miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets

AUXIN RESPONSIVE FACTOR8 regulates development of the feeding site induced by root-knot nematodes in tomato

Root-knot nematodes (RKN) from the genus Meloidogyne induce the dedifferentiation of root vascular cells into giant multinucleate feeding cells. These feeding cells result from an extensive reprogramming of gene expression, and auxin is known to be a key player in their development. However, little is known about how the auxin signal is transmitted during giant cell development. Integrative analyses combining transcriptome and small non-coding RNA datasets with the specific sequencing of cleaved transcripts identified genes targeted by miRNAs in tomato (Solanum lycopersicum) galls. The two auxin-responsive transcription factors ARF8A and ARF8B, and their miRNA167 regulators, were identified as robust gene-miRNA pair candidates to be involved in the tomato response to M. incognita. Spatiotemporal expression analysis using promoter-β-glucuronidase (GUS) fusions showed the up-regulation of ARF8A and ARF8B in RKN-induced feeding cells and surrounding cells. The generation and phenotyping of CRISPR (clustered regularly interspaced palindromic repeats) mutants demonstrated the role of ARF8A and ARF8B in giant cell development and allowed the characterization of their downstream regulated genes.

Genome-scale signatures of adaptive gene expression changes in an invasive seaweed Gracilaria vermiculophylla

Invasive species can successfully and rapidly colonize new niches and expand ranges via founder effects and enhanced tolerance towards environmental stresses. However, the underpinning molecular mechanisms (i.e., gene expression changes) facilitating rapid adaptation to harsh environments are still poorly understood. The red seaweed Gracilaria vermiculophylla, which is native to the northwest Pacific but invaded North American and European coastal habitats over the last 100 years, provides an excellent model to examine whether enhanced tolerance at the level of gene expression contributed to its invasion success. We collected G. vermiculophylla from its native range in Japan and from two non-native regions along the Delmarva Peninsula (Eastern United States) and in Germany. Thalli were reared in a common garden for 4 months at which time we performed comparative transcriptome (mRNA) and microRNA (miRNA) sequencing. MRNA-expression profiling identified 59 genes that were differently expressed between native and non-native thalli. Of these genes, most were involved in metabolic pathways, including photosynthesis, abiotic stress, and biosynthesis of products and hormones in all four non-native sites. MiRNA-based target-gene correlation analysis in native/non-native pairs revealed that some target genes are positively or negatively regulated via epigenetic mechanisms. Importantly, these genes are mostly associated with metabolism and defence capability (e.g., metal transporter Nramp5, senescence-associated protein, cell wall-associated hydrolase, ycf68 protein and cytochrome P450-like TBP). Thus, our gene expression results indicate that resource reallocation to metabolic processes is most likely a predominant mechanism contributing to the range-wide persistence and adaptation of G. vermiculophylla in the invaded range. This study, therefore, provides molecular insight into the speed and nature of invasion-mediated rapid adaption.

Degradome Assisted Plant MicroRNA Prediction Under Alternative Annotation Criteria

Current microRNA (miRNA) prediction methods are generally based on annotation criteria that tend to miss potential functional miRNAs. Recently, new miRNA annotation criteria have been proposed that could lead to improvements in miRNA prediction methods in plants. Here, we investigate the effect of the new criteria on miRNA prediction in Arabidopsis thaliana and present a new degradome assisted functional miRNA prediction approach. We investigated the effect by applying the new criteria, and a more permissive criteria on miRNA prediction using existing miRNA prediction tools. We also developed an approach to miRNA prediction that is assisted by the functional information extracted from the analysis of degradome sequencing. We demonstrate the improved performance of degradome assisted miRNA prediction compared to unassisted prediction and evaluate the approach using miRNA differential expression analysis. We observe how the miRNA predictions fit under the different criteria and show a potential novel miRNA that has been missed within Arabidopsis thaliana. Additionally, we introduce a freely available software 'PAREfirst' that employs the degradome assisted approach. The study shows that some miRNAs could be missed due to the stringency of the former annotation criteria, and combining a degradome assisted approach with more permissive miRNA criteria can expand confident miRNA predictions.

Interactive Web-based Annotation of Plant MicroRNAs with iwa-miRNA

MicroRNAs (miRNAs) are important regulators of gene expression. The large-scale detection and profiling of miRNAs have been 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 prediction tools. 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 in selecting promising miRNA candidates in an interactive mode, 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 the annotation of miRNAs in plant species with reference genomes. We also illustrate the application of iwa-miRNA for miRNA annotation using data from plant species with varying genomic complexity. The source codes and web server of iwa-miRNA are freely accessible at http://iwa-miRNA.omicstudio.cloud/.

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

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.

Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants

Plant transcriptomes encompass a large number of functional non-coding RNAs (ncRNAs), only some of which have protein-coding capacity. Since their initial discovery, ncRNAs have been classified into two broad categories based on their biogenesis and mechanisms of action, housekeeping ncRNAs and regulatory ncRNAs. With advances in RNA sequencing technology and computational methods, bioinformatics resources continue to emerge and update rapidly, including workflow for in silico ncRNA analysis, up-to-date platforms, databases, and tools dedicated to ncRNA identification and functional annotation. In this review, we aim to describe the biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs (miRNA, siRNA, tsRNA, circRNA, lncRNA) in plants. Then, we systematically summarize tools for analysis and prediction of plant ncRNAs, as well as databases. Furthermore, we discuss the silico analysis process of these ncRNAs and present a protocol for step-by-step computational analysis of ncRNAs. In general, this review will help researchers better understand the world of ncRNAs at multiple levels.

Establishment of Repertoire of Placentome-Associated MicroRNAs and Their Appearance in Blood Plasma Could Identify Early Establishment of Pregnancy in Buffalo (Bubalus bubalis)

Precise early pregnancy diagnosis in dairy animals is of utmost importance for an efficient dairy production system. Not detecting a dairy animal pregnant sufficiently early after the breeding results to extending the unproductive time of their milk production cycle and causes substantial economic loss for a dairy producer. At present, the most conventional and authentic pregnancy confirmation practice in cows and buffaloes is rectal palpation of the reproductive organs at Days 35–40 after insemination, which sometime leads to considering an animal as false pregnant. Other alternative methods available for early pregnancy diagnosis lack either accuracy or reproducibility or require elaborate instrumentation and laboratory setup not feasible to practice at farmers’ doorstep. The present study was aimed at establishment of the microRNA (miRNA) repertoire of the placentome in buffaloes, which could capture the event of the cross talk between a growing embryo and a dam, through fetal cotyledons and maternal caruncles, and thus could hint at the early pregnancy establishment event in ruminants. Total RNA was isolated from buffalo placentome tissues during early stages of pregnancy (at Day < 25 and Days 30–35), and global small RNA analysis was performed by using Illumina single-end read chemistry and Bubalus bubalis genome. A total of 2,199 miRNAs comprising 1,620 conserved and 579 non-conserved miRNAs were identified. Stringent functional miRNA selection criteria could predict 20 miRNAs worth evaluating for their abundance in the plasma of pregnant, non-pregnant, cyclic non-bred, and non-cyclic prepubertal animals. Eight of them (viz., miR-195-5p, miR-708-3p, miR-379-5p, miR-XX1, miR-XX2, miR-130a-3p, miR-200a-3p, and miR-27) displayed typical abundance patterns in the plasma samples of the animals on Day 19 as well as Day 25 post-insemination, thus making them ambiguous candidates for early pregnancy detection. Similarly, higher abundance of miR-200a-3p and miR130a-3p in non-pregnant animals was indicative of their utility for detecting the animals as not pregnant. Most interestingly, miR-XX1 and miR-XX2 were very characteristically abundant only in pregnant animals. In silico target prediction analysis confirmed that these two miRNAs are important regulators of cyclooxygenase-2 (COX-2) and cell adhesion molecule-2 (CADM-2), both of which play a significant role in the implantation process during feto-maternal cross talk. We interpret that circulatory miR-XX1 and miR-XX2 in blood plasma could be the potential biomarkers for early pregnancy detection in buffaloes.

noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise

High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimizing technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches.

MicroRNA annotation in plants: current status and challenges

Last two decades, the studies on microRNAs (miRNAs) and the numbers of annotated miRNAs in plants and animals have surged. Herein, we reviewed the current progress and challenges of miRNA annotation in plants. Via the comparison of plant and animal miRNAs, we pinpointed out the difficulties on plant miRNA annotation and proposed potential solutions. In terms of recalling the history of methods and criteria in plant miRNA annotation, we detailed how the major progresses made and evolved. By collecting and categorizing bioinformatics tools for plant miRNA annotation, we surveyed their advantages and disadvantages, especially for ones with the principle of mimicking the miRNA biogenesis pathway by parsing deeply sequenced small RNA (sRNA) libraries. In addition, we summarized all available databases hosting plant miRNAs, and posted the potential optimization solutions such as how to increase the signal-to-noise ratio (SNR) in these databases. Finally, we discussed the challenges and perspectives of plant miRNA annotations, and indicated the possibilities offered by an all-in-one tool and platform according to the integration of artificial intelligence.

MirCure: a tool for quality control, filter and curation of microRNAs of animals and plants

MOTIVATION

microRNAs (miRNAs) are essential components of gene expression regulation at the post-transcriptional level. miRNAs have a well-defined molecular structure and this has facilitated the development of computational and high-throughput approaches to predict miRNAs genes. However, due to their short size, miRNAs have often been incorrectly annotated in both plants and animals. Consequently, published miRNA annotations and miRNA databases are enriched for false miRNAs, jeopardizing their utility as molecular information resources. To address this problem, we developed MirCure, a new software for quality control, filtering and curation of miRNA candidates. MirCure is an easy-to-use tool with a graphical interface that allows both scoring of miRNA reliability and browsing of supporting evidence by manual curators.

RESULTS

Given a list of miRNA candidates, MirCure evaluates a number of miRNA-specific features based on gene expression, biogenesis and conservation data, and generates a score that can be used to discard poorly supported miRNA annotations. MirCure can also curate and adjust the annotation of the 5p and 3p arms based on user-provided small RNA-seq data. We evaluated MirCure on a set of manually curated animal and plant miRNAs and demonstrated great accuracy. Moreover, we show that MirCure can be used to revisit previous bona fide miRNAs annotations to improve miRNA databases.

AVAILABILITY AND IMPLEMENTATION

The MirCure software and all the additional scripts used in this project are publicly available at https://github.com/ConesaLab/MirCure. A Docker image of MirCure is available at https://hub.docker.com/r/conesalab/mircure.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq

Plant transcriptome encompasses numerous endogenous, regulatory non-coding RNAs (ncRNAs) that play a major biological role in regulating key physiological mechanisms. While studies have shown that ncRNAs are extremely diverse and ubiquitous, the functions of the vast majority of ncRNAs are still unknown. With ever-increasing ncRNAs under study, it is essential to identify, categorize and annotate these ncRNAs on a genome-wide scale. The use of high-throughput RNA sequencing (RNA-seq) technologies provides a broader picture of the non-coding component of transcriptome, enabling the comprehensive identification and annotation of all major ncRNAs across samples. However, the detection of known and emerging class of ncRNAs from RNA-seq data demands complex computational methods owing to their unique as well as similar characteristics. Here, we discuss major plant endogenous, regulatory ncRNAs in an RNA sample followed by computational strategies applied to discover each class of ncRNAs using RNA-seq. We also provide a collection of relevant software packages and databases to present a comprehensive bioinformatics toolbox for plant ncRNA researchers. We assume that the discussions in this review will provide a rationale for the discovery of all major categories of plant ncRNAs.

PmiREN: a comprehensive encyclopedia of plant miRNAs

MicroRNAs (miRNAs) are small non-coding RNA molecules that function as diverse endogenous gene regulators at the post-transcriptional level. In the past two decades, as research effort on miRNA identification, function and evolution has soared, so has the demand for miRNA databases. However, the current plant miRNA databases suffer from several typical drawbacks, including a lack of entries for many important species, uneven annotation standards across different species, abundant questionable entries, and limited annotation. To address these issues, we developed a knowledge-based database called Plant miRNA Encyclopedia (PmiREN, http://www.pmiren.com/), which was based on uniform processing of sequenced small RNA libraries using miRDeep-P2, followed by manual curation using newly updated plant miRNA identification criteria, and comprehensive annotation. PmiREN currently contains 16,422 high confidence novel miRNA loci in 88 plant species and 3,966 retrieved from miRBase. For every miRNA entry, information on precursor sequence, precursor secondary structure, expression pattern, clusters and synteny in the genome, potential targets supported by Parallel Analysis of RNA Ends (PARE) sequencing, and references is attached whenever possible. PmiREN is hierarchically accessible and has eight built-in search engines. We believe PmiREN is useful for plant miRNA cataloguing and data mining, therefore a resource for data-driven miRNA research in plants.

microRNA profiling in the Weddell seal suggests novel regulatory mechanisms contributing to diving adaptation

Background

The Weddell Seal (Leptonychotes weddelli) represents a remarkable example of adaptation to diving among marine mammals. This species is capable of diving > 900 m deep and remaining underwater for more than 60 min. A number of key physiological specializations have been identified, including the low levels of aerobic, lipid-based metabolism under hypoxia, significant increase in oxygen storage in blood and muscle; high blood volume and extreme cardiovascular control. These adaptations have been linked to increased abundance of key proteins, suggesting an important, yet still understudied role for gene reprogramming.

In this study, we investigate the possibility that post-transcriptional gene regulation by microRNAs (miRNAs) has contributed to the adaptive evolution of diving capacities in the Weddell Seal.

Results

Using small RNA data across 4 tissues (brain, heart, muscle and plasma), in 3 biological replicates, we generate the first miRNA annotation in this species, consisting of 559 high confidence, manually curated miRNA loci. Evolutionary analyses of miRNA gain and loss highlight a high number of Weddell seal specific miRNAs.

Four hundred sixteen miRNAs were differentially expressed (DE) among tissues, whereas 80 miRNAs were differentially expressed (DE) across all tissues between pups and adults and age differences for specific tissues were detected in 188 miRNAs. mRNA targets of these altered miRNAs identify possible protective mechanisms in individual tissues, particularly relevant to hypoxia tolerance, anti-apoptotic pathways, and nitric oxide signal transduction. Novel, lineage-specific miRNAs associated with developmental changes target genes with roles in angiogenesis and vasoregulatory signaling.

Conclusions

Altogether, we provide an overview of miRNA composition and evolution in the Weddell seal, and the first insights into their possible role in the specialization to diving.

Computational tools for plant small RNA detection and categorization

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

PAREameters: a tool for computational inference of plant miRNA-mRNA targeting rules using small RNA and degradome sequencing data

MicroRNAs (miRNAs) are short, non-coding RNAs that modulate the translation-rate of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to sequence-specific targets. In plants, this typically results in cleavage and subsequent degradation of the mRNA. Degradome sequencing is a high-throughput technique developed to capture cleaved mRNA fragments and thus can be used to support miRNA target prediction. The current criteria used for miRNA target prediction were inferred on a limited number of experimentally validated A. thaliana interactions and were adapted to fit these specific interactions; thus, these fixed criteria may not be optimal across all datasets (organisms, tissues or treatments). We present a new tool, PAREameters, for inferring targeting criteria from small RNA and degradome sequencing datasets. We evaluate its performance using a more extensive set of experimentally validated interactions in multiple A. thaliana datasets. We also perform comprehensive analyses to highlight and quantify the differences between subsets of miRNA-mRNA interactions in model and non-model organisms. Our results show increased sensitivity in A. thaliana when using the PAREameters inferred criteria and that using data-driven criteria enables the identification of additional interactions that further our understanding of the RNA silencing pathway in both model and non-model organisms.

The role of microRNAs in the legume-Rhizobium nitrogen-fixing symbiosis

Under nitrogen starvation, most legume plants form a nitrogen-fixing symbiosis with Rhizobium bacteria. The bacteria induce the formation of a novel organ called the nodule in which rhizobia reside as intracellular symbionts and convert atmospheric nitrogen into ammonia. During this symbiosis, miRNAs are essential for coordinating the various plant processes required for nodule formation and function. miRNAs are non-coding, endogenous RNA molecules, typically 20-24 nucleotides long, that negatively regulate the expression of their target mRNAs. Some miRNAs can move systemically within plant tissues through the vascular system, which mediates, for example, communication between the stem/leaf tissues and the roots. In this review, we summarize the growing number of miRNAs that function during legume nodulation focusing on two model legumes, Lotus japonicus and Medicago truncatula, and two important legume crops, soybean (Glycine max) and common bean (Phaseolus vulgaris). This regulation impacts a variety of physiological processes including hormone signaling and spatial regulation of gene expression. The role of mobile miRNAs in regulating legume nodule number is also highlighted.

Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach

Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miRNA identification in the yet poorly annotated porcine genome and demonstrated the usefulness of implementing a motif search positional refinement strategy for the accurate determination of precursor miRNA boundaries. The small RNA fraction from gluteus medius skeletal muscle of 48 Duroc gilts was sequenced and used for the prediction of novel miRNA loci. Additionally, we selected the human miRNA annotation for a homology-based search of porcine miRNAs with orthologous genes in the human genome. A total of 20 novel expressed miRNAs were identified in the porcine muscle transcriptome and 27 additional novel porcine miRNAs were also detected by homology-based search using the human miRNA annotation. The existence of three selected novel miRNAs (ssc-miR-483, ssc-miR484 and ssc-miR-200a) was further confirmed by reverse transcription quantitative real-time PCR analyses in the muscle and liver tissues of Göttingen minipigs. In summary, the eMIRNA pipeline presented in the current work allowed us to expand the catalogue of porcine miRNAs and showed better performance than other commonly used miRNA prediction approaches. More importantly, the flexibility of our pipeline makes possible its application in other yet poorly annotated non-model species.

The UEA sRNA Workbench (version 4.4): a comprehensive suite of tools for analyzing miRNAs and sRNAs

Motivation

RNA interference, a highly conserved regulatory mechanism, is mediated via small RNAs (sRNA). Recent technical advances enabled the analysis of larger, complex datasets and the investigation of microRNAs and the less known small interfering RNAs. However, the size and intricacy of current data requires a comprehensive set of tools, able to discriminate the patterns from the low-level, noise-like, variation; numerous and varied suggestions from the community represent an invaluable source of ideas for future tools, the ability of the community to contribute to this software is essential.

Results

We present a new version of the UEA sRNA Workbench, reconfigured to allow an easy insertion of new tools/workflows. In its released form, it comprises of a suite of tools in a user-friendly environment, with enhanced capabilities for a comprehensive processing of sRNA-seq data e.g. tools for an accurate prediction of sRNA loci (CoLIde) and miRNA loci (miRCat2), as well as workflows to guide the users through common steps such as quality checking of the input data, normalization of abundances or detection of differential expression represent the first step in sRNA-seq analyses.

Availability and implementation

The UEA sRNA Workbench is available at: http://srna-workbench.cmp.uea.ac.uk. The source code is available at: https://github.com/sRNAworkbenchuea/UEA_sRNA_Workbench

Supplementary information

Supplementary data are available at Bioinformatics online.

Trends in the development of miRNA bioinformatics tools

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.

PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules

Small RNAs (sRNAs) are short, non-coding RNAs that play critical roles in many important biological pathways. They suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to their sequence-specific mRNA target(s). In plants, this typically results in mRNA cleavage and subsequent degradation of the mRNA. The resulting mRNA fragments, or degradome, provide evidence for these interactions, and thus degradome analysis has become an important tool for sRNA target prediction. Even so, with the continuing advances in sequencing technologies, not only are larger and more complex genomes being sequenced, but also degradome and associated datasets are growing both in number and read count. As a result, existing degradome analysis tools are unable to process the volume of data being produced without imposing huge resource and time requirements. Moreover, these tools use stringent, non-configurable targeting rules, which reduces their flexibility. Here, we present a new and user configurable software tool for degradome analysis, which employs a novel search algorithm and sequence encoding technique to reduce the search space during analysis. The tool significantly reduces the time and resources required to perform degradome analysis, in some cases providing more than two orders of magnitude speed-up over current methods.

Target gene identification and functional characterization of miR-1704 in chicken

MiRNAs are small non-coding RNAs that negatively regulate gene expression at the post-transcriptional level. SNPs in miRNA genes may lead to phenotypic variation by altering miRNA expression and their targets. In this study, miR-1704 expression profiles in nine tissues at 1 d, 6 weeks and 16 weeks old Gushi chickens were detected. MiR-1704 was widely expressed in the detection of tissues. The expression in 1 d and 6 weeks old was low abundance, while its expression at 16 weeks was very high. An rs14668705 (C > G) SNP was detected within the pre-miR-1704 in an F₂ resource population of Gushi chicken crossed with Anka broiler. Bioinformatic analysis indicated that the C > G mutation could introduce a base-pair mismatch and cause the change of free energy. Experiments further revealed that the rs14668705 in precursor miR-1704 could significantly affect mature miR-1704 biogenesis and was significantly associated with body weight at the age of 0, 6, 8, 10, and 12 weeks, shank circumference at 4, 8, and 12 weeks, carcass weight, and semi-evisceration weight (p < 0.05). Insulin receptor 2 (IRS2) gene, one of the potential targets of miR-1704 was identified and further confirmed. These data suggested that miR-1704 targeted IRS2 and have an effect on body weight in chicken.

miPIE: NGS-based Prediction of miRNA Using Integrated Evidence

Methods for the de novo identification of microRNA (miRNA) have been developed using a range of sequence-based features. With the increasing availability of next generation sequencing (NGS) transcriptome data, there is a need for miRNA identification that integrates both NGS transcript expression-based patterns as well as advanced genomic sequence-based methods. While miRDeep2 does examine the predicted secondary structure of putative miRNA sequences, it does not leverage many of the sequence-based features used in state-of-the-art de novo methods. Meanwhile, other NGS-based methods, such as miRanalyzer, place an emphasis on sequence-based features without leveraging advanced expression-based features reflecting miRNA biosynthesis. This represents an opportunity to combine the strengths of NGS-based analysis with recent advances in de novo sequence-based miRNA prediction. We here develop a method, microRNA Prediction using Integrated Evidence (miPIE), which integrates both expression-based and sequence-based features to achieve significantly improved miRNA prediction performance. Feature selection identifies the 20 most discriminative features, 3 of which reflect strictly expression-based information. Evaluation using precision-recall curves, for six NGS data sets representing six diverse species, demonstrates substantial improvements in prediction performance compared to three methods: miRDeep2, miRanalyzer, and mirnovo. The individual contributions of expression-based and sequence-based features are also examined and we demonstrate that their combination is more effective than either alone.

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

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

miRNomes involved in imparting thermotolerance to crop plants

Thermal stress is one of the challenges to crop plants that negatively impacts crop yield. To overcome this ever-growing problem, utilization of regulatory mechanisms, especially microRNAs (miRNAs), that provide efficient and precise regulation in a targeted manner have been found to play determining roles. Besides their roles in plant growth and development, many recent studies have shown differential regulation of several miRNAs during abiotic stresses including heat stress (HS). Thus, understanding the underlying mechanism of miRNA-mediated gene expression during HS will enable researchers to exploit this regulatory mechanism to address HS responses. This review focuses on the miRNAs and regulatory networks that were involved in physiological, metabolic and morphological adaptations during HS in plant, specifically in crops. Illustrated examples including, the miR156-SPL, miR169-NF-YA5, miR395-APS/AST, miR396-WRKY, etc., have been discussed in specific relation to the crop plants. Further, we have also discussed the available plant miRNA databases and bioinformatics tools useful for miRNA identification and study of their regulatory role in response to HS. Finally, we have briefly discussed the future prospects about the miRNA-related mechanisms of HS for improving thermotolerance in crop plants.

The evolutionary dynamics of microRNAs in domestic mammals

MiRNAs are crucial regulators of gene expression found across both the plant and animal kingdoms. While the number of annotated miRNAs deposited in miRBase has greatly increased in recent years, few studies provided comparative analyses across sets of related species, or investigated the role of miRNAs in the evolution of gene regulation. We generated small RNA libraries across 5 mammalian species (cow, dog, horse, pig and rabbit) from 4 different tissues (brain, heart, kidney and testis). We identified 1676 miRBase and 413 novel miRNAs by manually curating the set of computational predictions obtained from miRCat and miRDeep2. Our dataset spanning five species has enabled us to investigate the molecular mechanisms and selective pressures driving the evolution of miRNAs in mammals. We highlight the important contributions of intronic sequences (366 orthogroups), duplication events (135 orthogroups) and repetitive elements (37 orthogroups) in the emergence of new miRNA loci. We use this framework to estimate the patterns of gains and losses across the phylogeny, and observe high levels of miRNA turnover. Additionally, the identification of lineage-specific losses enables the characterisation of the selective constraints acting on the associated target sites. Compared to the miRBase subset, novel miRNAs tend to be more tissue specific. 20 percent of novel orthogroups are restricted to the brain, and their target repertoires appear to be enriched for neuron activity and differentiation processes. These findings may reflect an important role for young miRNAs in the evolution of brain expression plasticity. Many seed sequences appear to be specific to either the cow or the dog. Analyses on the associated targets highlight the presence of several genes under artificial positive selection, suggesting an involvement of these miRNAs in the domestication process. Altogether, we provide an overview on the evolutionary mechanisms responsible for miRNA turnover in 5 domestic species, and their possible contribution to the evolution of gene regulation.

New 3D graphical representation for RNA structure analysis and its application in the pre-miRNA identification of plants

MicroRNAs (miRNAs) are a family of short non-coding RNAs that play significant roles as post-transcriptional regulators. Consequently, various methods have been proposed to identify precursor miRNAs (pre-miRNAs), among which the comparative studies of miRNA structures are the most important. To measure and classify the structural similarity of miRNAs, we propose a new three-dimensional (3D) graphical representation of the secondary structure of miRNAs, in which an miRNA secondary structure is initially transformed into a characteristic sequence based on physicochemical properties and frequency of base. A numerical characterization of the 3D graph is used to represent the miRNA secondary structure. We then utilize a novel Euclidean distance method based on this expression to compute the distance of different miRNA sequences for the sequence similarity analysis. Finally, we use this sequence similarity analysis method to identify plant pre-miRNAs among three commonly used datasets. Results show that the method is reasonable and effective.

microRNAs associated with early neural crest development in Xenopus laevis

BACKGROUND

The neural crest (NC) is a class of transitory stem cell-like cells unique to vertebrate embryos. NC cells arise within the dorsal neural tube where they undergo an epithelial to mesenchymal transition in order to migrate and differentiate throughout the developing embryo. The derivative cell types give rise to multiple tissues, including the craniofacial skeleton, peripheral nervous system and skin pigment cells. Several well-studied gene regulatory networks underpin NC development, which when disrupted can lead to various neurocristopathies such as craniofrontonasal dysplasia, DiGeorge syndrome and some forms of cancer. Small RNAs, such as microRNAs (miRNAs) are non-coding RNA molecules important in post-transcriptional gene silencing and critical for cellular regulation of gene expression.

RESULTS

To uncover novel small RNAs in NC development we used high definition adapters and next generation sequencing of libraries derived from ectodermal explants of Xenopus laevis embryos induced to form neural and NC tissue. Ectodermal and blastula animal pole (blastula) stage tissues were also sequenced. We show that miR-427 is highly abundant in all four tissue types though in an isoform specific manner and we define a set of 11 miRNAs that are enriched in the NC. In addition, we show miR-301a and miR-338 are highly expressed in both the NC and blastula suggesting a role for these miRNAs in maintaining the stem cell-like phenotype of NC cells.

CONCLUSION

We have characterised the miRNAs expressed in Xenopus embryonic explants treated to form ectoderm, neural or NC tissue. This has identified novel tissue specific miRNAs and highlighted differential expression of miR-427 isoforms.

Roles of Non-Coding RNA in Sugarcane-Microbe Interaction

Studies have highlighted the importance of non-coding RNA regulation in plant-microbe interaction. However, the roles of sugarcane microRNAs (miRNAs) in the regulation of disease responses have not been investigated. Firstly, we screened the sRNA transcriptome of sugarcane infected with Acidovorax avenae. Conserved and novel miRNAs were identified. Additionally, small interfering RNAs (siRNAs) were aligned to differentially expressed sequences from the sugarcane transcriptome. Interestingly, many siRNAs aligned to a transcript encoding a copper-transporter gene whose expression was induced in the presence of A. avenae, while the siRNAs were repressed in the presence of A. avenae. Moreover, a long intergenic non-coding RNA was identified as a potential target or decoy of miR408. To extend the bioinformatics analysis, we carried out independent inoculations and the expression patterns of six miRNAs were validated by quantitative reverse transcription-PCR (qRT-PCR). Among these miRNAs, miR408-a copper-microRNA-was downregulated. The cleavage of a putative miR408 target, a laccase, was confirmed by a modified 5'RACE (rapid amplification of cDNA ends) assay. MiR408 was also downregulated in samples infected with other pathogens, but it was upregulated in the presence of a beneficial diazotrophic bacteria. Our results suggest that regulation by miR408 is important in sugarcane sensing whether microorganisms are either pathogenic or beneficial, triggering specific miRNA-mediated regulatory mechanisms accordingly.

Identification and expression profiling of microRNAs involved in the stigma exsertion under high-temperature stress in tomato

Background

Autogamy in cultivated tomato varieties is a derived trait from wild type tomato plants, which are mostly allogamous. However, environmental stresses can cause morphological defects in tomato flowers and hinder autogamy. Under elevated temperatures, tomato plants usually exhibit the phenotype of stigma exsertion, with severely hindered self-pollination and fruit setting, whereas the inherent mechanism of stigma exsertion have been hitherto unknown. Numerous small RNAs (sRNAs) have been shown to play significant roles in plant development and stress responses, however, none of them have been studied with respect to stamen and pistil development under high-temperature conditions. We investigated the associations between stigma exsertion and small RNAs using high-throughput sequencing technology and molecular biology approaches.

Results

Sixteen sRNA libraries of Micro-Tom were constructed from plants stamen and pistil samples and sequenced after 2 d and 12 d of exposure to heat stress, respectively, from which a total of 110 known and 84 novel miRNAs were identified. Under heat stress conditions, 34 known and 35 novel miRNAs were differentially expressed in stamens, and 20 known and 10 novel miRNAs were differentially expressed in pistils. GO and KEGG pathway analysis showed that the predicted target genes of differentially expressed miRNAs were significantly enriched in metabolic pathways in both stamen and pistil libraries. Potential miRNA-target cleavage cascades that correlated with the regulation of stigma exsertion under heat stress conditions were found and validated through qRT-PCR and RLM-5′ RACE.

Conclusion

Overall, a global spectrum of known and novel miRNAs involved in tomato stigma exsertion and induced by high temperatures were identified using high-throughput sequencing and molecular biology approaches, laying a foundation for revealing the miRNA-mediated regulatory network involved in the development of tomato stamens and pistils under high-temperature conditions.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4238-9) contains supplementary material, which is available to authorized users.