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bioRxiv · 10.1101/604496

PyMethylProcess - highly parallelized preprocessing for DNA methylation array data

Levy, J. J.·Titus, A. J.·Salas, L. A.·Christensen, B.
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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.\n\nAvailability and ImplementationProject Name: PyMethylProcess\n\nProject Home Page: https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess. Available on PyPI as pymethylprocess.\n\nAvailable on DockerHub via joshualevy44/pymethylprocess.\n\nHelp Documentation: https://christensen-lab-dartmouth.github.io/PyMethylProcess/\n\nOperating Systems: Linux, MacOS, Windows (Docker)\n\nProgramming Language: Python, R\n\nOther Requirements: Python 3.6, R 3.5.1, Docker (optional) License: MIT\n\nContactjoshua.j.levy.gr@dartmouth.edu

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Levy, J. J., Titus, A. J., Salas, L. A., Christensen, B.. 2019-04-12. PyMethylProcess - highly parallelized preprocessing for DNA methylation array data. https://doi.org/10.1101/604496

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