deletions | additions       diff --git a/Methods.tex b/Methods.tex  index 54b65bf..aafed24 100644  --- a/Methods.tex  +++ b/Methods.tex 
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Roche 454 FLX system using titanium chemistry at Selah Genomics (Columbia,  SC).   SSU rRNA gene sequences were initially screened by maximum expected errors at a specific read length threshold (17) and \citep{edgar2013}. Reads that had less  than~0.5 expected errors at a length of 250 nt were then  aligned to the Silva reference Reference Alignment as provided in the Mothur software package  using the Mothur NAST aligner \citep{}(21, 22). Anomalous reads \citep{DeSantis2005,schloss2009}. Reads that did not align to the  expected region of the SSU rRNA gene  were discarded discarded. After expected error  and alignment based quality control,  87\% of original reads remained. Remaining The remaining  reads were annotated using the “UClust” taxonomic annotation framework in QIIME (18, 19) with \citep{caparaso2010,edgar2010}. We used  97\% cluster seeds from the Silva SSU rRNA database \citep{quast2013}  as reference for taxonomic annotation  (provided at QIIME website) (20). \citep{quast2013}.  Sequences were distributed into OTUs using the with a centroid based clustering algorithm (i.e.  UPARSE methodology (17). \citep{edgar2013}). The centroid selection also included robust chimera  screening \citep{edgar2013}.  OTU centroids were established at a threshold of 97\% sequence identity. identity and non-centroid sequences were mapped back to  centroids. Reads that could not be mapped to an OTU centroid at greater  than or equal to 97\% sequence identity were discarded.  For phylogenetic reconstruction, alignment was performed with SSU-Align (23, 24). \citep{nawrocki2009,nawrocki2013}.  Columns in the alignment that were aligned with poor confidence (< ($<$  95\% of characters had posterior probability alignment scores  > $>$  95\%) were masked. not considered when building  the phylogenetic tree.  FastTree (25) \citep{price2010}  was used with default parameters to build the phylogeny. NMDS ordination was performed on weighted Unifrac (32) \citep{lozupone2005}  distances. The Phyloseq (33) \citep{mcmurdie2013}  wrapper for Vegan (34) \citep{oksanen2015}  (both R packages) was used to compute sample values along NMDS axes. The 'adonis' function in Vegan was used to perform Adonis tests (default parameters) (36). \citep{Anderson2001a}.  We used DESeq2, DESeq2 (R package),  an RNA-Seq differential expression statistical framework (29), \citep{love2014},  to identify OTUs that were enriched in high density gradient fractions from $^{13}$C-treatments relative to corresponding density fractions from control treatments (for review of RNA-Seq differential expression statistics applied to microbiome OTU count data see (30)). We define "high density gradient fractions" as gradient fractions whose density falls between 1.7125 - and  1.755 g ml$^{-1}$. Briefly, DESeq2 includes several features that enable robust estimates of standard error in addition to reliable ranking of logarithmic fold change (LFC) in abundance (i.e. gamma-Poisson regression  coefficients) even with low count groups where LFC can often be noisy CITE.  Further, statistical evaluation of LFC can be performed with selected 
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one standard deviation above the mean of all LFC values. P-values were  corrected for multiple comparisons by using the Benjamini and Hochberg (BH)  method (31). Independent filtering was performed on the basis of sparsity prior  to correcting P-values for multiple comparisons. The sparsity threshold value  that yielded the most P-values less than 0.10 was used forsparsity  independent filtering. filtering by  sparsity.  Briefly, OTUs were eliminated if they failed to appear in at least XX\% 45\%  of high density gradient fractions for a given $^{13}$C and control treatment pair, these OTUs are unlikely to have sufficient data to allow for  the determination of statistical significance.