Open AccessPyMethylProcess—convenient high-throughput preprocessing workflow for DNA methylation data
Author(s) -
Joshua Levy,
Alexander Titus,
Lucas A. Salas,
Brock C. Christensen
Publication year - 2019
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btz594
Subject(s) - computer science , python (programming language) , workflow , preprocessor , scalability , pipeline (software) , operating system , pipeline transport , database , programming language , chemistry , organic chemistry
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.
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