Abstract Accurate identification of microRNA (miRNA) targets at base-pair resolution has been an open problem for over a decade. The recent discovery of miRNA isoforms (isomiRs) adds more complexity to this problem. Despite the existence of many methods, none considers isomiRs, and their performance is still suboptimal. We hypothesize that by taking the isomiR–mRNA interactions into account and applying a deep learning model to study miRNA–mRNA interaction features, we may improve the accuracy of miRNA target predictions. We developed a deep learning tool called DMISO to capture the intricate features of miRNA/isomiR–mRNA interactions. Based on tenfold cross-validation, DMISO showed high precision (95%) and recall (90%). Evaluated on three independent datasets, DMISO had superior performance to five tools, including three popular conventional tools and two recently developed deep learning-based tools. By applying two popular feature interpretation strategies, we demonstrated the importance of the miRNA regions other than their seeds and the potential contribution of the RNA-binding motifs within miRNAs/isomiRs and mRNAs to the miRNA/isomiR–mRNA interactions.
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miRador: a fast and precise tool for the prediction of plant miRNAs
Abstract Plant microRNAs (miRNAs) are short, noncoding RNA molecules that restrict gene expression via posttranscriptional regulation and function in several essential pathways, including development, growth, and stress responses. Accurately identifying miRNAs in populations of small RNA sequencing libraries is a computationally intensive process that has resulted in the misidentification of inaccurately annotated miRNA sequences. In recent years, criteria for miRNA annotation have been refined with the aim to reduce these misannotations. Here, we describe miRador, a miRNA identification tool that utilizes the most up-to-date, community-established criteria for accurate identification of miRNAs in plants. We combined target prediction and Parallel Analysis of RNA Ends (PARE) data to assess the precision of the miRNAs identified by miRador. We compared miRador to other commonly used miRNA prediction tools and found that miRador is at least as precise as other prediction tools while being substantially faster than other tools. miRador should be broadly useful for the plant community to identify and annotate miRNAs in plant genomes.
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- Award ID(s):
- 1754097
- PAR ID:
- 10404681
- Date Published:
- Journal Name:
- Plant Physiology
- Volume:
- 191
- Issue:
- 2
- ISSN:
- 0032-0889
- Page Range / eLocation ID:
- 894 to 903
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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