COmbinatorial PEptide POoling Design for TCR specificity
T cell receptor (TCR) repertoire diversity enables the antigen-specific immune responses against the vast space of possible pathogens. Identifying TCR-antigen binding pairs from the large TCR repertoire and antigen space is crucial for biomedical research. Here, we introduce copepodTCR, an open-access tool to design and interpret high-throughput experimental TCR specificity assays.
copepodTCR implements a combinatorial peptide pooling scheme for efficient experimental testing of T cell responses against large overlapping peptide libraries, that can be used to identify the specificity of (or "deorphanize") TCRs. The scheme detects experimental errors and, coupled with a hierarchical Bayesian model for unbiased interpretation, identifies the response-eliciting peptide sequence for a TCR of interest out of hundreds of peptides tested using a simple experimental set-up.
Documentation: copepodTCR.readthedocs.
Also you can use copepodTCR app.
Cite as
Kovaleva V. A., et al. "copepodTCR: Identification of Antigen-Specific T Cell Receptors with combinatorial peptide pooling." bioRxiv (2023): 2023-11.
Or use the following BibTeX entry:
@article{
kovaleva2023copepodtcr,
title = {copepodTCR: Identification of Antigen-Specific T Cell Receptors with combinatorial peptide pooling},
author = {Kovaleva, Vasilisa A and Pattinson, David J and He, Guanchen and Barton, Carl and Chapin, Sarah R and Minervina, Anastasia A and Huang, Qin and Thomas, Paul G and Pogorelyy, Mikhail V and Meyer, Hannah V},
year = 2023,
journal = {bioRxiv},
publisher = {Cold Spring Harbor Laboratory},
pages = {2023--11}
}
Installation
Can be installed with pip:
pip install copepodTCR
or conda:
conda install -c vasilisa.kovaleva copepodTCR
Then you need to install manifold3d, required for 3D modeling of masks. You can skip this step, if you don't plan to print masks for pooling step.
pip install manifold3d
Alternative to manifold3d is Blender, it can be installed from Blender official website (version 4.5 and higher).
You can use :func:cpp.pick_engine() to check with engines are available in you environment.
Requirements
Required packages should be installed simulataneously with the copepodTCR packages.
But if they were not, here is the list of requirements:
pip install "pandas>=1.5.3"
pip install "numpy>=1.23.5"
pip install "trimesh>=4.7.1"
pip install "pymc>=5.9.2"
pip install "arviz>=0.16.1"
pip install "matplotlib>=3.10.5"
pip install "seaborn>=0.13.2"
pip install "plotly>=6.2.0"
1 maintainer
Metadata
1 maintainer
Release files for copepodTCR 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| copepodtcr-1.0.0.tar.gz | 9.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| copepodtcr-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 9.9 MB
Release files / copepodtcr-1.0.0.tar.gz
| Download URL | copepodtcr-1.0.0.tar.gz |
|---|---|
| Size | 9.9 MB |
| Tags | Source |
|
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Release files / copepodtcr-1.0.0-py3-none-any.whl
| Download URL | copepodtcr-1.0.0-py3-none-any.whl |
|---|---|
| Size | 14.7 kB |
| Tags | Python 3 |
|
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