PyMethylProcess-适用于 DNA 甲基化数据的高通量预处理工作流。
PyMethylProcess-convenient high-throughput preprocessing workflow for DNA methylation data.
机构信息
Department of Epidemiology, Geisel School of Medicine at Dartmouth.
Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
出版信息
Bioinformatics. 2019 Dec 15;35(24):5379-5381. doi: 10.1093/bioinformatics/btz594.
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.
摘要
摘要
使用 Python 进行高度并行化的甲基化阵列数据预处理可以加速下游甲基化分析的数据准备，包括大规模的生产就绪机器学习管道。我们提出了一个高度可重复、可扩展的管道（PyMethylProcess），可以通过 Docker 和 PIP 快速设置和部署。
可用性和实现
项目主页：https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess。可在 PyPI（pymethylprocess）、Docker（joshualevy44/pymethylprocess）上获得。
补充信息
补充数据可在Bioinformatics 在线获得。
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