# 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 protein sequences and convert each of them into a graph. Graph neural network takes the input graphs to detect biosynthetic gene cluster..
- Developer : Jihun Jeung (jihun@gm.gist.ac.kr, jeung4705@gmail.com)
- Github repository : https://github.com/jihunni/BGCfinder
- PyPI project website : https://pypi.org/project/BGCfinder/
Installation requirement:
- PyTorch
- PyTorch Geometric
- Prodigal (https://github.com/hyattpd/Prodigal)
To construct the conda environment,
```bash
$ 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,
```bash
$ bgc-download
```
To find the protein-coding gene in bacterial genome (Installation of `Prodigal` is required for this step),
```bash
$ 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 (recommended),
```bash
$ 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,
```bash
$ 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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"description": "# BGCfinder : Biosynthetic Gene Cluster detection with Graph Neural Network\n\nBGCfinder detects biosynthetic gene clusters in bacterial genomes using deep learning. BGCfinder takes a fasta file containing protein sequences and convert each of them into a graph. Graph neural network takes the input graphs to detect biosynthetic gene cluster..\n\n- Developer : Jihun Jeung (jihun@gm.gist.ac.kr, jeung4705@gmail.com)\n- Github repository : https://github.com/jihunni/BGCfinder\n- PyPI project website : https://pypi.org/project/BGCfinder/\n\nInstallation requirement:\n- PyTorch\n- PyTorch Geometric\n- Prodigal (https://github.com/hyattpd/Prodigal)\n\n\nTo construct the conda environment, \n\n```bash\n$ conda create --name BGCfinder python=3.9\n$ conda init bash \n$ conda activate BGCfinder \n$ conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch\n$ conda install pyg -c pyg \n$ pip install BGCfinder \n```\n\n\nTo download the BGCfinder model and test files, \n```bash\n$ bgc-download\n```\n\nTo find the protein-coding gene in bacterial genome (Installation of `Prodigal` is required for this step),\n```bash\n$ prodigal -f gff -i bacterial_genome_seq.fasta -a bacterial_protein_seq.fasta -o bacterial_genome_seq.gff \n```\n\nTo run BGCfinder with a fasta file containing amino acid sequence with CPU (recommended), \n```bash\n$ bgcfinder bacterial_protein_seq.fasta -o output_filename_prefix -l log_record.log -d False\n```\n\nTo run BGCfinder with a fasta file containing amino acid sequence with GPU, \n```bash\n$ bgcfinder bacterial_protein_seq.fasta -o output_filename_prefix -l log_record.log -d True\n```\n\nThe development environment of BGCfinder : \n```\n'torch==1.10.0', \n'torch-geometric==2.0.2', \n'torch-scatter==2.0.9', \n'torch-sparse==0.6.12' \n```\n",
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