MicroRNAs (miRNA) are small endogenous RNA molecules, which regulate target gene expression at post-transcriptional level. Besides, miRNA activity can be controlled by a newly discovered regulatory mechanism called endogenous target mimicry (eTM). In target mimicry, eTMs bind to the corresponding miRNAs to block the binding of specific transcript leading to increase mRNA expression. Thus, miRNA-eTM-target-mRNA regulation modules involving a wide range of biological processes; an increasing need for a comprehensive eTM database arose. Except miRSponge with limited number of Arabidopsis eTM data no available database and/or repository was developed and released for plant eTMs yet. Here, we present an online plant eTM database, called PeTMbase (http://petmbase.org), with a highly efficient search tool. To establish the repository a number of identified eTMs was obtained utilizing from high-throughput RNA-sequencing data of 11 plant species. Each transcriptome libraries is first mapped to corresponding plant genome, then long non-coding RNA (lncRNA) transcripts are characterized. Furthermore, additional lncRNAs retrieved from GREENC and PNRD were incorporated into the lncRNA catalog. Then, utilizing the lncRNA and miRNA sources a total of 2,728 eTMs were successfully predicted. Our regularly updated database, PeTMbase, provides high quality information regarding miRNA:eTM modules and will aid functional genomics studies particularly, on miRNA regulatory networks.

A database of animal, plant and virus microRNA data maintained at the University of Poznan. The database provides: * 9980 miRNA candiates from 420 animal and plant species predicted in Expressed Sequence Tags * predicted targets for plant candidates * RNA-seq reads mapped to candidates from 29 species * external data from 12 databases that includes sequences, polymorphism, expression and regulation. miRNEST 1.0, it contains miRNA from 563 animals, plants and viruses plant species.

Additional file 2: Supplementary Table S1. Degradome/PARE-seq and small RNA-seq data used for TarDB database construction.

The miRBase Sequence Database -- Release 22
-------------------------------------------

1. SUMMARY

The miRBase database provides a searchable online repository for
published microRNA sequences and associated annotation. miRBase also
provides a gene naming and nomenclature function in the miRBase
Registry.

Release 22 of the database contains 38589 entries representing hairpin
precursor miRNAs, expressing 48885 mature miRNA products, in 271
species. The data are freely available to all through the web
interface at http://www.mirbase.org/ and in flatfile form from
ftp://mirbase.org/pub/mirbase/.


2. CHANGES SINCE RELEASE 21

10031 new hairpin sequences and 13149 new mature products have been
added, an increase in the number of sequences of over a third. The
first miRNAs in 48 new species are reported. 115 hairpins and 496
mature sequences have changed names; the overwhelming majority of
these changes are additions of lettered or numbered suffixes to
differentiate related loci. 87 misannotated and duplicate sequences
have been deleted. See the miRNA.diff file for the full description
of changes.

The "high confidence" microRNA dataset (as described in Kozomara and
Griffiths-Jones, NAR 2014) is available on the FTP site.


3. FILES

The following files are available from the above ftp site:

 miRNA.dat   - all entries in (almost) EMBL format
 hairpin.fa  - predicted miR stem-loop sequences in fasta format
 mature.fa   - mature sequences in fasta format
 miRNA.dead  - entries removed from the database
 miRNA.diff  - differences between the current and last release

The genomes/ directory contains gff files of genome coordinates for 152
species. See the individual files for the genome assemblies used.

The database_files/ directory contains dumps of the MySQL relational
database that is used to generate the web pages. The documentation
for this subset of files is non-existent - use at your peril!


4. LICENSE

miRBase is in the public domain. It is not copyrighted. You may
freely modify, redistribute, or use it for any purpose. See
ftp://mirbase.org/pub/mirbase/CURRENT/LICENSE for details.


5. HOW TO CITE THE miRNA DATABASE

The miRBase database is described in the following articles:

miRBase: annotating high confidence microRNAs using deep sequencing
data.
Kozomara A, Griffiths-Jones S.
Nucleic Acids Res. 2014 42:D68-D73

miRBase: integrating microRNA annotation and deep-sequencing data.
Kozomara A, Griffiths-Jones S.
Nucleic Acids Res. 2011 39:D152-D157

miRBase: tools for microRNA genomics.
Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ.
Nucleic Acids Res. 2008 36:D154-D158

miRBase: microRNA sequences, targets and gene nomenclature.
Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ.
Nucleic Acids Res. 2006 34:D140-D144

The miRNA Registry.
Griffiths-Jones S.
Nucleic Acids Res. 2004 32:D109-D111

Please cite us if you use the data we distribute, but also be sure to
cite the primary sources of miRNA sequences in your work.

Guidelines to miRNA annotation are published here:

A uniform system for microRNA annotation.
Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen X, 
Dreyfuss G, Eddy SR, Griffiths-Jones S, Marshall M, Matzke M,
Ruvkun G, Tuschl T. 
RNA 2003 9(3):277-279

Criteria for annotation of plant MicroRNAs.
Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, Bowman JL, Cao
X, Carrington JC, Chen X, Green PJ, Griffiths-Jones S, Jacobsen SE,
Mallory AC, Martienssen RA, Poethig RS, Qi Y, Vaucheret H, Voinnet O,
Watanabe Y, Weigel D, Zhu JK.
Plant Cell. 2008 20(12):3186-3190


6. FEEDBACK

Any queries about data, web services, naming requests or other
feedback should be directed to mirbase@manchester.ac.uk.


7. HISTORY

Version    Date    Entries

 1.0     12/02    218
 1.1     01/03    262
 1.2     04/03    295
 1.3     05/03    332
 1.4     07/03    345
 2.0     07/03    506
 2.1     09/03    558
 2.2     11/03    593
 3.0     01/04    719
 3.1     04/04    899
 4.0     07/04    1185
 5.0     09/04    1345
 5.1     12/04    1420
 6.0     04/05    1650
 7.0     06/05    2909
 7.1     10/05    3424
 8.0     02/06    3518
 8.1     05/06    3963
 8.2     07/06    4039
 9.0     10/06    4361
 9.1     02/07    4449
 9.2     05/07    4584
 10.0     08/07    5071
 10.1     12/07    5395
 11.0     04/08    6396
 12.0     09/08    8619
 13.0     03/09    9539
  14     09/09   10883
  15     04/10   14197
  16     08/10   15172
  17     04/11   16772
  18     11/11   18226
  19     08/12   21264
  20     06/13 24521
  21     06/14 28645
  22     03/18   38589

--
SGJ

MicroRNAs (miRNAs) are of significance in tuning and buffering gene expression. Despite abundant analysis tools that have been developed in the last two decades, plant miRNA identification from next-generation sequencing (NGS) data remains challenging. Here, we show that we can train a convolutional neural network to accurately identify plant miRNAs from NGS data. Based on our methods, we also present a user-friendly pure Java-based software package called Small RNA-related Intelligent and Convenient Analysis Tools (SRICATs). SRICATs encompasses all the necessary steps for plant miRNA analysis. Our results indicate that SRICATs outperforms currently popular software tools on the test data from five plant species. For non-commercial users, SRICATs is freely available at https://sourceforge.net/projects/sricats.

MicroRNAs (miRNAs) are important regulators of gene expression. The large-scale detection and profiling of miRNAs has accelerated with the development of high-throughput small RNA sequencing (sRNA-Seq) techniques and bioinformatics tools. However, generating high-quality comprehensive miRNA annotations remains challenging, due to the intrinsic complexity of sRNA-Seq data and inherent limitations of existing miRNA predictions. Here, we present iwa-miRNA, a Galaxy-based framework that can facilitate miRNA annotation in plant species by combining computational analysis and manual curation. iwa-miRNA is specifically designed to generate a comprehensive list of miRNA candidates, bridging the gap between already annotated miRNAs provided by public miRNA databases and new predictions from sRNA-Seq datasets. It can also assist users to select promising miRNA candidates in an interactive mode through the automated and manual steps, contributing to the accessibility and reproducibility of genome-wide miRNA annotation. iwa-miRNA is user-friendly and can be easily deployed as a web application for researchers without programming experience. With flexible, interactive, and easy-to-use features, iwa-miRNA is a valuable tool for annotation of miRNAs in plant species with reference genomes. We illustrated the application of iwa-miRNA for miRNA annotation of plant species with varying complexity. The sources codes and web server of iwa-miRNA is freely accessible at: http://iwa-miRNA.omicstudio.cloud/.

MicroRNAs (miRNAs) are of significance in tuning and buffering gene expression. Despite abundant analysis tools that have been developed in the last two decades, plant miRNA identification from next-generation sequencing (NGS) data remains challenging. Here, we show that we can train a convolutional neural network to accurately identify plant miRNAs from NGS data. Based on our methods, we also present a user-friendly pure Java-based software package called Small RNA-related Intelligent and Convenient Analysis Tools (SRICATs). SRICATs encompasses all the necessary steps for plant miRNA analysis. Our results indicate that SRICATs outperforms currently popular software tools on the test data from five plant species. For non-commercial users, SRICATs is freely available at https://sourceforge.net/projects/sricats.

MicroRNAs (miRNAs) are processed from longer precursors with fold-back structures. While animal MIRNA precursors have homogenous structures, plant precursors comprise a collection of fold-backs with variable size and shape. Here, we design an approach (SPARE) to systematically analyze miRNA processing intermediates and characterize the biogenesis of most of the evolutionary conserved miRNAs present in Arabidopsis thaliana. We found that plant MIRNAs are processed by four mechanisms, depending on the sequential direction of the processing machinery and the number of cuts required to release the miRNA. Classification of the precursors according to their processing mechanism revealed specific structural determinants for each group. We found that the complexity of the miRNA processing pathways occurs in both ancient and evolutionary young sequences, and that members of the same family can be processed in different ways. We observed that different structural determinants compete for the processing machinery and that alternative miRNAs can be generated from a single precursor. The results provide a mechanistic explanation for the structural diversity of MIRNA precursors in plants and new insights towards the understanding of the biogenesis of small RNAs. Approach to systematically analyze miRNA processing intermediates and characterize the biogenesis of conserved and young miRNAs present in Arabidopsis thaliana. MiRNA processing intermediates profiles of Wild type and Fiery mutants Arabidopsis plants were analyzed, using Illumina GAIIx.

MicroRNAs (miRNAs) are important post-transcriptional regulators of plant development and seed formation. In Brassica napus, an important edible oil crop, valuable lipids are synthesized and stored in specific seed tissues during embryogenesis. The miRNA transcriptome of B. napus is currently poorly characterized, especially at different seed developmental stages. This work aims to describe the miRNAome of developing seeds of B. napus by identifying plant-conserved and novel miRNAs and comparing miRNA abundance in mature versus developing seeds. Members of 59 miRNA families were detected through a computational analysis of a large number of reads obtained from deep sequencing two small RNA and two RNA-seq libraries of (i) pooled immature developing stages and (ii) mature B. napus seeds. Among these miRNA families, 17 families are currently known to exist in B. napus; additionally 29 families not reported in B. napus but conserved in other plant species were identified by alignment with known plant mature miRNAs. Assembled mRNA-seq contigs allowed for a search of putative new precursors and led to the identification of 13 novel miRNA families. Analysis of miRNA population between libraries reveals that several miRNAs and isomiRNAs have different abundance in developing stages compared to mature seeds. The predicted miRNA target genes encode a broad range of proteins related to seed development and energy storage. This work presents a comparative study of the miRNA transcriptome of mature and developing B. napus seeds and provides a basis for future research on individual miRNAs and their functions in embryogenesis, seed maturation and lipid accumulation in B. napus.

mirEX is a comprehensive platform for comparative analysis of primary microRNA expression data. quantitative real-time PCR-based gene expression profiles are stored in a universal and expandable database scheme and wrapped by an intuitive user-friendly interface. A new way of accessing gene expression data in mirEX includes a simple mouse operated querying system and dynamic graphs for data mining analyses. In contrast to other publicly available databases, the mirEX interface allows a simultaneous comparison of expression levels between various microRNA genes in diverse organs and developmental stages. Currently, mirEX integrates information about the expression profile of 190 Arabidopsis thaliana pri-miRNAs in seven different developmental stages: seeds, seedlings and various organs of mature plants. Additionally, by providing RNA structural models, publicly available deep sequencing results, experimental procedure details and careful selection of auxiliary data in the form of web links, mirEX can function as a one-stop solution for Arabidopsis microRNA information. This database aims to be useful to anyone investigating the role of microRNAs in shaping plant development, organ formation and response to different biotic and abiotic stresses. To start exploring the database just press the "Browse Atlas" button or search for a particular microRNA record by typing at least two numbers from its ID in the window.

microRNAs (miRNAs) play an important role as key regulators controlling the post-transcriptional events in plants across development, abiotic and biotic stress, tissue polarity and also in defining the evolutionary basis of the origin of the post-transcriptional machinery. Identifying patterns of regulated and co-regulated small RNAs, in particular miRNAs and their sequence variants with the availability of next generation sequencing approaches has widely demonstrated the role of miRNAs and their temporal regulation in maintaining plant development and their response to stress conditions. Although the role of canonical miRNAs has been widely explored and functional diversity is revealed, those works for isomiRs are still limited and urgent to be carried out across plants. This relative lack of information with respect to isomiRs might be attributed to the non-availability of large-scale detection of isomiRs across wide plant species. In the present research, we addressed this by developing Plant isomiR Atlas, which provides large-scale detection of isomiRs across 23 plant species utilizing 677 smallRNAs datasets and reveals a total of 98,374 templated and non-templated isomiRs from 6,167 precursors. Plant isomiR Atlas provides several visualization features such as species specific isomiRs, isomiRs and canonical miRNAs overlap, terminal modification classifications, target identification using psRNATarget and TargetFinder and also canonical miRNAs:target interactions. Plant isomiR Atlas will play a key role in understanding the regulatory nature of miRNAome and will accelerate to understand the functional role of isomiRs. Plant isomiR Atlas is available at www.mcr.org.in/isomir. One Sentence Summary? Plant isomiR Atlas will play a key role in understanding the regulatory nature of miRNAome and will accelerate the understanding and diversity of functional targets of plants isomiRs.

In plants, miRNA production is orchestrated by a suite of proteins that control transcription of the pri-miRNA gene, post-transcriptional processing and nuclear export of the mature miRNA. Post-transcriptional processing of miRNAs is controlled by a pair of physically-interacting proteins, HYL1 and DCL1. However, the evolutionary history and structural basis of the HYL1-DCL1 interaction is unknown. Here we use ancestral sequence reconstruction and functional characterization of ancestral HYL1 in vitro and in Arabidopsis thaliana to better understand the origin and evolution of the HYL1-DCL1 interaction and its impact on miRNA production and plant development. We found the ancestral plant HYL1 evolved high affinity for both double-stranded RNA (dsRNA) and its DCL1 partner before the divergence of mosses from seed plants (~500 Ma), and these high-affinity interactions remained largely conserved throughout plant evolutionary history. Structural modeling and molecular binding experiments suggest that the second of two double-stranded RNA-binding motifs (DSRMs) in HYL1 may interact tightly with the first of two C-terminal DCL1 DSRMs to mediate the HYL1-DCL1 physical interaction necessary for efficient miRNA production. Transgenic expression of the nearly 200 Ma-old ancestral flowering-plant HYL1 in A. thaliana was sufficient to rescue many key aspects of plant development disrupted by HYL1- knockout and restored near-native miRNA production, suggesting that the functional partnership of HYL1-DCL1 originated very early in and was strongly conserved throughout the evolutionary history of terrestrial plants. Overall, our results are consistent with a model in which miRNA-based gene regulation evolved as part of a conserved plant ‘developmental toolkit’.

Protein-Protein, Genetic, and Chemical Interactions for Speth C (2013):RACK1 scaffold proteins influence miRNA abundance in Arabidopsis. curated by BioGRID (https://thebiogrid.org); ABSTRACT: MicroRNAs (miRNAs) regulate plant development by post-transcriptional regulation of target genes. In Arabidopsis thaliana, DCL1 processes precursors (pri-miRNAs) to miRNA duplexes, which associate with AGO1. Additional proteins act in concert with DCL1 (e.g. HYL1 and SERRATE) or AGO1, respectively, to facilitate efficient and precise pri-miRNA processing and miRNA loading. In this study, we show that the accumulation of plant microRNAs depends on RECEPTOR FOR ACTIVATED C KINASE 1 (RACK1), a scaffold protein found in all higher eukaryotes. miRNA levels are reduced in rack1 mutants and our data suggest that RACK1 affects the microRNA pathway via several distinct mechanisms involving direct interactions with known microRNA factors: RACK1 ensures the accumulation and processing of some pri-miRNAs, directly interacts with SERRATE and is part of an AGO1 complex. As a result, mutations in RACK1 lead to an overaccumulation of miRNA target mRNAs, which are important for e.g. ABA responses and phyllotaxy. In conclusion, our study discovered complex functioning of RACK1 proteins in the Arabidopsis miRNA pathway, which are important for miRNA production and therefore plant development. MPI for Developmental Biology.

MicroRNAs (miRNAs) represent a class of endogenous non-coding small RNAs that play important roles in multiple biological processes by degrading targeted mRNAs or repressing mRNA translation. Thousands of miRNAs have been identified in many plant species, whereas only a limited number of miRNAs have been predicted in M. acuminata (A genome) and M. balbisiana (B genome). Here, previously known plant miRNAs were BLASTed against the Expressed Sequence Tag (EST) and Genomic Survey Sequence (GSS), a database of banana genes. A total of 32 potential miRNAs belonging to 13 miRNAs families were detected using a range of filtering criteria. 244 miRNA:target pairs were subsequently predicted, most of which encode transcription factors or enzymes that participate in the regulation of development, growth, metabolism, and other physiological processes. In order to validate the predicted miRNAs and the mutual relationship between miRNAs and their target genes, qRT-PCR was applied to detect the tissue-specific expression levels of 12 putative miRNAs and 6 target genes in roots, leaves, flowers, and fruits. This study provides some important information about banana pre-miRNAs, mature miRNAs, and miRNA target genes and these findings can be applied to future research of miRNA functions.

miRNEST is a database of animal, plant and virus microRNAs, containing miRNA predictions conducted on Expressed Sequence Tags of animal and plant species.

In recent years, an increasing number of studies have reported the presence of plant miRNAs in human samples, which resulted in a hypothesis asserting the existence of plant-derived exogenous microRNA (xenomiR). However, this hypothesis is not widely accepted in the scientific community due to possible sample contamination and the small sample size with lack of rigorous statistical analysis. This study provides a systematic statistical test that can validate (or invalidate) the plant-derived xenomiR hypothesis by analyzing 388 small RNA sequencing data from human samples in 11 types of body fluids/tissues. A total of 166 types of plant miRNAs were found in at least one human sample, of which 14 plant miRNAs represented more than 80% of the total plant miRNAs abundance in human samples. Plant miRNA profiles were characterized to be tissue-specific in different human samples. Meanwhile, the plant miRNAs identified from microbiome have an insignificant abundance compared to those from humans, while plant miRNA profiles in human samples were significantly different from those in plants, suggesting that sample contamination is an unlikely reason for all the plant miRNAs detected in human samples. This study also provides a set of testable synthetic miRNAs with isotopes that can be detected in situ after being fed to animals.

Protein-Protein, Genetic, and Chemical Interactions for Fang Y (2007):Identification of nuclear dicing bodies containing proteins for microRNA biogenesis in living Arabidopsis plants. curated by BioGRID (https://thebiogrid.org); ABSTRACT: MicroRNAs (miRNAs) are important for regulating gene expression in muticellular organisms. MiRNA processing is a two-step process. In animal cells, the first step is nuclear and the second step cytoplasmic, whereas in plant cells, both steps occur in the nucleus via the enzyme Dicer-like1 (DCL1) and other proteins including the zinc-finger-domain protein Serrate (SE) and a double-stranded RNA (dsRNA) binding-domain protein, Hyponastic Leaves1 (HYL1). Furthermore, plant miRNAs are methylated by Hua Enhancer (HEN1) at their 3' ends and loaded onto Argonaute1 (AGO1). However, little is known about the cellular basis of miRNA biogenesis. Using live-cell imaging, we show here that DCL1 and HYL1 colocalize in discrete nuclear bodies in addition to being present in a low-level diffuse nucleoplasmic distribution. These bodies, which we refer to as nuclear dicing bodies (D-bodies), differ from Cajal bodies. A mutated DCL1 with impaired function in miRNA processing fails to target to D-bodies, and an introduced primary (pri)-miRNA transcript is recruited to D-bodies. Furthermore, bimolecular fluorescence complementation (BiFC) shows that DCL1, HYL1, and SE interact in D-bodies. On the basis of these data, we propose that D-bodies are crucial for orchestrating pri-miRNA processing and/or storage/assembly of miRNA-processing complexes in the nuclei of plant cells.

In this study we investigated the transcriptome and epigenome dynamics of the tomato fruit during post-harvest in ‘Lucariello’. ‘Lucariello’ belongs to a group of landraces collectively known as ‘Piennolo del Vesuvio’. This dataset includes: - RPKM expression values from three post-harvest stages of Lucariello fruits, time points RD0, RD60 and RD150. Each group has three biological replicates. The listed loci are from the Lucariello reference genome - Raw expression counts of miRNA from the same samples - DNA gene methylation levels from the same samples, divided for CG, CHG and CHH context - ChIP-seq peaks for H3K4me3, H3K9K14ac, H3K27me3 from the same samples, two biological replicates - Supplementary tables described in the main manuscript providing lists of Differentially Methylated Regions (DMR) and Differentially Expressed Genes (DEGs) across the different time points

Database providing a systematic and comprehensive view of morphological phenotypes regulated by plant hormones, as well as regulatory genes participating in numerous plant hormone responses. By integrating the data from mutant studies, transgenic analysis and gene ontology annotation, genes related to the stimulus of eight plant hormones were identified, including abscisic acid, auxin, brassinosteroid, cytokinin, ethylene, gibberellin, jasmonic acid and salicylic acid. Another pronounced characteristics of this database is that a phenotype ontology was developed to precisely describe all kinds of morphological processes regulated by plant hormones with standardized vocabularies. To increase the coverage of phytohormone related genes, the database has been updated from AHD to AHD2.0 adding and integrating several pronounced features: (1) added 291 newly published Arabidopsis hormone related genes as well as corrected information (e.g. the arguable ABA receptors) based on the recent 2-year literature; (2) integrated orthologues of sequenced plants in OrthoMCLDB into each gene in the database; (3) integrated predicted miRNA splicing site in each gene in the database; (4) provided genetic relationship of these phytohormone related genes mining from literature, which represents the first effort to construct a relatively comprehensive and complex network of hormone related genes as shown in the home page of our database; (5) In convenience to in-time bioinformatics analysis, they also provided links to a powerful online analysis platform Weblab that they have recently developed, which will allow users to readily perform various sequence analysis with these phytohormone related genes retrieved from AHD2.0; (6) provided links to other protein databases as well as more expression profiling information that would facilitate users for a more systematic analysis related to phytohormone research. Please help to improve the database with your contributions.

The wild grass Brachypodium distachyon has emerged as a model system for temperate grasses and biofuel plants. However, the global analysis of miRNAs, molecules known to be key for eukaryotic gene regulation, has been limited in B. distachyon to studies examining a few samples or that rely on computational predictions. Similarly an in-depth global analysis of miRNA-mediated target cleavage using Parallel Analysis of RNA Ends (PARE) data is lacking in B. distachyon. B. distachyon small RNAs were cloned and deeply sequenced from 17 libraries that represent different tissues and stresses. Using a computational pipeline, we identified 116 miRNAs including not only conserved miRNAs that have not been reported in B. distachyon, but also non-conserved miRNAs that were not found in other plants. To investigate miRNA-mediated cleavage function, four PARE libraries were constructed from key tissues and sequenced to a total depth of approximately 70 million sequences. The roughly 5 million distinct genome-matched sequences that resulted represent an extensive dataset to analyze small RNA-guided cleavage events. Analysis of the PARE and miRNA data provided experimental evidence for miRNA-mediated cleavage of 264 sites in predicted miRNA targets. In addition, PARE analysis revealed that differentially expressed miRNAs in the same family guide specific target RNA cleavage in a correspondingly tissue-preferential manner. B. distachyon miRNAs and target RNAs were experimentally identified and analyzed. Knowledge gained from this study should provide insights into the roles of miRNAs and the regulation of their targets in B. distachyon and related plants. Examination of various tissues and stresses in Brachypodium by high throughput sequencing for small RNA profiling and PARE (Parallel Analysis of RNA Ends)