Limits in the detection of m6A changes using MeRIP/m6A-seq
Many cellular mRNAs contain the modified base m 6 A, and recent studies have suggested that various stimuli can lead to changes in m 6 A. The most common method to map m 6 A and to predict changes in m 6 A between conditions is methylated RNA immunoprecipitation sequencing (MeRIP-seq), through which...
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description | Many cellular mRNAs contain the modified base m
6
A, and recent studies have suggested that various stimuli can lead to changes in m
6
A. The most common method to map m
6
A and to predict changes in m
6
A between conditions is methylated RNA immunoprecipitation sequencing (MeRIP-seq), through which methylated regions are detected as peaks in transcript coverage from immunoprecipitated RNA relative to input RNA. Here, we generated replicate controls and reanalyzed published MeRIP-seq data to estimate reproducibility across experiments. We found that m
6
A peak overlap in mRNAs varies from ~30 to 60% between studies, even in the same cell type. We then assessed statistical methods to detect changes in m
6
A peaks as distinct from changes in gene expression. However, from these published data sets, we detected few changes under most conditions and were unable to detect consistent changes across studies of similar stimuli. Overall, our work identifies limits to MeRIP-seq reproducibility in the detection both of peaks and of peak changes and proposes improved approaches for analysis of peak changes. |
doi_str_mv | 10.1038/s41598-020-63355-3 |
format | Article |
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6
A, and recent studies have suggested that various stimuli can lead to changes in m
6
A. The most common method to map m
6
A and to predict changes in m
6
A between conditions is methylated RNA immunoprecipitation sequencing (MeRIP-seq), through which methylated regions are detected as peaks in transcript coverage from immunoprecipitated RNA relative to input RNA. Here, we generated replicate controls and reanalyzed published MeRIP-seq data to estimate reproducibility across experiments. We found that m
6
A peak overlap in mRNAs varies from ~30 to 60% between studies, even in the same cell type. We then assessed statistical methods to detect changes in m
6
A peaks as distinct from changes in gene expression. However, from these published data sets, we detected few changes under most conditions and were unable to detect consistent changes across studies of similar stimuli. Overall, our work identifies limits to MeRIP-seq reproducibility in the detection both of peaks and of peak changes and proposes improved approaches for analysis of peak changes.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-020-63355-3</identifier><identifier>PMID: 32313079</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/114/2415 ; 631/114/2416 ; 631/114/794 ; 631/1647/514/1949 ; 631/337/1645/2570 ; Gene expression ; Humanities and Social Sciences ; Immunoprecipitation ; multidisciplinary ; N6-methyladenosine ; Ribonucleic acid ; RNA ; Science ; Science (multidisciplinary) ; Statistical methods ; Transcription</subject><ispartof>Scientific reports, 2020-04, Vol.10 (1), p.6590-6590, Article 6590</ispartof><rights>The Author(s) 2020</rights><rights>The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c447t-305677561d392030bcafea91681a53c6066af6a542026f312a165ecb2409d9bb3</citedby><cites>FETCH-LOGICAL-c447t-305677561d392030bcafea91681a53c6066af6a542026f312a165ecb2409d9bb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170965/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170965/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,41096,42165,51551,53766,53768</link.rule.ids></links><search><creatorcontrib>McIntyre, Alexa B. R.</creatorcontrib><creatorcontrib>Gokhale, Nandan S.</creatorcontrib><creatorcontrib>Cerchietti, Leandro</creatorcontrib><creatorcontrib>Jaffrey, Samie R.</creatorcontrib><creatorcontrib>Horner, Stacy M.</creatorcontrib><creatorcontrib>Mason, Christopher E.</creatorcontrib><title>Limits in the detection of m6A changes using MeRIP/m6A-seq</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><description>Many cellular mRNAs contain the modified base m
6
A, and recent studies have suggested that various stimuli can lead to changes in m
6
A. The most common method to map m
6
A and to predict changes in m
6
A between conditions is methylated RNA immunoprecipitation sequencing (MeRIP-seq), through which methylated regions are detected as peaks in transcript coverage from immunoprecipitated RNA relative to input RNA. Here, we generated replicate controls and reanalyzed published MeRIP-seq data to estimate reproducibility across experiments. We found that m
6
A peak overlap in mRNAs varies from ~30 to 60% between studies, even in the same cell type. We then assessed statistical methods to detect changes in m
6
A peaks as distinct from changes in gene expression. However, from these published data sets, we detected few changes under most conditions and were unable to detect consistent changes across studies of similar stimuli. 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R. ; Gokhale, Nandan S. ; Cerchietti, Leandro ; Jaffrey, Samie R. ; Horner, Stacy M. ; Mason, Christopher E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c447t-305677561d392030bcafea91681a53c6066af6a542026f312a165ecb2409d9bb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>631/114/2415</topic><topic>631/114/2416</topic><topic>631/114/794</topic><topic>631/1647/514/1949</topic><topic>631/337/1645/2570</topic><topic>Gene expression</topic><topic>Humanities and Social Sciences</topic><topic>Immunoprecipitation</topic><topic>multidisciplinary</topic><topic>N6-methyladenosine</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Statistical methods</topic><topic>Transcription</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McIntyre, Alexa B. R.</creatorcontrib><creatorcontrib>Gokhale, Nandan S.</creatorcontrib><creatorcontrib>Cerchietti, Leandro</creatorcontrib><creatorcontrib>Jaffrey, Samie R.</creatorcontrib><creatorcontrib>Horner, Stacy M.</creatorcontrib><creatorcontrib>Mason, Christopher E.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McIntyre, Alexa B. R.</au><au>Gokhale, Nandan S.</au><au>Cerchietti, Leandro</au><au>Jaffrey, Samie R.</au><au>Horner, Stacy M.</au><au>Mason, Christopher E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Limits in the detection of m6A changes using MeRIP/m6A-seq</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><date>2020-04-20</date><risdate>2020</risdate><volume>10</volume><issue>1</issue><spage>6590</spage><epage>6590</epage><pages>6590-6590</pages><artnum>6590</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>Many cellular mRNAs contain the modified base m
6
A, and recent studies have suggested that various stimuli can lead to changes in m
6
A. The most common method to map m
6
A and to predict changes in m
6
A between conditions is methylated RNA immunoprecipitation sequencing (MeRIP-seq), through which methylated regions are detected as peaks in transcript coverage from immunoprecipitated RNA relative to input RNA. Here, we generated replicate controls and reanalyzed published MeRIP-seq data to estimate reproducibility across experiments. We found that m
6
A peak overlap in mRNAs varies from ~30 to 60% between studies, even in the same cell type. We then assessed statistical methods to detect changes in m
6
A peaks as distinct from changes in gene expression. However, from these published data sets, we detected few changes under most conditions and were unable to detect consistent changes across studies of similar stimuli. Overall, our work identifies limits to MeRIP-seq reproducibility in the detection both of peaks and of peak changes and proposes improved approaches for analysis of peak changes.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>32313079</pmid><doi>10.1038/s41598-020-63355-3</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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source | Nature Open Access; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Alma/SFX Local Collection; Free Full-Text Journals in Chemistry; Springer Nature OA Free Journals |
subjects | 631/114/2415 631/114/2416 631/114/794 631/1647/514/1949 631/337/1645/2570 Gene expression Humanities and Social Sciences Immunoprecipitation multidisciplinary N6-methyladenosine Ribonucleic acid RNA Science Science (multidisciplinary) Statistical methods Transcription |
title | Limits in the detection of m6A changes using MeRIP/m6A-seq |
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