Getting started

Installation

CRISPRzip is available on PyPi and can be installed with pip.

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 assuming you already have python installed in a bash-like terminal.

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.

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 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. If you would like to verify your local installation, please follow the Developer Installation instructions, then run the following test.

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.

# 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 for more examples how to explore sequence, time and concentration dependency.