Loading...
Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis
Clooney, Adam G Fouhy, Fiona Sleator, Roy D. O'Driscoll, Aisling STANTON, CATHERINE Cotter, Paul D. Claesson, Marcus J.
Clooney, Adam G
Fouhy, Fiona
Sleator, Roy D.
O'Driscoll, Aisling
STANTON, CATHERINE
Cotter, Paul D.
Claesson, Marcus J.
Citations
Altmetric:
Date
05/02/2016
Collections
Files
Loading...
e0148028.pdf
Adobe PDF, 2.39 MB
Research Projects
Organizational Units
Journal Issue
Citation
Clooney AG, Fouhy F, Sleator RD, O’Driscoll A, Stanton C, Cotter PD, et al. (2016)Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis. PLoS ONE 11(2): e0148028. doi:10.1371/journal.pone.0148028
Abstract
Rapid advancements in sequencing technologies along with falling costs present widespread
opportunities for microbiome studies across a vast and diverse array of environments. These
impressive technological developments have been accompanied by a considerable growth in
the number ofmethodological variables, including sampling, storage, DNA extraction, primer
pairs, sequencing technology, chemistry version, read length, insert size, and analysis pipelines,
amongst others. This increase in variability threatens to compromise both the reproducibility
and the comparability of studies conducted. Here we perform the first reported study
comparing both amplicon and shotgun sequencing for the three leading next-generation
sequencing technologies. These were applied to six human stool samples using Illumina
HiSeq, MiSeq and Ion PGM shotgun sequencing, as well as amplicon sequencing across two
variable 16S rRNA gene regions. Notably, we found that the factor responsible for the greatest
variance inmicrobiota composition was the chosen methodology rather than the natural
inter-individual variance, which is commonly one of the most significant drivers in microbiome
studies. Amplicon sequencing suffered from this to a large extent, and this issue was particularly
apparent when the 16S rRNA V1-V2 region amplicons were sequenced withMiSeq.
Somewhat surprisingly, the choice of taxonomic binning software for shotgun sequences
proved to be of crucial importance with even greater discriminatory power than sequencing
technology and choice of amplicon. Optimal N50 assembly values for the HiSeq was obtained
for 10million reads per sample, whereas the applied MiSeq and PGM sequencing depths
proved less sufficient for shotgun sequencing of stool samples. The latter technologies, on
the other hand, provide a better basis for functional gene categorisation, possibly due to their
longer read lengths. Hence, in addition to highlighting methodological biases, this study demonstrates
the risks associated with comparing data generated using different strategies. We
also recommend that laboratories with particular interests in certain microbes should optimise
their protocols to accurately detect these taxa using different techniques.