eprintid: 10101489
rev_number: 16
eprint_status: archive
userid: 608
dir: disk0/10/10/14/89
datestamp: 2020-06-19 15:55:19
lastmod: 2021-10-04 00:06:31
status_changed: 2020-06-19 15:55:19
type: article
metadata_visibility: show
creators_name: Capitanchik, C
creators_name: Toolan-Kerr, P
creators_name: Luscombe, NM
creators_name: Ule, J
title: How Do You Identify m⁶ A Methylation in Transcriptomes at High Resolution? A Comparison of Recent Datasets
ispublished: pub
divisions: UCL
divisions: B02
divisions: C07
divisions: D07
divisions: F85
divisions: C08
divisions: D09
divisions: F99
keywords: RNA, N6-methyladenosine, m6A, epitranscriptomics, bioinformatics
note: Copyright © 2020 Capitanchik, Toolan-Kerr, Luscombe and Ule. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
abstract: A flurry of methods has been developed in recent years to identify N6-methyladenosine (m6A) sites across transcriptomes at high resolution. This raises the need to understand both the common features and those that are unique to each method. Here, we complement the analyses presented in the original papers by reviewing their various technical aspects and comparing the overlap between m6A-methylated messenger RNAs (mRNAs) identified by each. Specifically, we examine eight different methods that identify m6A sites in human cells with high resolution: two antibody-based crosslinking and immunoprecipitation (CLIP) approaches, two using endoribonuclease MazF, one based on deamination, two using Nanopore direct RNA sequencing, and finally, one based on computational predictions. We contrast the respective datasets and discuss the challenges in interpreting the overlap between them, including a prominent expression bias in detected genes. This overview will help guide researchers in making informed choices about using the available data and assist with the design of future experiments to expand our understanding of m6A and its regulation.
date: 2020-05-20
date_type: published
official_url: https://doi.org/10.3389/fgene.2020.00398
oa_status: green
full_text_type: pub
pmcid: PMC7251061
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1790294
doi: 10.3389/fgene.2020.00398
lyricists_name: Luscombe, Nicholas
lyricists_name: Ule, Jernej
lyricists_id: NMLUS17
lyricists_id: JULEX61
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: Frontiers in Genetics
volume: 11
article_number: 398
event_location: Switzerland
citation:        Capitanchik, C;    Toolan-Kerr, P;    Luscombe, NM;    Ule, J;      (2020)    How Do You Identify m⁶ A Methylation in Transcriptomes at High Resolution? A Comparison of Recent Datasets.                   Frontiers in Genetics , 11     , Article 398.  10.3389/fgene.2020.00398 <https://doi.org/10.3389/fgene.2020.00398>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10101489/1/fgene-11-00398.pdf