ERKER to Phenopackets

v2.0.0: MC4R Pipeline finished

In this repository we are developing a pipeline mapping the ERKER Dataset to the Phenopackets format. The ERKER dataset is a collection of clinical data from the Charité Berlin. The Phenopackets format is a standard for the exchange of phenotypic and genomic data for patients with rare diseases. The goal of this project is to develop a general pipeline that can be used to map any clinical dataset to the Phenopackets format.

More information on Phenopackets:

• Phenopackets Documentation
• Article on Phenopackets in the journal Nature Biotechnology

Table of Contents

• Project Description
• Features
• Installation
• Running the Pipeline
• Resources
• License
• Acknowledgements

Project Description

Reliable exchange of medical data between medical facilities is essential for patient's medical care. Especially patients with rare diseases can profit of digital interoperability.

To capture and produce RD-specific and FAIR (Findability, Accessability, Interoperability, Reusability) data, the ERKER (ERDRI-CDS kompatible Erfassung in REDCap / ERDRI-CDS compatible data capture in REDCap) was developed within our research projects CORD-MI, Screen4Care, Fair4Rare and Lab4Rare. With the subproject ERKER2Phenopackets we develop the transfer of ERKER data to Phenopackets - a computable representation of clinical data enabling deep phenotyping. The MC4R-deficiency, a rare genetic disease resulting in severe obesity, is used to develop the first example pipeline.

Please find a set of 50 phenopackets that were created artificially in the ERKER2Phenopackets/data/out/phenopackets directory.

Features

REDCap is a clinical electronic data capture system, for which many university hospitals have licenses. The ERKER version 1.5 form (ERDRI-CDS kompatible Erfassung in REDCap / ERDRI-CDS compatible data capture in REDCap) can be downloaded for free here: https://github.com/BIH-CEI/ERKER. For disease specific RD data capture in .csv or Excel format, you can use the Python import template to capture the data semi automatically.

For further use of Phenopackets please read: https://www.nature.com/articles/s41587-022-01357-4. The overall pipeline from the ERKER format to Phenopackets will be developed here.

Installation

1. Installing Python

We recommend the installation using Anaconda

A. Installing Python with pip (Standard Python)

To install Python using the standard method with pip, follow these steps:

1. Check if Python is already installed: Open your terminal or command prompt and type python --version or python3 --version.
   a. If Python is installed, you'll see the version number. Please check that your version is at least 3.10 or higher. if not, run pip install --upgrade python
   b. If not, proceed with the installation.

2. Download Python: Visit the official Python website at python.org and download the latest version of Python for your operating system.

3. Run the Installer: Double-click the downloaded installer and follow the on-screen instructions. Make sure to check the box that says "Add Python to PATH" during installation to easily run Python from the command line.

4. Verify Installation: After installation, open your terminal or command prompt and type python --version or python3 --version to confirm that Python is installed and check the version.

B. Installing Python with Anaconda

Anaconda is a popular distribution of Python that comes with many pre-installed data science libraries and tools. To install Python using Anaconda, follow these steps:

1. Download Anaconda: Visit the Anaconda website at anaconda.com and download the Anaconda distribution for your operating system.

2. Run the Installer: Double-click the downloaded Anaconda installer and follow the on-screen instructions. Anaconda provides an installer with a graphical user interface that makes it easy to customize your installation.

3. Create an Environment (Optional, but highly recommended): Anaconda allows you to create isolated Python environments for different projects. You can use the Anaconda Navigator or the command line to create and manage environments. Anaconda Creating Environment Tutorial

4. Verify Installation: After installation, open your terminal or Anaconda Navigator and type python --version or python3 --version to confirm that Anaconda Python is installed and check the version.

Now, you have Python installed on your system, and you can start using it by running python in your terminal.

2. Clone this repository to your machine.

1. Open a git CMD
2. Navigate to the folder where you would like to install this git repository.
3. Run git clone https://github.com/BIH-CEI/ERKER2Phenopackets

3. Installing Required Python Packages

1. Open your Python CMD
2. Navigate into the repository folder
3. Run pip install .
4. (Optional): If you would like to install the required Python packages for testing run pip install .[test]

[Optional] 4. Installing phenopacket-tools

We can use phenopacket-tools to validate the created phenopackets. Unfortunately, as of writing this, there is no Python version of phenopacket-tools available. Therefore, we rely on the CLI version of phenopacket-tools, which is then automatically called upon if installed, when executing the pipeline command.

Note: During development we used the phenopacket-tools version v1.0.0-RC3.

To install phenopacket-tools follow these steps:

1. Check if Java is already installed: Open your terminal or command prompt and type java --version.
   a. If Java is installed, you'll see the version number.
   b. If not, proceed with the installation.
2. Download Java: Visit the official Java website at java.com and download the latest version of Java for your operating system. Follow the on-screen instructions.
3. Download the phenopacket-tools CLI: Visit the official phenopacket-tools releases page and download the latest version of the phenopacket-tools CLI.
4. Unzip the downloaded file and place the .jar file (e.g., phenopacket-tools-cli-1.0.0-RC3.jar) into the ERKER2Phenopackets/submodules/phenopacket-tools directory.
5. If you are using a different version of phenopacket-tools, please also change the path to the .jar file in the config.cfg configuration file under the header Paths at jar_path.

5. Installing MongoDB

Please follow the official MongoDB Installation Tutorial.

Running the Pipeline

To run the pipeline, you require a .csv file in ERKER format with filled columns that allow Phenopacket creation from MC4R data.

1. Follow the steps in the Installation section. (Especially important is the pip install . command)
2. Navigate to the root directory (top level ERKER2Phenopackets folder).
3. Run pipeline [-h] [-d | -t] [-p] [-v] data_path [out_dir_name]
   a. If you do not provide an output folder name, the output folder will be named according to the current date and time in the 'YYYY-MM-DD-hhmm' format.
   b. Running the command with the -v or -validate tag automatically calls validate on the created phenopackets. This is recommended, especially when using -p or --publish. c. To get more info on how to run this command, run pipeline -h or pipeline --help.
4. You can find the created phenopackets in the ERKER2Phenopackets/data/out/ folder. Do not upload real patient data to GitHub.

Validating Phenopackets

Run validate (optionally add path to a single phenopacket .json file or a folder that includes phenopackets), defaults to validating last created phenopackets.

Resources

Ontologies

• Human Phenotype Ontology (HP, version: 2023-06-06) 🔗
• Online Mendelian Inheritance in Man (OMIM, version: 2023-09-08) 🔗
• Orphanet Rare Disease Ontology (OPRHA, version: 2023-09-10) 🔗
• National Center for Biotechnology Information Taxonomy (NCBITaxon, version: 2023-02-21) 🔗
• Logical Observation Identifiers Names and Codes (LOINC, version: 2023-08-15) 🔗
• HUGO Gene Nomenclature Committee (HGNC, version: 2023-09-10) 🔗
• Gene Ontology (GENO, version: 2023-07-27) 🔗

License

This project is licensed under the terms of the MIT License

Acknowledgements

We would like to extend our thanks to Daniel Danis for his support in the development of this project.

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• Authors:
  • Adam Graefe
  • Filip Rehburg