Employing machine learning for reliable miRNA target identification in plants

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides

Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint's GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

Species-specific microRNA discovery and target prediction in the soybean cyst nematode

The soybean cyst nematode (SCN) is a devastating pathogen for economic and food security considerations. Although the SCN genome has recently been sequenced, the presence of any miRNA has not been systematically explored and reported. This paper describes the development of a species-specific SCN miRNA discovery pipeline and its application to the SCN genome. Experiments on well-documented model nematodes (Caenorhabditis elegans and Pristionchus pacificus) are used to tune the pipeline's hyperparameters and confirm its recall and precision. Application to the SCN genome identifies 3342 high-confidence putative SCN miRNA. Prediction specificity within SCN is confirmed by applying the pipeline to RNA hairpins from known exonic regions of the SCN genome (i.e., sequences known to not be miRNA). Prediction recall is confirmed by building a positive control set of SCN miRNA, based on a limited deep sequencing experiment. Interestingly, a number of novel miRNA are predicted to be encoded within the intronic regions of effector genes, known to be involved in SCN parasitism, suggesting that these miRNA may also be involved in the infection process or virulence. Beyond miRNA discovery, gene targets within SCN are predicted for all high-confidence novel miRNA using a miRNA:mRNA target prediction system. Lastly, cross-kingdom miRNA targeting is investigated, where putative soybean mRNA targets are identified for novel SCN miRNA. All predicted miRNA and gene targets are made available in appendix and through a Borealis DataVerse open repository ( https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/30DEXA ).

The Involvement of microRNAs in Plant Lignan Biosynthesis—Current View

Lignans, as secondary metabolites synthesized within a phenylpropanoid pathway, play various roles in plants, including their involvement in growth and plant defense processes. The health and nutritional benefits of lignans are unquestionable, and many studies have been devoted to these attributes. Although the regulatory role of miRNAs in the biosynthesis of secondary metabolites has been widely reported, there is no systematic review available on the miRNA-based regulatory mechanism of lignans biosynthesis. However, the genetic background of lignan biosynthesis in plants is well characterized. We attempted to put together a regulatory mosaic based on current knowledge describing miRNA-mediated regulation of genes, enzymes, or transcription factors involved in this biosynthesis process. At the same time, we would like to underline the fact that further research is necessary to improve our understanding of the miRNAs regulating plant lignan biosynthesis by exploitation of current approaches for functional identification of miRNAs.

Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction

Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms, have been widely applied to RRI prediction with remarkable success. This paper first gives a brief introduction to the traditional machine learning methods applied on RRI prediction and benchmark databases for training the models, and then provides a recent methodology overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions.

Dosage-sensitive miRNAs trigger modulation of gene expression during genomic imbalance in maize

The genomic imbalance caused by varying the dosage of individual chromosomes or chromosomal segments (aneuploidy) has more detrimental effects than altering the dosage of complete chromosome sets (ploidy). Previous analysis of maize (Zea mays) aneuploids revealed global modulation of gene expression both on the varied chromosome (cis) and the remainder of the genome (trans). However, little is known regarding the role of microRNAs (miRNAs) under genomic imbalance. Here, we report the impact of aneuploidy and polyploidy on the expression of miRNAs. In general, cis miRNAs in aneuploids present a predominant gene-dosage effect, whereas trans miRNAs trend toward the inverse level, although other types of responses including dosage compensation, increased effect, and decreased effect also occur. By contrast, polyploids show less differential miRNA expression than aneuploids. Significant correlations between expression levels of miRNAs and their targets are identified in aneuploids, indicating the regulatory role of miRNAs on gene expression triggered by genomic imbalance.

Plant RNA-mediated gene regulatory network

Not all transcribed RNAs are protein-coding RNAs. Many of them are non-protein-coding RNAs in diverse eukaryotes. However, some of them seem to be non-functional and are resulted from spurious transcription. A lot of non-protein-coding transcripts have a significant function in the translation process. Gene expressions depend on complex networks of diverse gene regulatory pathways. Several non-protein-coding RNAs regulate gene expression in a sequence-specific system either at the transcriptional level or post-transcriptional level. They include a significant part of the gene expression regulatory network. RNA-mediated gene regulation machinery is evolutionarily ancient. They well-evolved during the evolutionary time and are becoming much more complex than had been expected. In this review, we are trying to summarizing the current knowledge in the field of RNA-mediated gene silencing.

The first draft genome of Picrorhiza kurrooa, an endangered medicinal herb from Himalayas

Picrorhiza kurrooa is an endangered medicinal herb which is distributed across the Himalayan region at an altitude between 3000–5000 m above mean sea level. The medicinal properties of P. kurrooa are attributed to monoterpenoid picrosides present in leaf, rhizome and root of the plant. However, no genomic information is currently available for P. kurrooa, which limits our understanding about its molecular systems and associated responses. The present study brings the first assembled draft genome of P. kurrooa by using 227 Gb of raw data generated by Illumina and PacBio RS II sequencing platforms. The assembled genome has a size of n = ~ 1.7 Gb with 12,924 scaffolds. Four pronged assembly quality validations studies, including experimentally reported ESTs mapping and directed sequencing of the assembled contigs, confirmed high reliability of the assembly. About 76% of the genome is covered by complex repeats alone. Annotation revealed 24,798 protein coding and 9789 non-coding genes. Using the assembled genome, a total of 710 miRNAs were discovered, many of which were found responsible for molecular response against temperature changes. The miRNAs and targets were validated experimentally. The availability of draft genome sequence will aid in genetic improvement and conservation of P. kurrooa. Also, this study provided an efficient approach for assembling complex genomes while dealing with repeats when regular assemblers failed to progress due to repeats.

Genome wide in-silico miRNA and target network prediction from stress responsive Horsegram (Macrotyloma uniflorum) accessions

Horsegram (Macrotyloma uniflorum (Lam.) Verdc.) is a drought hardy food and fodder legume of Indo-African continents with diverse germplasm sources demonstrating alternating mechanisms depicting contrasting adaptations to different climatic zones. Tissue specific expression of genes contributes substantially to location specific adaptations. Regulatory networks of such adaptive genes are elucidated for downstream translational research. MicroRNAs are small endogenous regulatory RNAs which alters the gene expression profiles at a particular time and type of tissue. Identification of such small regulatory RNAs in low moisture stress hardy crops can help in cross species transfer and validation confirming stress tolerance ability. This study outlined prediction of conserved miRNAs from transcriptome shotgun assembled sequences and EST sequences of horsegram. We could validate eight out of 15 of the identified miRNAs to demonstrate their role in deficit moisture stress tolerance mechanism of horsegram variety Paiyur1 with their target networks. The putative mumiRs were related to other food legumes indicating the presence of gene regulatory networks. Differential miRNA expression among drought specific tissues indicted the probable energy conservation mechanism. Targets were identified for functional characterization and regulatory network was constructed to find out the probable pathways of post-transcriptional regulation. The functional network revealed mechanism of biotic and abiotic stress tolerance, energy conservation and photoperiod responsiveness.

Computational tools for plant small RNA detection and categorization

Small RNAs (sRNAs) are important short-length molecules with regulatory functions essential for plant development and plasticity. High-throughput sequencing of total sRNA populations has revealed that the largest share of sRNA remains uncategorized. To better understand the role of sRNA-mediated cellular regulation, it is necessary to create accurate and comprehensive catalogues of sRNA and their sequence features, a task that currently relies on nontrivial bioinformatic approaches. Although a large number of computational tools have been developed to predict features of sRNA sequences, these tools are mostly dedicated to microRNAs and none integrates the functionalities necessary to describe units from all sRNA pathways thus far discovered in plants. Here, we review the different classes of sRNA found in plants and describe available bioinformatics tools that can help in their detection and categorization.

An Introduction to Methods for Discovery and Functional Analysis of MicroRNAs in Plants

MicroRNAs play important roles in posttranscriptional regulation of plant development, metabolism, and abiotic stress responses. The recent generation of massive amounts of small RNA sequence data, along with development of bioinformatic tools to identify miRNAs and their mRNA targets, has led to an explosion of newly identified putative miRNAs in plants. Genome editing techniques like CRISPR-Cas9 will allow us to study the biological role of these potential novel miRNAs by efficiently targeting both the miRNA and its mRNA target. In this chapter, we review bioinformatic tools and experimental methods for the identification and functional characterization of miRNAs and their target mRNAs in plants.

Plant Regulomics Portal (PRP): a comprehensive integrated regulatory information and analysis portal for plant genomes

Gene regulation is a highly complex and networked phenomenon where multiple tiers of control determine the cell state in a spatio-temporal manner. Among these, the transcription factors, DNA and histone modifications, and post-transcriptional control by small RNAs like miRNAs serve as major regulators. An understanding of the integrative and spatio-temporal impact of these regulatory factors can provide better insights into the state of a ‘cell system’. Yet, there are limited resources available to this effect. Therefore, we hereby report an integrative information portal (Plant Regulomics Portal; PRP) for plants for the first time. The portal has been developed by integrating a huge amount of curated data from published sources, RNA-, methylome- and sRNA/miRNA sequencing, histone modifications and repeats, gene ontology, digital gene expression and characterized pathways. The key features of the portal include a regulatory search engine for fetching numerous analytical outputs and tracks of the abovementioned regulators and also a genome browser for integrated visualization of the search results. It also has numerous analytical features for analyses of transcription factors (TFs) and sRNA/miRNA, spot-specific methylation, gene expression and interactions and details of pathways for any given genomic element. It can also provide information on potential RdDM regulation, while facilitating enrichment analysis, generation of visually rich plots and downloading of data in a selective manner. Visualization of intricate biological networks is an important feature which utilizes the Neo4j Graph database making analysis of relationships and long-range system viewing possible. Till date, PRP hosts 571-GB processed data for four plant species namely Arabidopsis thaliana, Oryza sativa subsp. japonica, Zea mays and Glycine max.

Database URL: https://scbb.ihbt.res.in/PRP

Augmentation of crop productivity through interventions of omics technologies in India: challenges and opportunities

With the continuous increase in the population of developing countries and decline of natural resources, there is an urgent need to qualitatively and quantitatively augment crop productivity by using new tools and technologies for improvement of agriculturally important traits. The new scientific and technological omics-based approaches have enabled us to deal with several issues and challenges faced by modern agricultural system and provided us novel opportunities for ensuring food and nutritional security. Recent developments in sequencing techniques have made available huge amount of genomic and transcriptomic data on model and cultivated crop plants including Arabidopsis thaliana, Oryza sativa, Triticum aestivum etc. The sequencing data along with other data generated through several omics platforms have significantly influenced the disciplines of crop sciences. Gene discovery and expression profiling-based technologies are offering enormous opportunities to the scientific community which can now apply marker-assisted selection technology to assess and enhance diversity in their collected germplasm, introgress essential traits from new sources and investigate genes that control key traits of crop plants. Utilization of omics science and technologies for crop productivity, protection and management has recently been receiving a lot of attention; the majority of the efforts have been put into signifying the possible applications of various omics technologies in crop plant sciences. This article highlights the background of challenges and opportunities for augmentation of crop productivity through interventions of omics technologies in India.

miRNAs target databases: developmental methods and target identification techniques with functional annotations

PURPOSE

microRNA (miRNA) regulates diverse biological mechanisms and metabolisms in plants and animals. Thus, the discoveries of miRNA has revolutionized the life sciences and medical research.The miRNA represses and cleaves the targeted mRNA by binding perfect or near perfect or imperfect complementary base pairs by RNA-induced silencing complex (RISC) formation during biogenesis process. One miRNA interacts with one or more mRNA genes and vice versa, hence takes part in causing various diseases. In this paper, the different microRNA target databases and their functional annotations developed by various researchers have been reviewed. The concurrent research review aims at comprehending the significance of miRNA and presenting the existing status of annotated miRNA target resources built by researchers henceforth discovering the knowledge for diagnosis and prognosis.

METHODS AND RESULTS

This review discusses the applications and developmental methodologies for constructing target database as well as the utility of user interface design. An integrated architecture is drawn and a graphically comparative study of present status of miRNA targets in diverse diseases and various biological processes is performed. These databases comprise of information such as miRNA target-associated disease, transcription factor binding sites (TFBSs) in miRNA genomic locations, polymorphism in miRNA target, A-to-I edited target, Gene Ontology (GO), genome annotations, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, target expression analysis, TF-miRNA and miRNA-mRNA interaction networks, drugs-targets interactions, etc.

CONCLUSION

miRNA target databases contain diverse experimentally and computationally predicted target through various algorithms. The comparison of various miRNA target database has been performed on various parameters. The computationally predicted target databases suffer from false positive information as there is no common theory for prediction of miRNA targets. The review conclusion emphasizes the need of more intelligent computational improvement for the miRNA target identification, their functional annotations and datasbase development.

Rice Improvement Through Genome-Based Functional Analysis and Molecular Breeding in India

Rice is one of the main pillars of food security in India. Its improvement for higher yield in sustainable agriculture system is also vital to provide energy and nutritional needs of growing world population, expected to reach more than 9 billion by 2050. The high quality genome sequence of rice has provided a rich resource to mine information about diversity of genes and alleles which can contribute to improvement of useful agronomic traits. Defining the function of each gene and regulatory element of rice remains a challenge for the rice community in the coming years. Subsequent to participation in IRGSP, India has continued to contribute in the areas of diversity analysis, transcriptomics, functional genomics, marker development, QTL mapping and molecular breeding, through national and multi-national research programs. These efforts have helped generate resources for rice improvement, some of which have already been deployed to mitigate loss due to environmental stress and pathogens. With renewed efforts, Indian researchers are making new strides, along with the international scientific community, in both basic research and realization of its translational impact.

MiRNAting control of DNA methylation

DNA methylation is a type of epigenetic modification where a methyl group is added to the cytosine or adenine residue of a given DNA sequence. It has been observed that DNA methylation is achieved by some collaborative agglomeration of certain proteins and non-coding RNAs. The assembly of IDN2 and its homologous proteins with siRNAs recruits the enzyme DRM2, which adds a methyl group at certain cytosine residues within the DNA sequence. In this study, it was found that de novo DNA methylation might be regulated by miRNAs through systematic targeting of the genes involved in DNA methylation. A comprehensive genome-wide and system-level study of miRNA targeting, transcription factors, DNA-methylation-causing genes and their target genes has provided a clear picture of an interconnected relationship of all these factors which regulate DNA methylation in Arabidopsis. The study has identified a DNA methylation system that is controlled by four different genes: IDN2, IDNl1, IDNl2 and DRM2. These four genes along with various critical transcription factors appear to be controlled by five different miRNAs. Altogether, DNA methylation appears to be a finely tuned process of opposite control systems of DNAmethylation- causing genes and certain miRNAs pitted against each other.

ptRNApred: computational identification and classification of post-transcriptional RNA

Non-coding RNAs (ncRNAs) are known to play important functional roles in the cell. However, their identification and recognition in genomic sequences remains challenging. In silico methods, such as classification tools, offer a fast and reliable way for such screening and multiple classifiers have already been developed to predict well-defined subfamilies of RNA. So far, however, out of all the ncRNAs, only tRNA, miRNA and snoRNA can be predicted with a satisfying sensitivity and specificity. We here present ptRNApred, a tool to detect and classify subclasses of non-coding RNA that are involved in the regulation of post-transcriptional modifications or DNA replication, which we here call post-transcriptional RNA (ptRNA). It (i) detects RNA sequences coding for post-transcriptional RNA from the genomic sequence with an overall sensitivity of 91% and a specificity of 94% and (ii) predicts ptRNA-subclasses that exist in eukaryotes: snRNA, snoRNA, RNase P, RNase MRP, Y RNA or telomerase RNA.

AVAILABILITY

The ptRNApred software is open for public use on http://www.ptrnapred.org/.

Plant microRNA-target interaction identification model based on the integration of prediction tools and support vector machine

Background

Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA). Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA–target interactions.

Results

Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA) feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species.

Conclusions

The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided.

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

BACKGROUND

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.

RESULTS

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.

CONCLUSION

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.

Plant miRNAome and antiviral resistance: a retrospective view and prospective challenges

MicroRNAs (miRNAs) are small regulatory RNAs that play a defining role in post-transcriptional gene silencing of eukaryotes by either mRNA cleavage or translational inhibition. Plant miRNAs have been implicated in innumerable growth and developmental processes that extend beyond their ability to respond to biotic and abiotic stresses. Active in an organism's immune defence response, host miRNAs display a propensity to target viral genomes. During viral invasion, these virus-targeting miRNAs can be identified by their altered expression. All the while, pathogenic viruses, as a result of their long-term interaction with plants, have been evolving viral suppressors of RNA silencing (VSRs), as well as viral-encoded miRNAs as a counter-defence strategy. However, the gene silencing attribute of miRNAs has been ingeniously manipulated to down-regulate the expression of any gene of interest, including VSRs, in artificial miRNA (amiRNA)-based transgenics. Since we currently have a better understanding of the intricacies of miRNA-mediated gene regulation in plant-virus interactions, the majority of miRNAs manipulated to confer antiviral resistance to date are in plants. This review will share the insights gained from the studies of plant-virus combat and from the endeavour to manipulate miRNAs, including prospective challenges in the context of the evolutionary dynamics of the viral genome. Next generation sequencing technologies and bioinformatics analysis will further delineate the molecular details of host-virus interactions. The need for appropriate environmental risk assessment principles specific to amiRNA-based virus resistance is also discussed.

Biogenesis, turnover, and mode of action of plant microRNAs

MicroRNAs (miRNAs) are small RNAs that control gene expression through silencing of target mRNAs. Mature miRNAs are processed from primary miRNA transcripts by the endonuclease activity of the DICER-LIKE1 (DCL1) protein complex. Mechanisms exist that allow the DCL1 complex to precisely excise the miRNA from its precursor. Our understanding of miRNA biogenesis, particularly its intersection with transcription and other aspects of RNA metabolism such as splicing, is still evolving. Mature miRNAs are incorporated into an ARGONAUTE (AGO) effector complex competent for target gene silencing but are also subjected to turnover through a degradation mechanism that is beginning to be understood. The mechanisms of miRNA target silencing in plants are no longer limited to AGO-catalyzed slicing, and the contribution of translational inhibition is increasingly appreciated. Here, we review the mechanisms underlying the biogenesis, turnover, and activities of plant miRNAs.

miReader: Discovering Novel miRNAs in Species without Sequenced Genome

Along with computational approaches, NGS led technologies have caused a major impact upon the discoveries made in the area of miRNA biology, including novel miRNAs identification. However, to this date all microRNA discovery tools compulsorily depend upon the availability of reference or genomic sequences. Here, for the first time a novel approach, miReader, has been introduced which could discover novel miRNAs without any dependence upon genomic/reference sequences. The approach used NGS read data to build highly accurate miRNA models, molded through a Multi-boosting algorithm with Best-First Tree as its base classifier. It was comprehensively tested over large amount of experimental data from wide range of species including human, plants, nematode, zebrafish and fruit fly, performing consistently with >90% accuracy. Using the same tool over Illumina read data for Miscanthus, a plant whose genome is not sequenced; the study reported 21 novel mature miRNA duplex candidates. Considering the fact that miRNA discovery requires handling of high throughput data, the entire approach has been implemented in a standalone parallel architecture. This work is expected to cause a positive impact over the area of miRNA discovery in majority of species, where genomic sequence availability would not be a compulsion any more.

Finding microRNA targets in plants: current status and perspectives

MicroRNAs (miRNAs), a class of ∼20–24 nt long non-coding RNAs, have critical roles in diverse biological processes including development, proliferation, stress response, etc. With the development and availability of experimental technologies and computational approaches, the field of miRNA biology has advanced tremendously over the last decade. By sequence complementarity, miRNAs have been estimated to regulate certain mRNA transcripts. Although it was once thought to be simple and straightforward to find plant miRNA targets, this viewpoint is being challenged by genetic and biochemical studies. In this review, we summarize recent progress in plant miRNA target recognition mechanisms, principles of target prediction, and introduce current experimental and computational tools for plant miRNA target prediction. At the end, we also present our thinking on the outlook for future directions in the development of plant miRNA target finding methods.