A workflow for contextualization of antibiotic resistance in microbiomes.
We manage our dependencies trough conda.
Create the environment for telcomb and install snakemake.
conda create -c conda-forge -c bioconda -n telcomb snakemake gitClone the repository.
conda activate telcomb
git clone https://github.com/jonathan-bravo/TELCoMBThe telcomb workflows assumes that the fastq files will be stored in a directory called samples in the working directory. Here we show the usage and the directories structure that can be used with the default config.json file.
If all the databases are already available it is possible to avoid re-downloading them by specifying the directory in the config file. The names of the database have to be as in the following table. There is no need to copy them, a soft link is sufficient (ln -s).
| Database | File name in DATABASES_DIR |
|---|---|
| MGEs combined database | mges_combined.fasta |
| MEGARes Database | megares_full_database.fasta |
| MEGARes Ontology | megares_full_annotations.csv |
cd TELCoMB
mamba activate telcomb
# Create the directories structure
mkdir -p work_dir/samples work_dir/logs
# Move the fastq files in the samples directory
mv <your_data>.fastq work_dir/samples
# Run the workflow
snakemake -c <number of threads available> --use-conda --conda-frontend condaEdit the cluster.json file in order to fit your resources.
cd TELCoMB
# Create the directories structure
mkdir -p work_dir/samples work_dir/logs
# Move the fastq files in the samples directory
mv <your_data>.fastq work_dir/samples
# Run the workflow
mkdir -p logs
sbatch run.sh