PEA: an integrated R toolkit for plant epitranscriptome analysis

Abstract

Supplementary data are available at Bioinformatics online.

N6‐methyladenosine regulatory machinery in plants: composition, function and evolution

“…Using this method, two closely situated m6A sites could be detected at single‐nucleotide resolution (Hong et al ., ). Furthermore, the AthMethPre web server, an integrated R application PEA, m6ASNP web server and m6AVar database were developed to predict the target m6A modification sites (Jiang et al ., ; Xiang et al ., ; Zhai et al ., ). Zhou developed a computational pipeline termed AutoCirc, which identified thousands of cell‐specific m6A modifications in circRNAs (Zhou et al ., ).…”

Evolution of the RNA N6-Methyladenosine Methylome Mediated by Genomic Duplication

“…The trimmed B73 and Han21 m 6 A-seq reads were aligned to the maize B73 reference genome and Han21 pseudogenome, respectively, using the STAR v2.5.3a (Dobin et al, 2013) with parameters: alignIntronMin 5 20; alignIntronMax 5 10000; outFilterMultimapNmax 5 1; and out-FilterMismatchNmax 5 1. Peak calling was performed using a "sliding window" method slightly modified from previous analysis (Luo et al, 2014) and implemented with the R package PEA (Zhai et al, 2018). To call m 6 A peaks, the reference genome was scanned using a 25-bp sliding window.…”

Recent advances in functional annotation and prediction of the epitranscriptome

“…The first step of functional epitranscriptome prediction is to identify modification sites, either directly from high-throughput sequencing data or by using sequenced-based computational prediction tools. There exist a large number of sequencing technologies and software tools that can serve this purpose, including but not limited to those based on reverse transcription signature [55] , [56] , [57] , [58] , [59] , bisulfite treatment [39] , [60] , [61] , [62] , [63] , [64] , [65] , [66] , antibody [11] , [12] and the primary sequences of RNA molecules [67] , [68] , [69] , [70] , [71] , [72] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] , [81] , [82] , [83] , [84] . In the following paragraph, we cover primarily two most widely used approaches, including site detection from MeRIP-Seq data and sequence-based in silico prediction methods.…”