oncodrivefml 2.5.0

Identify signals of positive selection in somatic mutations

OncodriveFML

Recent years saw the development of methods to detect signals of positive selection in the pattern of somatic mutations in genes across cohorts of tumors, and the discovery of hundreds of driver genes. The next major challenge in tumor genomics is the identification of non-coding regions which may also drive tumorigenesis. We present OncodriveFML, a method that estimates the accumulated functional impact bias of somatic mutations in any genomic region of interest based on a local simulation of the mutational process affecting it. It may be applied to all genomic elements to detect likely drivers amongst them. OncodriveFML can discover signals of positive selection when only a small fraction of the genome, like a panel of genes, has been sequenced.

License

OncodriveFML is made available to the general public subject to certain conditions described in its LICENSE. For the avoidance of doubt, you may use the software and any data accessed through UPF software for academic, non-commercial and personal use only, and you may not copy, distribute, transmit, duplicate, reduce or alter in any way for commercial purposes, or for the purpose of redistribution, without a license from the Universitat Pompeu Fabra (UPF). Requests for information regarding a license for commercial use or redistribution of OncodriveFML may be sent via e-mail to [e-mail].

Usage

OncodriveFML is meant to be used through the command line.

By default, OncodriveFML is prepared to analyse mutations using HG19 reference genome. For other genomes, update the configuration accordingly.

Running OncodriveFML without installation (using Docker)

You can run OncodriveFML without having to install anything in your machine if you have Docker installed.

This is how you would run the example included in this repository:

docker run --rm -i \
		-v ${BGDATA_LOCAL:-${HOME}/.bgdata}:/root/.bgdata \
      -v $(pwd)/example:/data \
      --workdir /data \
		bbglab/oncodrivefml:2.5.0 \
         -i paad.txt.gz -e cds.tsv.gz --signature-correction wx --seed 123 --force

-v ${BGDATA_LOCAL:-${HOME}/.bgdata}:/root/.bgdata will allow the docker container to see the contents of your bgdata directory as defined by the environment variable BGDATA_LOCAL (or if it is not defined, the default ~/.bgdata).

-v $(pwd)/example:/data will allow the docker container to see the example data in ./example. You would need to replace $(pwd)/example by the folder where you have your own data.

--workdir /data will set the working directory to the data folders you specified before.

The results will be saved in a folder named cds under the ./example folder.

Installation

OncodriveFML can work with the Python versions 3.8 up to 3.11 (included).

The easiest way to install all this software stack is using the well known Anaconda Python distribution

conda install -c bbglab oncodrivefml

OncodriveFML can also be installed using pip:

pip install oncodrivefml

Finally, you can get the latest code from the repository and install it in development mode:

git clone https://github.com/bbglab/oncodrivefml.git
cd oncodrivefml
make build-dev
source .venv/bin/activate
oncodrivefml --help

[!NOTE] The first time that you run OncodriveFML it will download the genome reference from our servers. By default the downloaded datasets go to ~/.bgdata, but if you want to move these datasets to another folder you have to define the system environment variable BGDATA_LOCAL with an export command.

Running the example

Download and extract the example files (if you cloned the repository skip this step):

wget https://github.com/bbglab/oncodrivefml/archive/refs/tags/2.5.0.tar.gz
tar xvzf 2.5.0.tar.gz

To run this example OncodriveFML needs all the precomputed CADD scores, that is a 17Gb file, that will be downloaded automatically, together with the reference genome.

[!WARNING] The CADD scores are originally from http://cadd.gs.washington.edu/ and are freely available for all non-commercial applications. If you are planning on using them in a commercial application, please contact them at http://cadd.gs.washington.edu/contact.

To run the example, we have included a bash script (run.sh) that will execute OncodriveFML. The script should be executed in the folder where the files have been extracted:

cd oncodrivefml-2.5.0/example
./run.sh

The results will be saved in a folder named cds.

[!NOTE] It might fail to run in macOS. We recommend you to run it using the Docker image instead. See the section Running OncodriveFML without installation (using Docker) for details.

Configuring OncodriveFML

Although OncodriveFML includes a predefined configuration file, it is highly recommended to create one yourself. In fact, if you are interested in using a reference genome other than HG19, or a score other than CADD 1.0, it is mandatory. See the documentation for the configuration for more details.

Documentation

Find OncodriveFML documentation in ReadTheDocs.

You can also compile the documentation yourself using Sphinx, if you have cloned the repository. To do so, run the following command:

make docs
open docs/build/html/index.html