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ECOLE
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# ECOLE: Learning to call copy number variants on whole exome sequencing data [](https://zenodo.org/doi/10.5281/zenodo.10069036) > 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: <a href="https://www.biorxiv.org/content/10.1101/2022.11.17.516880v1" target="_blank">**ECOLE: Learning to call copy number variants on whole exome sequencing data**</a> > The repository with the necessary data and scripts to reproduce the results in the paper can be found here: <a href="https://zenodo.org/record/7317266#.Y3F0jS8w1hE" target="_blank">**ECOLE results reproduction**</a> > <a href="https://en.wikipedia.org/wiki/Deep_learning" target="_blank">**Deep Learning**</a>, <a href="https://en.wikipedia.org/wiki/Copy-number_variation" target="_blank">**Copy Number Variation**</a>, <a href="https://en.wikipedia.org/wiki/Exome_sequencing" target="_blank">**Whole Exome Sequencing**</a> --- ## Authors Berk Mandiracioglu, Furkan Ozden, Gun Kaynar, M. Alper Yilmaz, Can Alkan, A. Ercument Cicek --- ## Questions & comments [firstauthorname].[firstauthorsurname]@gmail.com [lastauthorsurname]@cs.bilkent.edu.tr --- ## 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](#license) section for more info. - [Installation](#installation) - [Features](#features) - [Instructions Manual](#instructions-manual) - [Usage Examples](#usage-examples) - [Citations](#citations) - [License](#license) --- ## 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: ```shell $ 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](#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: #### -conf, --confidenceThreshold - Confidence threshold for calling CNV labels. - Select higher values for more confident calls. #### -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 <a href="https://repo.anaconda.com/archive/" target="_blank">**Anaconda repo archive page**</a>, and select an appropriate version that you'd like to install. - Replace this `Anaconda3-version.num-Linux-x86_64.sh` with your choice ```shell $ 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: ```shell $ 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: ```shell $ 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: ```shell $ 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. <img src="./example_output.png" class="center"> ## 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 <a href="https://repo.anaconda.com/archive/" target="_blank">**Anaconda repo archive page**</a>, and select an appropriate version that you'd like to install. - Replace this `Anaconda3-version.num-Linux-x86_64.sh` with your choice ```shell $ 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: ```shell $ 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. <img src="./finetune_ground_truths.png" class="center"> - Please run the following line: ```shell $ 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: ```shell $ 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 <img src="./finetune_output.png" class="center"> --- ## Citations --- ## License - **[CC BY-NC-SA 2.0](https://creativecommons.org/licenses/by-nc-sa/2.0/)** - Copyright 2022 © ECOLE. - For commercial usage, please contact.
