/ECOLE

Primary LanguagePython

ECOLÉ: Learning to call copy number variants on whole exome sequencing data

ECOLÉ is a deep learning based software that performs CNV call predictions on WES data using read depth sequences.

The manuscript can be found here: ECOLÉ: Learning to call copy number variants on whole exome sequencing data

The repository with the necessary data and scripts to reproduce the results in the paper can be found here: ECOLE results reproduction

Deep Learning, Copy Number Variation, Whole Exome Sequencing

Authors

Berk Mandiracioglu, Furkan Ozden, Can Alkan, A. Ercument Cicek

Questions & comments

[firstauthorname].[firstauthorsurname]@epfl.ch

Table of Contents

Warning: Please note that ECOLÉ software is completely free for academic usage. However it is licenced for commercial usage. Please first refer to the License section for more info.

Installation

  • ECOLÉ is a python3 script and it is easy to run after the required packages are installed.

Requirements

For easy requirement handling, you can use ECOLE_environment.yml files to initialize conda environment with requirements installed:

$ conda env create --name ecole_env -f ECOLE_environment.yml
$ conda activate ecole_env

Note that the provided environment yml file is for Linux systems. For MacOS users, the corresponding versions of the packages might need to be changed.

Features

  • ECOLÉ provides GPU support optionally. See GPU Support section.

Instructions Manual

Important notice: Please call the ECOLÉ_call.py script from the scripts directory.

Required Arguments

-m, --model

  • Pretrained models of the paper, one of the following: (1) ecole, (2) ecole-ft-expert, (3) ecole-ft-somatic.

-bs, --batch_size

  • Batch size to be used to perform CNV call on the samples.

-i, --input

  • Relative or direct path for are the processed WES samples, including read depth data.

-o, --output

  • Relative or direct output directory path to write ECOLÉ output file.

-c, --cnv

  • Level of resolution you desire, choose one of the options: (1) exonlevel, (2) merged.

-n, --normalize

  • Relative or direct path for mean&std stats of read depth values to normalize. These values are obtained precalculated from the training dataset before the pretraining.

Optional Arguments

-g, --gpu

  • Set to PCI BUS ID of the gpu in your system.
  • You can check, PCI BUS IDs of the gpus in your system with various ways. Using gpustat tool check IDs of the gpus in your system like below:

-v, --version

-Check the version of ECOLÉ.

-h, --help

-See help page.

Usage Examples

Usage of ECOLÉ is very simple!

Step-1: Set Up your environment.

  • It is important to set up the conda environment which includes the necessary dependencies.
  • Please run the following lines to create and activate the environment:
$ conda env create --name ecole_env -f ECOLE_environment.yml
$ conda activate ecole_env

Step-2: Run the preprocessing script.

  • It is necessary to perform preprocessing on WES data samples to obtain read depth and other meta data and make them ready for CNV calling.
  • Please run the following line:
$ source preprocess_samples.sh

Step-3: Run ECOLÉ on data obtained in Step-2

  • Here, we demonstrate an example to run ECOLÉ on gpu device 0, and obtain exon-level CNV call.
  • Please run the following script:
$ source ecole_call.sh

You can change the argument parameters within the script to run it on cpu and/or to obtain merged CNV calls.

Output file of ECOLÉ

  • At the end of the CNV calling procedure, ECOLÉ will write its output file to the directory given with -o option. In this tutorial it is ./ecole_calls_output
  • Output file of ECOLÉ is a tab-delimited .bed like format.
  • Columns in the output file of ECOLÉ are the following with order: 1. Sample Name, 2. Chromosome, 3. CNV Start Index, 4. CNV End Index, 5. ECOLÉ Prediction
  • Following figure is an example of ECOLÉ output file.

Citations

License

  • CC BY-NC-SA 2.0
  • Copyright 2022 © ECOLÉ.
  • For commercial usage, please contact.