CpGtools: a python package for DNA methylation analysis

Abstract Motivation 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 ana...

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Veröffentlicht in:BIOINFORMATICS 2021-07, Vol.37 (11), p.1598-1599
Hauptverfasser: Wei, Ting, Nie, Jinfu, Larson, Nicholas B, Ye, Zhenqing, Eckel-Passow, Jeanette E, Robertson, Keith D, Kocher, Jean-Pierre A, Wang, Liguo
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container_end_page 1599
container_issue 11
container_start_page 1598
container_title BIOINFORMATICS
container_volume 37
creator Wei, Ting
Nie, Jinfu
Larson, Nicholas B
Ye, Zhenqing
Eckel-Passow, Jeanette E
Robertson, Keith D
Kocher, Jean-Pierre A
Wang, Liguo
description Abstract Motivation 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. Results 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: (i) ‘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; (ii) ‘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 (iii) ‘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. Availability and implementation 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 information Supplementary data are available at Bioinformatics online.
doi_str_mv 10.1093/bioinformatics/btz916
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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. Results 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: (i) ‘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; (ii) ‘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 (iii) ‘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. Availability and implementation 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 information Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1367-4811</identifier><identifier>EISSN: 1460-2059</identifier><identifier>DOI: 10.1093/bioinformatics/btz916</identifier><identifier>PMID: 31808791</identifier><language>eng</language><publisher>OXFORD: Oxford University Press</publisher><subject><![CDATA[Applications Notes ; Bayes Theorem ; Biochemical Research Methods ; Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Computer Science, Interdisciplinary Applications ; CpG Islands ; DNA Methylation ; High-Throughput Nucleotide Sequencing ; Humans ; Life Sciences & Biomedicine ; Mathematical & Computational Biology ; Mathematics ; Physical Sciences ; Science & Technology ; Sequence Analysis, DNA ; Statistics & Probability ; Technology]]></subject><ispartof>BIOINFORMATICS, 2021-07, Vol.37 (11), p.1598-1599</ispartof><rights>The Author(s) 2019. 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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. Results 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: (i) ‘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; (ii) ‘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 (iii) ‘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. Availability and implementation 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. 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subjects Applications Notes
Bayes Theorem
Biochemical Research Methods
Biochemistry & Molecular Biology
Biotechnology & Applied Microbiology
Computer Science
Computer Science, Interdisciplinary Applications
CpG Islands
DNA Methylation
High-Throughput Nucleotide Sequencing
Humans
Life Sciences & Biomedicine
Mathematical & Computational Biology
Mathematics
Physical Sciences
Science & Technology
Sequence Analysis, DNA
Statistics & Probability
Technology
title CpGtools: a python package for DNA methylation analysis
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