Example workflows

Starting with fastqs, get to just before OTU calling

For some forward reads for.fastq (with primer AAAA) and reverse reads rev.fastq (with primer TTTT), you'd want to merge reads, remove primers, demultiplex, and dereplicate.

Start by setting up your work environment (especially user.cfg, your .bashrc, and usearch). Run some pre-flight checks on your fastqs. Figure out how to split your fastqs: the N for splitting should be big enough so that each fastq is about 0.25 Gb. Then you can, ideally, get all this done in one shot with

/path/to/SmileTrain/otu_caller.py -f for.fastq -r rev.fastq -p AAAA -q TTTT --split 10 --merge --primers --qfilter --demultiplex --dereplicate

where I picked 10 for the number of fastqs to split into and I kept the default values for the merging, primer removal, and quality filtering.