Advertisement

Journal of Microbiology

, Volume 56, Issue 10, pp 693–705 | Cite as

Progress of analytical tools and techniques for human gut microbiome research

  • Eun-Ji Song
  • Eun-Sook Lee
  • Young-Do Nam
Minireview

Abstract

Massive DNA sequencing studies have expanded our insights and understanding of the ecological and functional characteristics of the gut microbiome. Advanced sequencing technologies allow us to understand the close association of the gut microbiome with human health and critical illnesses. In the future, analyses of the gut microbiome will provide key information associating with human individual health, which will help provide personalized health care for diseases. Numerous molecular biological analysis tools have been rapidly developed and employed for the gut microbiome researches; however, methodological differences among researchers lead to inconsistent data, limiting extensive share of data. It is therefore very essential to standardize the current methodologies and establish appropriate pipelines for human gut microbiome research. Herein, we review the methods and procedures currently available for studying the human gut microbiome, including fecal sample collection, metagenomic DNA extraction, massive DNA sequencing, and data analyses with bioinformatics. We believe that this review will contribute to the progress of gut microbiome research in the clinical and practical aspects of human health.

Keywords

gut microbiota microbiome NGS bioinformatics analytical process 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abrahamson, M., Hooker, E., Ajami, N.J., Petrosino, J.F., and Orwoll, E.S. 2017. Successful collection of stool samples for microbiome analyses from a large community-based population of elderly men. Contemp. Clin. Trials Commun. 7, 158–162.PubMedPubMedCentralCrossRefGoogle Scholar
  2. Abu-Ali, G.S., Mehta, R.S., Lloyd-Price, J., Mallick, H., Branck, T., Ivey, K.L., Drew, D.A., DuLong, C., Rimm, E., Izard, J., et al. 2018. Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nat. Microbiol. 3, 356–366.PubMedCrossRefGoogle Scholar
  3. Abubucker, S., Segata, N., Goll, J., Schubert, A.M., Izard, J., Cantarel, B.L., Rodriguez-Mueller, B., Zucker, J., Thiagarajan, M., Henrissat, B., et al. 2012. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358.CrossRefGoogle Scholar
  4. Ambardar, S., Gupta, R., Trakroo, D., Lal, R., and Vakhlu, J. 2016. High throughput sequencing: An overview of sequencing chemistry. Indian J. Microbiol. 56, 394–404.PubMedPubMedCentralCrossRefGoogle Scholar
  5. Anderson, E.L., Li, W., Klitgord, N., Highlander, S.K., Dayrit, M., Seguritan, V., Yooseph, S., Biggs, W., Venter, J.C., Nelson, K.E., et al. 2016. A robust ambient temperature collection and stabilization strategy: Enabling worldwide functional studies of the human microbiome. Sci. Rep. 6, 31731.PubMedPubMedCentralCrossRefGoogle Scholar
  6. Anhe, F.F., Varin, T.V., Le Barz, M., Desjardins, Y., Levy, E., Roy, D., and Marette, A. 2015. Gut microbiota dysbiosis in obesitylinked metabolic diseases and prebiotic potential of polyphenolrich extracts. Curr. Obes. Rep. 4, 389–400.PubMedCrossRefGoogle Scholar
  7. Armanhi, J.S.L., de Souza, R.S.C., de Araújo, L.M., Okura, V.K., Mieczkowski, P., Imperial, J., and Arruda, P. 2016. Multiplex amplicon sequencing for microbe identification in community-based culture collections. Sci. Rep. 6, 29543.PubMedPubMedCentralCrossRefGoogle Scholar
  8. Bag, S., Saha, B., Mehta, O., Anbumani, D., Kumar, N., Dayal, M., Pant, A., Kumar, P., Saxena, S., Allin, K.H., et al. 2016. An improved method for high quality metagenomics DNA extraction from human and environmental samples. Sci. Rep. 6, 26775.PubMedPubMedCentralCrossRefGoogle Scholar
  9. Bahl, M.I., Bergstrom, A., and Licht, T.R. 2012. Freezing fecal samples prior to DNA extraction affects the firmicutes to bacteroidetes ratio determined by downstream quantitative PCR analysis. FEMS Microbiol. Lett. 329, 193–197.PubMedCrossRefGoogle Scholar
  10. Ballester, L.Y., Luthra, R., Kanagal-Shamanna, R., and Singh, R.R. 2016. Advances in clinical next-generation sequencing: target enrichment and sequencing technologies. Expert Rev. Mol. Diagn. 16, 357–372.PubMedCrossRefGoogle Scholar
  11. Bashiardes, S., Zilberman-Schapira, G., and Elinav, E. 2016. Use of metatranscriptomics in microbiome research. Bioinform. Biol. Insights 10, 19–25.PubMedPubMedCentralCrossRefGoogle Scholar
  12. Bassis, C.M., Moore, N.M., Lolans, K., Seekatz, A.M., Weinstein, R.A., Young, V.B., and Hayden, M.K. 2017. Comparison of stool versus rectal swab samples and storage conditions on bacterial community profiles. BMC Microbiol. 17, 78.PubMedPubMedCentralCrossRefGoogle Scholar
  13. Bedarf, J.R., Hildebrand, F., Coelho, L.P., Sunagawa, S., Bahram, M., Goeser, F., Bork, P., and Wüllner, U. 2017. Functional implications of microbial and viral gut metagenome changes in early stage L-DOPA-naïve Parkinson’s disease patients. Genome Med. 9, 39.PubMedPubMedCentralCrossRefGoogle Scholar
  14. Benson, D.A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., and Sayers, E.W. 2013. GenBank. Nucleic Acids Res. 41, D36–42.PubMedCrossRefGoogle Scholar
  15. Bentley, D.R., Balasubramanian, S., Swerdlow, H.P., Smith, G.P., Milton, J., Brown, C.G., Hall, K.P., Evers, D.J., Barnes, C.L., Bignell, H.R., et al. 2008. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59.PubMedPubMedCentralCrossRefGoogle Scholar
  16. Bikel, S., Valdez-Lara, A., Cornejo-Granados, F., Rico, K., Canizales-Quinteros, S., Soberon, X., Del Pozo-Yauner, L., and Ochoa-Leyva, A. 2015. Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systemslevel understanding of human microbiome. Comput. Struct. Biotechnol. J. 13, 390–401.PubMedPubMedCentralCrossRefGoogle Scholar
  17. Boisvert, S., Raymond, F., Godzaridis, E., Laviolette, F., and Corbeil, J. 2012. Ray Meta: scalable de novo metagenome assembly and profiling. Genome Biol. 13, R122.PubMedPubMedCentralCrossRefGoogle Scholar
  18. Bray, J.R. and Curtis, J.T. 1957. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. 27, 326–349.CrossRefGoogle Scholar
  19. Budding, A.E., Grasman, M.E., Eck, A., Bogaards, J.A., Vandenbroucke-Grauls, C.M.J.E., van Bodegraven, A.A., and Savelkoul, P.H.M. 2014. Rectal swabs for analysis of the intestinal microbiota. PLoS One 9, e101344.CrossRefGoogle Scholar
  20. Byrne, A., Beaudin, A.E., Olsen, H.E., Jain, M., Cole, C., Palmer, T., DuBois, R.M., Forsberg, E.C., Akeson, M., and Vollmers, C. 2017. Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nat. Commun. 8, 16027.PubMedPubMedCentralCrossRefGoogle Scholar
  21. Cao, Y., Fanning, S., Proos, S., Jordan, K., and Srikumar, S. 2017. A review on the applications of next generation sequencing technologies as applied to food-related microbiome studies. Front. Microbiol. 8, 1829.PubMedPubMedCentralCrossRefGoogle Scholar
  22. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., et al. 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336.PubMedPubMedCentralCrossRefGoogle Scholar
  23. Carding, S., Verbeke, K., Vipond, D.T., Corfe, B.M., and Owen, L.J. 2015. Dysbiosis of the gut microbiota in disease. Microb. Ecol. Health Dis. 26, 26191.PubMedGoogle Scholar
  24. Cardona, S., Eck, A., Cassellas, M., Gallart, M., Alastrue, C., Dore, J., Azpiroz, F., Roca, J., Guarner, F., and Manichanh, C. 2012. Storage conditions of intestinal microbiota matter in metagenomic analysis. BMC Microbiol. 12, 158.PubMedPubMedCentralCrossRefGoogle Scholar
  25. Carozzi, F.M. and Sani, C. 2013. Fecal collection and stabilization methods for improved fecal DNA test for colorectal cancer in a screening setting. J. Cancer Res. 2013, 8.CrossRefGoogle Scholar
  26. Carroll, I.M., Ringel-Kulka, T., Siddle, J.P., Klaenhammer, T.R., and Ringel, Y. 2012. Characterization of the fecal microbiota using high-throughput sequencing reveals a stable microbial community during storage. PLoS One 7, e46953.CrossRefGoogle Scholar
  27. Chandler, J.A., Liu, R.M., and Bennett, S.N. 2015. RNA shotgun metagenomic sequencing of northern California (USA) mosquitoes uncovers viruses, bacteria, and fungi. Front. Microbiol. 6, 185.PubMedPubMedCentralCrossRefGoogle Scholar
  28. Chen, J., Domingue, J.C., and Sears, C.L. 2017a. Microbiota dysbiosis in select human cancers: Evidence of association and causality. Semin. Immunol. 32, 25–34.PubMedGoogle Scholar
  29. Chen, S.Y., Deng, F., Jia, X., Li, C., and Lai, S.J. 2017b. A transcriptome atlas of rabbit revealed by PacBio single-molecule longread sequencing. Sci. Rep. 7, 7648.PubMedPubMedCentralCrossRefGoogle Scholar
  30. Choo, J.M., Leong, L.E., and Rogers, G.B. 2015. Sample storage conditions significantly influence faecal microbiome profiles. Sci. Rep. 5, 16350.PubMedPubMedCentralCrossRefGoogle Scholar
  31. Claesson, M.J., Wang, Q., O’Sullivan, O., Greene-Diniz, R., Cole, J.R., Ross, R.P., and O’Toole, P.W. 2010. Comparison of two next-generation sequencing technologies for resolving highly complex microbiota composition using tandem variable 16S rRNA gene regions. Nucleic Acids Res. 38, e200.CrossRefGoogle Scholar
  32. Cole, J.R., Wang, Q., Fish, J.A., Chai, B., McGarrell, D.M., Sun, Y., Brown, C.T., Porras-Alfaro, A., Kuske, C.R., and Tiedje, J.M. 2014. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–642.PubMedCrossRefGoogle Scholar
  33. D’Argenio, V., Casaburi, G., Precone, V., and Salvatore, F. 2014. Comparative metagenomic analysis of human gut microbiome composition using two different bioinformatic pipelines. Biomed. Res. Int. 2014, 325340.PubMedPubMedCentralCrossRefGoogle Scholar
  34. de la Cuesta-Zuluaga, J. and Escobar, J.S. 2016. Considerations for optimizing microbiome analysis using a marker gene. Front. Nutr. 3, 26.PubMedPubMedCentralGoogle Scholar
  35. DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E.L., Keller, K., Huber, T., Dalevi, D., Hu, P., and Andersen, G.L. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072.PubMedPubMedCentralCrossRefGoogle Scholar
  36. Dominianni, C., Wu, J., Hayes, R.B., and Ahn, J. 2014. Comparison of methods for fecal microbiome biospecimen collection. BMC Microbiol. 14, 103.PubMedPubMedCentralCrossRefGoogle Scholar
  37. Edgar, R.C. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461.PubMedCrossRefGoogle Scholar
  38. Ehrlich, D. 2012. Vol.2018. http://www.mgps.eu (Accessed date: Aug. 20, 2018).Google Scholar
  39. Eid, J., Fehr, A., Gray, J., Luong, K., Lyle, J., Otto, G., Peluso, P., Rank, D., Baybayan, P., Bettman, B., et al. 2009. Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138.PubMedCrossRefGoogle Scholar
  40. Ercolini, D. 2013. High-throughput sequencing and metagenomics: moving forward in the culture-independent analysis of food microbial ecology. Appl. Environ. Microbiol. 79, 3148–3155.PubMedPubMedCentralCrossRefGoogle Scholar
  41. Flemer, B., Lynch, D.B., Brown, J.M.R., Jeffery, I.B., Ryan, F.J., Claesson, M.J., O’Riordain, M., Shanahan, F., and O’Toole, P.W. 2017. Tumour-associated and non-tumour-associated microbiota in colorectal cancer. Gut 66, 633–643.PubMedCrossRefGoogle Scholar
  42. Fouhy, F., Clooney, A.G., Stanton, C., Claesson, M.J., and Cotter, P.D. 2016. 16S rRNA gene sequencing of mock microbial populations-impact of DNA extraction method, primer choice and sequencing platform. BMC Microbiol. 16, 123.PubMedPubMedCentralCrossRefGoogle Scholar
  43. Fouhy, F., Deane, J., Rea, M.C., O’Sullivan, O., Ross, R.P., O’Callaghan, G., Plant, B.J., and Stanton, C. 2015. The effects of freezing on faecal microbiota as determined using MiSeq sequencing and culture-based investigations. PLoS One 10, e0119355.CrossRefGoogle Scholar
  44. Frankel, A.E., Froehlich, T.W., Kim, J., Coughlin, L.A., Xie, Y., Frenkel, E.P., and Koh, A.Y. 2017. Metagenomic shotgun sequencing to identify specific human gut microbes associated with immune checkpoint therapy efficacy in melanoma patients. J. Clin. Oncol. 35, 9516–9516.CrossRefGoogle Scholar
  45. Franzosa, E.A., Morgan, X.C., Segata, N., Waldron, L., Reyes, J., Earl, A.M., Giannoukos, G., Boylan, M.R., Ciulla, D., Gevers, D., et al. 2014. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl. Acad. Sci. USA 111, e2329–E2338.CrossRefGoogle Scholar
  46. Gerasimidis, K., Bertz, M., Quince, C., Brunner, K., Bruce, A., Combet, E., Calus, S., Loman, N., and Ijaz, U.Z. 2016. The effect of DNA extraction methodology on gut microbiota research applications. BMC Res. Notes 9, 365.PubMedPubMedCentralCrossRefGoogle Scholar
  47. Gigliucci, F., von Meijenfeldt, F.A.B., Knijn, A., Michelacci, V., Scavia, G., Minelli, F., Dutilh, B.E., Ahmad, H.M., Raangs, G.C., Friedrich, A.W., et al. 2018. Metagenomic characterization of the human intestinal microbiota in fecal samples from STEC-infected patients. Front. Cell. Infect. Microbiol. 8, 25.PubMedPubMedCentralCrossRefGoogle Scholar
  48. Gocayne, J., Robinson, D.A., FitzGerald, M.G., Chung, F.Z., Kerlavage, A.R., Lentes, K.U., Lai, J., Wang, C.D., Fraser, C.M., and Venter, J.C. 1987. Primary structure of rat cardiac beta-adrenergic and muscarinic cholinergic receptors obtained by automated DNA sequence analysis: further evidence for a multigene family. Proc. Natl. Acad. Sci. USA 84, 8296–8300.PubMedCrossRefGoogle Scholar
  49. Goff, S.A., Ricke, D., Lan, T.H., Presting, G., Wang, R., Dunn, M., Glazebrook, J., Sessions, A., Oeller, P., Varma, H., et al. 2002. A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 296, 92–100.PubMedCrossRefGoogle Scholar
  50. Gower, J.C. 1966. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53, 325–338.CrossRefGoogle Scholar
  51. Guo, F. and Zhang, T. 2013. Biases during DNA extraction of activated sludge samples revealed by high throughput sequencing. Appl. Microbiol. Biotechnol. 97, 4607–4616.PubMedCrossRefGoogle Scholar
  52. Hale, V.L., Tan, C.L., Knight, R., and Amato, K.R. 2015. Effect of preservation method on spider monkey (Ateles geoffroyi) fecal microbiota over 8 weeks. J. Microbiol. Methods 113, 16–26.PubMedCrossRefGoogle Scholar
  53. Hamad, I., Ranque, S., Azhar, E.I., Yasir, M., Jiman-Fatani, A.A., Tissot-Dupont, H., Raoult, D., and Bittar, F. 2017. Culturomics and amplicon-based metagenomic approaches for the study of fungal population in human gut microbiota. Sci. Rep. 7, 16788.PubMedPubMedCentralCrossRefGoogle Scholar
  54. Hodkinson, B.P. and Grice, E.A. 2015. Next-generation sequencing: A review of technologies and tools for wound microbiome research. Adv. Wound Care 4, 50–58.CrossRefGoogle Scholar
  55. Hooda, S., Boler, B.M.V., Serao, M.C.R., Brulc, J.M., Staeger, M.A., Boileau, T.W., Dowd, S.E., Fahey, J.G.C., and Swanson, K.S. 2012. 454 pyrosequencing reveals a shift in fecal microbiota of healthy adult men consuming polydextrose or soluble corn fiber. J. Nutr. 142, 1259–1265.PubMedGoogle Scholar
  56. Huang, K., Brady, A., Mahurkar, A., White, O., Gevers, D., Huttenhower, C., and Segata, N. 2014. MetaRef: a pan-genomic database for comparative and community microbial genomics. Nucleic Acids Res. 42, D617–624.Google Scholar
  57. International Human Genome Sequencing Consortium. 2001. Initial sequencing and analysis of the human genome. Nature 409, 860.CrossRefGoogle Scholar
  58. Jain, M., Olsen, H.E., Paten, B., and Akeson, M. 2016. The Oxford nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol. 17, 239.PubMedPubMedCentralCrossRefGoogle Scholar
  59. Jain, M., Tyson, J.R., Loose, M., Ip, C.L.C., Eccles, D.A., O’Grady, J., Malla, S., Leggett, R.M., Wallerman, O., Jansen, H.J., et al. 2017. MinION analysis and reference consortium: Phase 2 data release and analysis of R9.0 chemistry [version 1; referees: 1 approved, 2 approved with reservations]. F1000Res. 6, 760.PubMedPubMedCentralCrossRefGoogle Scholar
  60. Jha, A.R., Davenport, E.R., Gautam, Y., Bhandari, D., Tandukar, S., Ng, K., Holmes, S., Gautam, G.P., Sherchand, J.B., Bustamante, C., et al. 2018. Gut microbiome transition across a lifestyle gradient in Himalaya. bioRxiv 253450.Google Scholar
  61. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., and Tanabe, M. 2012. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–114.PubMedCrossRefGoogle Scholar
  62. Kchouk, M., Gibrat, J.F., and Elloumi, M. 2017. Generations of sequencing technologies: From first to next generation. Biol. Med. 9, 3.CrossRefGoogle Scholar
  63. Kelley, D.R., Liu, B., Delcher, A.L., Pop, M., and Salzberg, S.L. 2012. Gene prediction with glimmer for metagenomic sequences augmented by classification and clustering. Nucleic Acids Res. 40, e9.CrossRefGoogle Scholar
  64. Kennedy, N.A., Walker, A.W., Berry, S.H., Duncan, S.H., Farquarson, F.M., Louis, P., Thomson, J.M., Satsangi, J., Flint, H.J., Parkhill, J., et al. 2014. The impact of different DNA extraction kits and laboratories upon the assessment of human gut microbiota composition by 16S rRNA gene sequencing. PLoS One 9, e88982.CrossRefGoogle Scholar
  65. Kent, W.J. 2002. BLAT–the BLAST-like alignment tool. Genome Res. 12, 656–664.PubMedPubMedCentralCrossRefGoogle Scholar
  66. Kerkhof, L.J., Dillon, K.P., Häggblom, M.M., and McGuinness, L.R. 2017. Profiling bacterial communities by MinION sequencing of ribosomal operons. Microbiome 5, 116.PubMedPubMedCentralCrossRefGoogle Scholar
  67. Kim, Y., Han, M.S., Kim, J., Kwon, A., and Lee, K.A. 2014. Evaluation of three automated nucleic acid extraction systems for identification of respiratory viruses in clinical specimens by multiplex real-time PCR. Biomed. Res. Int. 2014, 430650.PubMedPubMedCentralGoogle Scholar
  68. Koren, S., Treangen, T.J., and Pop, M. 2011. Bambus 2: scaffolding metagenomes. Bioinformatics 27, 2964–2971.PubMedPubMedCentralCrossRefGoogle Scholar
  69. Langille, M.G., Zaneveld, J., Caporaso, J.G., McDonald, D., Knights, D., Reyes, J.A., Clemente, J.C., Burkepile, D.E., Vega Thurber, R.L., Knight, R., et al. 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821.PubMedPubMedCentralCrossRefGoogle Scholar
  70. Laserson, J., Jojic, V., and Koller, D. 2011. Genovo: de novo assembly for metagenomes. J. Comput. Biol. 18, 429–443.PubMedCrossRefGoogle Scholar
  71. Lauber, C.L., Zhou, N., Gordon, J.I., Knight, R., and Fierer, N. 2010. Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples. FEMS Microbiol. Lett. 307, 80–86.PubMedPubMedCentralCrossRefGoogle Scholar
  72. Laver, T., Harrison, J., O’Neill, P.A., Moore, K., Farbos, A., Paszkiewicz, K., and Studholme, D.J. 2015. Assessing the performance of the Oxford nanopore technologies MinION. Biomol. Detect. Quantif. 3, 1–8.PubMedPubMedCentralCrossRefGoogle Scholar
  73. Lazarevic, V., Whiteson, K., Huse, S., Hernandez, D., Farinelli, L., Osteras, M., Schrenzel, J., and Francois, P. 2009. Metagenomic study of the oral microbiota by Illumina high-throughput sequencing. J. Microbiol. Methods 79, 266–271.PubMedPubMedCentralCrossRefGoogle Scholar
  74. Lee, H., Gurtowski, J., Yoo, S., Nattestad, M., Marcus, S., Goodwin, S., McCombie, W.R., and Schatz, M. 2016. Third-generation sequencing and the future of genomics. bioRxiv 048603.Google Scholar
  75. Lee, J.H., Park, Y., Choi, J.R., Lee, E.K., and Kim, H.S. 2010. Comparisons of three automated systems for genomic DNA extraction in a clinical diagnostic laboratory. Yonsei Med. J. 51, 104–110.PubMedCrossRefGoogle Scholar
  76. Leggett, R.M., Alcon-Giner, C., Heavens, D., Caim, S., Brook, T.C., Kujawska, M., Hoyles, L., Clarke, P., Hall, L., and Clark, M.D. 2017. Rapid MinION metagenomic profiling of the preterm infant gut microbiota to aid in pathogen diagnostics. bioRxiv 180406.Google Scholar
  77. Li, R., Tun, H.M., Jahan, M., Zhang, Z., Kumar, A., Fernando, D., Farenhorst, A., and Khafipour, E. 2017. Comparison of DNA-, PMA-, and RNA-based 16S rRNA Illumina sequencing for detection of live bacteria in water. Sci. Rep. 7, 5752.PubMedPubMedCentralCrossRefGoogle Scholar
  78. Li, R., Zhu, H., Ruan, J., Qian, W., Fang, X., Shi, Z., Li, Y., Li, S., Shan, G., Kristiansen, K., et al. 2010. De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 20, 265–272.PubMedPubMedCentralCrossRefGoogle Scholar
  79. Li, W. and Godzik, A. 2006. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659.PubMedCrossRefGoogle Scholar
  80. Liang, D., Leung, R.K.K., Guan, W., and Au, W.W. 2018. Involvement of gut microbiome in human health and disease: brief overview, knowledge gaps and research opportunities. Gut. Pathogens. 10, 3.PubMedPubMedCentralCrossRefGoogle Scholar
  81. Lim, M.Y., Song, E.J., Kim, S.H., Lee, J., and Nam, Y.D. 2018. Comparison of DNA extraction methods for human gut microbial community profiling. Syst. Appl. Microbiol. 41, 151–157.PubMedCrossRefGoogle Scholar
  82. Ling, Z., Liu, X., Luo, Y., Yuan, L., Nelson, K.E., Wang, Y., Xiang, C., and Li, L. 2013. Pyrosequencing analysis of the human microbiota of healthy Chinese undergraduates. BMC Genomics 14, 390.PubMedPubMedCentralCrossRefGoogle Scholar
  83. Liu, L., Li, Y., Li, S., Hu, N., He, Y., Pong, R., Lin, D., Lu, L., and Law, M. 2012. Comparison of next-generation sequencing systems. J. Biomed. Biotechnol. 2012, 251364.PubMedPubMedCentralGoogle Scholar
  84. Loman, N.J., Misra, R.V., Dallman, T.J., Constantinidou, C., Gharbia, S.E., Wain, J., and Pallen, M.J. 2012. Performance comparison of benchtop high-throughput sequencing platforms. Nat. Biotechnol. 30, 434–439.PubMedCrossRefGoogle Scholar
  85. Lozupone, C., Hamady, M., and Knight, R. 2006. UniFrac–an online tool for comparing microbial community diversity in a phylogenetic context. BMC Bioinformatics 7, 371.PubMedPubMedCentralCrossRefGoogle Scholar
  86. Lu, S., Park, M., Ro, H.S., Lee, D.S., Park, W., and Jeon, C.O. 2006. Analysis of microbial communities using culture-dependent and culture-independent approaches in an anaerobic/aerobic SBR reactor. J. Microbiol. 44, 155–161.PubMedGoogle Scholar
  87. Mardis, E.R. 2013. Next-generation sequencing platforms. Annu. Rev. Anal. Chem. 6, 287–303.CrossRefGoogle Scholar
  88. Margulies, M., Egholm, M., Altman, W.E., Attiya, S., Bader, J.S., Bemben, L.A., Berka, J., Braverman, M.S., Chen, Y.J., Chen, Z., et al. 2005. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376–380.PubMedPubMedCentralCrossRefGoogle Scholar
  89. Markowitz, V.M., Chen, I.M., Chu, K., Szeto, E., Palaniappan, K., Pillay, M., Ratner, A., Huang, J., Pagani, I., Tringe, S., et al. 2014. IMG/M 4 version of the integrated metagenome comparative analysis system. Nucleic Acids Res. 42, D568–573.Google Scholar
  90. Markowitz, V.M., Chen, I.M., Palaniappan, K., Chu, K., Szeto, E., Grechkin, Y., Ratner, A., Jacob, B., Huang, J., Williams, P., et al. 2012. IMG: the integrated microbial genomes database and comparative analysis system. Nucleic Acids Res. 40, D115–122.Google Scholar
  91. Maukonen, J., Simoes, C., and Saarela, M. 2012. The currently used commercial DNA-extraction methods give different results of clostridial and actinobacterial populations derived from human fecal samples. FEMS Microbiol. Ecol. 79, 697–708.PubMedCrossRefGoogle Scholar
  92. Maxam, A.M. and Gilbert, W. 1977. A new method for sequencing DNA. Proc. Natl. Acad. Sci. USA 74, 560–564.PubMedCrossRefGoogle Scholar
  93. McCarthy, A. 2010. Third generation DNA sequencing: pacific biosciences’ single molecule real time technology. Chem. Biol. 17, 675–676.PubMedCrossRefGoogle Scholar
  94. Meyer, F., Paarmann, D., D’Souza, M., Olson, R., Glass, E.M., Kubal, M., Paczian, T., Rodriguez, A., Stevens, R., Wilke, A., et al. 2008. The metagenomics RAST server-a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9, 386.PubMedPubMedCentralCrossRefGoogle Scholar
  95. Myer, P.R., Kim, M., Freetly, H.C., and Smith, T.P.L. 2016. Metagenomic and near full-length 16S rRNA sequence data in support of the phylogenetic analysis of the rumen bacterial community in steers. Data Brief 8, 1048–1053.PubMedPubMedCentralCrossRefGoogle Scholar
  96. Namiki, T., Hachiya, T., Tanaka, H., and Sakakibara, Y. 2012. Meta-Velvet: an extension of velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res. 40, e155.CrossRefGoogle Scholar
  97. Neefs, J.M., Van de Peer, Y., De Rijk, P., Chapelle, S., and De Wachter, R. 1993. Compilation of small ribosomal subunit RNA structures. Nucleic Acids Res. 21, 3025–3049.PubMedGoogle Scholar
  98. Nguyen, L.D.N., Deschaght, P., Merlin, S., Loywick, A., Audebert, C., Van Daele, S., Viscogliosi, E., Vaneechoutte, M., and Delhaes, L. 2016. Effects of propidium monoazide (PMA) treatment on mycobiome and bacteriome analysis of cystic fibrosis airways during exacerbation. PLoS One 11, e0168860.Google Scholar
  99. Nilakanta, H., Drews, K.L., Firrell, S., Foulkes, M.A., and Jablonski, K.A. 2014. A review of software for analyzing molecular sequences. BMC Res. Notes 7, 830.PubMedPubMedCentralCrossRefGoogle Scholar
  100. Nocker, A., Cheung, C.Y., and Camper, A.K. 2006. Comparison of propidium monoazide with ethidium monoazide for differentiation of live vs. dead bacteria by selective removal of DNA from dead cells. J. Microbiol. Methods 67, 310–320.PubMedCrossRefGoogle Scholar
  101. Overbeek, R., Olson, R., Pusch, G.D., Olsen, G.J., Davis, J.J., Disz, T., Edwards, R.A., Gerdes, S., Parrello, B., Shukla, M., et al. 2014. The SEED and the rapid annotation of microbial genomes using subsystems technology (RAST). Nucleic Acids Res. 42, D206–D214.Google Scholar
  102. Parks, D.H., Tyson, G.W., Hugenholtz, P., and Beiko, R.G. 2014. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124.PubMedPubMedCentralCrossRefGoogle Scholar
  103. Peng, Y., Leung, H.C., Yiu, S.M., and Chin, F.Y. 2011. Meta-IDBA: a de novo assembler for metagenomic data. Bioinformatics 27, i94–101.Google Scholar
  104. Pillai, S., Gopalan, V., and Lam, A.K. 2017. Review of sequencing platforms and their applications in phaeochromocytoma and paragangliomas. Crit. Rev. Oncol. Hematol. 116, 58–67.PubMedCrossRefGoogle Scholar
  105. Pinchi, V., Focardi, M., Martinelli, D., Norelli, G.A., Carboni, I., Gozzini, A., Romolini, C., Torricelli, F., and Ricci, U. 2013. DNA extraction method from teeth using QIAcube. Forensic Sci. Int. Genet. Suppl. Ser. 4, e276–e277.CrossRefGoogle Scholar
  106. Plummer, E. Twin, J., Bulach, D.M., Garland, S.M., and Tabtizi, S.N. 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.CrossRefGoogle Scholar
  107. Powell, S., Forslund, K., Szklarczyk, D., Trachana, K., Roth, A., Huerta-Cepas, J., Gabaldon, T., Rattei, T., Creevey, C., Kuhn, M., et al. 2014. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Res. 42, D231–239.Google Scholar
  108. Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K.S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., Yamada, T., et al. 2010. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65.PubMedPubMedCentralCrossRefGoogle Scholar
  109. Quince, C., Walker, A.W., Simpson, J.T., Loman, N.J., and Segata, N. 2017. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35, 833.PubMedCrossRefGoogle Scholar
  110. Reuter, J.A., Spacek, D.V., and Snyder, M.P. 2015. High-throughput sequencing technologies. Mol. Cell. 58, 586–597.PubMedPubMedCentralCrossRefGoogle Scholar
  111. Rho, M., Tang, H., and Ye, Y. 2010. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 38, e191.CrossRefGoogle Scholar
  112. Rhoads, A. and Au, K.F. 2015. PacBio sequencing and its applications. Genomics Proteomics Bioinformatics 13, 278–289.PubMedPubMedCentralCrossRefGoogle Scholar
  113. Rintala, A., Pietilä, S., Munukka, E., Eerola, E., Pursiheimo, J.P., Laiho, A., Pekkala, S., and Huovinen, P. 2017. Gut microbiota analysis results are highly dependent on the 16S rRNA gene target region, whereas the impact of DNA extraction is minor. J. Biomol. Tech. 28, 19–30.PubMedPubMedCentralGoogle Scholar
  114. Rizzo, J.M. and Buck, M.J. 2012. Key principles and clinical applications of “next-generation” DNA sequencing. Cancer Prev. Res. (Phila) 5, 887–900.CrossRefGoogle Scholar
  115. Rochelle, P.A., Cragg, B.A., Fry, J.C., John Parkes, R., and Weightman, A.J. 1994. Effect of sample handling on estimation of bacterial diversity in marine sediments by 16S rRNA gene sequence analysis. FEMS Microbiol. Ecol. 15, 215–225.CrossRefGoogle Scholar
  116. Rodrigues Hoffmann, A., Proctor, L.M., Surette, M.G., and Suchodolski, J.S. 2016. The microbiome: The trillions of microorganisms that maintain health and cause disease in humans and companion animals. Vet. Pathol. 53, 10–21.PubMedCrossRefGoogle Scholar
  117. Ronaghi, M., Karamohamed, S., Pettersson, B., Uhlen, M., and Nyren, P. 1996. Real-time DNA sequencing using detection of pyrophosphate release. Anal. Biochem. 242, 84–89.PubMedCrossRefGoogle Scholar
  118. Rothberg, J.M., Hinz, W., Rearick, T.M., Schultz, J., Mileski, W., Davey, M., Leamon, J.H., Johnson, K., Milgrew, M.J., Edwards, M., et al. 2011. An integrated semiconductor device enabling nonoptical genome sequencing. Nature 475, 348–352.PubMedCrossRefGoogle Scholar
  119. Rothberg, J.M. and Leamon, J.H. 2008. The development and impact of 454 sequencing. Nat. Biotechnol. 26, 1117.PubMedCrossRefGoogle Scholar
  120. Salonen, A., Nikkila, J., Jalanka-Tuovinen, J., Immonen, O., Rajilic-Stojanovic, M., Kekkonen, R.A., Palva, A., and de Vos, W.M. 2010. Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. J. Microbiol. Methods 81, 127–134.PubMedCrossRefGoogle Scholar
  121. Sanger, F., Air, G.M., Barrell, B.G., Brown, N.L., Coulson, A.R., Fiddes, J.C., Hutchison Iii, C.A., Slocombe, P.M., and Smith, M. 1977a. Nucleotide sequence of bacteriophage φX174 DNA. Nature 265, 687.PubMedCrossRefGoogle Scholar
  122. Sanger, F., Coulson, A.R., Hong, G.F., Hill, D.F., and Petersen, G.B. 1982. Nucleotide sequence of bacteriophage λ DNA. J. Mol. Biol. 162, 729–773.PubMedCrossRefGoogle Scholar
  123. Sanger, F., Nicklen, S., and Coulson, A.R. 1977b. DNA sequencing with chain-terminating inhibitors. Proc. Nat. Acad. Sci. USA 74, 5463–5467.PubMedCrossRefGoogle Scholar
  124. Schloss, P.D., Jenior, M.L., Koumpouras, C.C., Westcott, S.L., and Highlander, S.K. 2016. Sequencing 16S rRNA gene fragments using the PacBio SMRT DNA sequencing system. PeerJ 4, e1869.CrossRefGoogle Scholar
  125. Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., Lesniewski, R.A., Oakley, B.B., Parks, D.H., Robinson, C.J., et al. 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541.PubMedPubMedCentralCrossRefGoogle Scholar
  126. Schmutz, J., Cannon, S.B., Schlueter, J., Ma, J., Mitros, T., Nelson, W., Hyten, D.L., Song, Q., Thelen, J.J., Cheng, J., et al. 2010. Genome sequence of the palaeopolyploid soybean. Nature 463, 178–183.PubMedCrossRefGoogle Scholar
  127. Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W.S., and Huttenhower, C. 2011. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60.Google Scholar
  128. Sharon, D., Tilgner, H., Grubert, F., and Snyder, M. 2013. A singlemolecule long-read survey of the human transcriptome. Nat. Biotechnol. 31, 1009.PubMedPubMedCentralCrossRefGoogle Scholar
  129. Shendure, J., Balasubramanian, S., Church, G.M., Gilbert, W., Rogers, J., Schloss, J.A., and Waterston, R.H. 2017. DNA sequencing at 40: past, present and future. Nature 550, 345–353.PubMedCrossRefGoogle Scholar
  130. Shendure, J. and Ji, H. 2008. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135.PubMedCrossRefGoogle Scholar
  131. Shepard, R.N. 1966. Metric structures in ordinal data. J. Math. Psychol. 3, 287–315.CrossRefGoogle Scholar
  132. Sheridan, G.E.C., Masters, C.I., Shallcross, J.A., and Mackey, B.M. 1998. Detection of mRNA by reverse transcription-PCR as an indicator of viability in Escherichia coli cells. Appl. Environ. Microbiol. 64, 1313–1318.PubMedPubMedCentralGoogle Scholar
  133. Siegwald, L., Audebert, C., Even, G., Viscogliosi, E., Caboche, S., and Chabé, M. 2017. Targeted metagenomic sequencing data of human gut microbiota associated with Blastocystis colonization. Sci. Data 4, 170081.PubMedPubMedCentralCrossRefGoogle Scholar
  134. Sinha, R., Chen, J., Amir, A., Vogtmann, E., Shi, J., Inman, K.S., Flores, R., Sampson, J., Knight, R., and Chia, N. 2016. Collecting fecal samples for microbiome analyses in epidemiology studies. Cancer Epidemiol. Biomarkers Prev. 25, 407–416.PubMedCrossRefGoogle Scholar
  135. Smith, B., Li, N., Andersen, A.S., Slotved, H.C., and Krogfelt, K.A. 2011. Optimising bacterial DNA extraction from faecal samples: comparison of three methods. Open Microbiol. J. 5, 14–17.PubMedPubMedCentralCrossRefGoogle Scholar
  136. Stadlbauer, V., Leber, B., Lemesch, S., Trajanoski, S., Bashir, M., Horvath, A., Tawdrous, M., Stojakovic, T., Fauler, G., Fickert, P., et al. 2015. Lactobacillus casei shirota supplementation does not restore gut microbiota composition and gut barrier in metabolic syndrome: A randomized pilot study. PLoS One 10, e0141399.Google Scholar
  137. Stinson, L.F., Keelan, J.A., and Payne, M.S. 2018. Comparison of meconium DNA extraction methods for use in microbiome studies. Front. Microbiol. 9, 270.PubMedPubMedCentralCrossRefGoogle Scholar
  138. Tantikachornkiat, M., Sakakibara, S., Neuner, M., and Durall, D.M. 2016. The use of propidium monoazide in conjunction with qPCR and Illumina sequencing to identify and quantify live yeasts and bacteria. Int. J. Food Microbiol. 234, 53–59.PubMedCrossRefGoogle Scholar
  139. The Arabidopsis Genome Initiative. 2000. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796.CrossRefGoogle Scholar
  140. The HMP consortium. 2012. A framework for human microbiome research. Nature 486, 215–221.CrossRefGoogle Scholar
  141. The Integrative HMP (iHMP) Research Network Consortium. 2014. The integrative human microbiome project: Dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe 16, 276–289.PubMedCentralCrossRefGoogle Scholar
  142. The UniProt Consortium. 2017. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169.CrossRefGoogle Scholar
  143. Thompson, L.R., Sanders, J.G., McDonald, D., Amir, A., Ladau, J., Locey, K.J., Prill, R.J., Tripathi, A., Gibbons, S.M., Ackermann, G., et al. 2017. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463.PubMedCrossRefPubMedCentralGoogle Scholar
  144. Tremblay, J., Singh, K., Fern, A., Kirton, E.S., He, S., Woyke, T., Lee, J., Chen, F., Dangl, J.L., and Tringe, S.G. 2015. Primer and platform effects on 16S rRNA tag sequencing. Front. Microbiol. 6, 771.PubMedPubMedCentralGoogle Scholar
  145. Tsai, Y.C., Conlan, S., Deming, C., Segre, J.A., Kong, H.H., Korlach, J., and Oh, J. 2016. Resolving the complexity of human skin metagenomes using single-molecule sequencing. MBio 7, e01948-01915.CrossRefGoogle Scholar
  146. Tyakht, A.V., Kostryukova, E.S., Popenko, A.S., Belenikin, M.S., Pavlenko, A.V., Larin, A.K., Karpova, I.Y., Selezneva, O.V., Semashko, T.A., Ospanova, E.A., et al. 2013. Human gut microbiota community structures in urban and rural populations in Russia. Nat. Commun. 4, 2469.PubMedPubMedCentralCrossRefGoogle Scholar
  147. Vaishampayan, P., Probst, A.J., La Duc, M.T., Bargoma, E., Benardini, J.N., Andersen, G.L., and Venkateswaran, K. 2013. New perspectives on viable microbial communities in low-biomass cleanroom environments. ISME J. 7, 312–324.PubMedCrossRefGoogle Scholar
  148. Veiga, P., Pons, N., Agrawal, A., Oozeer, R., Guyonnet, D., Brazeilles, R., Faurie, J.M., van Hylckama Vlieg, J.E.T., Houghton, L.A., Whorwell, P.J., et al. 2014. Changes of the human gut microbiome induced by a fermented milk product. Sci. Rep. 4, 6328.PubMedPubMedCentralCrossRefGoogle Scholar
  149. Wagner Mackenzie, B., Waite, D.W., and Taylor, M.W. 2015. Evaluating variation in human gut microbiota profiles due to DNA extraction method and inter-subject differences. Front. Microbiol. 6, 130.PubMedPubMedCentralCrossRefGoogle Scholar
  150. Warner, B.B., Deych, E., Zhou, Y., Hall-Moore, C., Weinstock, G.M., Sodergren, E., Shaikh, N., Hoffmann, J.A., Linneman, L.A., Hamvas, A., et al. 2016. Gut bacteria dysbiosis and necrotising enterocolitis in very low birthweight infants: a prospective casecontrol study. Lancet (London, England) 387, 1928–1936.CrossRefGoogle Scholar
  151. Weinstock, G.M. 2012. Genomic approaches to studying the human microbiota. Nature 489, 250–256.PubMedPubMedCentralCrossRefGoogle Scholar
  152. Wesolowska-Andersen, A., Bahl, M.I., Carvalho, V., Kristiansen, K., Sicheritz-Ponten, T., Gupta, R., and Licht, T.R. 2014. Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis. Microbiome 2, 19.PubMedPubMedCentralCrossRefGoogle Scholar
  153. Wu, W.K., Chen, C.C., Panyod, S., Chen, R.A., Wu, M.S., Sheen, L.Y., and Chang, S.C. 2018. Optimization of fecal sample processing for microbiome study–The journey from bathroom to bench. J. Formos. Med. Assoc. In-press.Google Scholar
  154. Yanagi, H., Tsuda, A., Matsushima, M., Takahashi, S., Ozawa, G., Koga, Y., and Takagi, A. 2017. Changes in the gut microbiota composition and the plasma ghrelin level in patients with Helicobacter pylori-infected patients with eradication therapy. BMJ Open Gastroenterol. 4, e000182.CrossRefGoogle Scholar
  155. Yilmaz, P., Parfrey, L.W., Yarza, P., Gerken, J., Pruesse, E., Quast, C., Schweer, T., Peplies, J., Ludwig, W., and Glockner, F.O. 2014. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 42, D643–648.Google Scholar
  156. Yin, Y., Mao, X., Yang, J., Chen, X., Mao, F., and Xu, Y. 2012. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 40, W445–451.Google Scholar
  157. Yuan, S., Cohen, D.B., Ravel, J., Abdo, Z., and Forney, L.J. 2012. Evaluation of methods for the extraction and purification of DNA from the human microbiome. PLoS One 7, e33865.CrossRefGoogle Scholar
  158. Zheng, Z., Zhong, W., Liu, L., Wu, C., Zhang, L., Cai, S., Xu, Q., Wu, L., Bi, Y., Cui, Y., and Qin, N. 2016. Bioinformatics approaches for human gut microbiome research. Infect. Dis. Transl. Med. 2, 69–79.Google Scholar
  159. Zhu, W., Lomsadze, A., and Borodovsky, M. 2010. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 38, e132.CrossRefGoogle Scholar

Copyright information

© The Microbiological Society of Korea and Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Research Group of HealthcareKorea Food Research InstituteWanjuRepublic of Korea
  2. 2.Department of Food BiotechnologyKorea University of Science and TechnologyDaejeonRepublic of Korea

Personalised recommendations