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

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

The manuscript can be found here: ECOLE: 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

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Authors

Berk Mandiracioglu, Furkan Ozden, Gun Kaynar, M. Alper Yilmaz, Can Alkan, A. Ercument Cicek

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Questions & comments

[firstauthorname].[firstauthorsurname]@gmail.com [lastauthorsurname]@cs.bilkent.edu.tr

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Table of Contents

Warning: Please note that ECOLE 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
• Features
• Instructions Manual
• Usage Examples
• Citations
• License

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Installation

• ECOLE 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

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

Instructions Manual for ECOLE

Important notice: Please call the ECOLE_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 ECOLE 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 ECOLE.

-h, --help

-See help page.

Usage Example

Usage of ECOLE is very simple!

Step-0: Install conda package management

• This project uses conda package management software to create virtual environment and facilitate reproducability.

• For Linux users:

• Please take a look at the Anaconda repo archive page, and select an appropriate version that you'd like to install.

• Replace this Anaconda3-version.num-Linux-x86_64.sh with your choice

$ wget -c https://repo.continuum.io/archive/Anaconda3-vers.num-Linux-x86_64.sh
$ bash Anaconda3-version.num-Linux-x86_64.sh

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 ECOLE on data obtained in Step-2

• Here, we demonstrate an example to run ECOLE 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 ECOLE

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

Instructions Manual for Finetuning ECOLE

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

Required Arguments

-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 ECOLE output file.

-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.

-e, --epochs

• The number of epochs the finetuning will be performed.

-lr, --learning_rate

• The learning rate to be used in finetuning

-lmp, --load_model_path

• The path for the pretrained model weights to be loaded for finetuning

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 ECOLE.

-h, --help

-See help page.

Finetune Example

We provide an ECOLE Finetuning example with WES sample of NA12891 using only chromosome 21. Step-0 and Step-1 are the same as the ECOLE call example.

Step-0: Install conda package management

• This project uses conda package management software to create virtual environment and facilitate reproducability.

• For Linux users:

• Please take a look at the Anaconda repo archive page, and select an appropriate version that you'd like to install.

• Replace this Anaconda3-version.num-Linux-x86_64.sh with your choice

$ wget -c https://repo.continuum.io/archive/Anaconda3-vers.num-Linux-x86_64.sh
$ bash Anaconda3-version.num-Linux-x86_64.sh

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 for preparing the samples for finetuning.

• It is necessary to perform preprocessing on WES data samples to obtain read depth and other meta data and make them ready for ECOLE finetuning.

• ECOLE Finetuning requires .bam and ground truth calls as provided under /finetune_example_data. Please see the below image for a sample ground truths format.

  

• Please run the following line:

$ source finetune_preprocess_samples.sh

Step-3: Start ECOLE Finetuning on data obtained in Step-2

• Here, we demonstrate an example to run ECOLE Finetuning on gpu device 0.
• Please run the following script:

$ source ecole_finetune.sh

You can change the argument parameters within the script to run it on cpu.

Output file of ECOLE

• At the end of ECOLE Finetuning, the script will save its model weights file to the directory given with -o option. In this tutorial it is ./ecole_finetuned_model_weights

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Citations

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License

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