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Comparison of the Performance of MiSeq and HiSeq 2500 in a Microbiome Study

  • Na, Hee Sam (Department of Oral Microbiology, School of Dentistry, Pusan National University) ;
  • Yu, Yeuni (Interdisplinary Program of Genomic Science, Pusan National University) ;
  • Kim, Si Yeong (Department of Oral Microbiology, School of Dentistry, Pusan National University) ;
  • Lee, Jae-Hyung (Department of Oral Microbiology, School of Dentistry, Kyung Hee University) ;
  • Chung, Jin (Department of Oral Microbiology, School of Dentistry, Pusan National University)
  • Received : 2020.08.06
  • Accepted : 2020.11.13
  • Published : 2020.12.28

Abstract

Next generation sequencing is commonly used to characterize the microbiome structure. MiSeq is commonly used to analyze the microbiome due to its relatively long read length. However, recently, Illumina introduced the 250x2 chip for HiSeq 2500. The purpose of this study was to compare the performance of MiSeq and HiSeq in the context of oral microbiome samples. The MiSeq Reagent Kit V3 and the HiSeq Rapid SBS Kit V2 were used for MiSeq and HiSeq 2500 analyses, respectively. Total read count, read quality score, relative bacterial abundance, community diversity, and relative abundance correlation were analyzed. HiSeq produced significantly more read sequences and assigned taxa compared to MiSeq. Conversely, community diversity was similar in the context of MiSeq and HiSeq. However, depending on the relative abundance, the correlation between the two platforms differed. The correlation between HiSeq and MiSeq sequencing data for highly abundant taxa (> 2%), low abundant taxa (2-0.2%), and rare taxa (0.2% >) was 0.994, 0.860, and 0.416, respectively. Therefore, HiSeq 2500 may also be compatible for microbiome studies. Importantly, the HiSeq platform may allow a high-resolution massive parallel sequencing for the detection of rare taxa.

Keywords

References

  1. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI. 2007. The human microbiome project. Nature 449: 804-810. https://doi.org/10.1038/nature06244
  2. Jorth P, Turner KH, Gumus P, Nizam N, Buduneli N, Whiteley M. 2014. Metatranscriptomics of the human oral microbiome during health and disease. mBio 5: e01012-01014.
  3. Dong TS, Gupta A. 2019. Influence of early life, diet, and the environment on the microbiome. Clin. Gastroenterol. Hepatol. 17: 231-242. https://doi.org/10.1016/j.cgh.2018.08.067
  4. Somineni HK, Kugathasan S. 2019. The microbiome in patients with inflammatory diseases. Clin. Gastroenterol. Hepatol. 17: 243-255. https://doi.org/10.1016/j.cgh.2018.08.078
  5. Zackular JP, Baxter NT, Iverson KD, Sadler WD, Petrosino JF, Chen GY, et al. 2013. The gut microbiome modulates colon tumorigenesis. mBio 4: e00692-00613.
  6. Liu XX, Jiao B, Liao XX, Guo LN, Yuan ZH, Wang X, et al. 2019. Analysis of salivary microbiome in patients with Alzheimer's disease. J. Alzheimers Dis. 72: 633-640. https://doi.org/10.3233/JAD-190587
  7. Frank DN, Robertson CE, Hamm CM, Kpadeh Z, Zhang T, Chen H, et al. 2011. Disease phenotype and genotype are associated with shifts in intestinal-associated microbiota in inflammatory bowel diseases. Inflamm. Bowel Dis. 17: 179-184. https://doi.org/10.1002/ibd.21339
  8. Kau AL, Ahern PP, Griffin NW, Goodman AL, Gordon JI. 2011. Human nutrition, the gut microbiome and the immune system. Nature 474: 327-336. https://doi.org/10.1038/nature10213
  9. Cani PD, Possemiers S, Van de Wiele T, Guiot Y, Everard A, Rottier O, et al. 2009. Changes in gut microbiota control inflammation in obese mice through a mechanism involving GLP-2-driven improvement of gut permeability. Gut 58: 1091-1103. https://doi.org/10.1136/gut.2008.165886
  10. Langdon A, Crook N, Dantas G. 2016. The effects of antibiotics on the microbiome throughout development and alternative approaches for therapeutic modulation. Genome Med. 8: 39. https://doi.org/10.1186/s13073-016-0294-z
  11. Human Microbiome Project Consortium. 2012. Structure, function and diversity of the healthy human microbiome. Nature 486: 207-214. https://doi.org/10.1038/nature11234
  12. Dewhirst FE, Chen T, Izard J, Paster BJ, Tanner AC, Yu WH, et al. 2010. The human oral microbiome. J. Bacteriol. 192: 5002-5017. https://doi.org/10.1128/JB.00542-10
  13. Abusleme L, Dupuy AK, Dutzan N, Silva N, Burleson JA, Strausbaugh LD, et al. 2013. The subgingival microbiome in health and periodontitis and its relationship with community biomass and inflammation. ISME J. 7: 1016-1025. https://doi.org/10.1038/ismej.2012.174
  14. Apatzidou D, Lappin DF, Hamilton G, Papadopoulos CA, Konstantinidis A, Riggio MP. 2017. Microbiome of peri-implantitis affected and healthy dental sites in patients with a history of chronic periodontitis. Arch. Oral Biol. 83: 145-152. https://doi.org/10.1016/j.archoralbio.2017.07.007
  15. Park OJ, Yi H, Jeon JH, Kang SS, Koo KT, Kum KY, et al. 2015. Pyrosequencing analysis of subgingival microbiota in distinct periodontal conditions. J. Dent. Res. 94: 921-927. https://doi.org/10.1177/0022034515583531
  16. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6: 1621-1624. https://doi.org/10.1038/ismej.2012.8
  17. Junemann S, Prior K, Szczepanowski R, Harks I, Ehmke B, Goesmann A, et al. 2012. Bacterial community shift in treated periodontitis patients revealed by ion torrent 16S rRNA gene amplicon sequencing. PLoS One 7: e41606. https://doi.org/10.1371/journal.pone.0041606
  18. Junemann S, Sedlazeck FJ, Prior K, Albersmeier A, John U, Kalinowski J, et al. 2013. Updating benchtop sequencing performance comparison. Nat. Biotechnol. 31: 294-296. https://doi.org/10.1038/nbt.2522
  19. NIH Human Microbiome Portfolio Analysis Team. 2019. A review of 10 years of human microbiome research activities at the US National Institutes of Health, Fiscal Years 2007-2016. Microbiome 7: 31. https://doi.org/10.1186/s40168-019-0620-y
  20. Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, et al. 2013. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10: 57-59. https://doi.org/10.1038/nmeth.2276
  21. Navas-Molina JA, Peraltz-Sanchez JM, Gonzalez A, McMurdie PJ, Vazquez-Baeza Y, Xu Z, et al. 2013. Advancing our understanding of the human microbiome using QIIME. Methods Enzymol. 531: 371-444. https://doi.org/10.1016/B978-0-12-407863-5.00019-8
  22. Kwon S, Park S, Lee B, Yoon S. 2013. In-depth analysis of interrelation between quality scores and real errors in illumina reads. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference. 2013.
  23. Kuczynski J, Liu Z, Lozupone C, McDonald D, Fierer N, Knight R. 2010. Microbial community resemblance methods differ in their ability to detect biologically relevant patterns. Nat. Methods 7: 813-819. https://doi.org/10.1038/nmeth.1499
  24. Sneath PH, Sokal RR. 1962. Numerical Taxonomy. Nature 193: 855-860. https://doi.org/10.1038/193855a0
  25. Drancourt M, Collet C, Carlioz C, Martelin R, Gayral JP, Raoult D. 2000. 16S ribosomal DNA sequence analysis of a large collection of environmental and clinical unidentifiable bacterial isolates. J. Clin. Microbiol. 38: 3623-3630. https://doi.org/10.1128/JCM.38.10.3623-3630.2000
  26. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6: 1621-1624. https://doi.org/10.1038/ismej.2012.8