There are some issues with this training that I collect here to fix step-by-step.
In the preprocessing part, all fastq outputs go into multiQC, but since multiQC only shows one output for identically named files, this is rather useless. Either fix multiQC, rename the files or run in twice.
Assign filted reads, after mapping (non chicken reads), to taxa using Kraken2 ([Wood and Salzberg 2014](https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/tutorial.html#Wood2014)) and Kalamari, a database of completed assemblies for metagenomics-related tasks used widely in contamination and host filtering -> this is not correct
There are some issues with this training that I collect here to fix step-by-step.
Assign filted reads, after mapping (non chicken reads), to taxa using Kraken2 ([Wood and Salzberg 2014](https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/tutorial.html#Wood2014)) and Kalamari, a database of completed assemblies for metagenomics-related tasks used widely in contamination and host filtering
-> this is not correct