deletions | additions       diff --git a/Materials_and_Methods.tex b/Materials_and_Methods.tex  index dd856e0..e410bd3 100644  --- a/Materials_and_Methods.tex  +++ b/Materials_and_Methods.tex 
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reactions, which were performed and subsequently enriched. Sequencing  followed established manufacture protocols (454 Life Sciences).   \subsection{Sequence Quality Control and Analysis}  \subsubsection{Quality Control} \subsubsection{Sequence quality control}  The 16S/23S sequence collections were demultiplexed and sequences with sample barcodes not matching expected barcodes were discarded. We used the maximum expected error metric \citep{23955772} calculated from sequence quality scores to cull poor quality sequences from the dataset. Specifically, we discarded any sequence with a maximum expected error count greater than 1 after truncating to 175 nt. The forward primer and barcode was trimmed from the remaining reads. We  checked that all primer trimmed, error screened and truncated sequences were  derived from the same region of the LSU or SSU rRNA gene (23S and 16S 
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rank, minimum sequence identity for hits at 90\% and the maximum accepted hits  value was set to 3).  \subsubsection{Clustering} \subsubsection{Sequence clustering}  Reads were clustered into OTUs following the UParse pipeline. Specifically USearch (version 7.0.1001) was used to establish cluster centroids at a 97\% sequence identity level from the quality controlled data and map quality controlled reads to the centroids. The initial centroid establishment algorithm incorporates a quality control step wherein potentially chimeric reads are not allowed to become cluster seeds. Additionally, we discarded singleton reads. Eighty-eight and 98\% of quality controlled reads could be mapped back to our cluster seeds at a 97\% identity cutoff for the 16S and 23S sequences, respectively. \subsubsection{Alpha and Beta diversity analyses} Alpha diversity calculations  were made using PyCogent Python bioinformatics modules \citep{17708774}.  
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(within sites) as weights. The principal coordinate OTU weighted averages were  then expanded to match the site-wise variances \citep{vegan}.  \subsubsection{Identifying Enriched enriched  OTUs} We used an RNA-Seq differential expression statistical framework to find OTUs enriched in the given sample  classes (R package DESeq2 developed by \citet{Love_2014}) (for review of  RNA-Seq differential expression statistics applied to microbiome OTU count data