Computational tools for plant small RNA detection and categorization

Identification of Arabidopsis thaliana small RNAs responsive to the fungal pathogen Botrytis cinerea at an early stage of interaction

In plants, small RNAs (sRNAs), mainly microRNAs (miRNAs) and small interfering RNAs (siRNAs), have been described as key regulators of plant development, growth, and abiotic and biotic responses. Despite reports indicating the involvement of certain sRNAs in regulating the interaction between Botrytis cinerea (a major necrotrophic fungal phytopathogen) and host plants, there remains a lack of analysis regarding the potential regulatory roles of plant sRNAs during early stages of the interaction despite early immune responses observed then during infection. We present the first transcriptome-wide analysis of small RNA expression on the early interaction between the necrotrophic fungus Botrytis cinerea and the model plant Arabidopsis thaliana. We found that evolutionary conserved A. thaliana miRNAs were the sRNAs that accumulated the most in the presence of B. cinerea. The upregulation of miR167, miR159 and miR319 was of particular interest because these, together with their target transcripts, are involved in the fine regulation of the plant hormone signaling pathways. We also describe that miR173, which triggers the production of secondary siRNAs from TAS1 and TAS2 loci, as well as secondary siRNAs derived from these loci, is upregulated in response to B. cinerea. Thus, at an early stage of the interaction there are transcriptional changes of sRNA-guided silencing pathway genes and of a subset of sRNAs that targeted genes from the PPR gene superfamily, and these may be important mechanisms regulating the interaction between A. thaliana and B. cinerea. This work provides the basis for a better understanding of the regulation mediated by sRNAs during early B. cinerea-plant interaction and may help in the development of more effective strategies for its control.

Plant biomarkers as early detection tools in stress management in food crops: a review

MAIN CONCLUSION

Plant Biomarkers are objective indicators of a plant's cellular state in response to abiotic and biotic stress factors. They can be explored in crop breeding and engineering to produce stress-tolerant crop species. Global food production safely and sustainably remains a top priority to feed the ever-growing human population, expected to reach 10 billion by 2050. However, abiotic and biotic stress factors negatively impact food production systems, causing between 70 and 100% reduction in crop yield. Understanding the plant stress responses is critical for developing novel crops that can adapt better to various adverse environmental conditions. Using plant biomarkers as measurable indicators of a plant's cellular response to external stimuli could serve as early warning signals to detect stresses before severe damage occurs. Plant biomarkers have received considerable attention in the last decade as pre-stress indicators for various economically important food crops. This review discusses some biomarkers associated with abiotic and biotic stress conditions and highlights their importance in developing stress-resilient crops. In addition, we highlighted some factors influencing the expression of biomarkers in crop plants under stress. The information presented in this review would educate plant researchers, breeders, and agronomists on the significance of plant biomarkers in stress biology research, which is essential for improving plant growth and yield toward sustainable food production.

Perspective for Studying the Relationship of miRNAs with Transposable Elements

Transposable elements are important sources of miRNA, long non-coding RNAs genes, and their targets in the composition of protein-coding genes in plants and animals. Therefore, the detection of expression levels of specific non-coding RNAs in various tissues and cells in normal and pathological conditions may indicate a programmed pattern of transposable elements' activation. This reflects the species-specific composition and distribution of transposable elements in genomes, which underlie gene regulation in every cell division, including during aging. TEs' expression is also regulated by epigenetic factors (DNA methylation, histone modifications), SIRT6, cytidine deaminases APOBEC3, APOBEC1, and other catalytic proteins, such as ERCC, TREX1, RB1, HELLS, and MEGP2. In evolution, protein-coding genes and their regulatory elements are derived from transposons. As part of non-coding regions and introns of genes, they are sensors for transcriptional and post-transcriptional control of expression, using miRNAs and long non-coding RNAs, that arose from transposable elements in evolution. Methods (Orbld, ncRNAclassifier) and databases have been created for determining the occurrence of miRNAs from transposable elements in plants (PlanTE-MIR DB, PlaNC-TE), which can be used to design epigenetic gene networks in ontogenesis. Based on the data accumulated in the scientific literature, the presence of 467 transposon-derived miRNA genes in the human genome has been reliably established. It was proposed to create an updated and controlled online bioinformatics database of miRNAs derived from transposable elements in healthy individuals, as well as expression changes of these miRNAs during aging and various diseases, such as cancer and difficult-to-treat diseases. The use of the information obtained can open new horizons in the management of tissue and organ differentiation to aging slow down. In addition, the created database could become the basis for clarifying the mechanisms of pathogenesis of various diseases (imbalance in the activity of transposable elements, reflected in changes in the expression of miRNAs) and designing their targeted therapy using specific miRNAs as targets. This article provides examples of the detection of transposable elements-derived miRNAs involved in the development of specific malignant neoplasms, aging, and idiopathic pulmonary fibrosis.

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/.

Role of phasiRNAs in plant-pathogen interactions: molecular perspectives and bioinformatics tools

The genome of an organism is regulated in concert with the organized action of various genetic regulators at different hierarchical levels. Small non-coding RNAs are one of these regulators, among which microRNAs (miRNAs), a distinguished sRNA group with decisive functions in the development, growth and stress-responsive activities of both plants as well as animals, are keenly explored over a good number of years. Recent studies in plants revealed that apart from the silencing activity exhibited by miRNAs on their targets, miRNAs of specific size and structural features can direct the phasing pattern of their target loci to form phased secondary small interfering RNAs (phasiRNAs). These trigger-miRNAs were identified to target both coding and long non-coding RNAs that act as potent phasiRNA precursors or PHAS loci. The phasiRNAs produced thereby exhibit a role in enhancing further downstream regulation either on their own precursors or on those transcripts that are distinct from their genetic source of origin. Hence, these tiny regulators can stimulate an elaborative cascade of interacting RNA networks via cis and trans-regulatory mechanisms. Our review focuses on the comprehensive understanding of phasiRNAs and their trigger miRNAs, by giving much emphasis on their role in the regulation of plant defense responses, together with a summary of the computational tools available for the prediction of the same.

Understanding small ORF diversity through a comprehensive transcription feature classification

Small open reading frames (small ORFs/sORFs/smORFs) are potentially coding sequences smaller than 100 codons that have historically been considered junk DNA by gene prediction software and in annotation screening; however, the advent of next-generation sequencing has contributed to the deeper investigation of junk DNA regions and their transcription products, resulting in the emergence of smORFs as a new focus of interest in systems biology. Several smORF peptides were recently reported in noncanonical mRNAs as new players in numerous biological contexts; however, their relevance is still overlooked in coding potential analysis. Hence, this review proposes a smORF classification based on transcriptional features, discussing the most promising approaches to investigate smORFs based on their different characteristics. First, smORFs were divided into nonexpressed (intergenic) and expressed (genic) smORFs. Second, genic smORFs were classified as smORFs located in noncoding RNAs (ncRNAs) or canonical mRNAs. Finally, smORFs in ncRNAs were further subdivided into sequences located in small or long RNAs, whereas smORFs located in canonical mRNAs were subdivided into several specific classes depending on their localization along the gene. We hope that this review provides new insights into large-scale annotations and reinforces the role of smORFs as essential components of a hidden coding DNA world.

Evaluation and application of tools for the identification of known microRNAs in plants

MicroRNAs (miRNAs), endogenous non-coding RNA regulators, post-transcriptionally inhibit the expression of their target genes. Several tools have been developed for predicting annotated known miRNAs, but there is no consensus about how to select the most suitable method for any given species. In this study, eight miRNA prediction tools (mirnovo, miRPlant, miRDeep-P2, miRExpress, miRkwood, miRDeep2, miR-PREFeR, and sRNAbench) were selected for evaluation. High-throughput small RNA sequencing data from four plant species (including C3 and C4 species, and both monocots and dicots, i.e., Arabidopsis thaliana, Oryza sativa, Triticum aestivum, and Zea mays) were used for the analysis. The sensitivity, accuracy, area under the curve, consistency, duration, and RAM usage of the known miRNA predictions were evaluated for each tool. The miRNA annotations were obtained using miRBase and sRNAanno. Algorithms, such as random forest, BLAST, and receiver operating characteristic curves, were used to evaluate accuracy. Of the tools evaluated, sRNAbench was found to be the most accurate, miRDeep-P2 was the most sensitive, miRDeep-P2 was the fastest, and miRkwood had the highest memory usage. Due to its large genome size, only three tools were able to successfully predict known miRNAs in wheat (Triticum aestivum). Our results enable us to recommend the tool best suited to a variety of researcher needs, which we hope will reduce confusion and enhance future work.

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.

Genome-wide discovery of pre-miRNAs: comparison of recent approaches based on machine learning

MOTIVATION

The genome-wide discovery of microRNAs (miRNAs) involves identifying sequences having the highest chance of being a novel miRNA precursor (pre-miRNA), within all the possible sequences in a complete genome. The known pre-miRNAs are usually just a few in comparison to the millions of candidates that have to be analyzed. This is of particular interest in non-model species and recently sequenced genomes, where the challenge is to find potential pre-miRNAs only from the sequenced genome. The task is unfeasible without the help of computational methods, such as deep learning. However, it is still very difficult to find an accurate predictor, with a low false positive rate in this genome-wide context. Although there are many available tools, these have not been tested in realistic conditions, with sequences from whole genomes and the high class imbalance inherent to such data.

RESULTS

In this work, we review six recent methods for tackling this problem with machine learning. We compare the models in five genome-wide datasets: Arabidopsis thaliana, Caenorhabditis elegans, Anopheles gambiae, Drosophila melanogaster, Homo sapiens. The models have been designed for the pre-miRNAs prediction task, where there is a class of interest that is significantly underrepresented (the known pre-miRNAs) with respect to a very large number of unlabeled samples. It was found that for the smaller genomes and smaller imbalances, all methods perform in a similar way. However, for larger datasets such as the H. sapiens genome, it was found that deep learning approaches using raw information from the sequences reached the best scores, achieving low numbers of false positives.

AVAILABILITY

The source code to reproduce these results is in: http://sourceforge.net/projects/sourcesinc/files/gwmirna Additionally, the datasets are freely available in: https://sourceforge.net/projects/sourcesinc/files/mirdata.

PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction

Rice microRNAs (miRNAs) are important post-transcriptional regulation factors and play vital roles in many biological processes, such as growth, development, and stress resistance. Identification of these molecules is the basis of dissecting their regulatory functions. Various machine learning techniques have been developed to identify precursor miRNAs (pre-miRNAs). However, no tool is implemented specifically for rice pre-miRNAs. This study aims at improving prediction performance of rice pre-miRNAs by constructing novel features with high discriminatory power and developing a training model with species-specific data. PlantMirP-rice, a stand-alone random forest-based miRNA prediction tool, achieves a promising accuracy of 93.48% based on independent (unseen) rice data. Comparisons with other competitive pre-miRNA prediction methods demonstrate that plantMirP-rice performs better than existing tools for rice and other plant pre-miRNA classification.

miRDetect: A combinatorial approach for automated detection of novel miRNA precursors from plant EST data using homology and Random Forest classification

Identification of microRNAs from plants is a crucial step for understanding the mechanisms of pathways and regulation of genes. A number of tools have been developed for the detection of microRNAs from small RNA-seq data. However, there is a lack of pipeline for detection of miRNA from EST dataset even when a huge resource is publicly available and the method is known. Here we present miRDetect, a python implementation to detect novel miRNA precursors from plant EST data using homology and machine learning approach. 10-fold cross validation was applied to choose best classifier based on ROC, accuracy, MCC and F1-scores using 112 features. miRDetect achieved a classification accuracy of 93.35% on a Random Forest classifier and outperformed other precursor detection tools in terms of performance. The miRDetect pipeline aids in identifying novel plant precursors using a mixed approach and will be helpful to researchers with less informatics background.

The Multiple Facets of Plant-Fungal Interactions Revealed Through Plant and Fungal Secretomics

The plant secretome is usually considered in the frame of proteomics, aiming at characterizing extracellular proteins, their biological roles and the mechanisms accounting for their secretion in the extracellular space. In this review, we aim to highlight recent results pertaining to secretion through the conventional and unconventional protein secretion pathways notably those involving plant exosomes or extracellular vesicles. Furthermore, plants are well known to actively secrete a large array of different molecules from polymers (e.g. extracellular RNA and DNA) to small compounds (e.g. ATP, phytochemicals, secondary metabolites, phytohormones). All of these play pivotal roles in plant-fungi (or oomycetes) interactions, both for beneficial (mycorrhizal fungi) and deleterious outcomes (pathogens) for the plant. For instance, recent work reveals that such secretion of small molecules by roots is of paramount importance to sculpt the rhizospheric microbiota. Our aim in this review is to extend the definition of the plant and fungal secretomes to a broader sense to better understand the functioning of the plant/microorganisms holobiont. Fundamental perspectives will be brought to light along with the novel tools that should support establishing an environment-friendly and sustainable agriculture.

Complexity measures of the mature miRNA for improving pre-miRNAs prediction

Motivation

The discovery of microRNA (miRNA) in the last decade has certainly changed the understanding of gene regulation in the cell. Although a large number of algorithms with different features have been proposed, they still predict an impractical amount of false positives. Most of the proposed features are based on the structure of precursors of the miRNA only, not considering the important and relevant information contained in the mature miRNA. Such new kind of features could certainly improve the performance of the predictors of new miRNAs.

Results

This paper presents three new features that are based on the sequence information contained in the mature miRNA. We will show how these new features, when used by a classical supervised machine learning approach as well as by more recent proposals based on deep learning, improve the prediction performance in a significant way. Moreover, several experimental conditions were defined and tested to evaluate the novel features impact in situations close to genome-wide analysis. The results show that the incorporation of new features based on the mature miRNA allows to improve the detection of new miRNAs independently of the classifier used.

Availability and implementation

https://sourceforge.net/projects/sourcesinc/files/cplxmirna/.

Supplementary information

Supplementary data are available at Bioinformatics online.

miRkwood: a tool for the reliable identification of microRNAs in plant genomes

BACKGROUND

MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulation of eukaryotic gene expression and are involved in many aspects of plant development. Although several prediction tools are available for metazoan genomes, the number of tools dedicated to plants is relatively limited.

RESULTS

Here, we present miRkwood, a user-friendly tool for the identification of miRNAs in plant genomes using small RNA sequencing data. Deep-sequencing data of Argonaute associated small RNAs showed that miRkwood is able to identify a large diversity of plant miRNAs and limits false positive predictions. Moreover, it outperforms current tools such as ShortStack and contrary to ShortStack, miRkwood provides a quality score allowing users to rank miRNA predictions.

CONCLUSION

miRkwood is a very efficient tool for the annotation of miRNAs in plant genomes. It is available as a web server, as a standalone version, as a docker image and as a Galaxy tool: http://bioinfo.cristal.univ-lille.fr/mirkwood.

miRkwood: a tool for the reliable identification of microRNAs in plant genomes

BACKGROUND

MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulation of eukaryotic gene expression and are involved in many aspects of plant development. Although several prediction tools are available for metazoan genomes, the number of tools dedicated to plants is relatively limited.

RESULTS

Here, we present miRkwood, a user-friendly tool for the identification of miRNAs in plant genomes using small RNA sequencing data. Deep-sequencing data of Argonaute associated small RNAs showed that miRkwood is able to identify a large diversity of plant miRNAs and limits false positive predictions. Moreover, it outperforms current tools such as ShortStack and contrary to ShortStack, miRkwood provides a quality score allowing users to rank miRNA predictions.

CONCLUSION

miRkwood is a very efficient tool for the annotation of miRNAs in plant genomes. It is available as a web server, as a standalone version, as a docker image and as a Galaxy tool: http://bioinfo.cristal.univ-lille.fr/mirkwood.

miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences

microRNAs (miRNAs) are short, noncoding regulatory RNAs derived from hairpin precursors (pre-miRNAs). In synergy with experimental approaches, computational approaches have become an invaluable tool for identifying miRNAs at the genome scale. We have recently reported a method called miRLocator, which applies machine learning algorithms to accurately predict the localization of most likely miRNAs within their pre-miRNAs. One major strength of miRLocator is the fact that the machine learning-based miRNA prediction model can be automatically trained using a set of miRNAs of particular interest, with informative features extracted from miRNA-miRNA* duplexes and the optimized ratio between positive and negative samples. Here, we present a detailed protocol for miRLocator that performs the training and prediction processes using a python implementation and web interface. The source codes, web interface, and manual documents are freely available to academic users at https://github.com/cma2015/miRLocator .