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Hotspot propensity across mutational processes

This repository contains the source code to reproduce hotspot propensity figures from the manuscript:

Arnedo-Pac C, Muiños F, Gonzalez-Perez A, Lopez-Bigas N. Hotspot propensity across mutational processes. Mol Syst Biol. 2023:1-22. doi: doi.org/10.1038/s44320-023-00001-w

Content

The repository contains the data and code to reproduce the main and Extended View figures figures of the manuscript. You can also check the resulting PNG images from running the code.

How to run

Requirements

This code has been developed in Python 3.6 and Jupyter Notebook version 5.0.0. You can check the list of additional requirements at env.yaml file.

Step 1. Create a conda environment and install the requirements

You can directly create a conda environment as:

~$ conda env create --name hotspot_propensity_env --file env.yaml

Step 2. Get a copy of hotspot_propensity repository

For example, you can clone the repository as:

~$ git clone [email protected]:bbglab/hotspot_propensity.git

Step 3. Download and uncompress source data

The source data is available within Github and Zenodo 10.5281/zenodo.10004773.

  1. Download hotspots identified across cancer types from Zenodo 10.5281/zenodo.10004773.
  2. Move hotspots.zip file to ./data directory and uncompress.
  3. The resulting ./data directory should look like this:
~$ ls ./data -1
colonic_crypts_mutations
ctcf
EV_datasets
expected_hotspot_propensity
fraction_samples_with_hotspot.json
genomic_bin_data
germline_mutations
hotspot_propensity_1000iter_100samples_100-300muts.txt.gz
hotspots
hotspots_SBS_prob__COADREAD.txt.gz
hotspots_SBS_prob__ESOPHA_STOMACH.txt.gz
hotspots_SBS_prob__SKCM.txt.gz
methylation
sample_signatures_fraction_activity_inside_outside.json
signatures_fold_change_inside_outside.txt
total_hotspots_per_sample.json
total_mutations_per_sample.json

Step 4. Activate the conda environment and run Jupyter Notebook

~$ conda activate hotspot_propensity_env
~$ jupyter notebook

Run the notebook of your interest. Figures will appear in the running directory of the notebook.

Additional information

Additional code to carry out the analyses described in the manuscript can be found in analysis. Note that the input data for this section must be downloaded from the original sources as described in the manuscript.

HotspotFinder algorithm is described in Materials and Methods section and Appendix Note 1 of the manuscript and its repository. You can download HotspotFinder at bitbucket.org/bbglab/hotspotfinder

You can read all details of the manuscript at: Arnedo-Pac C, Muiños F, Gonzalez-Perez A, Lopez-Bigas N. Hotspot propensity across mutational processes. bioRxiv 2022.09.14.507952; doi: 10.1101/2022.09.14.507952

How to cite

Arnedo-Pac C, Muiños F, Gonzalez-Perez A, Lopez-Bigas N. Hotspot propensity across mutational processes. Mol Syst Biol. 2023:1-22. doi: doi.org/10.1038/s44320-023-00001-w

License

This code is available to the general public subject to certain conditions described in its LICENSE.