- aragorn 1.2.41
- tRNAscan-SE 2.0.11
- barrnap 0.9
- biopython1.80
- glob2 0.7
- mmseqs 14.7e284
- sibeliaz 1.2.5
We have been successful installing the dependencies in a conda environment and running the code in that environment.
conda create -n gmi-kintun -c bioconda -c conda-forge "python>=3.10" biopython glob2 mmseqs2 sibeliaz pandas scikit-learn tqdm networkx
conda activate gmi-kintun
git clone https://github.com/GMI-Lab/Kintun-GIP.git
cd Kintun-GIP/
pip install .
This tool will annotate and cluster all the t(m)DNAs that are coded in a collection of strains. It is required that all chromosomes of the strains are fully assembled, i.e. each chromosome is in only one contig. It is mandatory to choose a prefix for a new nomenclature scheme to be resolved. This avoids confusing different schemes.
kintunGIP clust [-h] -i FASTA_DIR -x FILE_EXTENSION -o RESULTS_DIR -p PREFIX [-t THREADS] [-log LOG_FILE] [-v]
options:
-h, --help show this help message and exit
-i FASTA_DIR Directory with fasta files (REQUIRED)
-x FILE_EXTENSION Extension for fasta files (REQUIRED;default: fasta)
-o RESULTS_DIR Name for results directory (REQUIRED)
-p PREFIX Prefix for the t(m)DNA clustering scheme (REQUIRED)
-t THREADS Number of threads for nucmer and SibeliaZ
-log LOG_FILE Name of log file (Optional)
-v show program's version number and exit
This tool will use previous t(m)RNAs annotation to predict mobile genetic elements in chromosomes.