| Name | phold JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Phage Annotations using Protein Structures |
| upload_time | 2025-08-06 06:44:57 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <4,>=3.8 |
| license | None |
| keywords |
keyword
are
cool
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](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=250>
</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 1.36 million phage protein structures mostly predicted using [Colabfold](https://github.com/sokrypton/ColabFold).
<p align="center">
<img src="img/phold_workflow.png" alt="phold workflow" height=300>
</p>
Alternatively, you can specify protein structures that you have pre-computed for your phage(s) instead of using ProstT5 using the parameters `--structures` and `--structure_dir` with `phold compare`.
`phold` strongly outperforms sequence-based homology phage annotation tools like [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` + `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_transformer) uses 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`.
# 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 conda via [miniforge](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 [conda](https://github.com/conda-forge/miniforge):
```bash
conda 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 will install a CPU-only version.
If you have an NVIDIA GPU, please try:
```bash
conda create -n pholdENV -c conda-forge -c bioconda phold pytorch=*=cuda*
```
If you have a Mac running an Apple Silicon chip (M1/M2/M3/M4), `phold` should be able to use the GPU. Please try:
```bash
conda create -n pholdENV python==3.13
conda activate pholdENV
conda install pytorch::pytorch torchvision torchaudio -c pytorch
conda install -c conda-forge -c bioconda phold
```
If you are have a different non-NVIDIA GPU, or have 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 -t 8
```
If you have an NVIDIA GPU and can take advantage of Foldseek's GPU acceleration, instead run
```bash
phold install -t 8 --foldseek_gpu
```
* 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 have an NVIDIA GPU available, add `--foldseek_gpu`
* If you do not have any 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). With GPU it should complete in under 1 minute.
* `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 where GPUs are scarce, for large datasets it may be 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 per residue 3Di probabilities
from ProstT5. Mean per protein 3Di
probabilities will always be output.
--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
--mask_threshold FLOAT Masks 3Di residues below this value of
ProstT5 confidence for Foldseek searches
[default: 25]
--finetune Use gbouras13/ProstT5Phold encoder + CNN
model both finetuned on phage proteins
--vanilla Use vanilla CNN model (trained on CASP14)
with ProstT5Phold encoder instead of the one
trained on phage proteins
--hyps Use this to only annotate hypothetical
proteins from a Pharokka GenBank input
-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 E-value 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: 1000]
--ultra_sensitive Runs phold with maximum sensitivity by
skipping Foldseek prefilter. Not recommended
for large datasets.
--extra_foldseek_params TEXT Extra foldseek search params
--custom_db TEXT Path to custom database
--foldseek_gpu Use this to enable compatibility with
Foldseek-GPU search acceleration
```
# 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
{
"_id": null,
"home_page": null,
"name": "phold",
"maintainer": null,
"docs_url": null,
"requires_python": "<4,>=3.8",
"maintainer_email": null,
"keywords": "keyword, are, cool",
"author": null,
"author_email": "George Bouras <george.bouras@adelaide.edu.au>",
"download_url": "https://files.pythonhosted.org/packages/77/ab/1600aabf452f36557abc4dbff4d2966511191283a3ae8138e0c11227f9e4/phold-1.0.0.tar.gz",
"platform": null,
"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=250>\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 1.36 million phage protein structures mostly predicted using [Colabfold](https://github.com/sokrypton/ColabFold). \n\n<p align=\"center\">\n <img src=\"img/phold_workflow.png\" alt=\"phold workflow\" height=300>\n</p>\n\nAlternatively, you can specify protein structures that you have pre-computed for your phage(s) instead of using ProstT5 using the parameters `--structures` and `--structure_dir` with `phold compare`.\n\n`phold` strongly outperforms sequence-based homology phage annotation tools like [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` + `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_transformer) uses 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 \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 conda via [miniforge](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 [conda](https://github.com/conda-forge/miniforge):\n\n```bash\nconda 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 will install a CPU-only version. \n\nIf you have an NVIDIA GPU, please try:\n\n```bash\nconda 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/M4), `phold` should be able to use the GPU. Please try:\n\n```bash\nconda create -n pholdENV python==3.13 \nconda activate pholdENV\nconda install pytorch::pytorch torchvision torchaudio -c pytorch \nconda install -c conda-forge -c bioconda phold \n```\n\nIf you are have a different non-NVIDIA GPU, or have 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 -t 8\n```\n\nIf you have an NVIDIA GPU and can take advantage of Foldseek's GPU acceleration, instead run\n\n```bash\nphold install -t 8 --foldseek_gpu\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 have an NVIDIA GPU available, add `--foldseek_gpu`\n* If you do not have any 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). With GPU it should complete in under 1 minute.\n* `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 where GPUs are scarce, for large datasets it may be 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 per residue 3Di probabilities\n from ProstT5. Mean per protein 3Di\n probabilities will always be output.\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 --mask_threshold FLOAT Masks 3Di residues below this value of\n ProstT5 confidence for Foldseek searches\n [default: 25]\n --finetune Use gbouras13/ProstT5Phold encoder + CNN\n model both finetuned on phage proteins\n --vanilla Use vanilla CNN model (trained on CASP14)\n with ProstT5Phold encoder instead of the one\n trained on phage proteins\n --hyps Use this to only annotate hypothetical\n proteins from a Pharokka GenBank input\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 E-value 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: 1000]\n --ultra_sensitive Runs phold with maximum sensitivity by\n skipping Foldseek prefilter. Not recommended\n for large datasets.\n --extra_foldseek_params TEXT Extra foldseek search params\n --custom_db TEXT Path to custom database\n --foldseek_gpu Use this to enable compatibility with\n Foldseek-GPU search acceleration\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",
"bugtrack_url": null,
"license": null,
"summary": "Phage Annotations using Protein Structures",
"version": "1.0.0",
"project_urls": {
"Documentation": "https://phold.readthedocs.io/en/latest",
"Homepage": "https://github.com/gbouras13/phold"
},
"split_keywords": [
"keyword",
" are",
" cool"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "c39ae5d85211e84a46071c3a64a3d941476373a99024e68945f8ce24bdfc908e",
"md5": "6194d30c09c712832491bf96d7f5502c",
"sha256": "dcc83d9544f42a0dff24645784a06ec8bd347f31604da79e993aeec3b8d2bf8a"
},
"downloads": -1,
"filename": "phold-1.0.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "6194d30c09c712832491bf96d7f5502c",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<4,>=3.8",
"size": 3434850,
"upload_time": "2025-08-06T06:44:55",
"upload_time_iso_8601": "2025-08-06T06:44:55.889535Z",
"url": "https://files.pythonhosted.org/packages/c3/9a/e5d85211e84a46071c3a64a3d941476373a99024e68945f8ce24bdfc908e/phold-1.0.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "77ab1600aabf452f36557abc4dbff4d2966511191283a3ae8138e0c11227f9e4",
"md5": "f416733b784243bd0f9640e9d5f42209",
"sha256": "6991ad9ea5d4d277600d84144e281e1d571a1dc7448b1dfa9ca106d108d37973"
},
"downloads": -1,
"filename": "phold-1.0.0.tar.gz",
"has_sig": false,
"md5_digest": "f416733b784243bd0f9640e9d5f42209",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<4,>=3.8",
"size": 3438860,
"upload_time": "2025-08-06T06:44:57",
"upload_time_iso_8601": "2025-08-06T06:44:57.265461Z",
"url": "https://files.pythonhosted.org/packages/77/ab/1600aabf452f36557abc4dbff4d2966511191283a3ae8138e0c11227f9e4/phold-1.0.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-06 06:44:57",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "gbouras13",
"github_project": "phold",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "phold"
}
