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

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.

SUBMITTER: Levy JJ 

PROVIDER: S-EPMC6954637 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature