<h1 align="center">
<img src="https://git.mpi-cbg.de/tothpetroczylab/picnic/-/raw/main/branding/logo/logo_picnic_v1.96113169.png" width="300">
</h1><br>
# PICNIC
PICNIC (Proteins Involved in CoNdensates In Cells) is a machine learning-based model that predicts proteins involved in biomolecular condensates. The first model (PICNIC) is based on sequence-based features and structure-based features derived from Alphafold2 models. Another model includes extended set of features based on Gene Ontology terms (PICNIC-GO). Although this model is biased by the already available annotations on proteins, it provides useful insights about specific protein properties that are enriched in proteins of biomolecular condensate. Overall, we recommend using PICNIC that is an unbiased predictor, and using PICNIC-GO for specific cases, for example for experimental hypothesis generation.
# External software
*IUPred2A*
IUPred2A is a tool that predicts disordered protein regions. It is available for download via the link https://iupred2a.elte.hu/download_new
The downloaded archive should be unpacked into the "src/files/" directory.
*STRIDE*
STRIDE is a software for protein secondary structure assignment
Installation guide can be found here https://webclu.bio.wzw.tum.de/stride/
# Installation instructions
A binary installer for the latest released version is available at the Python Package Index (PyPI).
## Requirements
* Python versions >=3.9,<3.12
* Download and unpack IUPred2A
* Add IUPred2A to PYTHONPATH
* Download and unpack STRIDE
* Add STRIDE binary to your system PATH
## Install external requirements
### How to install STRIDE?
A complete installation guide can be found [here](https://webclu.bio.wzw.tum.de/stride/install.html) or simply
run the following commands:
```shell
$ mkdir stride
$ cd stride
$ curl -OL https://webclu.bio.wzw.tum.de/stride/stride.tar.gz
$ tar -zxf stride.tar.gz
$ make
$ export PATH="$PATH:$PWD"
```
### How to install IUPred2A?
IUPred2A software is available for free only for academic users and it cannot be used for commercial purpose.
If you are an academic user, then you can download IUPred2A by filling out the following form [here](https://iupred2a.elte.hu/download_new).
```shell
# Step 1: Fill out the form above and download the IUPred2A tar ball
$ tar -zxf iupred2a.tar.gz
$ cd iupred2a
$ export PYTHONPATH="$PWD"
```
## PICNIC is available on PyPI
PICNIC officially supports Python versions >=3.9,<3.12.
```shell
$ python3 --version
Python 3.11.5
$ python3 -m pip install picnic_bio
```
## PICNIC is also installable from source
```shell
$ git clone git@git.mpi-cbg.de:atplab/picnic.git
```
Once you have a copy of the source, you can embed it in your own Python package, or install it into your site-packages easily
```shell
$ cd picnic
$ python3 -m venv picnic-env
$ source picnic-env/bin/activate
(picnic-env) $ python -m pip install .
```
## How to install PICNIC using Conda?
There isn't any binary installer available on Conda yet. Though it is possible to install PICNIC within a virtual Conda environment.
Please note that in a conda environment you have to pre-install catboost, before installing picnic-bio itself, otherwise the installation will fail when compiling the catboost package from source code. Also it is recommended to use and set up [conda-forge](https://conda-forge.org/docs/user/introduction.html) to fetch pre-compiled versions of catboost.
Please also note that catboost=1.2.2 is incompatible with Python 3.12. The maintainers of catboost are working towards a fix right now.
We have documented how to get around the catboost installation issue.
```shell
$ conda config --add channels conda-forge
$ conda config --set channel_priority strict
$ conda create -n myenv python=[3.9, 3.10, 3.11] catboost=1.2.2
$ conda activate myenv
(myenv) $ python -m pip install picnic_bio
```
# How to use?
## Usage - Using PICNIC from command line
```
$ picnic <is_automated> <path_af> <protein_id> <is_go> --path_fasta_file <file>
usage: PICNIC [-h] [--path_fasta_file PATH_FASTA_FILE]
is_automated path_af protein_id is_go
PICNIC (Proteins Involved in CoNdensates In Cells) is a machine learning-based
model that predicts proteins involved in biomolecular condensates.
positional arguments:
is_automated True if automated pipeline (works for proteins with
length < 1400 aa, with precalculated Alphafold2 model,
deposited to UniprotKB), else manual pipeline
(protein_id, Alphafold2 model(s) and fasta file are
needed to be provided as input)
path_af directory with pdb files, created by Alphafold2 for
the protein in the format. For smaller proteins ( <
1400 aa length) AlphaFold2 provides one model, that
should be named: AF-protein_id-F1-v{j}.pdb, where j is
a version number. In case of large proteins Alphafold2
provides more than one file, and all of them should be
stored in one directory and named: 'AF-
protein_id-F{i}-v{j}.pdb', where i is a number of
model, j is a version number.
protein_id protein identifier in UniprotKB (should correspond to
the name 'protein_id' for Alphafold2 models, stored in
directory_af_models)
is_go boolean flag; if 'True', picnic_go score (picnic
version with Gene Ontology features) will be
calculated, Gene Ontology terms are retrieved in this
case from UniprotKB by protein_id identifier;
otherwise default picnic score will be printed
(without Gene Ontology annotation)
options:
-h, --help show this help message and exit
--path_fasta_file PATH_FASTA_FILE
directory with sequence file in fasta format
```
## Examples
Run automated pipeline for a given UniProt Id:
```shell
$ picnic True notebooks/test_files/Q99720/ Q99720 True
```
Run manual pipeline for a given UniProt Id:
```shell
$ picnic False 'notebooks/test_files/O95613/' 'O95613' False --path_fasta_file 'notebooks/test_files/O95613/O95613.fasta.txt'
```
Run manual pipeline for your own protein sequence called MY_PROTEIN, which has no reference to UniProt:
```shell
$ picnic False 'notebooks/test_files/MY_PROTEIN/' 'MY_PROTEIN' False --path_fasta_file 'notebooks/test_files/MY_PROTEIN/my_protein.fasta'
```
Examples of using PICNIC are shown in a jupyter-notebook in notebooks folder.
## How to run the provided Jupyter notebook?
Examples of how to use and run PICNIC are shown in a provided Jupyter notebook. The notebook can be found under the
**notebooks** folder.
### What is Jupyter Notebook?
Please read documentation [here](https://saturncloud.io/blog/how-to-launch-jupyter-notebook-from-your-terminal/#what-is-jupyter-notebook).
### How to create a virtual environment and install all required Python packages.
Create a virtual environment by executing the command venv:
```shell
$ python -m venv /path/to/new/virtual/environment
# e.g.
$ python -m venv my_jupyter_env
```
Then install the classic Jupyter Notebook with:
```shell
$ source my_jupyter_env/bin/activate
$ pip install notebook
```
Also install picnic-bio from source in the same virtual environment...
```shell
$ pip install .
```
### How to Launch Jupyter Notebook from Your Terminal?
In your terminal source the previously created virtual environment...
```shell
$ source my_jupyter_env/bin/activate
```
Launch Jupyter Notebook...
```shell
$ jupyter notebook
```
Open the example notebook called 'picnic_examples.ipynb' under the notebooks folder.
# Link to paper
[DOI: 10.1101/2023.06.01.543229](https://www.biorxiv.org/content/10.1101/2023.06.01.543229v2)
Raw data
{
"_id": null,
"home_page": "https://picnic.cd-code.org/",
"name": "picnic-bio",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "Biomolecular condensate, Scientific Annotation Tool, condensate, machine learning",
"author": "Anna Hadarovich <hadarovi@mpi-cbg.de>, Maxim Scheremetjew <schereme@mpi-cbg.de>",
"author_email": "picnic@cd-code.org",
"download_url": "https://files.pythonhosted.org/packages/77/7e/13f95f6d4e6daa2f6cac25db9817236111eaab48faf4fe20724f98d9aa43/picnic_bio-1.0.0b3.tar.gz",
"platform": null,
"description": "<h1 align=\"center\">\n<img src=\"https://git.mpi-cbg.de/tothpetroczylab/picnic/-/raw/main/branding/logo/logo_picnic_v1.96113169.png\" width=\"300\">\n</h1><br>\n\n# PICNIC\n\nPICNIC (Proteins Involved in CoNdensates In Cells) is a machine learning-based model that predicts proteins involved in biomolecular condensates. The first model (PICNIC) is based on sequence-based features and structure-based features derived from Alphafold2 models. Another model includes extended set of features based on Gene Ontology terms (PICNIC-GO). Although this model is biased by the already available annotations on proteins, it provides useful insights about specific protein properties that are enriched in proteins of biomolecular condensate. Overall, we recommend using PICNIC that is an unbiased predictor, and using PICNIC-GO for specific cases, for example for experimental hypothesis generation.\n\n# External software\n\n*IUPred2A*\n\nIUPred2A is a tool that predicts disordered protein regions. It is available for download via the link https://iupred2a.elte.hu/download_new\nThe downloaded archive should be unpacked into the \"src/files/\" directory.\n\n*STRIDE*\n\nSTRIDE is a software for protein secondary structure assignment \nInstallation guide can be found here https://webclu.bio.wzw.tum.de/stride/\n\n# Installation instructions\n\nA binary installer for the latest released version is available at the Python Package Index (PyPI).\n\n## Requirements\n\n* Python versions >=3.9,<3.12\n* Download and unpack IUPred2A\n * Add IUPred2A to PYTHONPATH\n* Download and unpack STRIDE\n * Add STRIDE binary to your system PATH\n\n\n## Install external requirements\n\n### How to install STRIDE?\n\nA complete installation guide can be found [here](https://webclu.bio.wzw.tum.de/stride/install.html) or simply\nrun the following commands:\n\n```shell\n$ mkdir stride\n$ cd stride\n$ curl -OL https://webclu.bio.wzw.tum.de/stride/stride.tar.gz\n$ tar -zxf stride.tar.gz\n$ make\n$ export PATH=\"$PATH:$PWD\"\n```\n\n### How to install IUPred2A?\n\nIUPred2A software is available for free only for academic users and it cannot be used for commercial purpose.\nIf you are an academic user, then you can download IUPred2A by filling out the following form [here](https://iupred2a.elte.hu/download_new).\n\n```shell\n# Step 1: Fill out the form above and download the IUPred2A tar ball\n$ tar -zxf iupred2a.tar.gz\n$ cd iupred2a\n$ export PYTHONPATH=\"$PWD\"\n```\n\n## PICNIC is available on PyPI\n\nPICNIC officially supports Python versions >=3.9,<3.12.\n\n```shell\n$ python3 --version\nPython 3.11.5\n$ python3 -m pip install picnic_bio\n```\n\n## PICNIC is also installable from source\n\n```shell\n$ git clone git@git.mpi-cbg.de:atplab/picnic.git\n```\n\nOnce you have a copy of the source, you can embed it in your own Python package, or install it into your site-packages easily\n\n```shell\n$ cd picnic\n$ python3 -m venv picnic-env\n$ source picnic-env/bin/activate\n(picnic-env) $ python -m pip install .\n```\n\n## How to install PICNIC using Conda?\n\nThere isn't any binary installer available on Conda yet. Though it is possible to install PICNIC within a virtual Conda environment.\n\nPlease note that in a conda environment you have to pre-install catboost, before installing picnic-bio itself, otherwise the installation will fail when compiling the catboost package from source code. Also it is recommended to use and set up [conda-forge](https://conda-forge.org/docs/user/introduction.html) to fetch pre-compiled versions of catboost.\n\nPlease also note that catboost=1.2.2 is incompatible with Python 3.12. The maintainers of catboost are working towards a fix right now.\n\nWe have documented how to get around the catboost installation issue.\n\n```shell\n$ conda config --add channels conda-forge\n$ conda config --set channel_priority strict\n$ conda create -n myenv python=[3.9, 3.10, 3.11] catboost=1.2.2\n$ conda activate myenv\n(myenv) $ python -m pip install picnic_bio\n```\n\n# How to use?\n\n## Usage - Using PICNIC from command line\n\n```\n$ picnic <is_automated> <path_af> <protein_id> <is_go> --path_fasta_file <file>\n\nusage: PICNIC [-h] [--path_fasta_file PATH_FASTA_FILE]\n is_automated path_af protein_id is_go\n\nPICNIC (Proteins Involved in CoNdensates In Cells) is a machine learning-based\nmodel that predicts proteins involved in biomolecular condensates.\n\npositional arguments:\n is_automated True if automated pipeline (works for proteins with\n length < 1400 aa, with precalculated Alphafold2 model,\n deposited to UniprotKB), else manual pipeline\n (protein_id, Alphafold2 model(s) and fasta file are\n needed to be provided as input)\n path_af directory with pdb files, created by Alphafold2 for\n the protein in the format. For smaller proteins ( <\n 1400 aa length) AlphaFold2 provides one model, that\n should be named: AF-protein_id-F1-v{j}.pdb, where j is\n a version number. In case of large proteins Alphafold2\n provides more than one file, and all of them should be\n stored in one directory and named: 'AF-\n protein_id-F{i}-v{j}.pdb', where i is a number of\n model, j is a version number.\n protein_id protein identifier in UniprotKB (should correspond to\n the name 'protein_id' for Alphafold2 models, stored in\n directory_af_models)\n is_go boolean flag; if 'True', picnic_go score (picnic\n version with Gene Ontology features) will be\n calculated, Gene Ontology terms are retrieved in this\n case from UniprotKB by protein_id identifier;\n otherwise default picnic score will be printed\n (without Gene Ontology annotation)\n\noptions:\n -h, --help show this help message and exit\n --path_fasta_file PATH_FASTA_FILE\n directory with sequence file in fasta format\n```\n\n## Examples\n\nRun automated pipeline for a given UniProt Id:\n```shell\n$ picnic True notebooks/test_files/Q99720/ Q99720 True\n```\nRun manual pipeline for a given UniProt Id:\n```shell\n$ picnic False 'notebooks/test_files/O95613/' 'O95613' False --path_fasta_file 'notebooks/test_files/O95613/O95613.fasta.txt'\n```\nRun manual pipeline for your own protein sequence called MY_PROTEIN, which has no reference to UniProt:\n```shell\n$ picnic False 'notebooks/test_files/MY_PROTEIN/' 'MY_PROTEIN' False --path_fasta_file 'notebooks/test_files/MY_PROTEIN/my_protein.fasta'\n```\nExamples of using PICNIC are shown in a jupyter-notebook in notebooks folder.\n\n## How to run the provided Jupyter notebook?\n\nExamples of how to use and run PICNIC are shown in a provided Jupyter notebook. The notebook can be found under the\n**notebooks** folder.\n\n### What is Jupyter Notebook?\n\nPlease read documentation [here](https://saturncloud.io/blog/how-to-launch-jupyter-notebook-from-your-terminal/#what-is-jupyter-notebook).\n\n\n### How to create a virtual environment and install all required Python packages.\n\nCreate a virtual environment by executing the command venv:\n```shell\n$ python -m venv /path/to/new/virtual/environment\n# e.g.\n$ python -m venv my_jupyter_env\n```\n\nThen install the classic Jupyter Notebook with:\n```shell\n$ source my_jupyter_env/bin/activate\n\n$ pip install notebook\n```\nAlso install picnic-bio from source in the same virtual environment...\n```shell\n$ pip install .\n```\n\n### How to Launch Jupyter Notebook from Your Terminal?\n\nIn your terminal source the previously created virtual environment...\n```shell\n$ source my_jupyter_env/bin/activate\n```\nLaunch Jupyter Notebook...\n```shell\n$ jupyter notebook\n```\nOpen the example notebook called 'picnic_examples.ipynb' under the notebooks folder. \n\n# Link to paper\n\n[DOI: 10.1101/2023.06.01.543229](https://www.biorxiv.org/content/10.1101/2023.06.01.543229v2)\n\n",
"bugtrack_url": null,
"license": null,
"summary": "PICNIC (Proteins Involved in CoNdensates In Cells) is a machine learning-based model that predicts proteins involved in biomolecular condensates.",
"version": "1.0.0b3",
"project_urls": {
"Documentation": "https://git.mpi-cbg.de/tothpetroczylab/picnic/-/blob/main/README.md",
"Funding": "https://picnic.cd-code.org/about-us",
"Homepage": "https://picnic.cd-code.org/",
"Source": "https://git.mpi-cbg.de/tothpetroczylab/picnic",
"Tracker": "https://git.mpi-cbg.de/tothpetroczylab/picnic/-/issues"
},
"split_keywords": [
"biomolecular condensate",
" scientific annotation tool",
" condensate",
" machine learning"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "be24fa62b33e7a708416af9c849352988587c48041c2ff88907444e8b11ac1fd",
"md5": "6c56ec2e2cd11218cc94e76ac070884c",
"sha256": "5e85b628be407c9480488ecc0b6b178c3ae49547afeaefc75ae6a62fe4306f58"
},
"downloads": -1,
"filename": "picnic_bio-1.0.0b3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "6c56ec2e2cd11218cc94e76ac070884c",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 2344110,
"upload_time": "2024-04-16T07:11:20",
"upload_time_iso_8601": "2024-04-16T07:11:20.047942Z",
"url": "https://files.pythonhosted.org/packages/be/24/fa62b33e7a708416af9c849352988587c48041c2ff88907444e8b11ac1fd/picnic_bio-1.0.0b3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "777e13f95f6d4e6daa2f6cac25db9817236111eaab48faf4fe20724f98d9aa43",
"md5": "6ca30791d4a29dda9fc7fd5d9956f51c",
"sha256": "533de37b85f8b0c64a12cfb6fd256db08f5cb18b23868f3344d069b90e9ea025"
},
"downloads": -1,
"filename": "picnic_bio-1.0.0b3.tar.gz",
"has_sig": false,
"md5_digest": "6ca30791d4a29dda9fc7fd5d9956f51c",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 2258842,
"upload_time": "2024-04-16T07:11:22",
"upload_time_iso_8601": "2024-04-16T07:11:22.292830Z",
"url": "https://files.pythonhosted.org/packages/77/7e/13f95f6d4e6daa2f6cac25db9817236111eaab48faf4fe20724f98d9aa43/picnic_bio-1.0.0b3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-16 07:11:22",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "picnic-bio"
}
JSON