Academic literature on the topic 'Pre-miRNA prediction'

MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that play an important role in post-transcriptional regulation of gene expression. In this paper, we present a web server for ab initio prediction of the human miRNAs and their precursors. The prediction methods are based on the hidden Markov Models and the context-structural characteristics. By taking into account the identified patterns of primary and secondary structures of the pre-miRNAs, a new HMM model is proposed and the existing context-structural Markov model is modified. The evaluation of the method performance has shown that it can accurately predict novel human miRNAs. Comparing with the existing methods we demonstrate that our method has a higher prediction quality both for human pre-miRNAs and miRNAs. The models have also showed good results in the prediction of the mouse miRNAs. The web server is available at http://wwwmgs.bionet.nsc.ru/mgs/programs/rnaanalys (mirror http://miRNA.at.nsu.ru ).

Background.MicroRNAs (miRNAs) are short noncoding RNAs integral for regulating gene expression at the posttranscriptional level. However, experimental methods often fall short in finding miRNAs expressed at low levels or in specific tissues. While several computational methods have been developed for predicting the localization of mature miRNAs within the precursor transcript, the prediction accuracy requires significant improvement.Methodology/Principal Findings.Here, we present MatPred, which predicts mature miRNA candidates within novel pre-miRNA transcripts. In addition to the relative locus of the mature miRNA within the pre-miRNA hairpin loop and minimum free energy, we innovatively integrated features that describe the nucleotide-specific RNA secondary structure characteristics. In total, 94 features were extracted from the mature miRNA loci and flanking regions. The model was trained based on a radial basis function kernel/support vector machine (RBF/SVM). Our method can predict precise locations of mature miRNAs, as affirmed by experimentally verified human pre-miRNAs or pre-miRNAs candidates, thus achieving a significant advantage over existing methods.Conclusions.MatPred is a highly effective method for identifying mature miRNAs within novel pre-miRNA transcripts. Our model significantly outperformed three other widely used existing methods. Such processing prediction methods may provide important insight into miRNA biogenesis.

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.

MicroRNAs (miRNA) are small regulatory, noncoding RNA molecules that are transcribed as primary miRNAs (pri-miRNA) from eukaryotic genomes. At least in plants, their regulatory activity is mediated through base-pairing with protein-coding messenger RNAs (mRNA) followed by mRNA degradation or translation repression. We describeNOVOMIR, a program for the identification of miRNA genes in plant genomes. It uses a series of filter steps and a statistical model to discriminate a pre-miRNA from other RNAs and does rely neither on prior knowledge of a miRNA target nor on comparative genomics. The sensitivity and specificity ofNOVOMIR for detection of premiRNAs fromArabidopsis thalianais ~0.83 and ~0.99, respectively. Plant pre-miRNAs are more heterogeneous with respect to size and structure than animal pre-miRNAs. Despite these difficulties,NOVOMIR is well suited to perform searches for pre-miRNAs on a genomic scale.NOVOMIR is written in Perl and relies on two additional, free programs for prediction of RNA secondary structure (RNALFOLD, RNASHAPES).

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.

Abstract 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.

MicroRNAs (miRNAs) are short, non-coding post-transcriptional gene regulators. In mammalian cells, mature miRNAs are produced from primary precursors (pri-miRNAs) using canonical protein machinery, which includes Drosha/DGCR8 and Dicer, or the non-canonical mirtron pathway. In plant cells, mature miRNAs are excised from pri-miRNAs by the DICER-LIKE1 (DCL1) protein complex. The involvement of multiple regulatory proteins that bind directly to distinct miRNA precursors in a sequence- or structure-dependent manner adds to the complexity of the miRNA maturation process. Here, we present a web server that enables searches for miRNA precursors that can be recognized by diverse RNA-binding proteins based on known sequence motifs to facilitate the identification of other proteins involved in miRNA biogenesis. The database used by the web server contains known human, murine, and Arabidopsis thaliana pre-miRNAs. The web server can also be used to predict new RNA-binding protein motifs based on a list of user-provided sequences. We show examples of miRNAmotif applications, presenting precursors that contain motifs recognized by Lin28, MCPIP1, and DGCR8 and predicting motifs within pre-miRNA precursors that are recognized by two DEAD-box helicases—DDX1 and DDX17. miRNAmotif is released as an open-source software under the MIT License. The code is available at GitHub (www.github.com/martynaut/mirnamotif). The webserver is freely available at http://mirnamotif.ibch.poznan.pl.

RNA Sequencing has recently emerged as a breakthrough technology for microRNA (miRNA) discovery. This technology has allowed the discovery of thousands of miRNAs in a large number of species. However, despite the benefits of this technology, it also carries its own limitations, including the need for sequencing read libraries and of the genome. Differently, ab initio computational methods need only the genome as input to search for genonic locus likely to give rise to novel miRNAs. In the core of most of these methods, there are predictive models induced by using data mining techniques able to distinguish between real (positive) and pseudo (negative) miRNA precursors (pre-miRNA). Nevertheless, the applicability of current literature ab initio methods have been compromised by high false detection rates and/or by other computational difficulties. In this work, we investigated how the main aspects involved in the induction of predictive models for pre-miRNA affect the predictive performance. Particularly, we evaluate the discriminant power of feature sets proposed in the literature, whose computational costs and composition vary widely. The computational experiments were carried out using sequence data from 45 species, which covered species from eight phyla. The predictive performance of the classification models induced using large training set sizes (&ge; 1; 608) composed of instances extracted from real and pseudo human pre-miRNA sequences did not differ significantly among the feature sets that lead to the maximal accuracies. Moreover, the differences in the predictive performances obtained by these models, due to the learning algorithms, were neglectable. Inspired by these results, we obtained a feature set which can be computed 34 times faster than the less costly among those feature sets, producing the maximal accuracies, albeit the proposed feature set has achieved accuracy within 0.1% of the maximal accuracies. When classification models using the elements previously discussed were induced using small training sets (120) from 45 species, we showed that the feature sets that produced the highest accuracies in the classification of human sequences were also more likely to produce higher accuracies for other species. Nevertheless, we showed that the learning complexity of pre-miRNAs vary strongly among species, even among those from the same phylum. These results showed that the existence of specie specific features indicated in previous studies may be correlated with the learning complexity. As a consequence, the predictive accuracies of models induced with different species and same features and instances spaces vary largely. In our results, we show that the use of training examples from species phylogenetically more complex may increase the predictive performances for less complex species. Finally, by using ensembles of computationally less costly feature sets, we showed alternative ways to increase the predictive performance for many species while keeping the computational costs of the analysis lower than those using the feature sets from the literature. Since in miRNA discovery the number of putative miRNA loci is in the order of millions, the analysis of putative miRNAs using a computationally expensive feature set and or inaccurate models would be wasteful or even unfeasible for large genomes. In this work, we explore most of the learning aspects implemented in current ab initio pre-miRNA prediction tools, which may lead to the development of new efficient ab initio pre-miRNA discovery tools<br>O sequenciamento de pequenos RNAs surgiu recentemente como uma tecnologia inovadora na descoberta de microRNAs (miRNA). Essa tecnologia tem facilitado a descoberta de milhares de miRNAs em um grande número de espécies. No entanto, apesar dos benefícios dessa tecnologia, ela apresenta desafios, como a necessidade de construir uma biblioteca de pequenos RNAs, além do genoma. Diferentemente, métodos computacionais ab initio buscam diretamente no genoma regiões prováveis de conter miRNAs. A maioria desses métodos usam modelos preditivos capazes de distinguir entre os verdadeiros (positivos) e pseudo precursores de miRNA - pre-miRNA - (negativos), os quais são induzidos utilizando técnicas de mineração de dados. No entanto, a aplicabilidade de métodos ab initio da literatura atual é limitada pelas altas taxas de falsos positivos e/ou por outras dificuldades computacionais, como o elevado tempo necessário para calcular um conjunto de atributos. Neste trabalho, investigamos como os principais aspectos envolvidos na indução de modelos preditivos de pre-miRNA afetam o desempenho preditivo. Particularmente, avaliamos a capacidade discriminatória de conjuntos de atributos propostos na literatura, cujos custos computacionais e a composição variam amplamente. Os experimentos computacionais foram realizados utilizando dados de sequências positivas e negativas de 45 espécies, cobrindo espécies de oito filos. Os resultados mostraram que o desempenho preditivo de classificadores induzidos utilizando conjuntos de treinamento com 1608 ou mais vetores de atributos calculados de sequências humanas não diferiram significativamente, entre os conjuntos de atributos que produziram as maiores acurácias. Além disso, as diferenças entre os desempenhos preditivos de classificadores induzidos por diferentes algoritmos de aprendizado, utilizando um mesmo conjunto de atributos, foram pequenas ou não significantes. Esses resultados inspiraram a obtenção de um conjunto de atributos menor e que pode ser calculado até 34 vezes mais rapidamente do que o conjunto de atributos menos custoso produzindo máxima acurácia, embora a acurácia produzida pelo conjunto proposto não difere em mais de 0.1% das acurácias máximas. Quando esses experimentos foram executados utilizando vetores de atributos calculados de sequências de outras 44 espécies, os resultados mostraram que os conjuntos de atributos que produziram modelos com as maiores acurácias utilizando vetores calculados de sequências humanas também produziram as maiores acurácias quando pequenos conjuntos de treinamento (120) calculados de exemplos de outras espécies foram utilizadas. No entanto, a análise destes modelos mostrou que a complexidade de aprendizado varia amplamente entre as espécies, mesmo entre aquelas pertencentes a um mesmo filo. Esses resultados mostram que a existência de características espécificas em pre-miRNAs de certas espécies sugerida em estudos anteriores pode estar correlacionada com a complexidade de aprendizado. Consequentemente, a acurácia de modelos induzidos utilizando um mesmo conjunto de atributos e um mesmo algoritmo de aprendizado varia amplamente entre as espécies. i Os resultados também mostraram que o uso de exemplos de espécies filogeneticamente mais complexas pode aumentar o desempenho preditivo de espécies menos complexas. Por último, experimentos computacionais utilizando técnicas de ensemble mostraram estratégias alternativas para o desenvolvimento de novos modelos para predição de pre-miRNA com maior probabilidade de obter maior desempenho preditivo do que estratégias atuais, embora o custo computacional dos atributos seja inferior. Uma vez que a descoberta de miRNAs envolve a análise de milhares de regiões genômicas, a aplicação prática de modelos preditivos de baixa acurácia e/ou que dependem de atributos computacionalmente custosos pode ser inviável em análises de grandes genomas. Neste trabalho, apresentamos e discutimos os resultados de experimentos computacionais investigando o potencial de diversas estratégias utilizadas na indução de modelos preditivos para predição ab initio de pre-miRNAs, que podem levar ao desenvolvimento de ferramentas ab initio de maior aplicabilidade prática

Neurotrophins and their receptors are key molecules in the development of the<br/>nervous system. Neurotrophin-3 binds preferentially to its high-affinity receptor<br/>NTRK3, which exists in two major isoforms in humans, the full-length kinaseactive<br/>form (150 kDa) and a truncated non-catalytic form (50 kDa). The two<br/>variants show different 3'UTR regions, indicating that they might be differentially<br/>regulated at the post-transcriptional level. In this work we explore how<br/>microRNAs take part in the regulation of full-length and truncated NTRK3,<br/>demonstrating that the two isoforms are targeted by different sets of microRNAs.<br/>We analyze the physiological consequences of the overexpression of some of the<br/>regulating microRNAs in human neuroblastoma cells. Finally, we provide<br/>preliminary evidence for a possible involvement of miR-124 - a microRNA with no<br/>putative target site in either NTRK3 isoform - in the control of the alternative<br/>spicing of NTRK3 through the downregulation of the splicing repressor PTBP1.<br>Las neurotrofinas y sus receptores constituyen una familia de factores cruciales<br/>para el desarrollo del sistema nervioso. La neurotrofina 3 ejerce su función<br/>principalmente a través de una unión de gran afinidad al receptor NTRK3, del cual<br/>se conocen dos isoformas principales, una larga de 150KDa con actividad de tipo<br/>tirosina kinasa y una truncada de 50KDa sin dicha actividad. Estas dos isoformas<br/>no comparten la misma región 3'UTR, lo que sugiere la existencia de una<br/>regulación postranscripcional diferente. En el presente trabajo se ha explorado<br/>como los microRNAs intervienen en la regulación de NTRK3, demostrando que las<br/>dos isoformas son reguladas por diferentes miRNAs. Se han analizado las<br/>consecuencias fisiológicas de la sobrexpresión de dichos microRNAs utilizando<br/>células de neuroblastoma. Finalmente, se ha estudiado la posible implicación del<br/>microRNA miR-124 en el control del splicing alternativo de NTRK3 a través de la<br/>regulación de represor de splicing PTBP1.

La principale contribution de cette thèse est le développement d'une méthode fiable, robuste, et rapide pour la prédiction des pré-miARNs. Deux objectifs avaient été assignés : efficacité et flexibilité. L'efficacité a été rendue possible au moyen d'un algorithme quadratique. La flexibilité repose sur deux aspects, la nature des données expérimentales et la position taxonomique de l'organisme (en particulier plantes ou animaux). Mirinho accepte en entrée des séquences de génomes complets mais aussi les très nombreuses séquences résultant d'un séquençage massif de type NGS de “RNAseq”. “L'universalité” taxonomique est obtenu par la possibilité de modifier les contraintes sur les tailles de la tige (double hélice) et de la boule terminale. Dans le cas de la prédiction des miARN de plantes la plus grande longueur de leur pré-miARN conduit à des méthodes d'extraction de la structure secondaire en tige-boule moins précises. Mirinho prend en compte ce problème lui permettant de fournir des structures secondaires de pré-miARN plus semblables à celles de miRBase que les autres méthodes disponibles. Mirinho a été utilisé dans le cadre de deux questions biologiques précises l'une concernant des RNAseq l'autre de l'ADN génomique. La première question a conduit au traitement et l'analyse des données RNAseq de Acyrthosiphon pisum, le puceron du pois. L'objectif était d'identifier les miARN qui sont différentiellement exprimés au cours des quatre stades de développement de cette espèce et sont donc des candidats à la régulation des gènes au cours du développement. Pour cette analyse, nous avons développé un pipeline, appelé MirinhoPipe. La deuxieme question a permis d'aborder les problèmes liés à la prévision et l'analyse des ARN non-codants (ARNnc) dans la bactérie Mycoplasma hyopneumoniae. Alvinho a été développé pour la prédiction de cibles des miRNA autour d'une segmentation d'une séquence numérique et de la détection de la conservation des séquences entre ncRNA utilisant un graphe k-partite. Nous avons finalement abordé un problème lié à la recherche de motifs conservés dans un ensemble de séquences et pouvant ainsi correspondre à des éléments fonctionnels<br>The main contribution of this thesis is the development of a reliable, robust, and much faster method for the prediction of pre-miRNAs. With this method, we aimed mainly at two goals: efficiency and flexibility. Efficiency was made possible by means of a quadratic algorithm. Flexibility relies on two aspects, the input type and the organism clade. Mirinho can receive as input both a genome sequence and small RNA sequencing (sRNA-seq) data of both animal and plant species. To change from one clade to another, it suffices to change the lengths of the stem-arms and of the terminal loop. Concerning the prediction of plant miRNAs, because their pre-miRNAs are longer, the methods for extracting the hairpin secondary structure are not as accurate as for shorter sequences. With Mirinho, we also addressed this problem, which enabled to provide pre-miRNA secondary structures more similar to the ones in miRBase than the other available methods. Mirinho served as the basis to two other issues we addressed. The first issue led to the treatment and analysis of sRNA-seq data of Acyrthosiphon pisum, the pea aphid. The goal was to identify the miRNAs that are expressed during the four developmental stages of this species, allowing further biological conclusions concerning the regulatory system of such an organism. For this analysis, we developed a whole pipeline, called MirinhoPipe, at the end of which Mirinho was aggregated. We then moved on to the second issue, that involved problems related to the prediction and analysis of non-coding RNAs (ncRNAs) in the bacterium Mycoplasma hyopneumoniae. A method, called Alvinho, was thus developed for the prediction of targets in this bacterium, together with a pipeline for the segmentation of a numerical sequence and detection of conservation among ncRNA sequences using a kpartite graph. We finally addressed a problem related to motifs, that is to patterns, that may be composed of one or more parts, that appear conserved in a set of sequences and may correspond to functional elements