Plant miRNA Identification Research Articles

Identification of plant microRNAs using convolutional neural network.

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.

PlantMirP2: An Accurate, Fast and Easy-To-Use Program for Plant Pre-miRNA and miRNA Prediction.

MicroRNAs (miRNAs) are a kind of short non-coding ribonucleic acid molecules that can regulate gene expression. The computational identification of plant miRNAs is of great significance to understanding biological functions. In our previous studies, we have put firstly forward and further developed a set of knowledge-based energy features to construct two plant pre-miRNA prediction tools (plantMirP and riceMirP). However, these two tools cannot be used for miRNA prediction from NGS (Next-Generation Sequencing) data. In addition, for further improving the prediction performance and accessibility, plantMirP2 has been developed. Based on the latest dataset, plantMirP2 achieves a promising performance: 0.9968 (Area Under Curve, AUC), 0.9754 (accuracy), 0.9675 (sensitivity) and 0.9876 (specificity). Additionally, the comparisons with other plant pre-miRNA tools show that plantMirP2 performs better. Finally, the webserver and stand-alone version of plantMirP2 are available.

Salient Features, Data and Algorithms for MicroRNA Screening from Plants: A Review on the Gains and Pitfalls of Machine Learning Techniques

The era of big data and high-throughput genomic technology has enabled scientists to have a clear view of plant genomic profiles. However, it has also led to a massive need for computational tools and strategies to interpret this data. In this scenario of huge data inflow, machine learning (ML) approaches are emerging to be the most promising for analysing heterogeneous and unstructured biological datasets. Extending its application to healthcare and agriculture, ML approaches are being useful for microRNA (miRNA) screening as well. Identification of miRNAs is a crucial step towards understanding post-transcriptional gene regulation and miRNA-related pathology. The use of ML tools is becoming indispensable in analysing such data and identifying species-specific, non-conserved miRNA. However, these techniques have their own benefits and lacunas. In this review, we will discuss the current scenario and pitfalls of ML-based tools for plant miRNA identification and provide some insights into the important features, the need for deep learning models and direction in which studies are needed.

Computational methods for the ab initio identification of novel microRNA in plants: a systematic review.

BackgroundMicroRNAs (miRNAs) play a vital role as post-transcriptional regulators in gene expression. Experimental determination of miRNA sequence and structure is both expensive and time consuming. The next-generation sequencing revolution, which facilitated the rapid accumulation of biological data has brought biology into the “big data” domain. As such, developing computational methods to predict miRNAs has become an active area of inter-disciplinary research.ObjectiveThe objective of this systematic review is to focus on the developments of ab initio plant miRNA identification methods over the last decade.Data sourcesFive databases were searched for relevant articles, according to a well-defined review protocol.Study selectionThe search results were further filtered using the selection criteria that only included studies on novel plant miRNA identification using machine learning.Data extractionRelevant data from each study were extracted in order to carry out an analysis on their methodologies and findings.ResultsResults depict that in the last decade, there were 20 articles published on novel miRNA identification methods in plants of which only 11 of them were primarily focused on plant microRNA identification. Our findings suggest a need for more stringent plant-focused miRNA identification studies.ConclusionOverall, the study accuracies are of a satisfactory level, although they may generate a considerable number of false negatives. In future, attention must be paid to the biological plausibility of computationally identified miRNAs to prevent further propagation of biologically questionable miRNA sequences.

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.

An emerging technique for reducing the response time in plant miRNA identification

The microRNA identification step is a part of a plant analytic pipeline. Researches on microRNA identification have been focused extensively on precision, recall, f-measure, and accuracy of computational techniques. However, as the database becomes larger, these computational techniques cannot reduce time complexity to handle the growth. As a result, the identification step becomes a bottleneck in the pipeline. To reduce the response time of the identification step, we proposed a new technique that can discover predictive results faster than the traditional technique. Our technique is based on reordering sequences. The sequences that have higher potential score values will be executed with higher priority. The proposed technique can accelerate the study of plant miRNAs and also can be applied to other computational techniques as well.

A Comprehensive Prescription for Plant miRNA Identification.

microRNAs (miRNAs) are tiny ribo-regulatory molecules involved in various essential pathways for persistence of cellular life, such as development, environmental adaptation, and stress response. In recent years, miRNAs have become a major focus in molecular biology because of their functional and diagnostic importance. This interest in miRNA research has resulted in the development of many specific software and pipelines for the identification of miRNAs and their specific targets, which is the key for the elucidation of miRNA-modulated gene expression. While the well-recognized importance of miRNAs in clinical research pushed the emergence of many useful computational identification approaches in animals, available software and pipelines are fewer for plants. Additionally, existing approaches suffers from mis-identification and annotation of plant miRNAs since the miRNA mining process for plants is highly prone to false-positives, particularly in cereals which have a highly repetitive genome. Our group developed a homology-based in silico miRNA identification approach for plants, which utilizes two Perl scripts “SUmirFind” and “SUmirFold” and since then, this method helped identify many miRNAs particularly from crop species such as Triticum or Aegliops. Herein, we describe a comprehensive updated guideline by the implementation of two new scripts, “SUmirPredictor” and “SUmirLocator,” and refinements to our previous method in order to identify genuine miRNAs with increased sensitivity in consideration of miRNA identification problems in plants. Recent updates enable our method to provide more reliable and precise results in an automated fashion in addition to solutions for elimination of most false-positive predictions, miRNA naming and miRNA mis-annotation. It also provides a comprehensive view to genome/transcriptome-wide location of miRNA precursors as well as their association with transposable elements. The “SUmirPredictor” and “SUmirLocator” scripts are freely available together with a reference high-confidence plant miRNA list.

MiRA: adaptable novel miRNA identification in plants using small RNA sequencing data.

BackgroundMicroRNAs (miRNAs) are short regulatory RNAs derived from longer precursor RNAs. miRNA biogenesis has been studied in animals and plants, recently elucidating more complex aspects, such as non-conserved, species-specific, and heterogeneous miRNA precursor populations. Small RNA sequencing data can help in computationally identifying genomic loci of miRNA precursors. The challenge is to predict a valid miRNA precursor from inhomogeneous read coverage from a complex RNA library: while the mature miRNA typically produces many sequence reads, the remaining part of the precursor is covered very sparsely. As recent results suggest, alternative miRNA biogenesis pathways may lead to a more diverse miRNA precursor population than previously assumed. In plants, the latter manifests itself in e.g. complex secondary structures and expression from multiple loci within precursors. Current miRNA identification algorithms often depend on already existing gene annotation, and/or make use of specific miRNA precursor features such as precursor lengths, secondary structures etc. Consequently and in view of the emerging new understanding of a more complex miRNA biogenesis in plants, current tools may fail to characterise organism-specific and heterogeneous miRNA populations.ResultsmiRA is a new tool to identify miRNA precursors in plants, allowing for heterogeneous and complex precursor populations. miRA requires small RNA sequencing data and a corresponding reference genome, and evaluates precursor secondary structures and precursor processing accuracy; key parameters can be adapted based on the specific organism under investigation. We show that miRA outperforms the currently best plant miRNA prediction tools both in sensitivity and specificity, for data involving Arabidopsis thaliana and the Volvocine algae Chlamydomonas reinhardtii; the latter organism has been shown to exhibit a heterogeneous and complex precursor population with little cross-species miRNA sequence conservation, and therefore constitutes an ideal model organism. Furthermore we identify novel miRNAs in the Chlamydomonas-related organism Volvox carteri.ConclusionsWe propose miRA, a new plant miRNA identification tool that is well adapted to complex precursor populations. miRA is particularly suited for organisms with no existing miRNA annotation, or without a known related organism with well characterized miRNAs. Moreover, miRA has proven its ability to identify species-specific miRNAs. miRA is flexible in its parameter settings, and produces user-friendly output files in various formats (pdf, csv, genome-browser-suitable annotation files, etc.). It is freely available at https://github.com/mhuttner/miRA.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0798-3) contains supplementary material, which is available to authorized users.

Discovering MicroRNAs and Their Targets in Plants

MicroRNAs (miRNAs) are a subset of endogenous approximate 22 nucleotide (nt), small non-coding regulatory RNA molecules that regulate gene expression post-transcriptionally by mediating mRNA degradation or translational repression in a sequence specific manner. Plant miRNA research gained momentum, 2002 onwards, which was accompanied by the discovery of plant proteins involved in miRNA biogenesis. Progress in genomic research of different plant species may open the door for discovering novel miRNAs and their characteristics. With the development and availability of experimental technologies and computational approaches, the field of miRNA biology has advanced tremendously over the last decade. This paper aims to introduce major computational approaches in the identification of plant miRNAs, including conservation based search, de novo prediction and mining in deep sequencing data. Computational tools for identification of miRNAs target is another intriguing perspective that has been examined in this review. All these approaches have certain advantages and disadvantages, which are reviewed in the present survey. Furthermore, in the present survey, we also present many experimental modalities such as Degradome sequencing, Immunoprecipitation of RISC components and their limitations for miRNAs targets identification. At the end, we also present the outlook for future directions in the development and improvement of plant miRNA discovery methods.

In silico identification of plant miRNAs in mammalian breast milk exosomes--a small step forward?

MicroRNAs (miRNAs) are a class of small RNA molecules that regulate gene expression by inhibiting the protein translation or targeting the mRNA cleavage. They play many important roles in living organism cells; however, the knowledge on miRNAs functions has become more extensive upon their identification in biological fluids and recent reports on plant-origin miRNAs abundance in human plasma and serum. Considering these findings, we performed a rigorous bioinformatics analysis of publicly available, raw data from high-throughput sequencing studies on miRNAs composition in human and porcine breast milk exosomes to identify the fraction of food-derived miRNAs. Several processing and filtering steps were applied to increase the accuracy, and to avoid false positives. Through aforementioned analysis, 35 and 17 miRNA species, belonging to 25 and 11 MIR families, were identified, respectively. In the human samples the highest abundance levels yielded the ath-miR166a, pab-miR951, ptc-miR472a and bdi-miR168, while in the porcine breast milk exosomes, the zma-miR168a, zma-miR156a and ath-miR166a have been identified in the largest amounts. The consensus prediction and annotation of potential human targets for select plant miRNAs suggest that the aforementioned molecules may interact with mRNAs coding several transcription factors, protein receptors, transporters and immune-related proteins, thus potentially influencing human organism. Taken together, the presented analysis shows proof of abundant plant miRNAs in mammal breast milk exosomes, pointing at the same time to the new possibilities arising from this discovery.

High-resolution experimental and computational profiling of tissue-specific known and novel miRNAs in Arabidopsis

Small non-coding RNAs (ncRNAs) are key regulators of plant development through modulation of the processing, stability, and translation of larger RNAs. We present small RNA data sets comprising more than 200 million aligned Illumina sequence reads covering all major cell types of the root as well as four distinct developmental zones. MicroRNAs (miRNAs) constitute a class of small ncRNAs that are particularly important for development. Of the 243 known miRNAs, 133 were found to be expressed in the root, and most showed tissue- or zone-specific expression patterns. We identified 66 new high-confidence miRNAs using a computational pipeline, PIPmiR, specifically developed for the identification of plant miRNAs. PIPmiR uses a probabilistic model that combines RNA structure and expression information to identify miRNAs with high precision. Knockdown of three of the newly identified miRNAs results in altered root growth phenotypes, confirming that novel miRNAs predicted by PIPmiR have functional relevance.

A Peep into the Plant miRNA World

microRNAs constitute a major class of the small regulatory molecules that are involved in regulating the intrin- sic normal growth of cells and development of organisms as well as in maintaining the integrity of genomes. The plant miRNA research gained momentum, 2002 onwards with identification of new miRNA molecules and their targets. This was accompanied by the discovery of plant homologs of proteins involved in miRNA biogenesis, including a new mem- ber SERRATE. The identification of several diverging and converging functions of miRNAs indicate that they play versa- tile roles in regulating cell differentiation and tissue development. This article provides an update on the conservation and identification of plant miRNAs. The classical miRNA biogenesis pathway and the associated proteins are discussed along with the emerging concept on the processing of miRNA-encoding introns (mirtrons). It also contains a concise account of plant miRNA targets and functions with focus on the recent successful attempt on engineering synthetic miRNAs to study gene function as well as to impart virus resistance in plants.