CHERRY-crispr version

CHERRY is a python library for predicting the interactions between viral and prokaryotic genomes. CHERRY is based on a deep learning model, which consists of a graph convolutional encoder and a link prediction decoder.

In this program, we provide an extension version of CHERRY, which uses the CRISPR information in CHERRY's database for multi-host (host range) prediction.

Input (provided by the user):
    1. phage contigs from their samples (FASTA files)

Output:
    The host range of the given phages (CSV files)

Required Dependencies

• Python 3.x
• Pandas
• Numpy
• Biopython
• NCBI BLAST+

An easiler way to install

We suggest you to install all the package using conda (both miniconda and Anaconda are ok) following the command lines as below:

conda create --name cherry_crispr_multihost python=3.8
conda activate cherry_crispr_multihost

conda install pandas numpy biopython
conda install blast -c bioconda

Usage

Once install the required environment, you need to activate it when you want to use:

conda activate cherry_crispr_multihost

Then, the command of multi-host extension can be called by:

python PATH_TO_cherry_crispr_multihost/Cherry_multihost.py --infile PATH_TO_FASTA --outfolder PATH_TO_OUTPUT_FOLDER --datasetpth [where you place the dataset folder provided in this GitHub] --threads NUM_OF_THREAD --ident IDENTITY_OF_ALIGNMENT --coverage COVERAGE_OF_ALIGNMENT

# example
python CHERRY_crispr_multihost/Cherry_multihost.py --infile nucl.fasta --outfolder test_out/ --datasetpth CHERRY_crispr_multihost/dataset --ident 75 --coverage 0.75

There are two thresholds for users:

1. --ident: the identity of the CRISPRs alignments (default: 75)
2. --coverage: the coverage of the CRISPRs alignments (default: 0.75)

Outputs

There are two output files in --outfolder PATH_TO_OUTPUT_FOLDER.

1. alignment_result.tab: BLASTN results between CRISPR and phage
2. prediction.csv: CSV files of the prediction (alignment > --ident IDENTITY_OF_ALIGNMENT && > --coverage COVERAGE_OF_ALIGNMENT)

Citation

If you use this program, please cite the following papers:

• CHERRY:

Jiayu Shang, Yanni Sun, CHERRY: a Computational metHod for accuratE pRediction of virus–pRokarYotic interactions using a graph encoder–decoder model, Briefings in Bioinformatics, 2022;, bbac182, https://doi.org/10.1093/bib/bbac182

The original version of CHERRY can be found via: CHERRY