Sequencing data from MinION, GridION or PromethION needs to be imported into a Nanopype compatible raw data folder structure. Recent MinKNOW versions write the reads into 4k batches per file. Nanopype expects a flat folder structure of sequencing runs with a reads subfolder containing the batches.
The provided test data contains three runs with one .fast5 batch each:
ls -l data/*/ ls -l data/*/reads
Note that each run contains a reads subfolder and one run is already indexed and contains a file reads.fofn. This read index is for instance needed by the demultiplexing module, where reads per barcode are extracted from the raw data archive and processed together.
For the remaining two runs create the read index as follows. Check the todo-list with a dry-run first:
snakemake --snakefile ~/src/nanopype/Snakefile data/20200227_FAL08241_FLO-MIN106_SQK-RNA002_sample/reads.fofn -n
With the -n, --dry-run flag, snakemake plots a list of jobs to execute without doing anything. You will see two types of jobs, storage_index_batch and storage_index_run. The first one(s) can be executed in parallel, the last one merges the results. Launch the snakemake command without -n to index the raw data:
snakemake --snakefile ~/src/nanopype/Snakefile data/20200227_FAL08241_FLO-MIN106_SQK-RNA002_sample/reads.fofn snakemake --snakefile ~/src/nanopype/Snakefile data/20200624_FAN48644_FLO-MIN106_SQK-DCS109_sample/reads.fofn
The created indices map each read ID to a batch file:
Mission accomplished! You executed your very first Nanopype workflow!