| Name | phold JSON |
| Version |
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| Summary | Phage Annotations using Protein Structures |
| upload_time | 2024-07-13 06:22:23 |
| maintainer | None |
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| author | None |
| requires_python | <3.12,>=3.8 |
| license | None |
| keywords |
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are
cool
|
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[](https://colab.research.google.com/github/gbouras13/phold/blob/main/run_pharokka_and_phold_and_phynteny.ipynb)
[](https://anaconda.org/bioconda/phold)
[](https://img.shields.io/conda/dn/bioconda/phold)
[](https://badge.fury.io/py/phold)
[](https://pepy.tech/project/phold)
# phold - Phage Annotation using Protein Structures
<p align="center">
<img src="img/phold_logo.png" alt="phold Logo" height=200>
</p>
`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 rapidly 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 over 1 million phage 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 with `phold compare`.
Benchmarking is ongoing, but `phold` strongly outperforms [Pharokka](https://github.com/gbouras13/pharokka), particularly for less characterised phages such as those from metagenomic datasets.
The below plot shows the percentage of annotated coding sequences (CDS) for 179 metagenomic phage genomes assembled with [phables](https://github.com/Vini2/phables). Phold v0.2.0 run both in default settings (with ProstT5) settings and where predicted protein structures (with Colabfold) were compared against Pharokka v1.7.0.
<p align="center">
<img src="img/phables_bench.jpeg" alt="phables benchmarking" height=200>
</p>
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` + `phynteny` use [this link](https://colab.research.google.com/github/gbouras13/phold/blob/main/run_pharokka_and_phold_and_phynteny.ipynb)
* [phynteny](https://github.com/susiegriggo/Phynteny) uses a long-short term memory model trained on phage synteny (the conserved gene order across phages) to assign hypothetical phage proteins to a PHROG category - it might help you add extra PHROG category annotations to hypothetical genes remaining after you run `phold`.
* Note: Phynteny will work only if your phage has fewer than 120 predicted proteins
* You can still use this notebook to run `phold` if your phage(s) are too big - just don't run the Phynteny step!
# 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 just over 8GB uncompressed).
# Quick Start
* `phold` takes a GenBank format file output from [pharokka](https://github.com/gbouras13/pharokka) or from [NCBI Genbank](https://www.ncbi.nlm.nih.gov/genbank/) 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...
install Installs ProstT5 model and phold database
plot Creates Phold Circular Genome Plots
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 (PhrostT5).
Experimental and not recommended for now
--finetune_path TEXT Path to finetuned model weights
--save_per_residue_embeddings Save the ProstT5 embeddings per resuide in a
h5 file
--save_per_protein_embeddings Save the ProstT5 embeddings as means per
protein in a h5 file
-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
--card_vfdb_evalue FLOAT Stricter Evalue threshold for Foldseek CARD
and VFDB hits [default: 1e-10]
--separate Output separate GenBank files for each 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: 10000]
--only_representatives Foldseek search only against the cluster
representatives (i.e. turn off --cluster-
search 1 Foldseek parameter)
--ultra_sensitive Runs phold with maximum sensitivity by
skipping Foldseek prefilter. Not recommended
for large datasets.
```
# 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 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:
* Pharokka - (https://github.com/gbouras13/pharokka) [Bouras G, Nepal R, Houtak G, Psaltis AJ, Wormald P-J, Vreugde S. Pharokka: a fast scalable bacteriophage annotation tool. Bioinformatics, Volume 39, Issue 1, January 2023, btac776](https://doi.org/10.1093/bioinformatics/btac776)
* 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)
* [Netflax](http://netflax.webflags.se) - [Karin Ernits, Chayan Kumar Saha, Tetiana Brodiazhenko, Bhanu Chouhan, Aditi Shenoy, Jessica A. Buttress, Julián J. Duque-Pedraza, Veda Bojar, Jose A. Nakamoto, Tatsuaki Kurata, Artyom A. Egorov, Lena Shyrokova, Marcus J. O. Johansson, Toomas Mets, Aytan Rustamova, Jelisaveta Džigurski, Tanel Tenson, Abel Garcia-Pino, Henrik Strahl, Arne Elofsson, Vasili Hauryliuk, and Gemma C. Atkinson, The structural basis of hyperpromiscuity in a core combinatorial network of type II toxin–antitoxin and related phage defense systems. PNAS (2023) https://doi.org/10.1073/pnas.2305393120](https://doi.org/10.1073/pnas.2305393120)
* [Netflax](http://netflax.webflags.se) - [Karin Ernits, Chayan Kumar Saha, Tetiana Brodiazhenko, Bhanu Chouhan, Aditi Shenoy, Jessica A. Buttress, Julián J. Duque-Pedraza, Veda Bojar, Jose A. Nakamoto, Tatsuaki Kurata, Artyom A. Egorov, Lena Shyrokova, Marcus J. O. Johansson, Toomas Mets, Aytan Rustamova, Jelisaveta Džigurski, Tanel Tenson, Abel Garcia-Pino, Henrik Strahl, Arne Elofsson, Vasili Hauryliuk, and Gemma C. Atkinson, The structural basis of hyperpromiscuity in a core combinatorial network of type II toxin–antitoxin and related phage defense systems. PNAS (2023) https://doi.org/10.1073/pnas.2305393120](https://doi.org/10.1073/pnas.2305393120)
Raw data
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"author_email": "George Bouras <george.bouras@adelaide.edu.au>, Susanna Grigson <susie.grigson@gmail.com>",
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"description": "[](https://colab.research.google.com/github/gbouras13/phold/blob/main/run_pharokka_and_phold_and_phynteny.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<p align=\"center\">\n <img src=\"img/phold_logo.png\" alt=\"phold Logo\" height=200>\n</p>\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 rapidly 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 over 1 million phage 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 with `phold compare`.\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\nThe below plot shows the percentage of annotated coding sequences (CDS) for 179 metagenomic phage genomes assembled with [phables](https://github.com/Vini2/phables). Phold v0.2.0 run both in default settings (with ProstT5) settings and where predicted protein structures (with Colabfold) were compared against Pharokka v1.7.0.\n\n<p align=\"center\">\n <img src=\"img/phables_bench.jpeg\" alt=\"phables benchmarking\" height=200>\n</p>\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` + `phynteny` use [this link](https://colab.research.google.com/github/gbouras13/phold/blob/main/run_pharokka_and_phold_and_phynteny.ipynb)\n * [phynteny](https://github.com/susiegriggo/Phynteny) uses a long-short term memory model trained on phage synteny (the conserved gene order across phages) to assign hypothetical phage proteins to a PHROG category - it might help you add extra PHROG category annotations to hypothetical genes remaining after you run `phold`. \n * Note: Phynteny will work only if your phage has fewer than 120 predicted proteins\n * You can still use this notebook to run `phold` if your phage(s) are too big - just don't run the Phynteny step!\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 just over 8GB uncompressed).\n\n# Quick Start\n\n* `phold` takes a GenBank format file output from [pharokka](https://github.com/gbouras13/pharokka) or from [NCBI Genbank](https://www.ncbi.nlm.nih.gov/genbank/) 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 install Installs ProstT5 model and phold database\n plot Creates Phold Circular Genome Plots\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\n nucleotide 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 (PhrostT5).\n Experimental and not recommended for now\n --finetune_path TEXT Path to finetuned model weights\n --save_per_residue_embeddings Save the ProstT5 embeddings per resuide in a\n h5 file\n --save_per_protein_embeddings Save the ProstT5 embeddings as means per\n protein in a h5 file\n -e, --evalue FLOAT Evalue threshold for Foldseek [default:\n 1e-3]\n -s, --sensitivity FLOAT Sensitivity parameter for foldseek [default:\n 9.5]\n --keep_tmp_files Keep temporary intermediate files,\n particularly the large foldseek_results.tsv\n of all Foldseek hits\n --card_vfdb_evalue FLOAT Stricter Evalue threshold for Foldseek CARD\n and VFDB hits [default: 1e-10]\n --separate Output separate GenBank files for each contig\n --max_seqs INTEGER Maximum results per query sequence allowed to\n pass the prefilter. You may want to reduce\n this to save disk space for enormous datasets\n [default: 10000]\n --only_representatives Foldseek search only against the cluster\n representatives (i.e. turn off --cluster-\n search 1 Foldseek parameter)\n --ultra_sensitive Runs phold with maximum sensitivity by\n skipping Foldseek prefilter. Not recommended\n for large datasets.\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 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* Pharokka - (https://github.com/gbouras13/pharokka) [Bouras G, Nepal R, Houtak G, Psaltis AJ, Wormald P-J, Vreugde S. Pharokka: a fast scalable bacteriophage annotation tool. Bioinformatics, Volume 39, Issue 1, January 2023, btac776](https://doi.org/10.1093/bioinformatics/btac776)\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* [Netflax](http://netflax.webflags.se) - [Karin Ernits, Chayan Kumar Saha, Tetiana Brodiazhenko, Bhanu Chouhan, Aditi Shenoy, Jessica A. Buttress, Juli\u00e1n J. Duque-Pedraza, Veda Bojar, Jose A. Nakamoto, Tatsuaki Kurata, Artyom A. Egorov, Lena Shyrokova, Marcus J. O. Johansson, Toomas Mets, Aytan Rustamova, Jelisaveta D\u017eigurski, Tanel Tenson, Abel Garcia-Pino, Henrik Strahl, Arne Elofsson, Vasili Hauryliuk, and Gemma C. Atkinson, The structural basis of hyperpromiscuity in a core combinatorial network of type II toxin\u2013antitoxin and related phage defense systems. PNAS (2023) https://doi.org/10.1073/pnas.2305393120](https://doi.org/10.1073/pnas.2305393120) \n* [Netflax](http://netflax.webflags.se) - [Karin Ernits, Chayan Kumar Saha, Tetiana Brodiazhenko, Bhanu Chouhan, Aditi Shenoy, Jessica A. Buttress, Juli\u00e1n J. Duque-Pedraza, Veda Bojar, Jose A. Nakamoto, Tatsuaki Kurata, Artyom A. Egorov, Lena Shyrokova, Marcus J. O. Johansson, Toomas Mets, Aytan Rustamova, Jelisaveta D\u017eigurski, Tanel Tenson, Abel Garcia-Pino, Henrik Strahl, Arne Elofsson, Vasili Hauryliuk, and Gemma C. Atkinson, The structural basis of hyperpromiscuity in a core combinatorial network of type II toxin\u2013antitoxin and related phage defense systems. PNAS (2023) https://doi.org/10.1073/pnas.2305393120](https://doi.org/10.1073/pnas.2305393120) \n\n\n",
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