CpGtools: A Python Package for DNA Methylation Analysis.

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2019
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Abstract
DNA methylation can be measured at the single CpG level using sodium bisulfite conversion of genomic DNA followed by sequencing or array hybridization. Many analytic tools have been developed, yet there is still a high demand for a comprehensive and multifaceted tool suite to analyze, annotate, QC, and visualize the DNA methylation data.We developed the CpGtools package to analyze DNA methylation data generated from bisulfite sequencing or Illumina methylation arrays. The CpGtools package consists of three types of modules: 1) "CpG position modules" focus on analyzing the genomic positions of CpGs, including associating other genomic and epigenomic features to a given list of CpGs, and generating the DNA motif logo enriched in the genomic contexts of a given list of CpGs; 2) "CpG signal modules" are designed to analyze DNA methylation values, such as performing the PCA or t-SNE analyses, using Bayesian Gaussian mixture modeling to classify CpG sites into fully methylated, partially methylated, and unmethylated groups, profiling the average DNA methylation level over user-specified genomics regions, and generating the bean/violin plots; and 3) "differential CpG analysis modules" focus on identifying differentially methylated CpGs between groups using different statistical methods including Fisher's Exact Test, Student's t-test, ANOVA, non-parametric tests, linear regression, logistic regression, beta-binomial regression, and Bayesian estimation.CpGtools is written in Python under the open-source GPL license. The source code and documentation are freely available at https://github.com/liguowang/cpgtools.Supplementary data are available at Bioinformatics online.
Reference Key
wei2019cpgtoolsbioinformatics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wei, Ting;Nie, Jinfu;Larson, Nicholas B;Ye, Zhenqing;Eckel Passow, Jeanette E;Robertson, Keith D;Kocher, Jean-Pierre A;Wang, Liguo;
Journal Bioinformatics
Year 2019
DOI
btz916
URL
Keywords
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