Quantitative profiling of N6-methyladenosine at single-base resolution in stem-differentiating xylem of Populus trichocarpa using Nanopore direct RNA sequencing

Abstract

There are no comprehensive methods to identify N6-methyladenosine (m6A) at single-base resolution for every single transcript, which is necessary for the estimation of m6A abundance. We develop a new pipeline called Nanom6A for the identification and quantification of m6A modification at single-base resolution using Nanopore direct RNA sequencing based on an XGBoost model. We validate our method using methylated RNA immunoprecipitation sequencing (MeRIP-Seq) and m6A-sensitive RNA-endoribonuclease–facilitated sequencing (m6A-REF-seq), confirming high accuracy. Using this method, we provide a transcriptome-wide quantification of m6A modification in stem-differentiating xylem and reveal that different alternative polyadenylation (APA) usage shows a different ratio of m6A.

Recent advances in functional annotation and prediction of the epitranscriptome

“…Recently, a computational model MetaTX shed some light on this issue from a statistical perspective by taking advantage of the overall distribution pattern of RNA modification [186] and a computational package Episo was also developed to quantify epitranscriptomal RNA m 5 C at the transcript isoform level [187] . The Nanopore technology provide a parallel experimental solution [188] , [189] , [190] , [191] , [192] , [193] , [194] . (4) Evolutionary conservation of individual RNA modification sites.…”

N6‐Methyladenosine mRNA methylation is important for salt stress tolerance in Arabidopsis

“…RNA modifications target different types of RNAs, including long non‐coding RNA (lncRNA), messenger RNA (mRNA), ribosomal RNA (rRNA) and transfer RNA (tRNA). Over 150 types of RNA modifications have been identified to date (Boccaletto et al , 2018; Xuan et al , 2018), and the transcriptome‐wide mapping of several of these modifications, such as N 1 ‐methyladenosine (m 1 A), N 6 ‐methyladenosine (m 6 A) and 5‐methylcytosine (m 5 C), has been achieved based on recent advances in sequencing technologies for mapping these epitranscriptomic marks, including methylated RNA immunoprecipitation‐sequencing (MeRIP‐seq), nanopore direct RNA sequencing, RNA bisulfite sequencing, m 6 A cross‐linking and immunoprecipitation and m 6 A‐selective chemical labeling (David et al , 2017; Dominissini et al , 2012, 2013; Gao et al , 2021; Grozhik et al , 2017; Li et al , 2016; Parker et al , 2020; Shu et al , 2020). Among these modifications, m 6 A RNA methylation is the most prevalent, dynamic and reversible mRNA modification (Liu and Pan, 2016), and is the most comprehensively elucidated epitranscriptomic modification, with known writers (RNA methyltransferases that deposit m 6 A marks) (Bodi et al , 2012; Růžička et al , 2017; Shen et al , 2016; Zhang et al , 2019; Zhong et al , 2008), erasers (RNA demethylases that remove m 6 A marks) (Duan et al , 2017; Martínez‐Pérez et al , 2017) and readers (RNA‐binding proteins that interpret m 6 A marks) (Scutenaire et al , 2018; Song et al , 2021; Wei et al , 2018) in plants.…”

Detection of m6A from direct RNA sequencing using a Multiple Instance Learning framework

“…Supervised detection of m6A modifications involves training a classifier using labels that can either be obtained from synthetically modified RNA samples or existing experimental protocols such as miCLIP, MeRIP-Seq or m6ACE-Seq . Methods such as EpiNano 44,45 , MINES 46 , nanom6A 47 , use training data to identify m6A using the sequencing error profile or shifts in the current signal intensity. Supervised methods can potentially be applied on a single sample, overcoming the main limitation of comparative methods for detection of specific RNA modifications.…”

“…Supervised detection of m6A modifications involves training a classifier using labels that can either be obtained from synthetically modified RNA samples or existing experimental protocols such as miCLIP, MeRIP-Seq or m6ACE-Seq . Methods such as EpiNano 44,45 , MINES 46 , nanom6A 47 , use training data to identify m6A using the . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.…”