ay-amityadav/HiFiBGC_analyses

Clone the repository

git clone https://github.com/ay-amityadav/HiFiBGC_analyses

Download dataset

Download HiFiBGC_0.1.13_Run.tar.gz from figshare or zenodo, uncompress it, and put it under folder HiFiBGC_analyses.

Setup conda environments

conda env create --file envs/snakemake.yml

conda env create --file envs/jupyterlab.yml

Run

Comparison between four methods

Run

conda activate snakemake

snakemake -s comparison_between_four_methods.smk --use-conda --cores 8 -p

Run

conda activate jupyterlab

jupyter lab

Thereafter, execute comparison_between_four_methods.ipynb

Clinker analysis

Run

conda activate snakemake

snakemake -s clinker.smk --use-conda --cores 8 -p

Run

conda activate jupyterlab

jupyter lab

Thereafter, execute clinker_analysis.ipynb with jupyter kernel created from conda environment envs/raincloudplots.yml.

Comparison within HiFiBGC (Upsetplots)

Run

conda activate snakemake

snakemake -s upsetplot.smk --use-conda --cores 8 -p

Complete BGC from unmapped reads

Run

conda activate jupyterlab

jupyter lab

Thereafter, execute complete_BGCs_from_unmapped-reads.ipynb with jupyter kernel created from conda environment envs/dna_features_viewer.yml.

Sequence statistics

Download raw-read files for SRA-Ids: SRR15275213 (Human), ERR7015089 (Sludge), SRR10963010 (Sheep) and SRR15214153 (Chicken). And put them under the folder raw with names human.fastq, sludge.fastq, sheep.fastq and chicken.fastq.

Run

conda activate snakemake

snakemake -s sequence_stats.smk --use-conda --cores 8 -p

BGC database statistics

Download mibig_gbk_3.1.tar.gz, uncompress it, and put it under folder HiFiBGC_analyses.

Download BiG-SLICE database, uncompress it, and put it under folder HiFiBGC_analyses.

Run

conda activate jupyterlab

jupyter lab

Thereafter, execute BGC_databases.ipynb