PEA is an integrated R toolkit that aims to facilitate the plant epitranscriptome analysis.
The main features:
1) PEA is a versatile epitranscriptome analysis pipeline covering CMR calling, prediction, and annotation. It generates comprehensive results for CMR (chemical modifications of RNA) calling from epitranscriptome sequencing data, CMR predictions at the transcriptome scale, and CMR annotation (location distribution analysis, motif scanning and discovery, and gene functional enrichment analysis).
2) 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).
3) PEA has been applied to predict N6-methyladenosine (m6A) modifications in Arabidopsis thaliana. Experimental results demonstrate that the toolkit achieved 71.6% sensitivity and 73.7% specificity on an independent test set, which is superior to existing m6A predictors.
4) The latest source codes and user manual of PEA are available at https://github.com/cma2015/PEA.
5) PEA is developed and maintained by the lab of Prof. Chuang Ma at the center of Bioinformatics, College of Life Sciences, Northwest A&F University. For comments/suggestions/error reports, please contact Jingjing Zhai (email@example.com) or Chuang Ma (firstname.lastname@example.org)