Inference and design of antibody specificity

This repository hosts the Julia code for the paper:

Inference and design of antibody specificity: from experiments to models and back

Jorge Fernandez-de-Cossio-Diaz, Guido Uguzzoni, Kévin Ricard, Francesca Anselmi, Clément Nizak, Andrea Pagnani, Olivier Rivoire

bioRxiv 2023.10.23.563570; doi: https://doi.org/10.1101/2023.10.23.563570

See the full paper at this link. If you use this code in your work, please cite using the included CITATION.bib file.

The code is written in Julia. To install Julia, follow the instructions at JuliaLang Downloads.

1. Clone this repository using git:

git clone https://github.com/2023ab4/ab4.git

2. In the Julia REPL, add the package:

import Pkg
Pkg.add("<path-to-cloned-repository>")

The notebooks directory contains Pluto notebooks that provide examples for:

• Training models.
• Performing various analyses.
• Reproducing the figures and plots from the paper.

We recommend using Julia 1.10 for the best compatibility. To launch these, open a Julia REPL, and run:

using Pluto
Pluto.run()

Then, navigate to the notebooks folder and select the desired notebook.

Jupyter notebooks are also available in the notebooks directory to help reproduce the results from the paper. To use them:

1. Install Python and Jupyter. For example, you can use https://www.anaconda.com/download.
2. Install the IJulia kernel. Follow instructions here: https://github.com/JuliaLang/IJulia.jl.
3. Start a Jupyter session:

jupyter notebook

4. Navigate to the notebooks directory, and ensure that the code is executed within the Ab4Paper2023 environment, and with the IJulia kernel selected.

As with Pluto, we suggest using Julia 1.10 for compatibility.

• src/: Contains the source code for the project.
• notebooks/: Contains Pluto and Jupyter notebooks for reproducing results.
• data/: Input data used in the paper's analysis.
• test/: Contains test module for simple testing functionality

If you encounter any problems, please open an issue on this GitHub repository. We also welcome contributions to the codebase. Please open a pull request with your improvements.

MIT license