model results in a more representative colonization by lactobacilli. Microb Biotechnol. 2012;5:106–115.PubMed
109.
go back to reference Auchtung JM, Robinson CD, Britton RA. Cultivation of stable, reproducible microbial communities from different fecal donors using minibioreactor arrays (MBRAs). Microbiome. 2015;3:42.PubMedPubMedCentral Auchtung JM, Robinson CD, Britton RA. Cultivation of stable, reproducible microbial communities from different fecal donors using minibioreactor arrays (MBRAs). Microbiome. 2015;3:42.PubMedPubMedCentral
110.
go back to reference Stevens LJ, van Lipzig MM, Erpelinck SL, et al. A higher throughput and physiologically relevant two-compartmental human ex vivo intestinal tissue system for studying gastrointestinal processes. Eur J Pharm Sci. 2019;137:104989.PubMed Stevens LJ, van Lipzig MM, Erpelinck SL, et al. A higher throughput and physiologically relevant two-compartmental human ex vivo intestinal tissue system for studying gastrointestinal processes. Eur J Pharm Sci. 2019;137:104989.PubMed
111.
go back to reference Nigro G, Hanson M, Fevre C, Lecuit M, Sansonetti PJ. Intestinal organoids as a novel tool to study microbes-epithelium interactions. Methods Mol Biol. 2019;1576:183–194.PubMed Nigro G, Hanson M, Fevre C, Lecuit M, Sansonetti PJ. Intestinal organoids as a novel tool to study microbes-epithelium interactions. Methods Mol Biol. 2019;1576:183–194.PubMed
112.
go back to reference Debelius J, Song SJ, Vazquez-Baeza Y, Xu ZZ, Gonzalez A, Knight R. Tiny microbes, enormous impacts: what matters in gut microbiome studies? Genome Biol. 2016;17:217.PubMedPubMedCentral Debelius J, Song SJ, Vazquez-Baeza Y, Xu ZZ, Gonzalez A, Knight R. Tiny microbes, enormous impacts: what matters in gut microbiome studies? Genome Biol. 2016;17:217.PubMedPubMedCentral
113.
go back to reference Poussin C, Sierro N, Boué S, et al. Interrogating the microbiome: experimental and computational considerations in support of study reproducibility. Drug Discov Today. 2018;23:1644–1657.PubMed Poussin C, Sierro N, Boué S, et al. Interrogating the microbiome: experimental and computational considerations in support of study reproducibility. Drug Discov Today. 2018;23:1644–1657.PubMed
114.
go back to reference David LA, Materna AC, Friedman J, et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 2014;15:R89.PubMedPubMedCentral David LA, Materna AC, Friedman J, et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 2014;15:R89.PubMedPubMedCentral
115.
116.
go back to reference Mehta RS, Abu-Ali GS, Drew DA, et al. Stability of the human faecal microbiome in a cohort of adult men. Nat Microbiol. 2018;3:347–355.PubMedPubMedCentral Mehta RS, Abu-Ali GS, Drew DA, et al. Stability of the human faecal microbiome in a cohort of adult men. Nat Microbiol. 2018;3:347–355.PubMedPubMedCentral
117.
go back to reference Faith JJ, Guruge JL, Charbonneau M, et al. The long-term stability of the human gut microbiota. Science. 2013;341:1237439.PubMedPubMedCentral Faith JJ, Guruge JL, Charbonneau M, et al. The long-term stability of the human gut microbiota. Science. 2013;341:1237439.PubMedPubMedCentral
118.
go back to reference Martinez I, Muller CE, Walter J. Long-term temporal analysis of the human fecal microbiota revealed a stable core of dominant bacterial species. PLoS ONE. 2013;8:e69621.PubMedPubMedCentral Martinez I, Muller CE, Walter J. Long-term temporal analysis of the human fecal microbiota revealed a stable core of dominant bacterial species. PLoS ONE. 2013;8:e69621.PubMedPubMedCentral
119.
go back to reference Baksi KD, Kuntal BK, Mande SS. ‘TIME’: a web application for obtaining insights into microbial ecology using longitudinal microbiome data. Front Microbiol. 2018;9:36.PubMedPubMedCentral Baksi KD, Kuntal BK, Mande SS. ‘TIME’: a web application for obtaining insights into microbial ecology using longitudinal microbiome data. Front Microbiol. 2018;9:36.PubMedPubMedCentral
120.
go back to reference Lugo-Martinez J, Ruiz-Perez D, Narasimhan D, Bar-Joseph Z. Dynamic interaction network inference from longitudinal microbiome data. Microbiome. 2019;7:54.PubMedPubMedCentral Lugo-Martinez J, Ruiz-Perez D, Narasimhan D, Bar-Joseph Z. Dynamic interaction network inference from longitudinal microbiome data. Microbiome. 2019;7:54.PubMedPubMedCentral
121.
go back to reference Cleary JG, Littin R, Trigg L, Irvine S, Hilbush B. Quantitative analysis of shotgun metagenomic data with the real time genomics platform. J Biomol Tech: JBT. 2013;24:S33.PubMedCentral Cleary JG, Littin R, Trigg L, Irvine S, Hilbush B. Quantitative analysis of shotgun metagenomic data with the real time genomics platform. J Biomol Tech: JBT. 2013;24:S33.PubMedCentral
122.
go back to reference UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43:D204–D212. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43:D204–D212.
123.
go back to reference Misra BB, Langefeld C, Olivier M, Cox LA. Integrated omics: tools, advances, and future approaches. J Mol Endocrinol. 2018;62:R21–R45. Misra BB, Langefeld C, Olivier M, Cox LA. Integrated omics: tools, advances, and future approaches. J Mol Endocrinol. 2018;62:R21–R45.
124.
go back to reference Noor E, Cherkaoui S, Sauer U. Biological insights through omics data integration. Current Opinion in Systems Biology. 2019;15:39–47. Noor E, Cherkaoui S, Sauer U. Biological insights through omics data integration. Current Opinion in Systems Biology. 2019;15:39–47.
125.
go back to reference Baron SA, Diene SM, Rolain J-M. Human microbiomes and antibiotic resistance. Human Microbiome Journal. 2018;10:43–52. Baron SA, Diene SM, Rolain J-M. Human microbiomes and antibiotic resistance. Human Microbiome Journal. 2018;10:43–52.
126.
go back to reference Escudeiro P, Pothier J, Dionisio F, Nogueira T. Antibiotic resistance gene diversity and virulence gene diversity are correlated in human gut and environmental microbiomes. mSphere. 2019;4:e00135-19.PubMedPubMedCentral Escudeiro P, Pothier J, Dionisio F, Nogueira T. Antibiotic resistance gene diversity and virulence gene diversity are correlated in human gut and environmental microbiomes. mSphere. 2019;4:e00135-19.PubMedPubMedCentral
128.
go back to reference Bernard G, Pathmanathan JS, Lannes R, Lopez R, Bapteste E. Microbial dark matter investigations: how microbial studies transform biological knowledge and empirically sketch a logic of scientific discovery. Genome Biol Evol. 2018;10:707–715.PubMedPubMedCentral Bernard G, Pathmanathan JS, Lannes R, Lopez R, Bapteste E. Microbial dark matter investigations: how microbial studies transform biological knowledge and empirically sketch a logic of scientific discovery. Genome Biol Evol. 2018;10:707–715.PubMedPubMedCentral
Metadata
Title
Tools for Analysis of the Microbiome
Authors
Jessica Galloway-Peña
Blake Hanson
Publication date
01-03-2020
Publisher
Springer US
Published in
Digestive Diseases and Sciences / Issue 3/2020
Print ISSN: 0163-2116
Electronic ISSN: 1573-2568
DOI
https://doi.org/10.1007/s10620-020-06091-y

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