Create a symbolic links to a copy of the Uniref50 protein fasta file, changing the following command to the actual PATH to that file on your system:
ln -s /storage/datasets/uniprot/uniref50.fasta data/uniref50.fasta
Tip: the file can be downloaded from this page (direct link to the file)
Also create a symbolic link to the directory in which the databases for eggnog-mapper have been placed:
ln -s /fast-storage/miniconda3/envs/eggnog-mapper/lib/python3.9/site-packages/data/ data/eggnog-mapper
Note: the above path would likely be different in your system. The best way to get these files is to install
eggnog-mapper using a conda environment and then use the download_eggnog_data.py command.
Place your genome assemblies in the data/gffs and data/fastas directories. The files should be named
SAMPLE.gff and SAMPLE.fasta, respectively, and the sample names should match those in the phenotype file
(i.e. data/data.tsv).
Create and activate a conda environment to run the bootstrapping script and the pipeline (named microGWAS, can be skipped if it's already present):
conda env create -f environment.yml
conda activate microGWAS
Then run the bootstrapping script to populate the input files for the pipeline and download the reference genomes used for annotation of hits and the rare variants analyses.
bash bootstrap.sh Escherichia coli IAI39 GCF_000013305.1,GCF_000007445.1,GCF_000026305.1,GCF_000026265.1,GCF_000026345.1,GCF_000005845.2,GCF_000026325.1,GCF_000013265.1
You are now ready to run the full pipeline! The following example runs all the analyses using 24 cores and mamba as the conda backend
to install each environment:
snakemake -p annotate_summary panfeed map_back heritability enrichment qq_plots find_amr_vag tree --cores 24 --use-conda --conda-frontend mamba
Marco Galardini ([email protected])