A pipeline to build Qiime2 taxonomy classifiers for the UNITE database.
If you are interested in Fungi 🍄🍄🟫 you could use their genomic fingerprint to identify them. Affordable PCR amplification and sequencing of the ITS gene gives you these nucleic acid fingerprints, and the UNITE team provides a database to gives these sequences a name.
We can predict the taxonomy of our fungal fingerprints using an old-school machine learning method: a supervised k-mer nb-classifier. But first, we need to prepare our database in a process called 'training.'
This is a pipeline that trains the UNITE ITS taxonomy database for use with Qiime2. You can run this pipeline yourself, but you don't have to! I've provided a ready to use pre-trained classifiers so you can simply run qiime feature-classifier classify-sklearn.
If you have questions about using Qiime2, ask on the Qiime2 forums.
If you have questions about the UNITE ITS database, contact the UNITE team.
If you have questions about this pipeline, please open a new issue!
Set up:
- Install qiime2-amplicon with conda
- Install Nextflow with conda
Configure & Run:
# edit config as needed (update path to qiime2 conda env)
open nextflow.config
# Run nextflow: All save report to ./results/
export NXF_OFFLINE=TRUE
nextflow run main.nf -resume
