diff --git a/README.md b/README.md
index 03b621d..7b31cb7 100644
--- a/README.md
+++ b/README.md
@@ -60,35 +60,35 @@ Results can be filtered by bootstrap support values to reduce false-positive ass
 snakemake --jobs 24 --snakefile snakefile_ESV --configfile config_ESV.yaml
 ```
 
-2. This workflow starts with the taxonomically assigned ESVs from the default dataflow and generates operational taxonomic units (OTUs) based on 97% sequence similarity.
+2. The OTU workflow starts with the taxonomically assigned ESVs from the default dataflow and generates operational taxonomic units (OTUs) based on 97% sequence similarity.
 
 ```linux
 # quickstart OTU pipeline
 snakemake --jobs 24 --snakefile snakefile_OTU --configfile config_OTU.yaml
 ```
 
-3. This workflow starts with the taxonomically assigned ESVs from the default dataflow and generates a GLOBAL set of ESV IDs consistent accross all samples *sequenced at different times* to which all ESVs will be mapped. This script may be useful when it is ideal to bioinformatically process samples one season at a time (or one trial at a time, or one year at a time) but still have a consistent set of equivalent ESV IDs project-wide to facilitate multi-season (or multi-trial, or multi-year) comparisons in downstream analyses.
+3. The global ESV workflow starts with the taxonomically assigned ESVs from the default dataflow and generates a GLOBAL set of ESV IDs consistent accross all samples *sequenced at different times* to which all ESVs will be mapped. This script may be useful when it is ideal to bioinformatically process samples one season at a time (or one trial at a time, or one year at a time) but still have a consistent set of equivalent ESV IDs project-wide to facilitate multi-season (or multi-trial, or multi-year) comparisons in downstream analyses.
 
 ```linux
 # quickstart global ESV pipeline
 snakemake --jobs 24 --snakefile snakefile_ESV_global --configfile config_ESV_global.yaml
 ```
 
-4. This workflow starts with the taxonomically assigned ESVs from the default dataflow and generates a GLOBAL set of OTU IDs consistent across all samples *sequenced at different times* to which all denoised ESVs willb e mapped.  This script may be useful when it is ideal to bioinformatically process samples one season at a time (or one trial at a time, or one year at a time) but still have a consistent set of equivalent OTU IDs project-wide to facilitate multi-season (or multi-trial, or multi-year) comparisons in downstream analyses.
+4. The global OTU workflow starts with the taxonomically assigned ESVs from the default dataflow and generates a GLOBAL set of OTU IDs consistent across all samples *sequenced at different times* to which all denoised ESVs willb e mapped.  This script may be useful when it is ideal to bioinformatically process samples one season at a time (or one trial at a time, or one year at a time) but still have a consistent set of equivalent OTU IDs project-wide to facilitate multi-season (or multi-trial, or multi-year) comparisons in downstream analyses.
 
 ```linux
 # quickstart global OTU pipeline
 snakemake --jbos 24 --snakefile snakefile_OTU_global --configfile config_OTU_global.yaml
 ```
 
-5. This workflow starts with Illumina paired-end reads that do NOT to overlap.  If you would like to process the R1 and R2 files separately this pipeline can be used with caution.  Note that if your reads overlap, they should be processed using the default pipeline because longer sequences tend to make better taxonomic assignments with higher accuracy.
+5. The single read workflow starts with Illumina paired-end reads that do NOT to overlap.  If you would like to process the R1 and R2 files separately this pipeline can be used with caution.  Note that if your reads overlap, they should be processed using the default pipeline because longer sequences tend to make better taxonomic assignments with higher accuracy.
 
 ```linux
 # quickstart single read pipeline
 snakemake --jobs 24 --snakefile snakefile_ESV_singleRead --configfile config_ESV_singleRead.yaml
 ```
 
-6. This workflow can handle dual-indexed amplicons sequenced from individuals (as opposed to bulk samples) that were pooled prior to Illumina indexing.  An extra mapping file is needed to sort out individuals and amplicon(s) and should contain the following fields: SampleID, Amplicon, Forward, Reverse.  This should be saved as a comma-separated values (csv) file.  An example of a DualIndexedSamples.csv is available in the 'testing' directory.  The SampleID column should contain the individual number associated with a particular dual-index combination.  The Forward column should contain the 5'-TagForwardPrimer sequence.  The Reverse column should contain the 5'-TagReversePrimer sequence. In our experiments, tags ranged from 5 - 6 bp.  The total TagPrimer length ranged from 25-27bp.  For maximum specificity, the cutadapt -O parameter in the configuration file should be set to the minimum TagPrimer length range, ex. 25bp in our example.
+6. The dual indexed individual sample workflow can handle dual-indexed amplicons sequenced from individuals (as opposed to bulk samples) that were pooled prior to Illumina indexing.  An extra mapping file is needed to sort out individuals and amplicon(s) and should contain the following fields: SampleID, Amplicon, Forward, Reverse.  This should be saved as a comma-separated values (csv) file.  An example of a DualIndexedSamples.csv is available in the 'testing' directory.  The SampleID column should contain the individual number associated with a particular dual-index combination.  The Forward column should contain the 5'-TagForwardPrimer sequence.  The Reverse column should contain the 5'-TagReversePrimer sequence. In our experiments, tags ranged from 5 - 6 bp.  The total TagPrimer length ranged from 25-27bp.  For maximum specificity, the cutadapt -O parameter in the configuration file should be set to the minimum TagPrimer length range, ex. 25bp in our example.
 
 ```linux
 # quickstart dual indexed sample pipeline