Comparison of Bioinformatics Pipelines and Operating Systems for the Analyses of 16S rRNA Gene Amplicon Sequences in Human Fecal Samples.
16S rRNA amplicon sequencing
QIIME2
UPARSE
bioconductor
fecal human samples
microbiome
mothur
Journal
Frontiers in microbiology
ISSN: 1664-302X
Titre abrégé: Front Microbiol
Pays: Switzerland
ID NLM: 101548977
Informations de publication
Date de publication:
2020
2020
Historique:
received:
23
10
2019
accepted:
18
05
2020
entrez:
9
7
2020
pubmed:
9
7
2020
medline:
9
7
2020
Statut:
epublish
Résumé
Amplicon high-throughput sequencing of 16S ribosomal RNA (rRNA) gene is currently the most widely used technique to investigate complex gut microbial communities. Microbial identification might be influenced by several factors, including the choice of bioinformatic pipelines, making comparisons across studies difficult. Here, we compared four commonly used pipelines (QIIME2, Bioconductor, UPARSE and mothur) run on two operating systems (OS) (Linux and Mac), to evaluate the impact of bioinformatic pipeline and OS on the taxonomic classification of 40 human stool samples. We applied the SILVA 132 reference database for all the pipelines. We compared phyla and genera identification and relative abundances across the four pipelines using the Friedman rank sum test. QIIME2 and Bioconductor provided identical outputs on Linux and Mac OS, while UPARSE and mothur reported only minimal differences between OS. Taxa assignments were consistent at both phylum and genus level across all the pipelines. However, a difference in terms of relative abundance was identified for all phyla (
Identifiants
pubmed: 32636817
doi: 10.3389/fmicb.2020.01262
pmc: PMC7318847
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1262Informations de copyright
Copyright © 2020 Marizzoni, Gurry, Provasi, Greub, Lopizzo, Ribaldi, Festari, Mazzelli, Mombelli, Salvatore, Mirabelli, Franzese, Soricelli, Frisoni and Cattaneo.
Références
mSystems. 2017 Mar 7;2(2):
pubmed: 28289731
F1000Res. 2016 Jun 24;5:1492
pubmed: 27508062
Biomed Res Int. 2014;2014:325340
pubmed: 24719854
PLoS One. 2012;7(6):e38234
pubmed: 22675527
Alzheimers Dement. 2018 Aug;14(8):1088-1098
pubmed: 29679576
Front Physiol. 2016 Dec 09;7:606
pubmed: 28018236
BMC Microbiol. 2017 Sep 13;17(1):194
pubmed: 28903732
Mov Disord. 2015 Mar;30(3):350-8
pubmed: 25476529
Bioinformatics. 2011 Feb 15;27(4):592-3
pubmed: 21169378
Rev Neurosci. 2018 Aug 28;29(6):629-643
pubmed: 29397391
Nat Biotechnol. 2019 Sep;37(9):1091
pubmed: 31399723
Gut. 2019 Jun;68(6):1014-1023
pubmed: 30045880
PLoS One. 2020 Jan 16;15(1):e0227434
pubmed: 31945086
Nat Methods. 2016 Jul;13(7):581-3
pubmed: 27214047
Appl Environ Microbiol. 2009 Dec;75(23):7537-41
pubmed: 19801464
PLoS One. 2013 Apr 22;8(4):e61217
pubmed: 23630581
Sci Rep. 2016 Oct 13;6:35216
pubmed: 27734914
BMC Bioinformatics. 2014 Aug 29;15:293
pubmed: 25176396
Nat Methods. 2013 Oct;10(10):996-8
pubmed: 23955772
Nucleic Acids Res. 2010 Dec;38(22):e200
pubmed: 20880993
PLoS One. 2015 Oct 19;10(10):e0140827
pubmed: 26479726
mSystems. 2018 Jan 30;3(1):
pubmed: 29404425
Front Microbiol. 2018 Dec 13;9:3010
pubmed: 30619117
Nat Rev Genet. 2012 Sep;13(9):667-72
pubmed: 22898652
Bioinformatics. 2010 Oct 1;26(19):2460-1
pubmed: 20709691
Gigascience. 2018 May 1;7(5):
pubmed: 29762668
FEMS Microbiol Ecol. 2014 Feb;87(2):303-14
pubmed: 24219358
Proc Natl Acad Sci U S A. 2011 Mar 15;108 Suppl 1:4516-22
pubmed: 20534432
Nucleic Acids Res. 2013 Jan;41(Database issue):D590-6
pubmed: 23193283
Neurobiol Aging. 2017 Jan;49:60-68
pubmed: 27776263
PLoS One. 2014 Feb 24;9(2):e88982
pubmed: 24586470
World J Biol Psychiatry. 2018 Dec;19(8):571-585
pubmed: 29383983
Front Psychiatry. 2018 Oct 22;9:513
pubmed: 30405455
Sci Rep. 2015 Nov 17;5:16350
pubmed: 26572876
Front Neuroinform. 2015 Apr 24;9:12
pubmed: 25964757