Biosynthetic Gene Cluster finder with Graph Neural Network
Project description
# BGCfinder : Biosynthetic Gene Cluster detection with Graph Neural Network
BGCfinder detects biosynthetic gene clusters in bacterial genomes using deep learning. BGCfinder takes a fasta file containing bacterial protein coding sequences and embed each protein sequence into a graph. Graph Neural Network takes the graphs to detect biosynthetic gene cluster.
Author : Jihun Jeung, jihun@gm.gist.ac.kr, jeung4705@gmail.com, https://github.com/jihunni/BGCfinder
Installation requirement: - PyTorch - PyTorch Geometric - Prodigal (https://github.com/hyattpd/Prodigal)
To construct the conda environment,
`` $ conda create –name BGCfinder python=3.9 $ conda init bash $ conda activate BGCfinder $ conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch $ conda install pyg -c pyg $ pip install BGCfinder ``
To download the BGCfinder model and test files, `` $ bgc-download ``
To find the protein-coding gene in bacterial genome, `` $ prodigal -f gff -i bacterial_genome_seq.fasta -a bacterial_protein_seq.fasta -o bacterial_genome_seq.gff ``
To run BGCfinder with a fasta file containing amino acid sequence with CPU, `` $ bgcfinder bacterial_protein_seq.fasta -o output_filename_prefix -l log_record.log -d False ``
To run BGCfinder with a fasta file containing amino acid sequence with GPU, `` $ bgcfinder bacterial_protein_seq.fasta -o output_filename_prefix -l log_record.log -d True ``
The development environment of BGCfinder : `` ‘torch==1.10.0’, ‘torch-geometric==2.0.2’, ‘torch-scatter==2.0.9’, ‘torch-sparse==0.6.12’ ``
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