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The storage module of Nanopype covers the import, indexing and extraction of raw nanopore reads. In general the raw data directory is expected to have one folder per flow cell with a subfolder reads containing the packed or bulk .fast5 files.

The folder structure for packeaged single read fast5 files is:

   |--20180101_FAH12345_FLO-MIN106_SQK-LSK108_WA01/   # One flow cell
         |--0.tar                                     # Packed .fast5
      |--reads.fofn                                   # Index file

For bulk-fast5 output from recent MinKNOW versions, the batches can be directly copied to the reads folder.

   |--20180101_FAH12345_FLO-MIN106_SQK-LSK108_WA01/   # One flow cell
         |--batch_0.fast5                             # Bulk-fast5
      |--reads.fofn                                   # Index file

Nanopype expects all batches to be found in the reads folder of a run. Restarting an experiment in MinKNOW results in a new raw output folder with batch numbers starting from zero. In current versions of MinKNOW a unique run-ID is part of the batch name, therefore bulk-fast5 files from multiple restarts can be copied into the same directory. After updating MinKNOW the output naming should be verified to avoid overwriting batches with equal names.


This section is for backwards compatibility with MinKNOW versions writing each read into a single .fast5 file. Recent versions create bulk-fast5 output which can be directly used with Nanopype

To pack reads e.g. from the MinKNOW output folder we provide an import script in the scripts folder of the repository. The basic usage is:

python3 scripts/ /data/raw/runname/ /path/to/import

You can specify one or more import directories, also by using wildcards in the path. This is useful after restarting an experiment and importing every folder containing a specific flow cell ID. Consider changing the batch size in case of amplicon or RNA sequencing with significantly more but in general shorter reads. The order of reads in the archives is not guaranteed to be the same as in the output folders of MinKNOW. Running the script with the same arguments twice will validate the import process and report any inconsistency between import and raw data directories.


An index file reads.fofn with one line per read containing the ID and the archive the read is stored in is helpful if later only a subset of the whole dataset needs to be processed. The indexing is triggered from the processing directory by executing e.g.:

snakemake --snakefile /path/to/nanopype/Snakefile /data/raw/20180101_FAH12345_FLO-MIN106_SQK-LSK108_WA01/reads.fofn

Together with the import, this is the only rule requiring write access to the raw data. We highly recommend, running it once after the experiment and making the run folder write protected afterwards with e.g.:

chmod 444 /data/raw/$run/reads/*
chmod 444 /data/raw/$run/reads.fofn
chmod 555 /data/raw/$run/reads
chmod 555 /data/raw/$run

Internally we use an isolated unix-user and group mduser and mdgrp owning the raw data. Setting permissions to e.g. 744 for the batch files and 755 for folders allows any analyst to securely read the raw data without accidentally compromising it.


It is possible to extract a subset of fast5 files from the packed and indexed run. Extraction requires a previously indexed run, a list of read IDs and works by requesting a directory from Nanopype:

snakemake --snakefile /path/to/nanopype/Snakefile subset/roi/20180101_FAH12345_FLO-MIN106_SQK-LSK108_WA01

With this command the extraction rule expects an input file subset/roi.txt with one read ID per line. Multiple regions of interest are supported by multiple ID files.


To extract reads covering a region of interest from multiple runs, provide a runnames.txt in the processing directory with one line per sequencing run:


And execute:

snakemake --snakefile /path/to/nanopype/Snakefile subset/roi.done

For this rule a flag file indicating completion is required since the exact output is unknown and the output directory might already be existent.