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Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses
Ben J. Callahan, Kris Sankaran, Julia A. Fukuyama and 2 more
F1000Research · 2016
doi:10.12688/f1000research.8986.2
Abstract
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 equalize 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 nonparametric. We provide examples of using the R packages dada2, phyloseq, DESeq2, ggplot2 and vegan to filter, visualize and test microbiome data. We also provide examples of supervised analyses using random forests and nonparametric testing using community networks and the ggnetwork package.
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Cited by papers we hold 3
- Parasite-Microbiota Interactions With the Vertebrate Gut: Synthesis Through an Ecological Lens · 2018No notice
- Host-genotype dependent gut microbiota drives zooplankton tolerance to toxic cyanobacteria · 2017No notice
- Microbial communities in placentas from term normal pregnancy exhibit spatially variable profiles · 2017No notice
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