PyMethylProcess - highly parallelized preprocessing for DNA methylation array data

Abstract SummaryThe ability to perform high-throughput preprocessing of methylation array data is essential in large scale methylation studies. While R is a convenient language for methylation analyses, performing highly parallelized preprocessing using Python can accelerate data preparation for downstream methylation analyses, including large scale production-ready machine learning pipelines. Here, we present a methylation data preprocessing pipeline called PyMethylProcess that is highly reproducible, scalable, and that can be quickly set-up and deployed through Docker and PIP. Availability and ImplementationProject Name: PyMethylProcessProject Home Page: https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess. Available on PyPI as pymethylprocess.Available on DockerHub via joshualevy44/pymethylprocess.Help Documentation: https://christensen-lab-dartmouth.github.io/PyMethylProcess/Operating Systems: Linux, MacOS, Windows (Docker)Programming Language: Python, ROther Requirements: Python 3.6, R 3.5.1, Docker (optional) License: MIT Contactjoshua.j.levy.gr@dartmouth.edu