Limits in the detection of m6A changes using MeRIP/m6A-seq

被引:140
作者
McIntyre, Alexa B. R. [1 ,2 ]
Gokhale, Nandan S. [3 ]
Cerchietti, Leandro [4 ]
Jaffrey, Samie R. [5 ]
Horner, Stacy M. [3 ,6 ]
Mason, Christopher E. [1 ,7 ,8 ,9 ]
机构
[1] Weill Cornell Med, Dept Physiol & Biophys, New York, NY 10065 USA
[2] Triinst Program Computat Biol & Med, New York, NY 10065 USA
[3] Duke Univ, Dept Mol Genet & Microbiol, Med Ctr, Durham, NC 27710 USA
[4] Weill Cornell Med, Div Hematol & Med Oncol, New York, NY 10065 USA
[5] Weill Cornell Med, Dept Pharmacol, New York, NY 10065 USA
[6] Duke Univ, Dept Med, Med Ctr, Durham, NC 27710 USA
[7] Weill Cornell Med, HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsau, New York, NY 10021 USA
[8] Weill Cornell Med, Feil Family Brain & Mind Res Inst, New York, NY 10065 USA
[9] Weill Cornell Med, WorldQuant Initiat Quantitat Predict, New York, NY 10021 USA
基金
加拿大自然科学与工程研究理事会; 美国国家卫生研究院;
关键词
MESSENGER-RNA METHYLATION; SINGLE-NUCLEOTIDE-RESOLUTION; CONTROLS CELL FATE; N-6-METHYLADENOSINE RNA; NUCLEAR-RNA; SEQ; TRANSLATION; REVEALS; N6-METHYLADENOSINE; REPRODUCIBILITY;
D O I
10.1038/s41598-020-63355-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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 similar to 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.
引用
收藏
页数:15
相关论文
共 90 条
  • [1] Coordination of m6A mRNA Methylation and Gene Transcription by ZFP217 Regulates Pluripotency and Reprogramming
    Aguilo, Francesca
    Zhang, Fan
    Sancho, Ana
    Fidalgo, Miguel
    Di Cecilia, Serena
    Vashisht, Ajay
    Lee, Dung-Fang
    Chen, Chih-Hung
    Rengasamy, Madhumitha
    Andino, Blanca
    Jahouh, Farid
    Roman, Angel
    Krig, Sheryl R.
    Wang, Rong
    Zhang, Weijia
    Wohlschlegel, James A.
    Wang, Jianlong
    Walsh, Martin J.
    [J]. CELL STEM CELL, 2015, 17 (06) : 689 - 704
  • [2] Dynamic m6A methylation facilitates mRNA triaging to stress granules
    Anders, Maximilian
    Chelysheva, Irina
    Goebel, Ingrid
    Trenkner, Timo
    Zhou, Jun
    Mao, Yuanhui
    Verzini, Silvia
    Qian, Shu-Bing
    Ignatova, Zoya
    [J]. LIFE SCIENCE ALLIANCE, 2018, 1 (04)
  • [3] m6aViewer: software for the detection, analysis, and visualization of N6-methyladenosine peaks from m6A-seq/ME-RIP sequencing data
    Antanaviciute, Agne
    Baquero-Perez, Belinda
    Watson, Christopher M.
    Harrison, Sally M.
    Lascelles, Carolina
    Crinnion, Laura
    Markham, Alexander F.
    Bonthron, David T.
    Whitehouse, Adrian
    Carr, Ian M.
    [J]. RNA, 2017, 23 (10) : 1493 - 1501
  • [4] The m6A Writer: Rise of a Machine for Growing Tasks
    Balacco, Dario L.
    Soller, Matthias
    [J]. BIOCHEMISTRY, 2019, 58 (05) : 363 - 378
  • [5] Promoter-bound METTL3 maintains myeloid leukaemia by m6A-dependent translation control
    Barbieri, Isaia
    Tzelepis, Konstantinos
    Pandolfini, Luca
    Shi, Junwei
    Millan-Zambrano, Gonzalo
    Robson, Samuel C.
    Aspris, Demetrios
    Migliori, Valentina
    Bannister, Andrew J.
    Han, Namshik
    De Braekeleer, Etienne
    Ponstingl, Hannes
    Hendrick, Alan
    Vakoc, Christopher R.
    Vassiliou, George S.
    Kouzarides, Tony
    [J]. NATURE, 2017, 552 (7683) : 126 - +
  • [6] m6A RNA Modification Controls Cell Fate Transition in Mammalian Embryonic Stem Cells
    Batista, Pedro J.
    Molinie, Benoit
    Wang, Jinkai
    Qu, Kun
    Zhang, Jiajing
    Li, Lingjie
    Bouley, Donna M.
    Lujan, Ernesto
    Haddad, Bahareh
    Daneshvar, Kaveh
    Carter, Ava C.
    Flynn, Ryan A.
    Zhou, Chan
    Lim, Kok-Seong
    Dedon, Peter
    Wernig, Marius
    Mullen, Alan C.
    Xing, Yi
    Giallourakis, Cosmas C.
    Chang, Howard Y.
    [J]. CELL STEM CELL, 2014, 15 (06) : 707 - 719
  • [7] The SMAD2/3 interactome reveals that TGFβ controls m6A mRNA methylation in pluripotency
    Bertero, Alessandro
    Brown, Stephanie
    Madrigal, Pedro
    Osnato, Anna
    Ortmann, Daniel
    Yiangou, Loukia
    Kadiwala, Juned
    Hubner, Nina C.
    de los Mozos, Igor Ruiz
    Sadee, Christoph
    Lenaerts, An-Sofie
    Nakanoh, Shota
    Grandy, Rodrigo
    Farnell, Edward
    Ule, Jernej
    Stunnenberg, Hendrik G.
    Mendjan, Sasha
    Vallier, Ludovic
    [J]. NATURE, 2018, 555 (7695) : 256 - +
  • [8] Trimmomatic: a flexible trimmer for Illumina sequence data
    Bolger, Anthony M.
    Lohse, Marc
    Usadel, Bjoern
    [J]. BIOINFORMATICS, 2014, 30 (15) : 2114 - 2120
  • [9] Near-optimal probabilistic RNA-seq quantification (vol 34, pg 525, 2016)
    Bray, Nicolas L.
    Pimentel, Harold
    Melsted, Pall
    Pachter, Lior
    [J]. NATURE BIOTECHNOLOGY, 2016, 34 (08) : 888 - 888
  • [10] Data Science Issues in Studying Protein-RNA Interactions with CLIP Technologies
    Chakrabarti, Anob M.
    Haberman, Nejc
    Praznik, Arne
    Luscombe, Nicholas M.
    Ule, Jernej
    [J]. ANNUAL REVIEW OF BIOMEDICAL DATA SCIENCE, VOL 1, 2018, 1 : 235 - 261