Bioconductor Microbiome Workflow Files

High-throughput sequencing of PCR-amplified taxonomic markers
(like the 16S rRNA gene) has enabled a new level of analysis of
complex bacterial communities known as microbiomes. Many tools exist
to quantify and compare abundance levels or OTU composition of
communities in different conditions. The sequencing reads have to be
denoised and assigned to the closest taxa from a reference
database. Common approaches use a notion of 97\% similarity and
normalize the data by subsampling to eqalize library sizes. In this
paper, we show that statistical models allow more accurate abundance
estimates. By providing a complete workflow in R, we enable the user
to do sophisticated downstream statistical analyses, whether
parametric or non-parametric. We provide examples of using the R
packages dada2, phyloseq, DESeq2 and vegan to filter, visualize and
test microbiome data and community networks.

Supplementary Files to:
Ben J. Callahan, Kris Sankaran, Julia A. Fukuyama, Paul J. McMurdie, Susan P. Holmes,
Bioconductor workflow for microbiome data analysis: from raw reads to community analyses,
F1000Research 2016, 5:1492 (doi: 10.12688/f1000research.8986.1).