Phylogenetic microbiota profiling in fecal samples depends on combination of sequencing depth and choice of NGS analysis method
Autoři:
Sukithar K. Rajan aff001; Mårten Lindqvist aff001; Robert Jan Brummer aff001; Ida Schoultz aff001; Dirk Repsilber aff001
Působiště autorů:
School of Medical Sciences, Örebro University, Örebro, Sweden
aff001
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0222171
Souhrn
The human gut microbiota is well established as an important factor in health and disease. Fecal sample microbiota are often analyzed as a proxy for gut microbiota, and characterized with respect to their composition profiles. Modern approaches employ whole genome shotgun next-generation sequencing as the basis for these analyses. Sequencing depth as well as choice of next-generation sequencing data analysis method constitute two main interacting methodological factors for such an approach. In this study, we used 200 million sequence read pairs from one fecal sample for comparing different taxonomy classification methods, using default and custom-made reference databases, at different sequencing depths. A mock community data set with known composition was used for validating the classification methods. Results suggest that sequencing beyond 60 million read pairs does not seem to improve classification. The phylogeny prediction pattern, when using the default databases and the consensus database, appeared to be similar for all three methods. Moreover, these methods predicted rather different species. We conclude that the choice of sequencing depth and classification method has important implications for taxonomy composition prediction. A multi-method-consensus approach for robust gut microbiota NGS analysis is recommended.
Klíčová slova:
Research and analysis methods – Database and informatics methods – Biological databases – Bioinformatics – Sequence analysis – Sequence databases – Sequencing techniques – Biology and life sciences – Molecular biology – Molecular biology techniques – Cloning – DNA cloning – Shotgun sequencing – Taxonomy – Microbial taxonomy – Ecology – Ecological metrics – Species diversity – Microbiology – Medical microbiology – Microbiome – Microbial genomics – Genetics – Genomics – Genome analysis – Genomic databases – Computational biology – Computer and information sciences – Data management – Ecology and environmental sciences
Zdroje
1. Bull MJ, Plummer NT. Part 1: The Human Gut Microbiome in Health and Disease. Integr Med (Encinitas). 2014;13(6):17–22. Epub 2016/01/16. 26770121; PubMed Central PMCID: PMCPMC4566439.
2. Natividad JM, Verdu EF. Modulation of intestinal barrier by intestinal microbiota: pathological and therapeutic implications. Pharmacol Res. 2013;69(1):42–51. Epub 2012/10/24. doi: 10.1016/j.phrs.2012.10.007 23089410.
3. Halfvarson J, Brislawn CJ, Lamendella R, Vazquez-Baeza Y, Walters WA, Bramer LM, et al. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol. 2017;2:17004. Epub 2017/02/14. doi: 10.1038/nmicrobiol.2017.4 28191884; PubMed Central PMCID: PMCPMC5319707.
4. Larroya-Garcia A, Navas-Carrillo D, Orenes-Pinero E. Impact of gut microbiota on neurological diseases: Diet composition and novel treatments. Crit Rev Food Sci Nutr. 2018:1–15. Epub 2018/06/06. doi: 10.1080/10408398.2018.1484340 29870270.
5. Savage DC. Microbial ecology of the gastrointestinal tract. Annu Rev Microbiol. 1977;31:107–33. Epub 1977/01/01. doi: 10.1146/annurev.mi.31.100177.000543 334036.
6. Icaza-Chavez ME. [Gut microbiota in health and disease]. Rev Gastroenterol Mex. 2013;78(4):240–8. Epub 2013/12/03. doi: 10.1016/j.rgmx.2013.04.004 24290319.
7. Rup L. The human microbiome project. Indian J Microbiol. 2012;52(3):315. Epub 2013/09/03. doi: 10.1007/s12088-012-0304-9 23997318; PubMed Central PMCID: PMCPMC3460114.
8. Blanco-Miguez A, Gutierrez-Jacome A, Fdez-Riverola F, Lourenco A, Sanchez B. MAHMI database: a comprehensive MetaHit-based resource for the study of the mechanism of action of the human microbiota. Database (Oxford). 2017;2017. Epub 2017/01/13. doi: 10.1093/database/baw157 28077565; PubMed Central PMCID: PMCPMC5225402.
9. Lewis CM Jr., Obregon-Tito A, Tito RY, Foster MW, Spicer PG. The Human Microbiome Project: lessons from human genomics. Trends Microbiol. 2012;20(1):1–4. Epub 2011/11/25. doi: 10.1016/j.tim.2011.10.004 22112388; PubMed Central PMCID: PMCPMC3709440.
10. Weinstock GM. Genomic approaches to studying the human microbiota. Nature. 2012;489(7415):250–6. Epub 2012/09/14. doi: 10.1038/nature11553 22972298; PubMed Central PMCID: PMCPMC3665339.
11. Olsen GJ, Lane DJ, Giovannoni SJ, Pace NR, Stahl DA. Microbial ecology and evolution: a ribosomal RNA approach. Annu Rev Microbiol. 1986;40:337–65. Epub 1986/01/01. doi: 10.1146/annurev.mi.40.100186.002005 2430518.
12. Oulas A, Pavloudi C, Polymenakou P, Pavlopoulos GA, Papanikolaou N, Kotoulas G, et al. Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies. Bioinform Biol Insights. 2015;9:75–88. Epub 2015/05/20. doi: 10.4137/BBI.S12462 25983555; PubMed Central PMCID: PMCPMC4426941.
13. Thomas T, Gilbert J, Meyer F. Metagenomics—a guide from sampling to data analysis. Microb Inform Exp. 2012;2(1):3. Epub 2012/05/17. doi: 10.1186/2042-5783-2-3 22587947; PubMed Central PMCID: PMCPMC3351745.
14. Yeh YC, Needham DM, Sieradzki ET, Fuhrman JA. Taxon Disappearance from Microbiome Analysis Reinforces the Value of Mock Communities as a Standard in Every Sequencing Run. mSystems. 2018;3(3). Epub 2018/04/10. doi: 10.1128/mSystems.00023-18 29629423; PubMed Central PMCID: PMCPMC5883066.
15. Brooks JP, Edwards DJ, Harwich MD, Rivera MC, Fettweis JM, Serrano MG, et al. The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies. Bmc Microbiology. 2015;15. ARTN 66 doi: 10.1186/s12866-015-0351-6 PubMed PMID: WOS:000353191800001. 25880246
16. Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods. 2012;9(8):811–4. Epub 2012/06/13. doi: 10.1038/nmeth.2066 22688413; PubMed Central PMCID: PMCPMC3443552.
17. Smith DP, Peay KG. Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing. PLoS One. 2014;9(2):e90234. Epub 2014/03/04. doi: 10.1371/journal.pone.0090234 24587293; PubMed Central PMCID: PMCPMC3938664.
18. Walsh AM, Crispie F, O'Sullivan O, Finnegan L, Claesson MJ, Cotter PD. Species classifier choice is a key consideration when analysing low-complexity food microbiome data. Microbiome. 2018;6. ARTN 50 doi: 10.1186/s40168-018-0437-0 PubMed PMID: WOS:000428216500003. 29554948
19. Siegwald L, Touzet H, Lemoine Y, Hot D, Audebert C, Caboche S. Assessment of Common and Emerging Bioinformatics Pipelines for Targeted Metagenomics. Plos One. 2017;12(1). ARTN e0169563 doi: 10.1371/journal.pone.0169563 PubMed PMID: WOS:000391621500068. 28052134
20. Bazinet AL, Cummings MP. A comparative evaluation of sequence classification programs. Bmc Bioinformatics. 2012;13. Artn 92 doi: 10.1186/1471-2105-13-92 PubMed PMID: WOS:000308069400001. 22574964
21. Fumagalli M. Assessing the effect of sequencing depth and sample size in population genetics inferences. PLoS One. 2013;8(11):e79667. Epub 2013/11/22. doi: 10.1371/journal.pone.0079667 24260275; PubMed Central PMCID: PMCPMC3832539.
22. Jovel J, Patterson J, Wang W, Hotte N, O'Keefe S, Mitchel T, et al. Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics. Frontiers in Microbiology. 2016;7. ARTN 459 doi: 10.3389/fmicb.2016.00459 PubMed PMID: WOS:000374371700001. 27148170
23. Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Gohl DM, Beckman KB, et al. Evaluating the Information Content of Shallow Shotgun Metagenomics. mSystems. 2018;3(6). Epub 2018/11/18. doi: 10.1128/mSystems.00069-18 30443602; PubMed Central PMCID: PMCPMC6234283.
24. Allali I, Arnold JW, Roach J, Cadenas MB, Butz N, Hassan HM, et al. A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome. BMC Microbiol. 2017;17(1):194. Epub 2017/09/15. doi: 10.1186/s12866-017-1101-8 28903732; PubMed Central PMCID: PMCPMC5598039.
25. Michel PO, Degen C, Hubert M, Baldi L, Hacker DL, Wurm FM. A NanoDrop-based method for rapid determination of viability decline in suspension cultures of animal cells. Anal Biochem. 2012;430(2):138–40. Epub 2012/09/11. doi: 10.1016/j.ab.2012.08.028 22960013.
26. Vandenberg N, van Oorschot RA. Extraction of human nuclear DNA from feces samples using the QIAamp DNA Stool Mini Kit. J Forensic Sci. 2002;47(5):993–5. Epub 2002/10/02. 12353586.
27. S. A. FastQC: a quality control tool for high throughput sequence data 2010 [cited 2018 08.29]. Available from: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
28. Ounit R, Wanamaker S, Close TJ, Lonardi S. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genomics. 2015;16:236. Epub 2015/04/17. doi: 10.1186/s12864-015-1419-2 25879410; PubMed Central PMCID: PMCPMC4428112.
29. Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 2014;15(3):R46. Epub 2014/03/04. doi: 10.1186/gb-2014-15-3-r46 24580807; PubMed Central PMCID: PMCPMC4053813.
30. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26(1):32–46. doi: 10.1046/j.1442-9993.2001.01070.x PubMed PMID: WOS:000167002000004.
31. Nudds TD. Variation in Richness, Evenness, and Diversity in Diving and Dabbling Duck Guilds in Prairie Pothole Habitats. Can J Zool. 1983;61(7):1547–50. doi: 10.1139/z83-208 PubMed PMID: WOS:A1983RD15800016.
32. Markowitz VM, Chen IM, Palaniappan K, Chu K, Szeto E, Grechkin Y, et al. IMG: the Integrated Microbial Genomes database and comparative analysis system. Nucleic Acids Res. 2012;40(Database issue):D115–22. Epub 2011/12/24. doi: 10.1093/nar/gkr1044 22194640; PubMed Central PMCID: PMCPMC3245086.
33. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods. 2010;7(5):335–6. doi: 10.1038/nmeth.f.303 PubMed PMID: WOS:000277175100003. 20383131
34. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl Environ Microb. 2009;75(23):7537–41. doi: 10.1128/Aem.01541-09 PubMed PMID: WOS:000271944800028. 19801464
35. Langmead B. Aligning short sequencing reads with Bowtie. Curr Protoc Bioinformatics. 2010;Chapter 11:Unit 11 7. Epub 2010/12/15. doi: 10.1002/0471250953.bi1107s32 21154709; PubMed Central PMCID: PMCPMC3010897.
36. Koslicki D, Falush D. MetaPalette: a k-mer Painting Approach for Metagenomic Taxonomic Profiling and Quantification of Novel Strain Variation. mSystems. 2016;1(3). Epub 2016/11/09. doi: 10.1128/mSystems.00020-16 27822531; PubMed Central PMCID: PMCPMC5069763.
37. Keegan KP, Glass EM, Meyer F. MG-RAST, a Metagenomics Service for Analysis of Microbial Community Structure and Function. Methods Mol Biol. 2016;1399:207–33. Epub 2016/01/23. doi: 10.1007/978-1-4939-3369-3_13 26791506.
38. Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:11257. Epub 2016/04/14. doi: 10.1038/ncomms11257 27071849; PubMed Central PMCID: PMCPMC4833860.
39. Uchiyama I, Mihara M, Nishide H, Chiba H. MBGD update 2015: microbial genome database for flexible ortholog analysis utilizing a diverse set of genomic data. Nucleic Acids Res. 2015;43(Database issue):D270–6. Epub 2014/11/16. doi: 10.1093/nar/gku1152 25398900; PubMed Central PMCID: PMCPMC4383954.
40. Jolley KA, Bray JE, Maiden MCJ. Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications. Wellcome Open Res. 2018;3:124. Epub 2018/10/23. doi: 10.12688/wellcomeopenres.14826.1 30345391; PubMed Central PMCID: PMCPMC6192448.
41. Li J, Jia H, Cai X, Zhong H, Feng Q, Sunagawa S, et al. An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol. 2014;32(8):834–41. Epub 2014/07/07. doi: 10.1038/nbt.2942 24997786.
42. Treangen TJ, Koren S, Sommer DD, Liu B, Astrovskaya I, Ondov B, et al. MetAMOS: a modular and open source metagenomic assembly and analysis pipeline. Genome Biol. 2013;14(1):R2. Epub 2013/01/17. doi: 10.1186/gb-2013-14-1-r2 23320958; PubMed Central PMCID: PMCPMC4053804.
43. Bokulich NA, Rideout JR, Mercurio WG, Shiffer A, Wolfe B, Maurice CF, et al. mockrobiota: a Public Resource for Microbiome Bioinformatics Benchmarking. mSystems. 2016;1(5). Epub 2016/11/09. doi: 10.1128/mSystems.00062-16 27822553; PubMed Central PMCID: PMCPMC5080401.
44. Lim MY, Song EJ, Kim SH, Lee J, Nam YD. Comparison of DNA extraction methods for human gut microbial community profiling. Syst Appl Microbiol. 2018;41(2):151–7. Epub 2018/01/07. doi: 10.1016/j.syapm.2017.11.008 29305057.
45. Wen Y, Xiao F, Wang C, Wang Z. The impact of different methods of DNA extraction on microbial community measures of BALF samples based on metagenomic data. Am J Transl Res. 2016;8(3):1412–25. Epub 2016/05/18. 27186268; PubMed Central PMCID: PMCPMC4858570.
46. Mao X, Cai T, Olyarchuk JG, Wei L. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics. 2005;21(19):3787–93. Epub 2005/04/09. doi: 10.1093/bioinformatics/bti430 15817693.
47. Tatusov RL, Koonin EV, Lipman DJ. A genomic perspective on protein families. Science. 1997;278(5338):631–7. doi: 10.1126/science.278.5338.631 PubMed PMID: WOS:A1997YC32300041. 9381173
48. Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Research. 2016;44(D1):D279–D85. doi: 10.1093/nar/gkv1344 PubMed PMID: WOS:000371261700038.26673716
Článek vyšel v časopise
PLOS One
2019 Číslo 9
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy