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.
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Zhai, J., Song, J., Cheng, Q., Tang, Y., Ma, C. (2018). PEA: an integrated R toolkit for plant epitranscriptome analysis Bioinformatics 34(21), 3747-3749. https://dx.doi.org/10.1093/bioinformatics/bty421
Chuang Ma
Professor, Doctoral Supervisor
My research interests include artificial intelligence, abiotic stress and plant breeding.