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A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction

Prashant K Srivastava14, Taraka Ramji Moturu1, Priyanka Pandey2, Ian T Baldwin3 and Shree P Pandey1*

Author Affiliations

1 Department of Biological Sciences, Indian Institute of Science Education and Research- Kolkata, Mohanpur Campus, Mohanpur 741252, West Bengal, India

2 National Institute of Biomedical Genomics, Kalyani 741251, West Bengal, India

3 Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knoell Str. 8, 07745 Jena, Germany

4 Current address: Integrative Genomics and Medicine, MRC clinical sciences, Imperial College, London, UK

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BMC Genomics 2014, 15:348  doi:10.1186/1471-2164-15-348

Published: 8 May 2014



Deep-sequencing has enabled the identification of large numbers of miRNAs and siRNAs, making the high-throughput target identification a main limiting factor in defining their function. In plants, several tools have been developed to predict targets, majority of them being trained on Arabidopsis datasets. An extensive and systematic evaluation has not been made for their suitability for predicting targets in species other than Arabidopsis. Nor, these have not been evaluated for their suitability for high-throughput target prediction at genome level.


We evaluated the performance of 11 computational tools in identifying genome-wide targets in Arabidopsis and other plants with procedures that optimized score-cutoffs for estimating targets. Targetfinder was most efficient [89% ‘precision’ (accuracy of prediction), 97% ‘recall’ (sensitivity)] in predicting ‘true-positive’ targets in Arabidopsis miRNA-mRNA interactions. In contrast, only 46% of true positive interactions from non-Arabidopsis species were detected, indicating low ‘recall’ values. Score optimizations increased the ‘recall’ to only 70% (corresponding ‘precision’: 65%) for datasets of true miRNA-mRNA interactions in species other than Arabidopsis. Combining the results of Targetfinder and psRNATarget delivers high true positive coverage, whereas the intersection of psRNATarget and Tapirhybrid outputs deliver highly ‘precise’ predictions. The large number of ‘false negative’ predictions delivered from non-Arabidopsis datasets by all the available tools indicate the diversity in miRNAs-mRNA interaction features between Arabidopsis and other species. A subset of miRNA-mRNA interactions differed significantly for features in seed regions as well as the total number of matches/mismatches.


Although, many plant miRNA target prediction tools may be optimized to predict targets with high specificity in Arabidopsis, such optimized thresholds may not be suitable for many targets in non-Arabidopsis species. More importantly, non-conventional features of miRNA-mRNA interaction may exist in plants indicating alternate mode of miRNA target recognition. Incorporation of these divergent features would enable next-generation of algorithms to better identify target interactions.

miRNA; Target prediction; Plants; Deep-sequencing; Non-model plants