PEA is a docker-based integrated R toolkit that aims to facilitate the plant epitranscriptome analysis. This toolkit contains a comprehensive collection of functions required for read mapping, CMR calling, motif scanning and discovery, and gene functional enrichment analysis. PEA also takes advantage of machine learning technologies for transcriptome-scale CMR prediction, with high prediction accuracy, using the Positive Samples Only Learning algorithm, which addresses the two-class classification problem by using only positive samples (CMRs), in the absence of negative samples (non-CMRs). Hence PEA is a versatile epitranscriptome analysis pipeline covering CMR calling, prediction, and annotation.

alternative text for search engines Figure .The schematic overview of PEA. A. Three functional modules of PEA for CMR calling, CMR prediction, and CMR annotation. B. CMR identification from epitranscriptom sequencing data through reads mapping and peak calling. C. CMR prediction using machine learning algorithms. D. CMR annotation and functional analysis at the transcriptome scale.

Zhai J, Song J, Cheng Q, et al. PEA: an integrated R toolkit for plant epitranscriptome analysis[J]. Bioinformatics, 2018.