Merge pull request #95 from vallenderlab/dev-master

Updated master with dev-master.

inst/paper/paper.bib

@@ -80,3 +80,11 @@ @Article{silva_2
   pages = {D643-D648},
   doi = {10.1093/nar/gkt1209},
 }
+
+@Manual{microbiome,
+  title = {microbiome: Tools for microbiome analysis in R},
+  author = {L Lahti and S Shetty and T Blake and J Salojarvi},
+  year = {2017},
+  note = {R package version 1.5.28},
+  url = {http://microbiome.github.com/microbiome},
+}

inst/paper/paper.md

@@ -29,7 +29,7 @@ bibliography: paper.bib
 
 The term “microbiome” refers to the microbial community in a given environment. In particular, it has recently risen to prominence in medicine to refer to the human oral, skin, urogenital, and digestive tract commensal bacterial communities. Modern technologies allow for the characterization of microbiome composition by high-throughput sequencing of 16S ribosomal DNA that effectively serves as identifying “bar codes”. Research studies focus on comparing differences in microbiomes between environments or changes within environments over time. To ensure study rigor and reproducibility it is important that data processing be clearly described and standardized. This package aims to unify the processing, analysis, and visualization tools necessary for modern microbiome studies under a transparent and straightforward implementation that facilitates standardization in analysis and reporting.
 
-Previous open source microbiome packages available for R [@r-core] (e.g. **vegan** and **microbiome**) have been developed, but lack the functionality afforded to more modern tools including **phyloseq** [@phyloseq] and **metacoder** [@metacoder]. Both of these packages, however, provide different degrees of functionality as it relates to data wrangling, statistical methods, and visualization.  Phyloseq, for instance, relies on **base** R functions such as _subset_ to extract or manipulate data, while metacoder uses a more modern approach like the **tidyverse**. Additionally, metacoder is built on top of the **taxa** package and uses a _“taxmap object”_, which allows for direct manipulation of hierarchical taxonomic data and associated application-specific data [@taxa].  Phyloseq, on the other hand, provides an excellent means for importing data into R as a _“phyloseq object”_, which can be used with various proven methods for analysis.  In order to bridge the gap, we have developed **MicrobiomeR**, to provide new tools and a comprehensive workflow based on concepts found in the phyloseq package and newer technologies being developed in the metacoder package.
+Previous open source microbiome packages available for R [@r-core] (e.g. **vegan** and **microbiome** [@microbiome]) have been developed, but lack the functionality afforded to more modern tools including **phyloseq** [@phyloseq] and **metacoder** [@metacoder]. Both of these packages, however, provide different degrees of functionality as it relates to data wrangling, statistical methods, and visualization.  Phyloseq, for instance, relies on **base** R functions such as _subset_ to extract or manipulate data, while metacoder uses a more modern approach like the **tidyverse**. Additionally, metacoder is built on top of the **taxa** package and uses a _“taxmap object”_, which allows for direct manipulation of hierarchical taxonomic data and associated application-specific data [@taxa].  Phyloseq, on the other hand, provides an excellent means for importing data into R as a _“phyloseq object”_, which can be used with various proven methods for analysis.  In order to bridge the gap, we have developed **MicrobiomeR**, to provide new tools and a comprehensive workflow based on concepts found in the phyloseq package and newer technologies being developed in the metacoder package.
 
 # Workflow