The discriminant power of RNA features for pre-miRNA recognition

Computational discovery of microRNAs (miRNA) is based on pre-determined sets of features from miRNA precursors (pre-miRNA). These feature sets used by current tools for pre-miRNA recognition differ in construction and dimension. Some feature sets are composed of sequence-structure patterns commonly found in pre-miRNAs, while others are a combination of more sophisticated RNA features. Current tools achieve similar predictive performance even though the feature sets used - and their computational cost - differ widely. In this work, we analyze the discriminant power of seven feature sets, which are used in six pre-miRNA prediction tools. The analysis is based on the classification performance achieved with these feature sets for the training algorithms used in these tools. We also evaluate feature discrimination through the F-score and feature importance in the induction of random forests. More diverse feature sets produce classifiers with significantly higher classification performance compared to feature sets composed only of sequence-structure patterns. However, small or non-significant differences were found among the estimated classification performances of classifiers induced using sets with diversification of features, despite the wide differences in their dimension. Based on these results, we applied a feature selection method to reduce the computational cost of computing the feature set, while maintaining discriminant power. We obtained a lower-dimensional feature set, which achieved a sensitivity of 90% and a specificity of 95%. Our feature set achieves a sensitivity and specificity within 0.1% of the maximal values obtained with any feature set while it is 34x faster to compute. Even compared to another feature set, which is the computationally least expensive feature set of those from the literature which perform within 0.1% of the maximal values, it is 34x faster to compute.Comment: Submitted to BMC Bioinformatics in October 25, 2013. The material to reproduce the main results from this paper can be downloaded from http://bioinformatics.rutgers.edu/Static/Software/discriminant.tar.g

A two-step site and mRNA-level model for predicting microRNA targets

<p>Abstract</p> <p>Background</p> <p>Despite experiments showing that the number of microRNA (miRNA) target sites is critical for miRNA targeting, most existing methods focus on identifying individual miRNA target sites and do not model contributions of multiple target sites to miRNA regulation. To address this possible fault, we developed a miRNA target prediction model that recognizes the individual characteristics of functional binding sites and the global characteristics of miRNA-targeted mRNAs.</p> <p>Results</p> <p>Benchmark experiments showed that this two-step model generally had a higher overall performance than other established miRNA target prediction algorithms and that the model was especially suited to identify true miRNA targets among genes that all contain conserved target sites.</p> <p>Conclusions</p> <p>This improved performance could partly be explained by the model not relying on conservation when predicting targets. The critical factors for the model's performance, however, were mRNA-level features that characterized the number and strength of individual target sites within the mRNA. The model is available for online predictions or as pre-computed predictions on the human genome <url>http://tare.medisin.ntnu.no/mirna_target</url>.</p

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.info:eu-repo/semantics/acceptedVersio

MiRNAfe: A comprehensive tool for feature extraction in microRNA prediction

miRNAfe is a comprehensive tool to extract features from RNA sequences. It is freely available as a web service, allowing a single access point to almost all state-of-the-art feature extraction methods used today in a variety of works from different authors. It has a very simple user interface, where the user only needs to load a file containing the input sequences and select the features to extract. As a result, the user obtains a text file with the features extracted, which can be used to analyze the sequences or as input to a miRNA prediction software.The tool can calculate up to 80 features where many of them are multidimensional arrays. In order to simplify the web interface, the features have been divided into six pre-defined groups, each one providing information about: primary sequence, secondary structure, thermodynamic stability, statistical stability, conservation between genomes of different species and substrings analysis of the sequences. Additionally, pre-trained classifiers are provided for prediction in different species. All algorithms to extract the features have been validated, comparing the results with the ones obtained from software of the original authors.The source code is freely available for academic use under GPL license at http://sourceforge.net/projects/sourcesinc/files/mirnafe/0.90/. A user-friendly access is provided as web interface at http://fich.unl.edu.ar/sinc/web-demo/mirnafe/. A more configurable web interface can be accessed at http://fich.unl.edu.ar/sinc/web-demo/mirnafe-full/.Fil: Yones, Cristian Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Kamenetzky, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

A network approach for low dimensional signatures from high throughput data

: One of the main objectives of high-throughput genomics studies is to obtain a low-dimensional set of observables-a signature-for sample classification purposes (diagnosis, prognosis, stratification). Biological data, such as gene or protein expression, are commonly characterized by an up/down regulation behavior, for which discriminant-based methods could perform with high accuracy and easy interpretability. To obtain the most out of these methods features selection is even more critical, but it is known to be a NP-hard problem, and thus most feature selection approaches focuses on one feature at the time (k-best, Sequential Feature Selection, recursive feature elimination). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised network-based signature identification method. This method implements a network-based heuristic to generate one or more signatures out of the best performing feature pairs. The algorithm is easily scalable, allowing efficient computing for high number of observables ([Formula: see text]-[Formula: see text]). We show applications on real high-throughput genomic datasets in which our method outperforms existing results, or is compatible with them but with a smaller number of selected features. Moreover, the geometrical simplicity of the resulting class-separation surfaces allows a clearer interpretation of the obtained signatures in comparison to nonlinear classification models

The impact of microRNAs on transcriptional heterogeneity and gene co-expression across single embryonic stem cells

MicroRNAs act posttranscriptionally to suppress multiple target genes within a cell population. To what extent this multi-target suppression occurs in individual cells and how it impacts transcriptional heterogeneity and gene co-expression remains unknown. Here we used single-cell sequencing combined with introduction of individual microRNAs. miR-294 and let-7c were introduced into otherwise microRNA-deficient Dgcr8 knockout mouse embryonic stem cells. Both microRNAs induce suppression and correlated expression of their respective gene targets. The two microRNAs had opposing effects on transcriptional heterogeneity within the cell population, with let-7c increasing and miR-294 decreasing the heterogeneity between cells. Furthermore, let-7c promotes, whereas miR-294 suppresses, the phasing of cell cycle genes. These results show at the individual cell level how a microRNA simultaneously has impacts on its many targets and how that in turn can influence a population of cells. The findings have important implications in the understanding of how microRNAs influence the co-expression of genes and pathways, and thus ultimately cell fate

Joint analysis of transcriptional and post- transcriptional brain tumor data: searching for emergent properties of cellular systems

<p>Abstract</p> <p>Background</p> <p>Advances in biotechnology offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. However, to date, most computational and algorithmic efforts have been directed at mining data from each of these molecular <it>levels </it>(genomic, transcriptional, etc.) separately. In view of the rapid advances in technology (new generation sequencing, high-throughput proteomics) it is important to address the problem of analyzing these data as a whole, i.e. preserving the emergent properties that appear in the cellular system when all molecular levels are interacting. We analyzed one of the (currently) few datasets that provide both transcriptional and post-transcriptional data of the same samples to investigate the possibility to extract more information, using a joint analysis approach.</p> <p>Results</p> <p>We use Factor Analysis coupled with pre-established knowledge as a theoretical base to achieve this goal. Our intention is to identify structures that contain information from both mRNAs and miRNAs, and that can explain the complexity of the data. Despite the small sample available, we can show that this approach permits identification of meaningful structures, in particular two polycistronic miRNA genes related to transcriptional activity and likely to be relevant in the discrimination between gliosarcomas and other brain tumors.</p> <p>Conclusions</p> <p>This suggests the need to develop methodologies to simultaneously mine information from different levels of biological organization, rather than linking separate analyses performed in parallel.</p

Application of a Naïve Bayes Classifier to Assign Polyadenylation Sites from 3\u27 End Deep Sequencing Data: A Dissertation

Cleavage and polyadenylation of a precursor mRNA is important for transcription termination, mRNA stability, and regulation of gene expression. This process is directed by a multitude of protein factors and cis elements in the pre-mRNA sequence surrounding the cleavage and polyadenylation site. Importantly, the location of the cleavage and polyadenylation site helps define the 3’ untranslated region of a transcript, which is important for regulation by microRNAs and RNA binding proteins. Additionally, these sites have generally been poorly annotated. To identify 3’ ends, many techniques utilize an oligo-dT primer to construct deep sequencing libraries. However, this approach can lead to identification of artifactual polyadenylation sites due to internal priming in homopolymeric stretches of adenines. Previously, simple heuristic filters relying on the number of adenines in the genomic sequence downstream of a putative polyadenylation site have been used to remove these sites of internal priming. However, these simple filters may not remove all sites of internal priming and may also exclude true polyadenylation sites. Therefore, I developed a naïve Bayes classifier to identify putative sites from oligo-dT primed 3’ end deep sequencing as true or false/internally primed. Notably, this algorithm uses a combination of sequence elements to distinguish between true and false sites. Finally, the resulting algorithm is highly accurate in multiple model systems and facilitates identification of novel polyadenylation sites

Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs

© 2018 The Author(s). Background: Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. Results: We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. Conclusions: The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs identification. The source codes of our method can be downloaded from https://github.com/myl446/MicroRNA-NHPred