Degradome Assisted Plant MicroRNA Prediction under Alternative Annotation Criteria

Salma Alzahrani, Christopher Applegate, David Swarbreck, Tamas Dalmay, Leighton Folkes, VIncent Moulton

Research output: Contribution to journalArticlepeer-review

Abstract

Current microRNA (miRNA) prediction methods are generally based on annotation criteria that tend to miss potential functional miRNAs. Recently, new miRNA annotation criteria have been proposed that could lead to improvements in miRNA prediction methods in plants. Here, we investigate the effect of the new criteria on miRNA prediction in Arabidopsis thaliana and present a new degradome assisted functional miRNA prediction approach. We investigated the effect by applying the new criteria, and a more permissive criteria on miRNA prediction using existing miRNA prediction tools. We also developed an approach to miRNA prediction that is assisted by the functional information extracted from the analysis of degradome sequencing. We demonstrate the improved performance of degradome assisted miRNA prediction compared to unassisted prediction and evaluate the approach using miRNA differential expression analysis. We observe how the miRNA predictions fit under the different criteria and show a potential novel miRNA that has been missed within Arabidopsis thaliana. Additionally, we introduce a freely available software ‘PAREfirst’ that employs the degradome assisted approach. The study shows that some miRNAs could be missed due to the stringency of the former annotation criteria, and combining a degradome assisted approach with more permissive miRNA criteria can expand confident miRNA predictions.
Original languageEnglish
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Early online date24 Sep 2021
DOIs
Publication statusE-pub ahead of print - 24 Sep 2021

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