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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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 |
format | Article |
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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.</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. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2019</rights><rights>The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>15</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000703906200016</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c452t-84002025eb2ead578fd4b507c6c03495f313d922a630c5d640beb3bc592c98743</citedby><cites>FETCH-LOGICAL-c452t-84002025eb2ead578fd4b507c6c03495f313d922a630c5d640beb3bc592c98743</cites><orcidid>0000-0003-2072-4826 ; 0000-0002-3468-4215</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275978/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275978/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,315,728,781,785,886,1605,27929,27930,39263,53796,53798</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/bioinformatics/btz916$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31808791$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Valencia, Alfonso</contributor><creatorcontrib>Wei, Ting</creatorcontrib><creatorcontrib>Nie, Jinfu</creatorcontrib><creatorcontrib>Larson, Nicholas B</creatorcontrib><creatorcontrib>Ye, Zhenqing</creatorcontrib><creatorcontrib>Eckel-Passow, Jeanette E</creatorcontrib><creatorcontrib>Robertson, Keith D</creatorcontrib><creatorcontrib>Kocher, Jean-Pierre A</creatorcontrib><creatorcontrib>Wang, Liguo</creatorcontrib><title>CpGtools: a python package for DNA methylation analysis</title><title>BIOINFORMATICS</title><addtitle>BIOINFORMATICS</addtitle><addtitle>Bioinformatics</addtitle><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.</description><subject>Applications Notes</subject><subject>Bayes Theorem</subject><subject>Biochemical Research Methods</subject><subject>Biochemistry & Molecular Biology</subject><subject>Biotechnology & Applied Microbiology</subject><subject>Computer Science</subject><subject>Computer Science, Interdisciplinary Applications</subject><subject>CpG Islands</subject><subject>DNA Methylation</subject><subject>High-Throughput Nucleotide Sequencing</subject><subject>Humans</subject><subject>Life Sciences & Biomedicine</subject><subject>Mathematical & Computational Biology</subject><subject>Mathematics</subject><subject>Physical Sciences</subject><subject>Science & Technology</subject><subject>Sequence Analysis, DNA</subject><subject>Statistics & Probability</subject><subject>Technology</subject><issn>1367-4803</issn><issn>1367-4811</issn><issn>1460-2059</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>EIF</sourceid><recordid>eNqNkUtv1TAQhS0Eog_4CaAskdCl41dss0CqQimVKrqBtWU7Tq8hiUPsFF1-PW5TrtpdVzPSfOfM6AxCbzB8wKDoiQ0xjF2cB5ODSyc2_1W4foYOMa3FhkmMn-97oAfoKKWfAMCB1y_RAcUSpFD4EIlmOs8x9uljZappl7dxrCbjfplrXxX36vO302rwebvry54yM6PpdymkV-hFZ_rkX9_XY_Tjy9n35uvm8ur8ojm93DjGSd5IBkCAcG-JNy0XsmuZ5SBc7YAyxTuKaasIMTUFx9uagfWWWscVcUoKRo_Rp9V3WuzgW-fHPJteT3MYzLzT0QT9eDKGrb6ON1oSwZWQxeDdvcEcfy8-ZT2E5Hzfm9HHJWlCSSEFY3VB-Yq6OaY0-26_BoO-DV0_Dl2voRfd24c37lX_Uy7A-xX4423skgt-dH6PlbcIoApqUro7O_l0ugn57jFNXMZcpLBK4zI98fh_zb-1VA</recordid><startdate>20210712</startdate><enddate>20210712</enddate><creator>Wei, Ting</creator><creator>Nie, Jinfu</creator><creator>Larson, Nicholas B</creator><creator>Ye, Zhenqing</creator><creator>Eckel-Passow, Jeanette E</creator><creator>Robertson, Keith D</creator><creator>Kocher, Jean-Pierre A</creator><creator>Wang, Liguo</creator><general>Oxford University Press</general><general>Oxford Univ Press</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2072-4826</orcidid><orcidid>https://orcid.org/0000-0002-3468-4215</orcidid></search><sort><creationdate>20210712</creationdate><title>CpGtools: a python package for DNA methylation analysis</title><author>Wei, Ting ; Nie, Jinfu ; Larson, Nicholas B ; Ye, Zhenqing ; Eckel-Passow, Jeanette E ; Robertson, Keith D ; Kocher, Jean-Pierre A ; Wang, Liguo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c452t-84002025eb2ead578fd4b507c6c03495f313d922a630c5d640beb3bc592c98743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Applications Notes</topic><topic>Bayes Theorem</topic><topic>Biochemical Research Methods</topic><topic>Biochemistry & Molecular Biology</topic><topic>Biotechnology & Applied Microbiology</topic><topic>Computer Science</topic><topic>Computer Science, Interdisciplinary Applications</topic><topic>CpG Islands</topic><topic>DNA Methylation</topic><topic>High-Throughput Nucleotide Sequencing</topic><topic>Humans</topic><topic>Life Sciences & Biomedicine</topic><topic>Mathematical & Computational Biology</topic><topic>Mathematics</topic><topic>Physical Sciences</topic><topic>Science & Technology</topic><topic>Sequence Analysis, DNA</topic><topic>Statistics & Probability</topic><topic>Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wei, Ting</creatorcontrib><creatorcontrib>Nie, Jinfu</creatorcontrib><creatorcontrib>Larson, Nicholas B</creatorcontrib><creatorcontrib>Ye, Zhenqing</creatorcontrib><creatorcontrib>Eckel-Passow, Jeanette E</creatorcontrib><creatorcontrib>Robertson, Keith D</creatorcontrib><creatorcontrib>Kocher, Jean-Pierre A</creatorcontrib><creatorcontrib>Wang, Liguo</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BIOINFORMATICS</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wei, Ting</au><au>Nie, Jinfu</au><au>Larson, Nicholas B</au><au>Ye, Zhenqing</au><au>Eckel-Passow, Jeanette E</au><au>Robertson, Keith D</au><au>Kocher, Jean-Pierre A</au><au>Wang, Liguo</au><au>Valencia, Alfonso</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CpGtools: a python package for DNA methylation analysis</atitle><jtitle>BIOINFORMATICS</jtitle><stitle>BIOINFORMATICS</stitle><addtitle>Bioinformatics</addtitle><date>2021-07-12</date><risdate>2021</risdate><volume>37</volume><issue>11</issue><spage>1598</spage><epage>1599</epage><pages>1598-1599</pages><issn>1367-4803</issn><eissn>1367-4811</eissn><eissn>1460-2059</eissn><abstract>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.</abstract><cop>OXFORD</cop><pub>Oxford University Press</pub><pmid>31808791</pmid><doi>10.1093/bioinformatics/btz916</doi><tpages>2</tpages><orcidid>https://orcid.org/0000-0003-2072-4826</orcidid><orcidid>https://orcid.org/0000-0002-3468-4215</orcidid><oa>free_for_read</oa></addata></record> |
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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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