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

Joshua J Levy; Alexander J Titus; Lucas A Salas; Brock C Christensen

doi:10.1093/bioinformatics/btz594

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

Bioinformatics. 2019 Dec 15;35(24):5379-5381. doi: 10.1093/bioinformatics/btz594.

Authors

Joshua J Levy^{1

2}, Alexander J Titus¹, Lucas A Salas¹, Brock C Christensen^{1

3}

Affiliations

¹ Department of Epidemiology, Geisel School of Medicine at Dartmouth.
² Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
³ Department of Molecular and Systems Biology, Hanover, NH, USA.

Abstract

Summary: 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.

Availability and implementation: Project Home Page: https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess. Available on PyPI (pymethylprocess), Docker (joshualevy44/pymethylprocess).

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology
DNA Methylation*
Machine Learning
Software
Workflow*

Abstract

Publication types

MeSH terms

Grants and funding