PyMethylProcess - convenient high-throughput preprocessing workflow for DNA methylation data

Joshua J. Levy, Alexander J. Titus, Lucas A. Salas, Brock C. Christensen

Abstract: Performing highly parallelized preprocessing of methylation array data using Python can accelerate data preparation for downstream methylation analyses, including large scale production-ready machine learning pipelines. We present a highly reproducible, scalable pipeline (PyMethylProcess) that can be quickly set-up and deployed through Docker and PIP.