| Name | phold JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | Phage Annotations using Protein Structures |
| upload_time | 2024-03-26 00:31:43 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.12,>=3.8 |
| license | None |
| keywords |
keyword
are
cool
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
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|
[](https://colab.research.google.com/github/gbouras13/phold/blob/main/run_pharokka_and_phold.ipynb)
[](https://anaconda.org/bioconda/phold)
[](https://img.shields.io/conda/dn/bioconda/phold)
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[](https://pepy.tech/project/phold)
# phold - Phage Annotation using Protein Structures
`phold` is a sensitive annotation tool for bacteriophage genomes and metagenomes using protein structural homology.
`phold` uses the [ProstT5](https://github.com/mheinzinger/ProstT5) protein language model to translate protein amino acid sequences to the 3Di token alphabet used by [Foldseek](https://github.com/steineggerlab/foldseek). Foldseek is then used to search these against a database of 803k protein structures mostly predicted using [Colabfold](https://github.com/sokrypton/ColabFold).
Alternatively, you can specify protein structures that you have pre-computed for your phage(s) instead of using ProstT5.
Benchmarking is ongoing but `phold` strongly outperforms [Pharokka](https://github.com/gbouras13/pharokka), particularly for less characterised phages such as those from metagenomic datasets.
If you have already annotated your phage(s) with Pharokka, `phold` takes the Genbank output of Pharokka as an input option, so you can easily update the annotation with more functional predictions!
# Tutorial
Check out the `phold` tutorial at [https://phold.readthedocs.io/en/latest/tutorial/](https://phold.readthedocs.io/en/latest/tutorial/).
# Google Colab Notebooks
If you don't want to install `phold` locally, you can run it without any code using one of the following Google Colab notebooks:
* To run `pharokka` + `phold` (recommended) [https://colab.research.google.com/github/gbouras13/phold/blob/main/run_pharokka_and_phold.ipynb](https://colab.research.google.com/github/gbouras13/phold/blob/main/run_pharokka_and_phold.ipynb)
* To run only `phold` [https://colab.research.google.com/github/gbouras13/phold/blob/main/run_phold.ipynb](https://colab.research.google.com/github/gbouras13/phold/blob/main/run_phold.ipynb)
# Table of Contents
- [phold - Phage Annotation using Protein Structures](#phold---phage-annotation-using-protein-structures)
- [Tutorial](#tutorial)
- [Google Colab Notebooks](#google-colab-notebooks)
- [Table of Contents](#table-of-contents)
- [Documentation](#documentation)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Output](#output)
- [Usage](#usage)
- [Plotting](#plotting)
- [Citation](#citation)
# Documentation
Check out the full documentation at [https://phold.readthedocs.io](https://phold.readthedocs.io).
# Installation
For more details (particularly if you are using a non-NVIDIA GPU), check out the [installation documentation](https://phold.readthedocs.io/en/latest/install/).
The best way to install `phold` is using [mamba](https://github.com/conda-forge/miniforge), as this will install [Foldseek](https://github.com/steineggerlab/foldseek) (the only non-Python dependency) along with the Python dependencies.
To install `phold` using [mamba](https://github.com/conda-forge/miniforge):
```bash
mamba create -n pholdENV -c conda-forge -c bioconda phold
```
To utilise `phold` with GPU, a GPU compatible version of `pytorch` must be installed. By default conda/mamba will install a CPU-only version.
If you have an NVIDIA GPU, please try:
```bash
mamba create -n pholdENV -c conda-forge -c bioconda phold pytorch=*=cuda*
```
If you have a Mac running an Apple Silicon chip (M1/M2/M3), `phold` should be able to use the GPU. Please try:
```bash
mamba create -n pholdENV python==3.11
conda activate pholdENV
mamba install pytorch::pytorch torchvision torchaudio -c pytorch
mamba install -c conda-forge -c bioconda phold
```
If you are having trouble with `pytorch` see [this link](https://pytorch.org) for more instructions. If you have an older version of CUDA installed, then you might find [this link useful](https://pytorch.org/get-started/previous-versions/).
Once `phold` is installed, to download and install the database run:
```bash
phold install
```
* Note: You will need at least 8GB of free space (the `phold` databases including ProstT5 are 7.7GB uncompressed).
# Quick Start
* `phold` takes a GenBank format file output from [pharokka](https://github.com/gbouras13/pharokka) as its input by default.
* If you are running `phold` on a local work station with GPU available, using `phold run` is recommended. It runs both `phold predict` and `phold compare`
``` bash
phold run -i tests/test_data/NC_043029.gbk -o test_output_phold -t 8
```
* If you do not have a GPU available, add `--cpu`.
* `phold run` will run in a reasonable time for small datasets with CPU only (e.g. <5 minutes for a 50kbp phage).
* However, `phold predict` will complete much faster if a GPU is available, and is necessary for large metagenomic datasets to run in a reasonable time.
* In a cluster environment, it is most efficient to run `phold` in 2 steps for optimal resource usage.
1. Predict the 3Di sequences with ProstT5 using `phold predict`. This is massively accelerated if a GPU available.
```bash
phold predict -i tests/test_data/NC_043029.gbk -o test_predictions
```
2. Compare the the 3Di sequences to the `phold` structure database with Foldseek using `phold compare`. This does not utilise a GPU.
```bash
phold compare -i tests/test_data/NC_043029.gbk --predictions_dir test_predictions -o test_output_phold -t 8
```
# Output
* The primary outputs are:
* `phold_3di.fasta` containing the 3Di sequences for each CDS
* `phold_per_cds_predictions.tsv` containing detailed annotation information on every CDS
* `phold_all_cds_functions.tsv` containing counts per contig of CDS in each PHROGs category, VFDB, CARD, ACRDB and Defensefinder databases (similar to the `pharokka_cds_functions.tsv` from Pharokka)
* `phold.gbk`, which contains a GenBank format file including these annotations, and keeps any other genomic features (tRNA, CRISPR repeats, tmRNAs) included from the `pharokka` Genbank input file if provided
# Usage
```bash
Usage: phold [OPTIONS] COMMAND [ARGS]...
Options:
-h, --help Show this message and exit.
-V, --version Show the version and exit.
Commands:
citation Print the citation(s) for this tool
compare Runs Foldseek vs phold db
createdb Creates foldseek DB from AA FASTA and 3Di FASTA input...
predict Uses ProstT5 to predict 3Di tokens - GPU recommended
proteins-compare Runs Foldseek vs phold db on proteins input
proteins-predict Runs ProstT5 on a multiFASTA input - GPU recommended
remote Uses foldseek API to run ProstT5 then foldseek locally
run phold predict then comapare all in one - GPU recommended
```
```bash
Usage: phold run [OPTIONS]
phold predict then comapare all in one - GPU recommended
Options:
-h, --help Show this message and exit.
-V, --version Show the version and exit.
-i, --input PATH Path to input file in Genbank format or nucleotide
FASTA format [required]
-o, --output PATH Output directory [default: output_phold]
-t, --threads INTEGER Number of threads [default: 1]
-p, --prefix TEXT Prefix for output files [default: phold]
-d, --database TEXT Specific path to installed phold database
-f, --force Force overwrites the output directory
--batch_size INTEGER batch size for ProstT5. 1 is usually fastest.
[default: 1]
--cpu Use cpus only.
--omit_probs Do not output 3Di probabilities from ProstT5
--finetune Use finetuned ProstT5 model
--finetune_path TEXT Path to finetuned model weights
-e, --evalue FLOAT Evalue threshold for Foldseek [default: 1e-3]
-s, --sensitivity FLOAT sensitivity parameter for Foldseek [default: 9.5]
--keep_tmp_files Keep temporary intermediate files, particularly
the large foldseek_results.tsv of all Foldseek
hits
--split Split the Foldseek searches by ProstT5 probability
--split_threshold FLOAT ProstT5 probability to split by [default: 60]
--card_vfdb_evalue FLOAT Stricter Evalue threshold for Foldseek CARD and
VFDB hits [default: 1e-10]
--separate Output separate genbank files for every contig
--max_seqs INTEGER Maximum results per query sequence allowed to pass
the prefilter. You may want to reduce this to save
disk space for enormous datasets [default: 1000]
```
# Plotting
`phold plot` will allow you to create Circos plots with [pyCirclize](https://github.com/moshi4/pyCirclize) for all your phage(s). For example:
```bash
phold plot -i tests/test_data/NC_043029_phold_output.gbk -o NC_043029_phold_plots -t '${Stenotrophomonas}$ Phage SMA6'
```
<p align="center">
<img src="img/NC_043029.png" alt="NC_043029" height=600>
</p>
# Citation
`phold` is a work in progress, a preprint will be coming hopefully soon - if you use it please cite the GitHub repository https://github.com/gbouras13/phold for now.
Please be sure to cite the following core dependencies and PHROGs database:
* Foldseek - (https://github.com/steineggerlab/foldseek) [van Kempen M, Kim S, Tumescheit C, Mirdita M, Lee J, Gilchrist C, Söding J, and Steinegger M. Fast and accurate protein structure search with Foldseek. Nature Biotechnology, doi:10.1038/s41587-023-01773-0 (2023)](https://www.nature.com/articles/s41587-023-01773-0)
* ProstT5 - (https://github.com/mheinzinger/ProstT5) [Michael Heinzinger, Konstantin Weissenow, Joaquin Gomez Sanchez, Adrian Henkel, Martin Steinegger, Burkhard Rost. ProstT5: Bilingual Language Model for Protein Sequence and Structure. bioRxiv doi:10.1101/2023.07.23.550085 (2023)](https://www.biorxiv.org/content/10.1101/2023.07.23.550085v1)
* Colabfold - (https://github.com/sokrypton/ColabFold) [Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S and Steinegger M. ColabFold: Making protein folding accessible to all. Nature Methods (2022) doi: 10.1038/s41592-022-01488-1 ](https://www.nature.com/articles/s41592-022-01488-1)
* PHROGs - (https://phrogs.lmge.uca.fr) [Terzian P., Olo Ndela E., Galiez C., Lossouarn J., Pérez Bucio R.E., Mom R., Toussaint A., Petit M.A., Enault F., "PHROG : families of prokaryotic virus proteins clustered using remote homology", NAR Genomics and Bioinformatics, (2021) https://doi.org/10.1093/nargab/lqab067](https://doi.org/10.1093/nargab/lqab067)
Please also consider citing these supplementary databases where relevant:
* [CARD](https://card.mcmaster.ca) - [Alcock B.P. et al, CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database Nucleic Acids Research (2022) https://doi.org/10.1093/nar/gkac920](https://doi.org/10.1093/nar/gkac920)
* [VFDB](http://www.mgc.ac.cn/VFs/main.htm) - [Chen L., Yang J., Yao Z., Sun L., Shen Y., Jin Q., "VFDB: a reference database for bacterial virulence factors", Nucleic Acids Research (2005) https://doi.org/10.1093/nar/gki008](https://doi.org/10.1093/nar/gki008)
* [Defensefinder](https://defensefinder.mdmlab.fr) - [ F. Tesson, R. Planel, A. Egorov, H. Georjon, H. Vaysset, B. Brancotte, B. Néron, E. Mordret, A Bernheim, G. Atkinson, J. Cury. A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and Databases. bioRxiv (2024) https://doi.org/10.1101/2024.01.25.577194](https://doi.org/10.1101/2024.01.25.577194)
* [acrDB](https://bcb.unl.edu/AcrDB/) - please cite the original acrDB database paper [Le Huang, Bowen Yang, Haidong Yi, Amina Asif, Jiawei Wang, Trevor Lithgow, Han Zhang, Fayyaz ul Amir Afsar Minhas, Yanbin Yin, AcrDB: a database of anti-CRISPR operons in prokaryotes and viruses. Nucleic Acids Research (2021) https://doi.org/10.1093/nar/gkaa857](https://doi.org/10.1093/nar/gkaa857) AND the paper that generated the structures for these protein used by `phold` [Harutyun Sahakyan, Kira S. Makarova, and Eugene V. Koonin. Search for Origins of Anti-CRISPR Proteins by Structure Comparison. The CRISPR Journal (2023)](https://doi.org/10.1089/crispr.2023.0011)
Raw data
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"download_url": "https://files.pythonhosted.org/packages/75/34/8a51b08d4140709c20068e3e32b14eee1985197b6ea3be56f9b676c69ff6/phold-0.1.4.tar.gz",
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"description": "[](https://colab.research.google.com/github/gbouras13/phold/blob/main/run_pharokka_and_phold.ipynb)\n\n[](https://anaconda.org/bioconda/phold)\n[](https://img.shields.io/conda/dn/bioconda/phold)\n[](https://badge.fury.io/py/phold)\n[](https://pepy.tech/project/phold)\n\n# phold - Phage Annotation using Protein Structures\n\n`phold` is a sensitive annotation tool for bacteriophage genomes and metagenomes using protein structural homology. \n\n`phold` uses the [ProstT5](https://github.com/mheinzinger/ProstT5) protein language model to translate protein amino acid sequences to the 3Di token alphabet used by [Foldseek](https://github.com/steineggerlab/foldseek). Foldseek is then used to search these against a database of 803k protein structures mostly predicted using [Colabfold](https://github.com/sokrypton/ColabFold). \n\nAlternatively, you can specify protein structures that you have pre-computed for your phage(s) instead of using ProstT5.\n\nBenchmarking is ongoing but `phold` strongly outperforms [Pharokka](https://github.com/gbouras13/pharokka), particularly for less characterised phages such as those from metagenomic datasets.\n\nIf you have already annotated your phage(s) with Pharokka, `phold` takes the Genbank output of Pharokka as an input option, so you can easily update the annotation with more functional predictions!\n\n# Tutorial\n\nCheck out the `phold` tutorial at [https://phold.readthedocs.io/en/latest/tutorial/](https://phold.readthedocs.io/en/latest/tutorial/).\n\n# Google Colab Notebooks\n\nIf you don't want to install `phold` locally, you can run it without any code using one of the following Google Colab notebooks:\n\n* To run `pharokka` + `phold` (recommended) [https://colab.research.google.com/github/gbouras13/phold/blob/main/run_pharokka_and_phold.ipynb](https://colab.research.google.com/github/gbouras13/phold/blob/main/run_pharokka_and_phold.ipynb)\n* To run only `phold` [https://colab.research.google.com/github/gbouras13/phold/blob/main/run_phold.ipynb](https://colab.research.google.com/github/gbouras13/phold/blob/main/run_phold.ipynb)\n\n# Table of Contents\n\n- [phold - Phage Annotation using Protein Structures](#phold---phage-annotation-using-protein-structures)\n- [Tutorial](#tutorial)\n- [Google Colab Notebooks](#google-colab-notebooks)\n- [Table of Contents](#table-of-contents)\n- [Documentation](#documentation)\n- [Installation](#installation)\n- [Quick Start](#quick-start)\n- [Output](#output)\n- [Usage](#usage)\n- [Plotting](#plotting)\n- [Citation](#citation)\n\n# Documentation\n\nCheck out the full documentation at [https://phold.readthedocs.io](https://phold.readthedocs.io).\n\n# Installation\n\nFor more details (particularly if you are using a non-NVIDIA GPU), check out the [installation documentation](https://phold.readthedocs.io/en/latest/install/).\n\nThe best way to install `phold` is using [mamba](https://github.com/conda-forge/miniforge), as this will install [Foldseek](https://github.com/steineggerlab/foldseek) (the only non-Python dependency) along with the Python dependencies.\n\nTo install `phold` using [mamba](https://github.com/conda-forge/miniforge):\n\n```bash\nmamba create -n pholdENV -c conda-forge -c bioconda phold \n```\n\nTo utilise `phold` with GPU, a GPU compatible version of `pytorch` must be installed. By default conda/mamba will install a CPU-only version. \n\nIf you have an NVIDIA GPU, please try:\n\n```bash\nmamba create -n pholdENV -c conda-forge -c bioconda phold pytorch=*=cuda*\n```\n\nIf you have a Mac running an Apple Silicon chip (M1/M2/M3), `phold` should be able to use the GPU. Please try:\n\n```bash\nmamba create -n pholdENV python==3.11 \nconda activate pholdENV\nmamba install pytorch::pytorch torchvision torchaudio -c pytorch \nmamba install -c conda-forge -c bioconda phold \n```\n\nIf you are having trouble with `pytorch` see [this link](https://pytorch.org) for more instructions. If you have an older version of CUDA installed, then you might find [this link useful](https://pytorch.org/get-started/previous-versions/).\n\nOnce `phold` is installed, to download and install the database run:\n\n```bash\nphold install\n```\n\n* Note: You will need at least 8GB of free space (the `phold` databases including ProstT5 are 7.7GB uncompressed).\n\n# Quick Start\n\n* `phold` takes a GenBank format file output from [pharokka](https://github.com/gbouras13/pharokka) as its input by default. \n* If you are running `phold` on a local work station with GPU available, using `phold run` is recommended. It runs both `phold predict` and `phold compare`\n\n``` bash\nphold run -i tests/test_data/NC_043029.gbk -o test_output_phold -t 8\n```\n\n* If you do not have a GPU available, add `--cpu`.\n* `phold run` will run in a reasonable time for small datasets with CPU only (e.g. <5 minutes for a 50kbp phage).\n* However, `phold predict` will complete much faster if a GPU is available, and is necessary for large metagenomic datasets to run in a reasonable time. \n\n* In a cluster environment, it is most efficient to run `phold` in 2 steps for optimal resource usage.\n\n1. Predict the 3Di sequences with ProstT5 using `phold predict`. This is massively accelerated if a GPU available.\n\n```bash\nphold predict -i tests/test_data/NC_043029.gbk -o test_predictions \n```\n\n2. Compare the the 3Di sequences to the `phold` structure database with Foldseek using `phold compare`. This does not utilise a GPU. \n\n```bash\nphold compare -i tests/test_data/NC_043029.gbk --predictions_dir test_predictions -o test_output_phold -t 8 \n```\n\n# Output\n\n* The primary outputs are:\n * `phold_3di.fasta` containing the 3Di sequences for each CDS\n * `phold_per_cds_predictions.tsv` containing detailed annotation information on every CDS\n * `phold_all_cds_functions.tsv` containing counts per contig of CDS in each PHROGs category, VFDB, CARD, ACRDB and Defensefinder databases (similar to the `pharokka_cds_functions.tsv` from Pharokka)\n * `phold.gbk`, which contains a GenBank format file including these annotations, and keeps any other genomic features (tRNA, CRISPR repeats, tmRNAs) included from the `pharokka` Genbank input file if provided\n\n# Usage\n\n```bash\nUsage: phold [OPTIONS] COMMAND [ARGS]...\n\nOptions:\n -h, --help Show this message and exit.\n -V, --version Show the version and exit.\n\nCommands:\n citation Print the citation(s) for this tool\n compare Runs Foldseek vs phold db\n createdb Creates foldseek DB from AA FASTA and 3Di FASTA input...\n predict Uses ProstT5 to predict 3Di tokens - GPU recommended\n proteins-compare Runs Foldseek vs phold db on proteins input\n proteins-predict Runs ProstT5 on a multiFASTA input - GPU recommended\n remote Uses foldseek API to run ProstT5 then foldseek locally\n run phold predict then comapare all in one - GPU recommended\n```\n\n```bash\nUsage: phold run [OPTIONS]\n\n phold predict then comapare all in one - GPU recommended\n\nOptions:\n -h, --help Show this message and exit.\n -V, --version Show the version and exit.\n -i, --input PATH Path to input file in Genbank format or nucleotide\n FASTA format [required]\n -o, --output PATH Output directory [default: output_phold]\n -t, --threads INTEGER Number of threads [default: 1]\n -p, --prefix TEXT Prefix for output files [default: phold]\n -d, --database TEXT Specific path to installed phold database\n -f, --force Force overwrites the output directory\n --batch_size INTEGER batch size for ProstT5. 1 is usually fastest.\n [default: 1]\n --cpu Use cpus only.\n --omit_probs Do not output 3Di probabilities from ProstT5\n --finetune Use finetuned ProstT5 model\n --finetune_path TEXT Path to finetuned model weights\n -e, --evalue FLOAT Evalue threshold for Foldseek [default: 1e-3]\n -s, --sensitivity FLOAT sensitivity parameter for Foldseek [default: 9.5]\n --keep_tmp_files Keep temporary intermediate files, particularly\n the large foldseek_results.tsv of all Foldseek\n hits\n --split Split the Foldseek searches by ProstT5 probability\n --split_threshold FLOAT ProstT5 probability to split by [default: 60]\n --card_vfdb_evalue FLOAT Stricter Evalue threshold for Foldseek CARD and\n VFDB hits [default: 1e-10]\n --separate Output separate genbank files for every contig\n --max_seqs INTEGER Maximum results per query sequence allowed to pass\n the prefilter. You may want to reduce this to save\n disk space for enormous datasets [default: 1000]\n ```\n\n# Plotting \n\n`phold plot` will allow you to create Circos plots with [pyCirclize](https://github.com/moshi4/pyCirclize) for all your phage(s). For example:\n\n```bash\nphold plot -i tests/test_data/NC_043029_phold_output.gbk -o NC_043029_phold_plots -t '${Stenotrophomonas}$ Phage SMA6' \n```\n\n<p align=\"center\">\n <img src=\"img/NC_043029.png\" alt=\"NC_043029\" height=600>\n</p>\n\n# Citation\n\n`phold` is a work in progress, a preprint will be coming hopefully soon - if you use it please cite the GitHub repository https://github.com/gbouras13/phold for now.\n\nPlease be sure to cite the following core dependencies and PHROGs database:\n\n* Foldseek - (https://github.com/steineggerlab/foldseek) [van Kempen M, Kim S, Tumescheit C, Mirdita M, Lee J, Gilchrist C, S\u00f6ding J, and Steinegger M. Fast and accurate protein structure search with Foldseek. Nature Biotechnology, doi:10.1038/s41587-023-01773-0 (2023)](https://www.nature.com/articles/s41587-023-01773-0)\n* ProstT5 - (https://github.com/mheinzinger/ProstT5) [Michael Heinzinger, Konstantin Weissenow, Joaquin Gomez Sanchez, Adrian Henkel, Martin Steinegger, Burkhard Rost. ProstT5: Bilingual Language Model for Protein Sequence and Structure. bioRxiv doi:10.1101/2023.07.23.550085 (2023)](https://www.biorxiv.org/content/10.1101/2023.07.23.550085v1)\n* Colabfold - (https://github.com/sokrypton/ColabFold) [Mirdita M, Sch\u00fctze K, Moriwaki Y, Heo L, Ovchinnikov S and Steinegger M. ColabFold: Making protein folding accessible to all. Nature Methods (2022) doi: 10.1038/s41592-022-01488-1 ](https://www.nature.com/articles/s41592-022-01488-1)\n* PHROGs - (https://phrogs.lmge.uca.fr) [Terzian P., Olo Ndela E., Galiez C., Lossouarn J., P\u00e9rez Bucio R.E., Mom R., Toussaint A., Petit M.A., Enault F., \"PHROG : families of prokaryotic virus proteins clustered using remote homology\", NAR Genomics and Bioinformatics, (2021) https://doi.org/10.1093/nargab/lqab067](https://doi.org/10.1093/nargab/lqab067)\n\nPlease also consider citing these supplementary databases where relevant:\n\n* [CARD](https://card.mcmaster.ca) - [Alcock B.P. et al, CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database Nucleic Acids Research (2022) https://doi.org/10.1093/nar/gkac920](https://doi.org/10.1093/nar/gkac920)\n* [VFDB](http://www.mgc.ac.cn/VFs/main.htm) - [Chen L., Yang J., Yao Z., Sun L., Shen Y., Jin Q., \"VFDB: a reference database for bacterial virulence factors\", Nucleic Acids Research (2005) https://doi.org/10.1093/nar/gki008](https://doi.org/10.1093/nar/gki008)\n* [Defensefinder](https://defensefinder.mdmlab.fr) - [ F. Tesson, R. Planel, A. Egorov, H. Georjon, H. Vaysset, B. Brancotte, B. N\u00e9ron, E. Mordret, A Bernheim, G. Atkinson, J. Cury. A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and Databases. bioRxiv (2024) https://doi.org/10.1101/2024.01.25.577194](https://doi.org/10.1101/2024.01.25.577194)\n* [acrDB](https://bcb.unl.edu/AcrDB/) - please cite the original acrDB database paper [Le Huang, Bowen Yang, Haidong Yi, Amina Asif, Jiawei Wang, Trevor Lithgow, Han Zhang, Fayyaz ul Amir Afsar Minhas, Yanbin Yin, AcrDB: a database of anti-CRISPR operons in prokaryotes and viruses. Nucleic Acids Research (2021) https://doi.org/10.1093/nar/gkaa857](https://doi.org/10.1093/nar/gkaa857) AND the paper that generated the structures for these protein used by `phold` [Harutyun Sahakyan, Kira S. Makarova, and Eugene V. Koonin. Search for Origins of Anti-CRISPR Proteins by Structure Comparison. The CRISPR Journal (2023)](https://doi.org/10.1089/crispr.2023.0011)\n\n\n",
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