Source code to reproduce the figures of the paper:
In silico saturation mutagenesis of cancer genes
Ferran Muiños, Francisco Martinez-Jimenez, Oriol Pich, Abel Gonzalez-Perez, Nuria Lopez-Bigas
DOI: https://doi.org/10.1038/s41586-021-03771-1
This repo contains the source code to reproduce the main and extended figures of the paper.
Each figure has its own jupyter notebook to render the figure's panels.
Figure 1: [ipynb] [pdf]
Figure 2: [ipynb] [pdf]
Figure 3: [ipynb] [pdf]
Figure 4: [ipynb] [pdf]
Extended Figure 1: [ipynb] [pdf]
Extended Figure 2: [ipynb] [pdf]
Extended Figure 3: [ipynb] [pdf]
Extended Figure 4: [ipynb] [pdf]
Extended Figure 5: [ipynb] [pdf]
Extended Figure 6: [ipynb] [pdf]
Extended Figure 7: [ipynb] [pdf]
Extended Figure 8: [ipynb] [pdf]
Extended Figure 9: [ipynb] [pdf]
You can access to boostDM source code and documentation in the boostDM pipeline repository.
You can explore and download the main outputs of boostDM in the boostDM website.
All the code features in this repo feeds on source data.
Make sure that you download a stable copy of the source data from zenodo and keep it in the root of the repo from zenodo as follows:
$ pip install zenodo_get
$ bash get.sh
$ tar -xvf source-data/source-data-zenodo.tar.gz
$ cp -r source-data/boostdm-analyses .
The notebooks must be run on a jupyter-notebook or jupyter-lab session launched from
Singularity image that already satisfies all the dependencies for the notebooks to run.
Follow these steps:
-
Install the latest Singularity release
-
Create a singularity image using the Singularity recipe:
$ sudo singularity build boostdm-analyses.sif Singularity
- Now you can run the notebooks from singularity:
$ singularity exec boostdm-analyses.sif jupyter-lab