Metadata-Version: 2.4
Name: crisprzip
Version: 1.2.2
Summary: A kinetic model of CRISPR-Cas target recognition.
Author-email: HS Offerhaus <hsofferhaus@gmail.com>
Maintainer-email: HS Offerhaus <hsofferhaus@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Hidde Offerhaus
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
Project-URL: Homepage, https://github.com/hiddeoff/crisprzip
Project-URL: Documentation, https://hiddeoff.github.io/crisprzip/
Project-URL: Source, https://github.com/hiddeoff/crisprzip
Keywords: crispr-cas9,crispr,bioinformatics,genome editing,genetic engineering
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: <3.13,>=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.20
Requires-Dist: scipy>=1.7
Requires-Dist: matplotlib>=3.7
Requires-Dist: numba>=0.55
Provides-Extra: docs
Requires-Dist: sphinx>=1.3; extra == "docs"
Requires-Dist: myst-nb>=1.1; extra == "docs"
Requires-Dist: sphinx-rtd-theme>=3.0; extra == "docs"
Provides-Extra: jupyter
Requires-Dist: jupyter>=1.0; extra == "jupyter"
Provides-Extra: tests
Requires-Dist: pytest>=6.0; extra == "tests"
Requires-Dist: pandas>=2.0; extra == "tests"
Provides-Extra: distr
Requires-Dist: build; extra == "distr"
Requires-Dist: twine; extra == "distr"
Dynamic: license-file

# CRISPRzip
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
![Test Status](https://github.com/hiddeoff/crisprzip/actions/workflows/test_pipeline.yml/badge.svg)
![TUDelft DCC](https://img.shields.io/badge/tu_delft-DCC-black?style=flat&label=TU%20Delft&labelColor=%2300A6D6%20&color=%23000000&link=https%3A%2F%2Fdcc.tudelft.nl%2F) 

Welcome to the codebase of CRISPRzip from the [Depken Lab](https://depkenlab.tudelft.nl/) at TU
Delft.

## About the project
![Activity prediction with CRISPRzip](https://raw.githubusercontent.com/hiddeoff/crisprzip/main/img/activity_prediction.png)

CRISPRzip is a physics-based model to study the target 
recognition dynamics of CRISPR-associated nucleases like Cas9
([Eslami-Mossalam, 2022](#references)). Their interactions with target DNA is represented 
as an energy landscape, with which you can simulate binding and cleavage
kinetics. The parameters have been obtained by machine learning on 
high-throughput data. CRISPRzip makes quantitative predictions of on-target 
efficiency and off-target risks of different guide RNAs.

With CRISPRzip, we hope to contribute to assessing
the risks that come with particular choices in CRISPR application, and as such
contribute to the development of safe gene editing technology.

### References
Eslami-Mossallam B et al. (2022) *A kinetic model predicts SpCas9 activity,
improves off-target classification, and reveals the physical basis of
targeting fidelity.* Nature Communications.
[10.1038/s41467-022-28994-2](https://doi.org/10.1038/s41467-022-28994-2)

## Installation
CRISPRzip is available on [PyPi](https://pypi.org/) and can be installed 
with [pip](https://pip.pypa.io/en/stable/).

### User installation
Although CRISPRzip can be directly installed from pip, creating a virtual 
environment makes it easier to manage dependencies. Below you can see some 
intructions on how to generate a virtual environment with 
[venv](https://docs.python.org/3/library/venv.html) assuming you already 
have python installed in a bash-like terminal.

```shell
python -m venv crisprzip-venv
source crisprzip-venv/bin/activate
pip install crisprzip
```

### Developer installation
To be able to make changes and contributions to CRISPRzip, you will need to 
get your own copy of the source code and install software dependencies on 
your own. Assuming you have a python and git installed in a bash-like 
terminal, the installation process can be done with the following 
instructions.

```shell
git clone https://github.com/hiddeoff/crisprzip.git
cd crisprzip
python -m venv crisprzip-venv
source crisprzip-venv/bin/activate
pip install -e .
```

Please use our 
[contributing guidelines](https://github.com/hiddeoff/crisprzip/blob/main/CONTRIBUTING.md)
 if you would like us to consider your developments in a future CRISPRzip 
release

### Verifying installation
CRISPRzip development includes a cross-platform compatibility and test 
[workflow](.github/workflows/compatibility-test.yml). If you would like to 
verify your local installation, please follow the Developer Installation 
instructions, then run the following test.

```shell
source crisprzip-venv/bin/activate
pip install -e '.[tests]'  # installs pytest and pandas
pytest tests/cleavage_binding_prediction/test_cleavage_binding_prediction.py -v
```

You can also follow the user installation and execute the code in the 
Usage section below.

## Usage
CRISPRzip makes predictions about cleavage and binding activity on on- and
off-targets. First, you define the protospacer and target sequence, and then,
you can predict the fraction cleaved or bound.

```python
# 1. load parameter set
from crisprzip.kinetics import load_landscape
searcher = load_landscape("sequence_params")

# 2. define Cas9, gRNA and DNA target
searchertargetcomplex = searcher.probe_sequence(
    protospacer = "AGACGCATAAAGATGAGACGCTGG",
    target_seq  = "AGACCCATTAAGATGAGACGCGGG",  # A13T G17C
)

# 3. predict activity
f_clv = searchertargetcomplex.get_cleaved_fraction(
    time=600,  # 10 minutes
    on_rate=1E-1
)
f_bnd = searchertargetcomplex.get_bound_fraction(
    time=600,  # 10 minutes
    on_rate=1E-1
)

# 4. format output
print(f"After 10 minutes, the target (A13T G17C) is ...")
print(f"- cleaved for {100 * f_clv:.1f}% by Cas9")
print(f"    or  ")
print(f"- bound for {100 * f_bnd:.1f}% by dCas9")
```
Output:
```
After 10 minutes, the target (A13T G17C) is ...
- cleaved for 10.5% by Cas9
    or  
- bound for 94.2% by dCas9
```

See the [tutorial](examples/tutorial.ipynb) or the
[docs](https://hiddeoff.github.io/crisprzip/) for more examples how to explore 
sequence, time and concentration dependency.

## Contributing
We encourage contributions in any form - reporting bugs, suggesting features,
drafting code changes. Read our [Contributing guidelines](./CONTRIBUTING.md) and 
our [Code of Conduct](./CODE_OF_CONDUCT.md).

## Acknowledgements
Many thanks to [Elviss Dvinskis](https://github.com/edvinskis),
[Raúl Ortiz](https://github.com/rortizmerino) and [Aysun Urhan](https://github.com/aysunrhn)
from the [DCC team at TU Delft](https://www.tudelft.nl/en/library/support/library-for-researchers/setting-up-research/dcc)
for their support to get this package released!

## Waiver
Technische Universiteit Delft hereby disclaims all copyright interest in the
program “CRISPRzip” (a physics-based CRISPR activity predictor)
written by the Author(s).
Paulien Herder, Dean of Applied Sciences

(c) 2024, Hidde Offerhaus, Delft, The Netherlands.
