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Review

The Multiverse of Plant Small RNAs: How Can We Explore It?

by
Zdravka Ivanova
1,†,
Georgi Minkov
1,2,†,
Andreas Gisel
3,
Galina Yahubyan
2,
Ivan Minkov
1,4,
Valentina Toneva
1,2 and
Vesselin Baev
1,2,*
1
Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria
2
Department of Plant Physiology and Molecular Biology, University of Plovdiv, 4000 Plovdiv, Bulgaria
3
Institute of Biomedical Technologies (ITB), CNR, 70126 Bari, Italy
4
Center of Plant System Biology and Biotechnology, 4000 Plovdiv, Bulgaria
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2022, 23(7), 3979; https://doi.org/10.3390/ijms23073979
Submission received: 1 March 2022 / Revised: 28 March 2022 / Accepted: 31 March 2022 / Published: 2 April 2022
(This article belongs to the Special Issue The World of Plant Non-coding RNAs)
Browse Figure
Versions Notes

Abstract

:
Plant small RNAs (sRNAs) are a heterogeneous group of noncoding RNAs with a length of 20–24 nucleotides that are widely studied due to their importance as major regulators in various biological processes. sRNAs are divided into two main classes—microRNAs (miRNAs) and small interfering RNAs (siRNAs)—which differ in their biogenesis and functional pathways. Their identification and enrichment with new structural variants would not be possible without the use of various high-throughput sequencing (NGS) techniques, allowing for the detection of the total population of sRNAs in plants. Classifying sRNAs and predicting their functional role based on such high-performance datasets is a nontrivial bioinformatics task, as plants can generate millions of sRNAs from a variety of biosynthetic pathways. Over the years, many computing tools have been developed to meet this challenge. Here, we review more than 35 tools developed specifically for plant sRNAs over the past few years and explore some of their basic algorithms for performing tasks related to predicting, identifying, categorizing, and quantifying individual sRNAs in plant samples, as well as visualizing the results of these analyzes. We believe that this review will be practical for biologists who want to analyze their plant sRNA datasets but are overwhelmed by the number of tools available, thus answering the basic question of how to choose the right one for a particular study.

1. Structural Diversity—The Big Bang of Small RNAs in Plants

Since the discovery that double-stranded RNAs (dsRNAs) can trigger gene silencing in Caenorhabditis elegans [1], small RNAs (sRNAs) have become recognized in plants as key molecules for maintaining plant genome integrity and regulating plant development and stress responses. Plant sRNAs are short molecules of various lengths (typically 20–24 nucleotide—nt), processed from single-stranded hairpin RNA or double-stranded RNA precursors [2,3,4]. Next-generation sequencing (NGS) techniques have greatly expanded the possibilities for identifying sRNAs by providing a massive number of sequences from a single plant sample. Additionally bioinformatics and computational tools play a vital role in these growing technologies, being an essential part of identifying and quantifying sRNAs and understanding the world of plant sRNAs. Advances in this field have led to a better understanding of the different types of sRNA as well as the various complex roles and interactions these molecules have within the context of gene expression.
Plant endogenous sRNAs are classified into two major types: microRNAs (miRNAs) and small-interfering RNAs (siRNAs). The latter group includes several different subtypes: natural antisense siRNAs (natsiRNAs), secondary siRNAs (phasiRNAs and tasiRNAs), and heterochromatic siRNAs (hcsiRNAs). Exogenous sRNAs, virus- or viroid-genome-derived siRNAs (vsiRNAs), on the other hand, are products of a host plant’s RNA silencing pathway and have been implicated in the plant’s defense response (Figure 1) [5,6,7].
Many miRNA-encoding genes (MIR) have been found in the intronic regions of so-called “host genes” within plant genomes, as well as in other genomic loci. They are transcribed by RNA polymerase II, forming a long primary RNA transcript (pri-miRNA) [8]. Part of the pri-miRNA sequence folds into a perfect stem–loop structure, stabilized by the RNA-binding protein DDL, to form a precursor miRNA (pre-miRNA). The precursor is recognized by an endoribonuclease Dicer-like (DCL1), cleaved to the miRNA:miRNA* duplex, and exported to the cytoplasm [9,10,11]. Finally, the mature (canonical) miRNA is loaded into the RNA-induced gene silencing complex (RISC), which guides an ARGONAUTE 1 protein (AGO1) to target mRNAs. Recent studies have shown that miRNA* may not necessarily be degraded as previously thought, but rather have a functional role—to recognize specific target genes. It has been found that most miRNA target genes of miRNAs are transcription factors (TF) [12,13].
As a result of inaccurate Dicer processing, pre-miRNAs can produce miRNAs:miRNA* duplexes shifted from the canonical miRNA, resulting in the generation of so-called isomiRs. This phenomenon is not only observed as a result of an enzymatic error, but isomiRs are molecules that seem to be programmed to be generated. It is also evidenced by their presence in various tissues and under specific environmental conditions, where their expression levels may differ accordingly [14]. Due to the altered production, and therefore depending on their sequence, the targets of isomiR molecules may or may not differ from mature miRNA targets [15].
Unlike miRNAs, which have relatively defined genes in the plant genome, siRNAs can be generated from ideally double-stranded RNAs (dsRNAs), generated from a variety of sources, such as the following: RNAs transcribed from inverted repeats; natural cis-antisense transcript pairs; genome-rich loci with retroelements; or even from an exogenous viral source. Depending on the specific catalytic activity of DCLs, these dsRNAs may be cleaved into molecules of different lengths, usually 21–24 nt. For example, natsiRNAs originate from dsRNAs derived either from overlapping transcripts (cis-natsiRNAs) or from highly complementary transcripts derived from different loci (trans-natsiRNAs) that form dsRNAs and are cleaved by DCL1, DCL2, or DCL3 [16,17,18].
The biogenesis of secondary siRNAs—phased siRNAs (phasiRNAs), including trans-acting siRNAs (tasiRNAs)—is more complex, involving Pol II transcription of noncoding or protein-coding loci as well as transposable elements. Producing the secondary, RNA-dependent RNA polymerase, RDR-dependent sRNAs, requires transcript targeting by miRNAs [19]. Cleaved targets are converted into dsRNA by RDR6 and cut by DCL2 and DCL4 to siRNAs of size 21 or 22 nt. We should point out that the production of secondary siRNAs (phasiRNAs and tasiRNAs) from mRNAs, noncoding RNAs, requires miRNAs to target mRNA sources. The name of trans-acting RNAs (tasiRNAs), found in some types of phasiRNAs, comes from their ability to function as miRNAs in a homologously dependent manner, directing the cleavage of mRNAs other than those at their source.
Both exogenous derived and endogenous sRNAs can guide transcriptional gene silencing (TGS) or posttranscriptional gene silencing (PTGS). miRNAs can regulate many biological processes by gene silencing at the PTGS level [20]. They pair with target mRNAs initiating cleavage or inhibition protein translation [20]. miRNAs, natsiRNAs, and phasiRNAs (including tasiRNAs) function primarily at the PTGS level via cleavage or translational suppression of target transcripts; although, several findings have been reported indicating that they can induce DNA methylation [21,22].
Like miRNAs, siRNAs are loaded into AGO-containing RISC complexes in order to guide target silencing at PTGS. The 24 nt hcsiRNAs (sometimes known as heterochromatic sRNA (hetsiRNAs) or repeat-associated sRNAs (rasiRNAs)) are the most abundant sRNAs in plants, responsible for inducing transcriptional silencing of transposons and pericentromeric repeats via RdDM. The generation of hctsiRNAs requires Pol IV transcription, followed by dsRNA synthesis by RDR2 and processing by DCL3 (Figure 1). Usually, hcsiRNAs promote DNA or histone modifications at the loci which produce them, including retrotransposons, 5S rDNA, and centromeric repeats (the reason some authors call them rasiRNAs) [23,24,25,26].
vsiRNAs are produced when exogenous viral transcripts are converted to dsRNAs by various mechanisms, including RDRs, with the help of DCL4, DCL2, and DCL3. These vsiRNAs are loaded into RISC complexes and eventually slice the hostile viral transcripts or the target host mRNAs [27,28,29].

2. Functional Diversity—The Expanse of the sRNAs World

Functional analysis of miRNAs showed their crucial involvement in many plant biological processes by regulating various genes at PTGS. These small but powerful molecules have been widely studied and found to play a key role in various development processes, including, among others, the following: meristem boundary identity [30,31,32]; auxin signaling [33,34]; organ separation, leaf development [35,36,37]; lateral root formation [38,39,40]; juvenile-to-adult vegetative phase [41,42,43]; leave development [35,36,37]; floral organ development [33,34,35,36]; flowering time [36,43,44]; control of cell growth and proliferation [45,46].
The functional role of miRNA also includes plant responses to biotic and abiotic stresses. Environmental changes can trigger plants to adapt their miRNA expression or produce new miRNAs to cope with stress. Various stress-dependent miRNAs have been identified and annotated in plants under numerous abiotic stress conditions, including: hypoxia and oxidative stress [47,48,49,50]; drought [51,52,53,54]; nutrient homeostasis [50,55,56,57,58,59,60,61,62]; cold [53,63,64,65]; heat [19]; salinity [53,66]; UV-B radiation [57,67]; mechanical stress [68]; heavy metals [69]. Furthermore, some miRNAs are linked to biotic responses in bacterial and fungal interactions with plants. For example, plants effectively utilize miRNAs to finetune their phytohormone pathways, along with genes involved in pathogenic virulence [70].
The discovery of isomiRs has highlighted the functional significance of miRNAs in gene regulation. Their altered sequences, as compared to canonical mature miRNA and miRNA*, may lead to a new set of target molecules, adding another level of complexity to miRNAs function [68,71]. In addition, some, if not all, miRNAs may reversibly interact with their targets, suggesting that miRNAs may regulate their own biogenesis pathways and supporting the possibility that miRNA regulatory roles may not be limited to protein-encoding transcripts. [72].
Although some sRNAs may still be unknown or less studied, other classes have important functional roles in plants. Secondary sRNAs (phasiRNAs and their subgroups of tasiRNAs) are known or predicted to function in various biological processes. However, given the many PHAS loci and phasiRNA members found in different plants, their exact functional role remains poorly documented overall. Triggered by miRNAs, these secondary sRNAs are found to be involved in several important biological processes. tasiRNAs can target the pentatricopeptide repeat-containing genes (PPR), many of which are abiotic-stress-related genes. Recent findings suggest that some tasiRNAs are involved in the thermotolerance of plants through the regulation of heat-stress-related TF [73]. Other tasiRNAs can target ARFs, which are related to the auxin-mediated control of developmental processes, including leaf morphogenesis, developmental timing, lateral root growth, and somatic embryogenesis [74,75,76,77,78,79,80,81]. Another relatively highly conserved tasiRNA, TAS4, is related to MYBs regulation [82], which is associated with lignin biosynthesis, bioflavonoid biosynthesis, and fruit development [82,83]. Different studies suggested that phasiRNAs are associated with plant immunity [84,85,86], as they can target disease resistance genes [87,88,89]. Interestingly phasiRNAs are also involved in plant parasitism. The authors in [90] found that a parasitic plant uses trans-species silencing to repress transcripts within the host plant, thereby facilitating its parasitism.
Some cis-natural antisense transcripts (cis-NATs) have been reported to generate natsiRNAs in response to abiotic and biotic stresses [16,91,92,93] or to accumulate in specific developmental stages [17,94]. natsiRNAs are the least studied small RNAs in terms of their functional roles in plants; however, some studies suggest that their role may be related to various mechanisms of plant development and stress response, such as pathogen resistance [95], salt tolerance [5], and cell wall biosynthesis [96].
Although most sRNAs function through PTGS, some perform their biological role through TGS through the RdDM mechanism. The hcsiRNAs are very important in maintaining genome stability and gene regulation as they induce epigenetic modification in repeat elements. Plant genomes consist of large portions of such repeats [97], having the ability of genome jumping and multiplication, causing gene disruption. Such events require plants to have protective mechanisms, where hcsiRNAs step in and prevent mobilization of the transposons. Repeats are also located in the promoter regions of protein-coding genes, which generate 24 nt sRNAs. hcsiRNAs can control these regulatory elements via RdDM mechanism, the methylation status of which can also affect the downstream gene expression [98]. Another functional role of hcsiRNAs is their participation in plant reproduction, including methylation programs of the gamete cells observed within the endosperm and zygote [99].
The interactions between host plants, viruses, and various abundant vsiRNAs are highly complex. vsiRNAs, produced through the host RNA silencing pathways, have been implicated in the host defense response [100]. Studies point out that the disease symptoms in the infected plant are a consequences of RNA silencing directed against important host genes by the same vsiRNAs [101,102]. They can induce antiviral defense through PTGS or TGS of viral RNA, as well as hijack the host’s RNA silencing system, in order to target complementary host transcripts.

3. Bioinformatics Tools for Exploration and Analysis of the World of Small RNAs

In the post-genomics era, NGS technologies provide quantitative evaluation and single-base resolution of known and novel sRNAs through various sRNA-seq methods. Various computational tools have been developed to analyze sRNA NGS data, allowing for not only the detection, profiling, and annotation of different classes of sRNAs, but also comparing the sRNA expression levels between samples. Over the past decade, these bioinformatics methods and software applications have become an inseparable part of exploring the sRNAs world, creating a need for more user-friendly tools, viable for biologists without programming knowledge or advanced bioinformatics skills. Most of the tools that analyze sRNA-seq data are run on Linux/Unix servers or clusters. This often requires knowledge of command-line workflows and interpreting large amounts of data files, which can often be a difficult task for biologists. A user-friendly graphic interface is a must for such software.
The next pitfall is that some tools require complex installations, dependencies, and third-party modules requiring further IT knowledge, which may disrupt the smooth analysis process. Furthermore, some tools are also tailored to operate on a specific operating system, which adds another layer of complexity. While some tools attempt to address these disadvantages by providing direct web access with a GUI and no upfront setup requirement, this has its own flaws. Users may need to work with large data files, which can be hard to upload and store, or use-sensitive, proprietary data, which cannot be stored off site. In conclusion, almost every bioinformatics tool has its pros and cons, and therefore it is crucial to select the right tool for each application. We provide a review of recently developed tools for sRNAs data analysis as a support for people searching help to enter in the world of sRNA or keep updated in this fast-developing field.
In this current review, we have gathered more than 35 tools developed over the past five years that deal with the analysis of sRNAs, including miRNAs, isomiRs, natsiRNA, phasiRNAs, and vsiRNAs, with most using sRNA-seq data as their primary source of sRNAs from the wet-lab experiment (Table 1). Not surprisingly, a significant part of these tools are dedicated to miRNAs as being the most famous and explored sRNA class. These miRNA-related tools have a variety of purposes—profiling and annotation of miRNAs and their isomiRs; identification of new MIR genes and putative precursors; discovery of miRNA targets and miRNA–mRNA interactions.
There is no unified protocol for analyzing miRNAs and their isoforms from sRNA-sequencing data. Nevertheless, most tools use similar major processing stages. Usually, the discovery stage includes several preprocessing steps of the raw reads from the sRNA-seq data, including sequencing library QC, adapter trimming, and cleaning low-quality reads, size filtering, etc. These steps generally integrate various widely accepted third-party tools that are integrated into the main analysis software, for example, FastQC, Prinseq, Cutadapt, FASTX and Trimommatic, TrimGalore, etc. (IsomiR_Window, miRDis, miRPursuit, MiRkwood, MirGalaxy, IsoMiRmap, miRge, sRNAnalyzer, isomiR2Function, MirCure, QuickMIRSeq). Next, the clean reads are mapped to a reference database in order to recognize known miRNAs or their isoforms across the processed samples. Here, the majority of the tools used Bowtie for third-party aligner, though some used others such as PatMaN (miRCat2, miRPursuit) and BWA (miRKwood, sRNAnalyser). The most frequently used reference miRNAs databases used are miRBase and miRGene [140,141,142].
It is worth noting that some tools can process unique molecular identifiers (UMIs) if the source files require that analysis protocol (sRNAbench, miRge, sRNAtools). When it comes to normalizing and quantifying the miRNAs identified in samples by differential expression analysis, most of the developed software uses external specially dedicated statistical packages, such as DESeq2, EdgeR, and EBSeq (SRNAbench, MirGalaxy, IsoMiRmap, miRge, IsomiR_Window, isomiR2Function, SRNAbench, miRDis, isomiR2Function, etc.), or RPM normalization (QuickMIRSeq, SRIS, sRNAnalyzer, PmiRDiscValie). Recently, the isomiR phenomenon has attracted the attention of researchers. Due to their potential importance, an increasing number of tools have provided distinctive features, such as comprehensive isomiR exploration, including further specific profile analysis (sRNAbench, miRGalaxy, isoMiRmap, miRge, IsomiR_Window, sRNAnalyser, isomiR2Function, miRDis, QuickMIRSeq, etc.).
Besides discovering known miRNAs, the identification of novel plant MIR genes is also an essential part of the comprehensive sRNAs analysis. Recently, various computational tools have been developed for identifying miRNAs with the help of supporting data from next-generation sequencing datasets, along with applying criteria based on the features of the miRNA biogenesis. Usually, these tools use aligners, such as Bowtie or ParMaN, to map reads to reference sequences or genome (miRKwood, miRDeep-P2, miRge, miRCat2, microRPM) and then searches for clusters of sRNAs that can be produced from potential precursor as miRNA:miRNA*. These clusters are carefully examined on various criteria to ensure they are consistent with miRNA biogenesis rules. Putative precursors are passed to a third-party package for thermodynamic calculation and RNA folding, for which most tools use ViennaRNA [143]. Some tools employ the help of the machine learning algorithms to strengthen their discovery approaches (miRDetect, miRHunter, miRge, microRPM, iwa-miRNA). The standard approach to discover new miRNA genes and their precursor is using sRNA-seq data and a reference genome. Still, some specific tools can use different NGS data (PmiRDiscVali) or even perform analysis without a reference genome (Mirnovo, miRDetect, microRPM), which can include other than sRNA-seq data, or support sRNA analysis of non-model plants where only a low-quality or even no reference genome exists.
Recently, different bioinformatics tools, such as Targetfinder [144], psRNATarget [120], comTAR [145], psRobot [146], CleaveLand [147,148], and sPARTA [149], have been developed to predict miRNA targets in plants, and were reviewed before. These tools have since been implemented in other, newer miRNA software, where they serve a similar purpose. There are not many new tools for target discovery, especially for plants. The psRNATarget tool was updated with the ability to use NGS data, along with an improved scoring schema for target site prediction, including a better-weighted method for the mismatch-sensitive “seed” region. This tool also considers mRNA target accessibility, i.e., the energy required to open mRNA secondary structure near the target, by calculating unpaired energy (UPE), using the most popular tools of the ViennaRNA package [150]. TarHunter is a new development that relies on orthologous miRNA clustering from desired species and cross-species conservation filters and implementing RNAhybrid (ViennaRNA) to search for miRNA targets. Importantly, target discovery may take advantages of novel approaches, including not only sRNA-seq data but also degradome sequences (SRIS, TarHunter, PAREsnip2). Degradome sequences represent fragments of mRNA cleaved by miRNAs or siRNA [151,152,153,154,155]. Usually, to discover potential target sites, degradome sequences are mapped to a transcriptome using Bowtie in order to provide information for the 5’ cleavage of the mRNA [156].
miRNA-dedicated tools seem to rule the computational universe when it comes to sRNAs exploration. Nevertheless, there are a number of software packages dedicated to identifying other sRNAs (natsiRNAs, phasi- and tasi-RNAs, tRNA-derived sRNAs, 24 nt siRNA, etc.). Their profiling often includes the same computational stages mentioned above, such as preprocessing of sRNA data and mapping, etc. Here, the miRNA reference databases are extended beside miRbase, with Ensembl, RfamBD, RefSeq, tRNA, and rRNA databases, and genomes with already specific annotations for small RNAs (unitas, SRIS, sRNAtools, sRNAbench, SRNAnalyser). In addition to broad profiling of sRNAs, there are some more specific tools for exploring phasiRNAs annotation (PHASIS, PhasiRNAanalyser, unitas), which are based on sequence homology instead of particular biogenesis rules.
The PHASIS suite provides comprehensive tools for de novo prediction and characterization of PHAS loci, emphasizing plants, where these loci are numerous. The recently developed PhasiRNAnalyzer also can provide identification of all crucial components in phasiRNAs’ regulatory pathway, along with furthermore verification of the interactions between phasiRNAs and their target genes based on degradome data. Moreover, the tool can perform differential expression analysis of phasiRNAs on each PHAS gene locus between different samples. natsiRNAs can be predicted with the tool NATpare. It requires sRNA, transcriptome, and optionally degradome data as input and enables the identification of both cis- and trans-natsiRNAs. The tool identifies potential NAT pairs and potential natsiRNAs, and, if degradome data is provided, the candidate natsiRNAs are subject to functional analysis using PAREsnip2 [125] to search for potential mRNA targets.
Virus infections are recognized as a significant threat to agricultural production and plant health. Efficient and accurate detection of vsiRNAs and therefore of viruses and viroids in plants is essential for the development of effective strategies to manage the spread and impact of viral diseases. For this reason, some bioinformatics tools with the ability to detect these exogenous small RNAs (VirusDetect, sRNAprofiler, SRIS, VSD toolkit) have recently been developed. This detection is carried out by mapping sRNA-seq data to viroid and/or virus sequence repositories by Bowtie, BWA, or BLAST. Additionally, some of the tools provide the ability to assemble and reconstruct the viral genome by gathering all vsiRNAs to aid in the discovery of new pathogen species or strains (using third-party assemblers such as SPades, Velvet, etc.).

4. Conclusions and Future Perspectives

Plant research is increasing slowly keeping up with popular research target, such as animals or humans, visible also in how many bioinformatics tools and algorithms became available in recent years for analysing sRNAs in plants. Due to the relatively complex biology of sRNAs, these tools are far from perfect and request further development to approach questions about the creation, involvement, and regulation of sRNA. Most of the tools have their pros and cons and can tackle a specific argument for a specific type of sRNA.
Currently, most of the tools are designed to handle miRNA analysis, leaving a gap for tools dedicated to other plant sRNAs species. This is because the research for miRNA is much more advanced than other sRNAs and can help to train and validate new algorithms. Another bottleneck is that many of these tools still require significant bioinformatics skills, creating difficulties in applicability and usability. Unfortunately, we are still confronted with the fact that, rather than unifying standards of analysis input and outputs, some software applications diverge further by implementing their own bespoke formats, which cannot be exchanged between tools. Nevertheless, computational tools that can bring comprehensive solutions and support wet-lab processes are needed now more than ever to expand our knowledge of sRNAs in revealing the full scope of their universe in a user-friendly way for basic research, but also for application in agronomy and breeding.

Author Contributions

Conceptualization, V.B., Z.I. and G.M.; resources, Z.I. and G.M.; writing—original draft preparation, V.B., Z.I. and G.M.; writing—review and editing, G.Y., A.G. and I.M.; visualization, V.B.; supervision, V.B., V.T. and I.M.; project administration, V.T. and I.M.; funding acquisition, V.T. and I.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the European Union’s Horizon 2020 research and innovation program, project PlantaSYST (SGA-CSA No. 739582 under FPA No. 664620).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Fire, A.; Xu, S.; Montgomery, M.K.; Kostas, S.A.; Driver, S.E.; Mello, C.C. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 1998, 391, 6669. [Google Scholar] [CrossRef]
  2. Song, X.; Li, Y.; Cao, X.; Qi, Y. MicroRNAs and Their Regulatory Roles in Plant-Environment Interactions. Annu. Rev. Plant Biol. 2019, 70, 489–525. [Google Scholar] [CrossRef]
  3. Fang, X.; Qi, Y. Rnai in plants: An argonaute-centered view. Plant Cell 2015, 28, 272–285. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Baulcombe, D. RNA silencing in plants. RNA silencing in plants. Nature 2004, 431, 356–363. [Google Scholar] [CrossRef] [PubMed]
  5. Chapman, E.J.; Carrington, J.C. Specialization and evolution of endogenous small RNA pathways. Nat. Rev. Genet. 2007, 8, 884–896. [Google Scholar] [CrossRef] [PubMed]
  6. Axtell, M.J. Classification and comparison of small RNAs from plants. Annu. Rev. Plant Biol. 2013, 64, 137–159. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Bernstein, E.; Caudy, A.A.; Hammond, S.M.; Hannon, G.J. Role for a bidentate ribonuclease in the initiation step of RNA interference. Nature 2001, 409, 363–366. [Google Scholar] [CrossRef] [PubMed]
  8. D’Ario, M.; Griffiths-Jones, S.; Kim, M. Small RNAs: Big Impact on Plant Development. Trends Plant Sci. 2017, 22, 1056–1068. [Google Scholar] [CrossRef] [Green Version]
  9. Ramachandran, V.; Chen, X. Small RNA metabolism in Arabidopsis. Trends Plant Sci. 2008, 13, 368–374. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Xie, M.; Zhang, S.; Yu, B. microRNA biogenesis, degradation and activity in plants. Cell. Mol. Life Sci. 2015, 72, 87–99. [Google Scholar] [CrossRef]
  11. Park, W.; Li, J.; Song, R.; Messing, J.; Chen, X. Carpel Factory, a Dicer homolog, and HEN1, a novel protein, act in microRNA metabolism in Arabidopsis thaliana. Curr. Biol. 2002, 12, 1484–1495. [Google Scholar] [CrossRef] [Green Version]
  12. Tang, J.; Chu, C. MicroRNAs in crop improvement: Fine-tuners for complex traits. Nat. Plants 2017, 3, 17077. [Google Scholar] [CrossRef] [PubMed]
  13. Zhang, B.; Unver, T. A critical and speculative review on microRNA technology in crop improvement: Current challenges and future directions. Plant Sci. 2018, 274, 193–200. [Google Scholar] [CrossRef] [PubMed]
  14. Guo, L.; Liang, T.; Lu, Z. A comprehensive study of multiple mapping and feature selection for correction strategy in the analysis of small RNAs from SOLiD sequencing. BioSystems 2011, 104, 87–93. [Google Scholar] [CrossRef] [PubMed]
  15. Fard, E.M.; Moradi, S.; Salekdeh, N.N.; Bakhshi, B.; Ghaffari, M.R.; Zeinalabedini, M.; Salekdeh, G.H. Plant isomiRs: Origins, biogenesis, and biological functions. Genomics 2020, 112, 3382–3395. [Google Scholar] [CrossRef]
  16. Borsani, O.; Zhu, J.; Verslues, P.E.; Sunkar, R.; Zhu, J.-K. Endogenous siRNAs Derived from a Pair of Natural cis-Antisense Transcripts Regulate Salt Tolerance in Arabidopsis. Cell 2005, 123, 1279–1291. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Ron, M.; Saez, M.A.; Williams, L.E.; Fletcher, J.C.; McCormick, S. Proper regulation of a sperm-specific cis-nat-siRNA is essential for double fertilization in Arabidopsis. Genes Dev. 2010, 24, 1010–1021. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Katiyar-Agarwal, S.; Gao, S.; Vivian-Smith, A.; Jin, H. A novel class of bacteria-induced small RNAs in Arabidopsis. Genes Dev. 2007, 21, 3123–3134. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Xin, M.; Wang, Y.; Yao, Y.; Xie, C.; Peng, H.; Ni, Z.; Sun, Q. Diverse set of microRNAs are responsive to powdery mildew infection and heat stress in wheat (Triticum aestivum L.). BMC Plant Biol. 2010, 10, 123. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Jones-Rhoades, M.W.; Bartel, D.P.; Bartel, B. MicroRNAs and their regulatory roles in plants. Annu. Rev. Plant Biol. 2006, 57, 19–53. [Google Scholar] [CrossRef]
  21. Wu, L.; Zhou, H.; Zhang, Q.; Zhang, J.; Ni, F.; Liu, C.; Qi, Y. DNA Methylation Mediated by a MicroRNA Pathway. Mol. Cell 2010, 38, 465–475. [Google Scholar] [CrossRef] [PubMed]
  22. Wu, L.; Mao, L.; Qi, Y. Roles of DICER-LIKE and ARGONAUTE Proteins in TAS-Derived Small Interfering RNA-Triggered DNA Methylation. Plant Physiol. 2012, 160, 990–999. [Google Scholar] [CrossRef] [Green Version]
  23. Chan, S.W.-L.; Henderson, I.; Jacobsen, S.E. Gardening the genome: DNA methylation in Arabidopsis thaliana. Nat. Rev. Genet. 2005, 6, 351–360. [Google Scholar] [CrossRef]
  24. Xie, Z.; Johansen, L.K.; Gustafson, A.M.; Kasschau, K.D.; Lellis, A.D.; Zilberman, D.; Jacobsen, S.E.; Carrington, J.C. Genetic and Functional Diversification of Small RNA Pathways in Plants. PLoS Biol. 2004, 2, e104. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Rosa, C.; Kuo, Y.-W.; Yan, Z.; Falk, B.W. RNA Interference Mechanisms and Applications in Plant Pathology. Annu. Rev. Phytopathol. 2018, 56, 581–610. [Google Scholar] [CrossRef] [PubMed]
  26. Vazquez, F.; Hohn, T. Biogenesis and Biological Activity of Secondary siRNAs in Plants. Scientifica 2013, 2013, 783253. [Google Scholar] [CrossRef]
  27. Xia, Z.; Peng, J.; Li, Y.; Chen, L.; Li, S.; Zhou, T.; Fan, Z. Characterization of Small Interfering RNAs Derived from Sugarcane Mosaic Virus in Infected Maize Plants by Deep Sequencing. PLoS ONE 2014, 9, e97013. [Google Scholar] [CrossRef] [PubMed]
  28. Agrawal, N.; Dasaradhi, P.V.N.; Mohmmed, A.; Malhotra, P.; Bhatnagar, R.K.; Mukherjee, S.K. RNA Interference: Biology, Mechanism, and Applications. Microbiol. Mol. Biol. Rev. 2003, 67, 657–685. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Csorba, T.; Kontra, L.; Burgyán, J. viral silencing suppressors: Tools forged to fine-tune host-pathogen coexistence. Virology 2015, 479-480, 85–103. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Nikovics, K.; Blein, T.; Peaucelle, A.; Ishida, T.; Morin, H.; Aida, M.; Laufs, P. The Balance between the MIR164A and CUC2 Genes Controls Leaf Margin Serration inArabidopsis. Plant Cell 2006, 18, 2929–2945. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Laufs, P.; Peaucelle, A.; Morin, H.; Traas, J. MicroRNA regulation of the CUC genes is required for boundary size control in Arabidopsis meristems. Development 2004, 131, 4311–4322. [Google Scholar] [CrossRef] [Green Version]
  32. Raman, S.; Greb, T.; Peaucelle, A.; Blein, T.; Laufs, P.; Theres, K. Interplay of miR164, CUP-SHAPED COTYLEDON genes and LATERAL SUPPRESSOR controls axillary meristem formation in Arabidopsis thaliana. Plant J. 2008, 55, 65–76. [Google Scholar] [CrossRef]
  33. Wang, J.-W.; Wang, L.-J.; Mao, Y.-B.; Cai, W.-J.; Xue, H.-W.; Chen, X.-Y. Control of Root Cap Formation by MicroRNA-Targeted Auxin Response Factors in Arabidopsis. Plant Cell 2005, 17, 2204–2216. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Mallory, A.C.; Bartel, D.P.; Bartel, B. MicroRNA-Directed Regulation of Arabidopsis AUXIN RESPONSE FACTOR17 is Essential for Proper Development and Modulates Expression of Early Auxin Response Genes. Plant Cell 2005, 17, 1360–1375. [Google Scholar] [CrossRef] [Green Version]
  35. Palatnik, J.; Allen, E.; Wu, X.; Schommer, C.; Schwab, R.; Carrington, J.; Weigel, D. Control of leaf morphogenesis by microRNAs. Nature 2003, 425, 257–263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Schwab, R.; Palatnik, J.; Riester, M.; Schommer, C.; Schmid, M.; Weigel, D. Specific Effects of MicroRNAs on the Plant Transcriptome. Dev. Cell 2005, 8, 517–527. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Achard, P.; Herr, A.; Baulcombe, D.; Harberd, N.P. Modulation of floral development by a gibberellin-regulated microRNA. Development 2004, 131, 3357–3365. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Yang, L.; Liu, Z.; Lu, F.; Dong, A.; Huang, H. SERRATE is a novel nuclear regulator in primary microRNA processing in Arabidopsis. Plant J. 2006, 47, 841–850. [Google Scholar] [CrossRef]
  39. Lobbes, D.; Rallapalli, G.; Schmidt, D.D.; Martin, C.; Clarke, J. SERRATE: A new player on the plant microRNA scene. EMBO Rep. 2006, 7, 1052–1058. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Grigg, S.P.; Canales, C.; Hay, A.; Tsiantis, M. SERRATE coordinates shoot meristem function and leaf axial patterning in Arabidopsis. Nature 2005, 437, 1022–1026. [Google Scholar] [CrossRef] [PubMed]
  41. Wu, G.; Park, M.Y.; Conway, S.R.; Wang, J.-W.; Weigel, D.; Poethig, R.S. The Sequential Action of miR156 and miR172 Regulates Developmental Timing in Arabidopsis. Cell 2009, 138, 750–759. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. He, J.; Xu, M.; Willmann, M.R.; McCormick, K.; Hu, T.; Yang, L.; Starker, C.; Voytas, D.; Meyers, B.C.; Poethig, R.S. Threshold-dependent repression of SPL gene expression by miR156/miR157 controls vegetative phase change in Arabidopsis thaliana. PLoS Genet. 2018, 14, e1007337. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Wu, G.; Poethig, R.S. Temporal regulation of shoot development in Arabidopsis thaliana by miR156 and its target SPL3. Development 2006, 133, 3539–3547. [Google Scholar] [CrossRef] [Green Version]
  44. Wang, J.-W.; Schwab, R.; Czech, B.; Mica, E.; Weigel, D. Dual Effects of miR156-Targeted SPL Genes and CYP78A5/KLUH on Plastochron Length and Organ Size in Arabidopsis thaliana. Plant Cell 2008, 20, 1231–1243. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Ori, N.; Cohen, A.R.; Etzioni, A.; Brand, A.; Yanai, O.; Shleizer, S.; Menda, N.; Amsellem, Z.; Efroni, I.; Pekker, I.; et al. Regulation of LANCEOLATE by miR319 is required for compound-leaf development in tomato. Nat. Genet. 2007, 39, 787–791. [Google Scholar] [CrossRef] [PubMed]
  46. Palatnik, J.; Wollmann, H.; Schommer, C.; Schwab, R.; Boisbouvier, J.; Rodriguez, R.; Warthmann, N.; Allen, E.; Dezulian, T.; Huson, D.; et al. Sequence and Expression Differences Underlie Functional Specialization of Arabidopsis MicroRNAs miR159 and miR319. Dev. Cell 2007, 13, 115–125. [Google Scholar] [CrossRef] [Green Version]
  47. Moldovan, D.; Spriggs, A.; Yang, J.; Pogson, B.J.; Dennis, E.S.; Wilson, I.W. Hypoxia-responsive microRNAs and trans-acting small interfering RNAs in Arabidopsis. J. Exp. Bot. 2009, 61, 165–177. [Google Scholar] [CrossRef] [Green Version]
  48. Zhang, Z.; Wei, L.; Zou, X.; Tao, Y.; Liu, Z.; Zheng, Y. Submergence-responsive MicroRNAs are Potentially Involved in the Regulation of Morphological and Metabolic Adaptations in Maize Root Cells. Ann. Bot. 2008, 102, 509–519. [Google Scholar] [CrossRef] [Green Version]
  49. Li, T.; Li, H.; Zhang, Y.-X.; Liu, J.-Y. Identification and analysis of seven H2O2-responsive miRNAs and 32 new miRNAs in the seedlings of rice (Oryza sativa L. ssp. indica). Nucleic Acids Res. 2010, 39, 2821–2833. [Google Scholar] [CrossRef] [Green Version]
  50. Sunkar, R.; Kapoor, A.; Zhu, J.K. Erratum: Posttranscriptional induction of two Cu/Zn superoxide dismutase genes in Arabidopsis is mediated by downregulation of miR398 and important for oxidative stress. Plant Cell 2006, 18, 2051–2065. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Zhou, L.; Liu, Y.; Liu, Z.; Kong, D.; Duan, M.; Luo, L. Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. J. Exp. Bot. 2010, 61, 4157–4168. [Google Scholar] [CrossRef] [PubMed]
  52. Zhao, B.; Liang, R.; Ge, L.; Li, W.; Xiao, H.; Lin, H.; Ruan, K.; Jin, Y. Identification of drought-induced microRNAs in rice. Biochem. Biophys. Res. Commun. 2007, 354, 585–590. [Google Scholar] [CrossRef]
  53. Liu, H.-H.; Tian, X.; Li, Y.-J.; Wu, C.-A.; Zheng, C.-C. Microarray-based analysis of stress-regulated microRNAs in Arabidopsis thaliana. RNA 2008, 14, 836–843. [Google Scholar] [CrossRef] [Green Version]
  54. Huertero, C.A.; Pérez, B.; Rabanal, F.; Blanco-Melo, D.; De La Rosa, C.; Estrada-Navarrete, G.; Sanchez, F.; Covarrubias, A.A.; Reyes, J.L. Conserved and novel miRNAs in the legume Phaseolus vulgaris in response to stress. Plant Mol. Biol. 2009, 70, 385–401. [Google Scholar] [CrossRef]
  55. Fujii, H.; Chiou, T.-J.; Lin, S.-I.; Aung, K.; Zhu, J.-K. A miRNA Involved in Phosphate-Starvation Response in Arabidopsis. Curr. Biol. 2005, 15, 2038–2043. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Bari, R.; Pant, B.D.; Stitt, M.; Golm, S.P. PHO2, MicroRNA399, and PHR1 Define a Phosphate-Signaling Pathway in Plants. Plant Physiol. 2006, 141, 988–999. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Jia, X.; Ren, L.; Chen, Q.-J.; Li, R.; Tang, G. UV-B-responsive microRNAs in Populus tremula. J. Plant Physiol. 2009, 166, 2046–2057. [Google Scholar] [CrossRef] [PubMed]
  58. Yamasaki, H.; Abdel-Ghany, S.E.; Cohu, C.M.; Kobayashi, Y.; Shikanai, T.; Pilon, M. Regulation of Copper Homeostasis by Micro-RNA in Arabidopsis. J. Biol. Chem. 2007, 282, 16369–16378. [Google Scholar] [CrossRef] [Green Version]
  59. Jones-Rhoades, M.W.; Bartel, D.P. Computational Identification of Plant MicroRNAs and Their Targets, Including a Stress-Induced miRNA The primary method of identifying miRNA genes has been to isolate, reverse transcribe, clone, and sequence small cellular RNAs. Mol. Cell 2004, 14, 787–799. [Google Scholar] [CrossRef] [PubMed]
  60. Rubio, V.; Linhares, F.; Solano, R.; Martín, A.C.; Iglesias, J.; Leyva, A.; Paz-Ares, J. A conserved MYB transcription factor involved in phosphate starvation signaling both in vascular plants and in unicellular algae. Genes Dev. 2001, 15, 2122–2133. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Franco-Zorrilla, J.M.; González, E.; Bustos, R.; Linhares, F.; Leyva, A.; Paz-Ares, J. The transcriptional control of plant responses to phosphate limitation. J. Exp. Bot. 2004, 55, 285–293. [Google Scholar] [CrossRef] [PubMed]
  62. Zeng, H.; Wang, G.; Hu, X.; Wang, H.; Du, L.; Zhu, Y. Role of microRNAs in plant responses to nutrient stress. Plant Soil 2013, 374, 1005–1021. [Google Scholar] [CrossRef]
  63. Zhou, X.; Wang, G.; Sutoh, K.; Zhu, J.-K.; Zhang, W. Identification of cold-inducible microRNAs in plants by transcriptome analysis. Biochim. Biophys. Acta Gene Regul. Mech. 2008, 1779, 780–788. [Google Scholar] [CrossRef] [PubMed]
  64. Sunkar, R.; Zhu, J.-K. Novel and Stress-Regulated MicroRNAs and Other Small RNAs from Arabidopsis. Plant Cell 2004, 16, 2001–2019. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Lu, S.; Sun, Y.-H.; Chiang, V.L. Stress-responsive microRNAs in Populus. Plant J. 2008, 55, 131–151. [Google Scholar] [CrossRef] [PubMed]
  66. Sunkar, R.; Zhou, X.; Zheng, Y.; Zhang, W.; Zhu, J.-K. Identification of novel and candidate miRNAs in rice by high throughput sequencing. BMC Plant Biol. 2008, 8, 25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Zhou, X.; Wang, G.; Zhang, W. UV-B responsive microRNA genes in Arabidopsis thaliana. Mol. Syst. Biol. 2007, 3, 103. [Google Scholar] [CrossRef] [PubMed]
  68. Lu, S.; Sun, Y.-H.; Shi, R.; Clark, C.; Li, L.; Chiang, V.L. Novel and Mechanical Stress–Responsive MicroRNAs in Populus trichocarpa That Are Absent from Arabidopsis. Plant Cell 2005, 17, 2186–2203. [Google Scholar] [CrossRef] [Green Version]
  69. Ding, Y.; Chen, Z.; Zhu, C. Microarray-based analysis of cadmium-responsive microRNAs in rice (Oryza sativa). J. Exp. Bot. 2011, 62, 3563–3573. [Google Scholar] [CrossRef] [PubMed]
  70. Zhang, X.; Yuan, Y.-R.; Pei, Y.; Lin, S.-S.; Tuschl, T.; Patel, D.J.; Chua, N.-H. Cucumber mosaic virus-encoded 2b suppressor inhibits Arabidopsis Argonaute1 cleavage activity to counter plant defense. Genes Dev. 2006, 20, 3255–3268. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Kim, V. MicroRNA precursors in motion: Exportin-5 mediates their nuclear export. Trends Cell Biol. 2004, 14, 156–159. [Google Scholar] [CrossRef]
  72. Zhao, Y.; He, S.; Liu, C.; Ru, S.; Zhao, H.; Yang, Z.; Yang, P.; Yuan, X.; Sun, S.; Bu, D.; et al. MicroRNA regulation of messenger-like noncoding RNAs: A network of mutual microRNA control. Trends Genet. 2008, 24, 323–327. [Google Scholar] [CrossRef] [PubMed]
  73. Li, S.; Liu, J.; Liu, Z.; Li, X.; Wu, F.; He, Y. HEAT-INDUCED TAS1 TARGET1 Mediates Thermotolerance via HEAT STRESS TRANSCRIPTION FACTOR A1a–Directed Pathways in Arabidopsis. Plant Cell 2014, 26, 1764–1780. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Adenot, X.; Elmayan, T.; Lauressergues, D.; Boutet, S.; Bouché, N.; Gasciolli, V.; Vaucheret, H. DRB4-Dependent TAS3 trans-Acting siRNAs Control Leaf Morphology through AGO7. Curr. Biol. 2006, 16, 927–932. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Fahlgren, N.; Montgomery, T.; Howell, M.D.; Allen, E.; Dvorak, S.K.; Alexander, A.L.; Carrington, J.C. Regulation of AUXIN RESPONSE FACTOR3 by TAS3 ta-siRNA Affects Developmental Timing and Patterning in Arabidopsis. Curr. Biol. 2006, 16, 939–944. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Marin, E.; Jouannet, V.; Herz, A.; Lokerse, A.S.; Weijers, D.; Vaucheret, H.; Nussaume, L.; Crespi, M.D.; Maizel, A. miR390, Arabidopsis TAS3 tasiRNAs, and Their AUXIN RESPONSE FACTOR Targets Define an Autoregulatory Network Quantitatively Regulating Lateral Root Growth. Plant Cell 2010, 22, 1104–1117. [Google Scholar] [CrossRef] [Green Version]
  77. Cho, S.H.; Coruh, C.; Axtell, M.J. miR156 and miR390 Regulate tasiRNA Accumulation and Developmental Timing in Physcomitrella patens. Plant Cell 2012, 24, 4837–4849. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Yifhar, T.; Pekker, I.; Peled, D.; Friedlander, G.; Pistunov, A.; Sabban, M.; Wachsman, G.; Alvarez, J.P.; Amsellem, Z.; Eshed, Y. Failure of the Tomato Trans-Acting Short Interfering RNA Program to Regulate AUXIN response factor3 and ARF4 Underlies the Wiry Leaf Syndrome. Plant Cell 2012, 24, 3575–3589. [Google Scholar] [CrossRef] [Green Version]
  79. Zhou, C.; Han, L.; Fu, C.; Wen, J.; Cheng, X.; Nakashima, J.; Ma, J.; Tang, Y.; Tan, Y.; Tadege, M.; et al. The Trans-Acting Short Interfering RNA3 Pathway and NO APICAL MERISTEM Antagonistically Regulate Leaf Margin Development and Lateral Organ Separation, as Revealed by Analysis of an argonaute7/lobed leaflet1 Mutant in Medicago truncatula. Plant Cell 2013, 25, 4845–4862. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Lin, Y.; Lin, L.; Lai, R.; Liu, W.; Chen, Y.; Zhang, Z.; XuHan, X.; Lai, Z. MicroRNA390-Directed TAS3 Cleavage Leads to the Production of tasiRNA-ARF3/4 During Somatic Embryogenesis in Dimocarpus longan Lour. Front. Plant Sci. 2015, 6, 1119. [Google Scholar] [CrossRef] [PubMed]
  81. Hobecker, K.V.; Reynoso, M.A.; Bustos-Sanmamed, P.; Wen, J.; Mysore, K.; Crespi, M.; Blanco, F.A.; Zanetti, M.E. The MicroRNA390/TAS3 Pathway Mediates Symbiotic Nodulation and Lateral Root Growth. Plant Physiol. 2017, 174, 2469–2486. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  82. Xia, R.; Zhu, H.; An, Y.-Q.; Beers, E.P.; Liu, Z. Apple miRNAs and tasiRNAs with novel regulatory networks. Genome Biol. 2012, 13, R47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Rock, C.D. Trans-acting small interfering RNA4: Key to nutraceutical synthesis in grape development? Trends Plant Sci. 2013, 18, 601–610. [Google Scholar] [CrossRef] [Green Version]
  84. Fei, Q.; Zhang, Y.; Xia, R.; Meyers, B.C. Small RNAs Add Zing to the Zig-Zag-Zig Model of Plant Defenses. Mol. Plant-Microbe Interact. 2016, 29, 165–169. [Google Scholar] [CrossRef] [Green Version]
  85. Xia, R.; Xu, J.; Arikit, S.; Meyers, B.C. Extensive Families of miRNAs andPHASLoci in Norway Spruce Demonstrate the Origins of Complex phasiRNA Networks in Seed Plants. Mol. Biol. Evol. 2015, 32, 2905–2918. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Fei, Q.; Xia, R.; Meyers, B.C. Phased, Secondary, Small Interfering RNAs in Posttranscriptional Regulatory Networks. Plant Cell 2013, 25, 2400–2415. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Zhao, M.; Meyers, B.C.; Cai, C.; Xu, W.; Ma, J. Evolutionary Patterns and Coevolutionary Consequences of MIRNA Genes and MicroRNA Targets Triggered by Multiple Mechanisms of Genomic Duplications in Soybean. Plant Cell 2015, 27, 546–562. [Google Scholar] [CrossRef] [Green Version]
  88. Fei, Q.; Li, P.; Teng, C.; Meyers, B.C. Secondary siRNAs fromMedicago NB-LRRsmodulated via miRNA-target interactions and their abundances. Plant J. 2015, 83, 451–465. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  89. Zhai, J.; Jeong, D.-H.; De Paoli, E.; Park, S.; Rosen, B.D.; Li, Y.; González, A.J.; Yan, Z.; Kitto, S.L.; Grusak, M.A.; et al. MicroRNAs as master regulators of the plant NB-LRR defense gene family via the production of phased, trans-acting siRNAs. Genes Dev. 2011, 25, 2540–2553. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  90. Shahid, S.; Kim, G.; Johnson, N.R.; Wafula, E.; Wang, F.; Coruh, C.; Bernal-Galeano, V.; Phifer, T.; Depamphilis, C.W.; Westwood, J.H.; et al. MicroRNAs from the parasitic plant Cuscuta campestris target host messenger RNAs. Nature 2018, 553, 82–85. [Google Scholar] [CrossRef] [PubMed]
  91. Jin, H.; Vacic, V.; Girke, T.; Lonardi, S.; Zhu, J.-K. Small RNAs and the regulation of cis-natural antisense transcripts in Arabidopsis. BMC Mol. Biol. 2008, 9, 6. [Google Scholar] [CrossRef] [Green Version]
  92. Katiyar-Agarwal, S.; Morgan, R.; Dahlbeck, D.; Borsani, O.; Villegas, A.; Zhu, J.-K.; Staskawicz, B.J.; Jin, H. A pathogen-inducible endogenous siRNA in plant immunity. Proc. Natl. Acad. Sci. USA 2006, 103, 18002–18007. [Google Scholar] [CrossRef] [Green Version]
  93. Jin, H. Endogenous small RNAs and antibacterial immunity in plants. FEBS Lett. 2008, 582, 2679–2684. [Google Scholar] [CrossRef] [Green Version]
  94. Zubko, E.; Meyer, P. A natural antisense transcript of thePetunia hybrida Shogene suggests a role for an antisense mechanism in cytokinin regulation. Plant J. 2007, 52, 1131–1139. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Matzke, M.A.; Birchler, J.A. RNAi-mediated pathways in the nucleus. Nat. Rev. Genet. 2005, 6, 24–35. [Google Scholar] [CrossRef] [PubMed]
  96. Zamore, P.D.; Haley, B. Ribo-gnome: The Big World of Small RNAs. Science 2005, 309, 1519–1524. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Lisch, D. Epigenetic Regulation of Transposable Elements in Plants. Annu. Rev. Plant Biol. 2009, 60, 43–66. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Won, S.Y.; Yumul, R.E.; Chen, X. Small RNAs in plants. In Molecular Biology; Springer: New York, NY, USA, 2014; pp. 95–127. [Google Scholar] [CrossRef]
  99. Castel, S.E.; Martienssen, R.A. RNA interference in the nucleus: Roles for small RNAs in transcription, epigenetics and beyond. Nat. Rev. Genet. 2013, 14, 100–112. [Google Scholar] [CrossRef] [PubMed]
  100. Hamilton, A.J.; Baulcombe, D.C. A Species of Small Antisense RNA in Posttranscriptional Gene Silencing in Plants. Science 1999, 286, 950–952. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  101. Addo-Quaye, C.; Eshoo, T.W.; Bartel, D.P.; Axtell, M.J. Endogenous siRNA and miRNA Targets Identified by Sequencing of the Arabidopsis Degradome. Curr. Biol. 2008, 18, 758–762. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  102. Wang, M.-B.; Bian, X.-Y.; Wu, L.-M.; Liu, L.-X.; Smith, N.A.; Isenegger, D.; Wu, R.-M.; Masuta, C.; Vance, V.B.; Watson, J.M.; et al. On the role of RNA silencing in the pathogenicity and evolution of viroids and viral satellites. Proc. Natl. Acad. Sci. USA 2004, 101, 3275–3280. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  103. Vasconcelos, A.M.; Carmo, M.B.; Ferreira, B.; Viegas, I.; Gama-Carvalho, M.; Ferreira, A.; Amaral, A.J. IsomiR_Window: A system for analyzing small-RNA-seq data in an integrative and user-friendly manner. BMC Bioinform. 2021, 22, 37. [Google Scholar] [CrossRef] [PubMed]
  104. Thody, J.; Moulton, V.; Mohorianu, I. PAREameters: A tool for computational inference of plant miRNA–mRNA targeting rules using small RNA and degradome sequencing data. Nucleic Acids Res. 2020, 48, 2258–2270. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  105. Zhao, S.; Gordon, W.; Du, S.; Zhang, C.; He, W.; Xi, L.; Mathur, S.; Agostino, M.; Paradis, T.; Von Schack, D.; et al. QuickMIRSeq: A pipeline for quick and accurate quantification of both known miRNAs and isomiRs by jointly processing multiple samples from microRNA sequencing. BMC Bioinform. 2017, 18, 180. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Zhang, T.; Zhai, J.; Zhang, X.; Ling, L.; Li, M.; Xie, S.; Song, M.; Ma, C. Interactive Web-based Annotation of Plant MicroRNAs with iwa-miRNA. Genom. Proteom. Bioinform. 2021. [Google Scholar] [CrossRef] [PubMed]
  107. Paicu, C.; Mohorianu, I.; Stocks, M.; Xu, P.; Coince, A.; Billmeier, M.; Dalmay, T.; Moulton, V.; Moxon, S. miRCat2: Accurate prediction of plant and animal microRNAs from next-generation sequencing datasets. Bioinformatics 2017, 33, 2446–2454. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  108. Vitsios, D.; Kentepozidou, E.; Quintais, L.; Gutierrez, E.B.; Van Dongen, S.; Davis, M.P.; Enright, A. Mirnovo: Genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests. Nucleic Acids Res. 2017, 45, e177. [Google Scholar] [CrossRef] [PubMed]
  109. Chaves, I.; Costa, B.V.; Rodrigues, A.S.; Bohn, A.; Miguel, C.M. miRPursuit-a pipeline for automated analyses of small RNAs in model and nonmodel plants. FEBS Lett. 2017, 591, 2261–2268. [Google Scholar] [CrossRef] [PubMed]
  110. Yang, K.; Sablok, G.; Qiao, G.; Nie, Q.; Wen, X. isomiR2Function: An Integrated Workflow for Identifying MicroRNA Variants in Plants. Front. Plant Sci. 2017, 8, 322. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  111. Ma, X.; Liu, C.; Gu, L.; Mo, B.; Cao, X.; Chen, X. TarHunter, a tool for predicting conserved microRNA targets and target mimics in plants. Bioinformatics 2017, 34, 1574–1576. [Google Scholar] [CrossRef] [PubMed]
  112. Ylla, G.; Liu, T.; Conesa, A. MirCure: A tool for quality control, filter and curation of microRNAs of animals and plants. Bioinformatics 2020, 36, i618–i624. [Google Scholar] [CrossRef] [PubMed]
  113. Zhang, H.; Wang, H.; Yao, Y.; Yi, M. PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction. Genes 2020, 11, 662. [Google Scholar] [CrossRef] [PubMed]
  114. Guigon, I.; Legrand, S.; Berthelot, J.-F.; Bini, S.; Lanselle, D.; Benmounah, M.; Touzet, H. miRkwood: A tool for the reliable identification of microRNAs in plant genomes. BMC Genom. 2019, 20, 532. [Google Scholar] [CrossRef] [PubMed]
  115. Zhang, T.; Ju, L.; Zhai, J.; Song, Y.; Song, J.; Ma, C. miRLocator: A python implementation and web server for predicting miRNAs from Pre-miRNA sequences. Methods Mol. Biol. 2019, 1932, 89–97. [Google Scholar] [CrossRef] [PubMed]
  116. Natsidis, P.; Kappas, I.; Karlowski, W.M. StarSeeker: An automated tool for mature duplex microRNA sequence identification based on secondary structure modeling of precursor molecule. J. Biol. Res. 2018, 25, 11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  117. Koh, I.; Kim, K.-B. miRHunter: A tool for predicting microRNA precursors based on combined computational method. BioChip J. 2017, 11, 164–171. [Google Scholar] [CrossRef]
  118. Wu, X.; Kim, T.-K.; Baxter, D.; Scherler, K.; Gordon, A.; Fong, O.; Etheridge, A.; Galas, D.J.; Wang, K. sRNAnalyzer—A flexible and customizable small RNA sequencing data analysis pipeline. Nucleic Acids Res. 2017, 45, 12140–12151. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  119. Glogovitis, I.; Yahubyan, G.; Würdinger, T.; Koppers-Lalic, D.; Baev, V. miRGalaxy: Galaxy-Based Framework for Interactive Analysis of microRNA and isomiR Sequencing Data. Cancers 2021, 13, 5663. [Google Scholar] [CrossRef] [PubMed]
  120. Dai, X.; Zhao, P.X. psRNATarget: A plant small RNA target analysis server. Nucleic Acids Res. 2011, 39, W155–W159. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  121. Fan, D.; Yao, Y.; Yi, M. PlantMirP2: An Accurate, Fast and Easy-To-Use Program for Plant Pre-miRNA and miRNA Prediction. Genes 2021, 12, 1280. [Google Scholar] [CrossRef] [PubMed]
  122. Yu, D.; Wan, Y.; Ito, H.; Ma, X.; Xie, T.; Wang, T.; Shao, C.; Meng, Y. PmiRDiscVali: An integrated pipeline for plant microRNA discovery and validation. BMC Genom. 2019, 20, 133. [Google Scholar] [CrossRef] [PubMed]
  123. Kuang, Z.; Wang, Y.; Li, L.; Yang, X. miRDeep-P2: Accurate and fast analysis of the microRNA transcriptome in plants. Bioinformatics 2018, 35, 2521–2522. [Google Scholar] [CrossRef] [PubMed]
  124. Thody, J.; Folkes, L.; Medina-Calzada, Z.; Xu, P.; Dalmay, T.; Moulton, V. PAREsnip2: A tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules. Nucleic Acids Res. 2018, 46, 8730–8739. [Google Scholar] [CrossRef] [PubMed]
  125. Zhang, H.; Silva, B.V.R.; Cui, J. miRDis: A Web tool for endogenous and exogenous microRNA discovery based on deep-sequencing data analysis. Brief. Bioinform. 2017, 19, 415–424. [Google Scholar] [CrossRef] [Green Version]
  126. Ayachit, G.; Pandya, H.; Das, J. miRDetect: A combinatorial approach for automated detection of novel miRNA precursors from plant EST data using homology and Random Forest classification. Genomics 2020, 112, 3201–3206. [Google Scholar] [CrossRef] [PubMed]
  127. Tseng, K.-C.; Chiang-Hsieh, Y.-F.; Pai, H.; Chow, C.-N.; Lee, S.-C.; Zheng, H.-Q.; Kuo, P.-L.; Li, G.-Z.; Hung, Y.-C.; Lin, N.-S.; et al. microRPM: A microRNA prediction model based only on plant small RNA sequencing data. Bioinformatics 2017, 34, 1108–1115. [Google Scholar] [CrossRef]
  128. Patil, A.H.; Halushka, M.K. miRge3.0: A comprehensive microRNA and tRF sequencing analysis pipeline. NAR Genom. Bioinform. 2021, 3, lqab068. [Google Scholar] [CrossRef] [PubMed]
  129. Thody, J.; Folkes, L.; Moulton, V. NATpare: A pipeline for high-throughput prediction and functional analysis of nat-siRNAs. Nucleic Acids Res. 2020, 48, 6481–6490. [Google Scholar] [CrossRef] [PubMed]
  130. Fei, Y.; Feng, J.; Wang, R.; Zhang, B.; Zhang, H.; Huang, J. PhasiRNAnalyzer: An integrated analyser for plant phased siRNAs. RNA Biol. 2021, 18, 1622–1629. [Google Scholar] [CrossRef] [PubMed]
  131. Adkar-Purushothama, C.; Iyer, P.; Sano, T.; Perreault, J.-P. sRNA Profiler: A User-Focused Interface for Small RNA Mapping and Profiling. Cells 2021, 10, 1771. [Google Scholar] [CrossRef] [PubMed]
  132. Zheng, Y.; Gao, S.; Padmanabhan, C.; Li, R.; Galvez, M.; Gutierrez, D.; Fuentes, S.; Ling, K.-S.; Kreuze, J.; Fei, Z. VirusDetect: An automated pipeline for efficient virus discovery using deep sequencing of small RNAs. Virology 2017, 500, 130–138. [Google Scholar] [CrossRef] [PubMed]
  133. Barrero, R.A.; Napier, K.R.; Cunnington, J.; Liefting, L.; Keenan, S.; Frampton, R.A.; Szabó, T.O.; Bulman, S.; Hunter, A.; Ward, L.; et al. An internet-based bioinformatics toolkit for plant biosecurity diagnosis and surveillance of viruses and viroids. BMC Bioinform. 2017, 18, 26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  134. Liu, Q.; Ding, C.; Lang, X.; Guo, G.; Chen, J.; Su, X.; Liu, Q.; Ding, C.; Lang, X.; Guo, G.; et al. Small noncoding RNA discovery and profiling with sRNAtools based on high-throughput sequencing. Brief. Bioinform. 2019, 22, 463–473. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  135. Tseng, K.-C.; Chiang-Hsieh, Y.-F.; Pai, H.; Wu, N.-Y.; Zheng, H.-Q.; Chow, C.-N.; Lee, T.-Y.; Chang, S.-B.; Lin, N.-S.; Chang, W.-C. sRIS: A Small RNA Illustration System for Plant Next-Generation Sequencing Data Analysis. Plant Cell Physiol. 2020, 61, 1204–1212. [Google Scholar] [CrossRef] [PubMed]
  136. Gebert, D.; Hewel, C.; Rosenkranz, D. unitas: The universal tool for annotation of small RNAs. BMC Genom. 2017, 18, 644. [Google Scholar] [CrossRef] [PubMed]
  137. Shi, J.; Ko, E.A.; Sanders, K.M.; Chen, Q.; Zhou, T. SPORTS1.0: A tool for annotating and profiling non-coding RNAs optimized for rRNA- and tRNA-derived small RNAs. Genom. Proteom. Bioinform. 2018, 16, 144–151. [Google Scholar] [CrossRef]
  138. Fletcher, S.J.; Boden, M.; Mitter, N.; Carroll, B. SCRAM: A pipeline for fast index-free small RNA read alignment and visualization. Bioinformatics 2018, 34, 2670–2672. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  139. Aparicio-Puerta, E.; Lebrón, R.; Rueda, A.; Gómez-Martín, C.; Giannoukakos, S.; Jáspez, D.; Medina, J.M.; Zubković, A.; Jurak, I.; Fromm, B.; et al. sRNAbench and sRNAtoolbox 2019: Intuitive fast small RNA profiling and differential expression. Nucleic Acids Res. 2019, 47, W530–W535. [Google Scholar] [CrossRef] [Green Version]
  140. Kozomara, A.; Birgaoanu, M.; Griffiths-Jones, S. miRBase: From microRNA sequences to function. Nucleic Acids Res. 2019, 47, D155–D162. [Google Scholar] [CrossRef] [PubMed]
  141. Kozomara, A.; Griffiths-Jones, S. miRBase: Integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2010, 39, D152–D157. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  142. Fromm, B.; Høye, E.; Domanska, D.; Zhong, X.; Aparicio-Puerta, E.; Ovchinnikov, V.; Umu, S.U.; Chabot, P.J.; Kang, W.; Aslanzadeh, M.; et al. MirGeneDB 2.1: Toward a complete sampling of all major animal phyla. Nucleic Acids Res. 2021, 50, D204–D210. [Google Scholar] [CrossRef] [PubMed]
  143. Hofacker, I.L. Vienna RNA secondary structure server. Nucleic Acids Res. 2003, 31, 3429–3431. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  144. Fahlgren, N.; Carrington, J.C. miRNA Target Prediction in Plants. Methods Mol. Biol. 2010, 592, 51–57. [Google Scholar] [CrossRef] [PubMed]
  145. Chorostecki, U.P.; Palatnik, J.F. comTAR: A web tool for the prediction and characterization of conserved microRNA targets in plants. Bioinformatics 2014, 30, 2066–2067. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  146. Wu, H.-J.; Ma, Y.-K.; Chen, T.; Wang, M.; Wang, X.-J. PsRobot: A web-based plant small RNA meta-analysis toolbox. Nucleic Acids Res. 2012, 40, W22–W28. [Google Scholar] [CrossRef] [PubMed]
  147. Addo-Quaye, C.; Miller, W.; Axtell, M.J. CleaveLand: A pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics 2008, 25, 130–131. [Google Scholar] [CrossRef] [PubMed]
  148. Brousse, C.; Liu, Q.; Beauclair, L.; Deremetz, A.; Axtell, M.J.; Bouché, N. A non-canonical plant microRNA target site. Nucleic Acids Res. 2014, 42, 5270–5279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  149. Kakrana, A.; Hammond, R.; Patel, P.; Nakano, M.; Meyers, B.C. sPARTA: A parallelized pipeline for integrated analysis of plant miRNA and cleaved mRNA data sets, including new miRNA target-identification software. Nucleic Acids Res. 2014, 42, e139. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  150. Lorenz, R.; Bernhart, S.H.; Honer Zu Siederdissen, C.; Tafer, H.; Flamm, C.; Stadler, P.F.; Hofacker, I.L. ViennaRNA Package 2.0. Algorithms Mol. Biol. 2011, 6, 26. [Google Scholar] [CrossRef]
  151. Zhai, J.; Arikit, S.; Simon, S.A.; Kingham, B.F.; Meyers, B.C. Rapid construction of parallel analysis of RNA end (PARE) libraries for Illumina sequencing. Methods 2014, 67, 84–90. [Google Scholar] [CrossRef] [PubMed]
  152. German, M.A.; Pillay, M.; Jeong, D.-H.; Hetawal, A.; Luo, S.; Janardhanan, P.; Kannan, V.; Rymarquis, A.L.; Nobuta, K.; German, R.; et al. Global identification of microRNA–target RNA pairs by parallel analysis of RNA ends. Nat. Biotechnol. 2008, 26, 941–946. [Google Scholar] [CrossRef] [PubMed]
  153. Zhou, M.; Gu, L.; Li, P.; Song, X.; Wei, L.; Chen, Z.; Cao, X. Degradome sequencing reveals endogenous small RNA targets in rice (Oryza sativa L. ssp. indica). Front. Biol. 2010, 5, 67–90. [Google Scholar] [CrossRef]
  154. Shamimuzzaman, M.; Vodkin, L. Identification of soybean seed developmental stage-specific and tissue-specific miRNA targets by degradome sequencing. BMC Genom. 2012, 13, 310. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  155. Zhao, M.; Tai, H.; Sun, S.; Zhang, F.; Xu, Y.; Li, W.-X. Cloning and Characterization of Maize miRNAs Involved in Responses to Nitrogen Deficiency. PLoS ONE 2012, 7, e29669. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  156. Langmead, B.; Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 2012, 9, 357–359. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Ijms 23 03979 g001 550
Figure 1. Biogenesis pathways of endogenous and exogenous small RNAs in plants. A majority of miRNAs are encoded by their own genes and transcribed by Pol II. Primary transcripts (pri-miRNA) are processed by DCL1, producing the miRNA hairpin precursor (pre-miRNA), which is further cleaved by DCL1 to generate a miRNA:miRNA* duplex. Unlike miRNAs, the other plant small RNAs are not encoded by genes. Instead, natsiRNAs originate from overlapping transcripts; secondary small RNAs originate from noncoding or coding loci, or TE; and hetsiRNA are transcribed from TE and repeat elements of pericentromeric chromatin. Downstream, the resulting transcripts (including the exogenous viral transcripts and viral RNAs) fall into processing pathways to generate double-stranded RNAs by RDRs, which are cleaved into small RNAs by DCLs. Small RNAs are loaded into an RNA-induced silencing complex (RISC), triggering posttranscriptional gene silencing—PTGS (miRNAs, natsiRNAs, secondary siRNA, vsiRNAs)—or into chromatin remodelling complexes (CRC) to induce transcriptional gene silencing—TGS—by RNA-dependent DNA methylation—RdDM (hetsiRNAs, vsiRNAs). (Created with BioRender.com, accessed on 27 March 2022).
Figure 1. Biogenesis pathways of endogenous and exogenous small RNAs in plants. A majority of miRNAs are encoded by their own genes and transcribed by Pol II. Primary transcripts (pri-miRNA) are processed by DCL1, producing the miRNA hairpin precursor (pre-miRNA), which is further cleaved by DCL1 to generate a miRNA:miRNA* duplex. Unlike miRNAs, the other plant small RNAs are not encoded by genes. Instead, natsiRNAs originate from overlapping transcripts; secondary small RNAs originate from noncoding or coding loci, or TE; and hetsiRNA are transcribed from TE and repeat elements of pericentromeric chromatin. Downstream, the resulting transcripts (including the exogenous viral transcripts and viral RNAs) fall into processing pathways to generate double-stranded RNAs by RDRs, which are cleaved into small RNAs by DCLs. Small RNAs are loaded into an RNA-induced silencing complex (RISC), triggering posttranscriptional gene silencing—PTGS (miRNAs, natsiRNAs, secondary siRNA, vsiRNAs)—or into chromatin remodelling complexes (CRC) to induce transcriptional gene silencing—TGS—by RNA-dependent DNA methylation—RdDM (hetsiRNAs, vsiRNAs). (Created with BioRender.com, accessed on 27 March 2022).
Ijms 23 03979 g001
Table
Table 1. List of bioinformatics tools for plant small RNA analysis and their main features.
Table 1. List of bioinformatics tools for plant small RNA analysis and their main features.
miRNA and isomiR Tools:
Tool Name1. IsomiR_Window
TypeLocal, VM
Description
  • Features: isomiRs identification; identification of noncoding RNAs; miRNA prediction; miRNA and isomiR quantification and functional analysis.
  • Third-party tools: Bowtie, DEseq2, MirDeep2, miRDP2, Miranda, TargetFinder, etc.
  • Output: visualizations and results as tables and interactive graphs.
Ref. and tool URLVasconcelos et al. [103] https://github.com/andreiaamaral/IsomiR-Window/, accessed on 27 March 2022
Tool Name2. PAREameters
TypeLocal
Description
  • Features: miRNA-mRNA interaction analysis; small RNA and degradome data; non-model organisms.
  • Third-party tools: miRCat2, miRPlant, PAREsnip2.
  • Output: set of data-inferred thresholds for a rule-based prediction of miRNA–mRNA interactions.
Ref. and tool URLThody et al. [104] https://github.com/sRNAworkbenchuea/UEA_sRNA_Workbench, accessed on 27 March 2022
Tool Name3. QuickMIRSeq
TypeLocal, Linux
Description
  • Features: miRNA and isomiR quantification.
  • Third-party tools and DB: miRBase
  • Output: User friendly report; Rich visualisation via set of QC metrics and plots.
Ref. and tool URLZhao et al. [105] https://sourceforge.net/projects/quickmirseq/files/, accessed on 27 March 2022
Tool Name4. Iwa-miRNA
TypeWeb, Galaxy
Description
  • Features: miRNA annotation; miRNA quantification.
  • Third-party tools and DB: miRDeepP2 and miRCat2; miRBase, PmiREN, sRNAanno, and PsRNA.
  • Output: genomic distribution, expression of miRNAs and their host genes, subgenome bias, genomic duplication and miRNA expansion, SNP effect.
Ref. and tool URLZhang et al. [106] http://iwa-mirna.omicstudio.cloud/, accessed on 27 March 2022
Tool Name5. miRCat2
TypeLocal, MAC, Linux and Windows
Description
  • Features: miRNA prediction.
  • Third-party tools: RNAFold (Vienna RNA Package), PatMaN.
  • Output: graphical representation of the hairpin structures and plots of sequence alignments of secondary structure.
Ref. and tool URLPaicu et al. [107] https://github.com/sRNAworkbenchuea/UEA_sRNA_Workbench, accessed on 27 March 2022
Tool Name6. Mirnovo
TypeWeb, Local, Mac, Linux
Description
  • Features: miRNA prediction; miRNA precursor prediction; non-reference genome support.
  • Third-party tools: Bowtie2, Random Forest R package.
  • Output: distribution of all feature values (coverage, sequence complexity and genomic) and visualization by QC-plots.
Ref. and tool URLVitsios et al. [108] https://github.com/dvitsios/mirnovo, accessed on 27 March 2022
Tool Name7. MiRPursuit
TypeLocal, Linux, Unix, Mac
Description
  • Features: miRNA identification; tasiRNAs identification; non-model organisms.
  • Third-party tools: FASTQC, FASTX; UEA sRNA Workbench; PatMaN; UEAsRNA Workbench (miRProf, miRCat); UEA sRNA Workbench (tasiPredictor).
  • Output: detailed report from the outputs of each process in miRPursuit along with a matrix with the raw counts exportable to other programs.
Ref. and tool URLChaves et al. [109] https://github.com/forestbiotech-lab/miRPursuit, accessed on 27 March 2022
Tool Name8. isomiR2Function
TypeLocal, Linux, Mac
Description
  • Features: isomiR identification; templated and non-templated isomiRs; isomiR quantification; target identification.
  • Third-party tools: DSeq and Ebseq.
  • Output: detailed isomer identification report; provides support for the visualisation of read mapping on corresponding precursor sequences.
Ref. and tool URLYang et al. [110] https://github.com/347033139/isomiR2Function, accessed on 27 March 2022
Tool Name9. TarHunter
TypeLocal, Linux
Description
  • Features: miRNA target identification; degradome data support.
  • Third-party tools: UBLAST, MUSCLE, USEARCH.
  • Output: files, containing all predicted miRNA targets.
Ref. and tool URLMa et al. [111] https://github.com/XMaBio, accessed on 27 March 2022
Tool Name10. MirCure
TypeLocal, Linux, MAC OS
Description
  • Features: miRNA prediction; miRNA annotation.
  • Third-party tools: DNApi, Cutadapt, Bowtie2, Samtools, Genomic Alignments R package.
  • Output: calculated score of secondary structures, gene expression, graphical visualizations.
Ref. and tool URLYlla et al. [112] https://github.com/ConesaLab/MirCure, accessed on 27 March 2022
Tool Name11. PlantMiRP-Rice
TypeLocal, Linux, Win
Description
  • Features: miRNA prediction; rice pre-miRNA prediction.
  • Third-party tools: miRBase, PlantGDB databases, RNAFold.
  • Output: three-column contents of the identifier, predicted label (positive or negative), and corresponding score for each testing sample.
Ref. and tool URLZhang et al. [113] https://github.com/yygen89/riceMirP, accessed on 27 March 2022
Tool Name12. mirKwood
TypeWeb, Galaxy, Docker, Local, Unix
Description
  • Features: miRNA identification.
  • Third-party tools: Bowtie2 or BWA; miRbase; RNAFold.
  • Output: web page where miRNA precursors are displayed in a table; various formats: CSV, FASTA, GFF, text report in ORG mode and read clouds.
Ref. and tool URLGuigon et al. [114] https://bioinfo.cristal.univ-lille.fr/mirkwood/mirkwood.php, accessed on 27 March 2022
Tool Name13. miRLocator
TypeLocal, Win, MacOS, Linux, Docker, web
Description
  • Features: miRNA prediction.
  • Third-party tools: Vienna RNA package; MiRBase; miRNEST.
  • Output: predicted miRNA and its corresponding passenger strand for the tested pre-miRNA sequence.
Ref. and tool URLZhang et al. [115] https://github.com/cma2015/miRLocator, accessed on 27 March 2022
Tool Name14. StarSeeker
TypePhyton, Local
Description
  • Features: miRNA identification.
  • Third-party tools and DB: miRbase.
  • Output: putative miRNA sequence given the precursor and the mature sequences.
Ref. and tool URLNatsidis et al. [116] https://biopython.org/, accessed on 27 March 2022
Tool Name15. miRHunter
TypeWeb
Description
  • Features: miRNA precursor identification; comparative and non-comparative ab initio prediction.
  • Third-party tools: BLAST, RNAFold.
  • Output: potential pre-miRNAs
Ref. and tool URLKoh et al. [117] https://repository.hanyang.ac.kr/handle/20.500.11754/114034, accessed on 27 March 2022
Tool Name16. sRNAnalyzer
TypeLocal, Linux
Description
  • Features: miRNA identification; miRNA SNPs detection.
  • Third-party tools: Bowtie, Fastx_toolkit, Prinseq, Cutadapt, MirGeneDB.
  • Output: detailed feature and profile text files.
Ref. and tool URLWu et al. [118] http://srnanalyzer.systemsbiology.net/, accessed on 27 March 2022
Tool Name17. mirGalaxy
TypeWeb, Docker, Mac, Win, Linux
Description
  • Features: miRNA identification; templated and non-templated isomiRs identification; miRNA and isomiR quantification
  • Third-party tools: Galaxy, TrimGalore, FastQC, Bowtie, DeSeq2, EdgeR.
  • Output: detailed reports from the differential expression analysis, PDF report, charts and plots visualization.
Ref. and tool URLGlogovitis et al. [119] https://hub.docker.com/r/glogobyte/mirgalaxy, accessed on 27 March 2022
Tool Name18. psRNATarget
TypeWeb
Description
  • Features: miRNA target identification.
  • Third-party tools: RNAup program in Vienna package, BioGrid platform.
  • Output: comprehensive list of small RNA/target pairs with ranking scores.
Ref. and tool URLDai et al. [120] http://plantgrn.noble.org/psRNATarget/, accessed on 27 March 2022
Tool Name19. PlantMirP2
TypeLocal, Docker, Web
Description
  • Features: miRNA prediction; miRNA precursor prediction.
  • Third-party tools and DB: miRbase.
  • Output: charts and plots visualizations.
Ref. and tool URLFan et al. [121] https://github.com/wuqiansibai/plantMiRP2/releases/tag/v1.0/, accessed on 27 March 2022
Tool Name20. PmiRDiscVali
TypeLocal, Perl
Description
  • Features: miRNA prediction, degradome data support.
  • Third-party tools: miRDeep-P, SVG, bowtie, ViennaRNA package.
  • Output: predicted miRNA precursors and a visual representation of their secondary structure.
Ref. and tool URLYu et al. [122] https://github.com/unincrna/pmirdv, accessed on 27 March 2022
Tool Name21. miRDeep-P2 (update)
TypeLocal, Linux
Description
  • Features: miRNA identification.
  • Third-party tools: Bowtie, ViennaRNA package.
  • Output: putative pre-miRNA with folded structure, sRNA mapping to pre-miRNA visualization.
Ref. and tool URLWang et al. [123] https://sourceforge.net/projects/mirdp2/, accessed on 27 March 2022
Tool Name22. PAREsnip2
TypeLocal
Description
  • Features: sRNA target identification, degradome data support.
  • Output: in CSV format, transcript peak information, visual representation of the sRNA–mRNA duplex.
Ref. and tool URLThody et al. [124] https://github.com/sRNAworkbenchuea/UEA_sRNA_Workbench/, accessed on 27 March 2022
Tool Name23. miRDis
TypeWeb
Description
  • Features: miRNA identification; miRNA prediction; miRNA quantification.
  • Third-party tools: BLAST, RNAfold, infernal, EdgeR, FASTQC, Cutadapt.
  • Output: summary, candidate list, annotation details, differential analysis, heatmaps.
Ref. and tool URLZhang et al. [125] http://sbbi-panda.unl.edu/miRDis/download.php, accessed on 27 March 2022
Tool Name24. miRDetect
TypeLocal, Python
Description
  • Features: miRNA prediction; miRNA precursor prediction; plant EST dataset support.
  • Third-party tools: BLAST standalone, ViennaRNA package,
  • Output: list of identified putative miRNA precursors.
Ref. and tool URLAyachit et al. [126] https://github.com/Garima268/miRDetect, accessed on 27 March 2022
Tool Name25. microRPM
TypeLocal, Perl
Description
  • Features: miRNA prediction; non-model organisms; non-reference genome support.
  • Third-party tools: Bowtie; Vienna RNA; Trinity; LibSVM; Structure RNA sequences.
  • Output: miRNA mature duplex report.
Ref. and tool URLK. C. Tseng et al. [127] http://microrpm.itps.ncku.edu.tw/, accessed on 27 March 2022
Tool Name26. miRge3.0
TypeLocal, Docker, Python
Description
  • Features: miRNA identification; isomiR identification; miRNA and isomiR quantification; miRNa prediction; UMIs support.
  • Third-party tools: Cutadapt, Bowtie, ViennaRNA, SAMtools, biopython, sklearn, numPy, SciPy, reportlab, DESeq2.
  • Output: summary report, files with count reads for each miRNA species, isomiR results in GFF3, BAM for visualization.
Ref. and tool URLPatil et al. [128] https://github.com/mhalushka/miRge3.0, accessed on 27 March 2022
natsiRNA tools
Tool Name1. NATpare
TypeJava, Mac, Win, Linux
Description
  • Features: natsiRNAs prediction and identification; degradome data support.
  • Third-party tools: PARESnip2, UEA sRNA Workbench.
  • Output: candidat natsiRNA, comma-separated value (CSV) format.
Ref. and tool URLThody et al. [129] https://github.com/sRNAworkbenchuea/UEA_sRNA_Workbench/, accessed on 27 March 2022
phasi/tasiRNA tools
Tool Name1. PhasiRNAnalyzer
TypeWeb
Description
  • Features: phasiRNAs prediction; phasiRNAs target genes prediction.
  • Output: list of predicted PHAS genes, phasiRNA clusters and phase-initiators.
Ref. and tool URLFei et al. [130] https://cbi.njau.edu.cn/PPSA/, accessed on 27 March 2022
vsiRNA tools
Tool Name1. sRNAProfiler
TypeLocal, MacOS, Unix, Windows
Description
  • Features: vsiRNA identification; viroid sRNA mapping.
  • Output: summary of the sRNA mapping data and graphical visualization.
Ref. and tool URLAdkar-Purushothama et al. [131] https://github.com/paviudes/vbind, accessed on 27 March 2022
Tool Name2. VirusDetect
TypeLocal, Linux
Description
  • Features: vsiRNA identification; virus sRNA mapping; virus assembly.
  • Third-party tools: BWA, Velvet, BLASTN, BLASTX.
  • Output: sequences of detected viruses in fasta format.
Ref. and tool URLZheng et al. [132] http://virusdetect.feilab.net/cgi-bin/virusdetect/vd_download.cgi, accessed on 27 March 2022
Tool Name3. VSD toolkit
TypeWeb
Description
  • Features: vsiRNA identification; vsiRNA assembly; hcsiRNAs assembly; virus and viroid detection.
  • Third-party tools: FASTx, fastqc, ConDeTri, SPAdes, CAP3, BLAST, Bowtie.
  • Output: detected viruses and viroids, vsiRNAs covarage across viral genomes, assembly of virus and viroids.
Ref. and tool URLBarrero et al. [133] https://github.com/muccg/yabi, accessed on 27 March 2022
Misc. tools
Tool Name1. sRNAtools
TypeWeb, Docker
Description
  • Features: miRNA identification; isomiR identification; piRNA identification; natsiRNA identification; other sncRNAs identification and annotation; sncRNA function analysis; sncRNA quantification.
  • Third-party tools: Cutadapt, FASTX-Toolkit, Bowtie, miRD-eep, Mireap, Tapirhybrid, Targetfinder, RNAhybrid, miRanda.
  • Output: sncRNA list, statistics and graphics, sncRNA differential expression, targets genes.
Ref. and tool URLLiu et al. [134] https://bioinformatics.caf.ac.cn/sRNAtools, accessed on 27 March 2022
Tool Name2. sRIS (Small RNA Illustration System)
TypeWeb, Linux
Description
  • Features: miRNA identification; vsiRNA identification; sRNA characterization; target identification; degradome data support.
  • Third-party tools and DB: Bowtie, FASTX toolkit, Rfam, miRbase, microRPM.
  • Output: sRNA library statistics, miRNA and vsiRNA profiles, sRNA-target profiles, genomic hotspots of vsiRNAs, miRNA heatmap, etc.
Ref. and tool URLTseng et al. [135] http://sris.itps.ncku.edu.tw/, accessed on 27 March 2022
Tool Name3. Unitas
TypeLocal, Linux, Mac, Windows
Description
  • Features: miRNA annotation, phasiRNA identification, piRNA annotation.
  • Third-party tools and DB: Ensembl, miRBase, GtRNAdb, SILVA rRNA, piRNA cluster databases.
  • Output: sequence length distribution, miRNA annotation table, sRNA annotation summary, phasiRNA annotation, etc.
Ref. and tool URLGebert et al. [136] https://sourceforge.net/projects/unitas/, accessed on 27 March 2022
Tool Name4. SPORTS1.0
TypeLocal, Linux
Description
  • Features: tsRNAs, rsRNAs, piRNA analysis.
  • Third-party tools: Cutadapt; MiRDeep2; miRBase, rRNA database, GtRNAdb, piRNA, Ensembl, Rfam databases.
  • Output: sRNA annotation, sRNA length distribution, sRNA summary, mismatch summary, tRNA, piRNA, ncRNA visualizations.
Ref. and tool URLShi et al. [137] https://github.com/junchaoshi/sports1.1, accessed on 27 March 2022
Tool Name5. SCRAM
TypeLocal, Docker
Description
  • Features: sRNA alignment and visualization
  • Output: plots and visualizations
Ref. and tool URLFletcher et al. [138] https://sfletc.github.io/scram/, accessed on 27 March 2022
Tool Name6. sRNAbench and sRNAtoolbox (update)
TypeWeb server, Docker
Description
  • Features: miRNA identification; isomiR identification; miRNA quantification; sncRNA; vsiRNA, etc.
  • Third-party tools and DB: miRbase, miRGeneDB, Deseq2, edgeR and UpsetR.
  • Output: interactive heatmaps, box-plots, volcano-plots, genome mapping visualization, consensus tables and graphical representation of differential expression.
Ref. and tool URLAparicio-Puerta et al. [139] https://arn.ugr.es/srnatoolbox/, accessed on 27 March 2022
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Ivanova, Z.; Minkov, G.; Gisel, A.; Yahubyan, G.; Minkov, I.; Toneva, V.; Baev, V. The Multiverse of Plant Small RNAs: How Can We Explore It? Int. J. Mol. Sci. 2022, 23, 3979. https://doi.org/10.3390/ijms23073979

AMA Style

Ivanova Z, Minkov G, Gisel A, Yahubyan G, Minkov I, Toneva V, Baev V. The Multiverse of Plant Small RNAs: How Can We Explore It? International Journal of Molecular Sciences. 2022; 23(7):3979. https://doi.org/10.3390/ijms23073979

Chicago/Turabian Style

Ivanova, Zdravka, Georgi Minkov, Andreas Gisel, Galina Yahubyan, Ivan Minkov, Valentina Toneva, and Vesselin Baev. 2022. "The Multiverse of Plant Small RNAs: How Can We Explore It?" International Journal of Molecular Sciences 23, no. 7: 3979. https://doi.org/10.3390/ijms23073979

APA Style

Ivanova, Z., Minkov, G., Gisel, A., Yahubyan, G., Minkov, I., Toneva, V., & Baev, V. (2022). The Multiverse of Plant Small RNAs: How Can We Explore It? International Journal of Molecular Sciences, 23(7), 3979. https://doi.org/10.3390/ijms23073979

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