Basecalling workflow

The basecalling script NanoMetaBasecall.py is separate from the main pipeline and can be run with: python NanoMetaBasecall.py -p path/to/guppy_basecaller -r path/to/raw/reads -o path/to/output/directory -m hac

flowchart LR
	a[/Reads/] 
	a1[Guppy basecaller]
	a2{High or Super accuracy model?}
	a3[\Basecalled reads\]
a4[To demultiplexing workflow]
a --> a1
a1 --> a2
a2 --> a3
a3 --> a4
linkStyle 0 stroke: #adff2f
linkStyle 1 stroke: #adff2f
linkStyle 2 stroke: #adff2f
linkStyle 3 stroke: #adff2f

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Required Arguments

• -p or --program: Full path to the guppy_basecaller e.g. path/to/ont_guppy_v6.0.1/bin/guppy_basecaller program or if the bin folder is in the PATH just use guppy_baseller. Default: guppy_basecaller
• -r or --raw: Full path to the fast5 folder of the raw sequenced reads. No defaults.
• -o or --output_directory: Full path to the folder to save outputs
• -m or --model: The model to use for basecalling. HAC is the high accuracy model that has been in used a few years now. It is the default model in both guppy and this pipeline. SUP is the super accuracy model which is more recent and marginally more accurate than HAC however will take considerably longer to complete.

Optional Arguments

• -k or --kit: The sequencing kit used for this experiment. Default is SQK-LSK109
• -f or --flowcell: The flowcell used for the experiment. Default is FLO-MIN106

Workflow

• The script creates a Basecalled folder to hold the eventual outputs
• The script checks the arguments chosen by the user and runs different guppy basecaller commands depending on whether the high-accuracy (hac) or super-accuracy (sup) algorithms was chosen
• The script outputs messages to the user.