miRRim: a novel system to find conserved miRNAs with high sensitivity and specificity

Computational Detection of Pre-microRNAs

MicroRNA (miRNA) studies have been one of the most popular research areas in recent years. Although thousands of miRNAs have been detected in several species, the majority remains unidentified. Thus, finding novel miRNAs is a vital element for investigating miRNA mediated posttranscriptional gene regulation machineries. Furthermore, experimental methods have challenging inadequacies in their capability to detect rare miRNAs, and are also limited to the state of the organism under examination (e.g., tissue type, developmental stage, stress-disease conditions). These issues have initiated the creation of high-level computational methodologies endeavoring to distinguish potential miRNAs in silico. On the other hand, most of these tools suffer from high numbers of false positives and/or false negatives and as a result they do not provide enough confidence for validating all their predictions experimentally. In this chapter, computational difficulties in detection of pre-miRNAs are discussed and a machine learning based approach that has been designed to address these issues is reviewed.

Roles of Noncoding RNAs in Islet Biology

The discovery that most mammalian genome sequences are transcribed to ribonucleic acids (RNA) has revolutionized our understanding of the mechanisms governing key cellular processes and of the causes of human diseases, including diabetes mellitus. Pancreatic islet cells were found to contain thousands of noncoding RNAs (ncRNAs), including micro-RNAs (miRNAs), PIWI-associated RNAs, small nucleolar RNAs, tRNA-derived fragments, long non-coding RNAs, and circular RNAs. While the involvement of miRNAs in islet function and in the etiology of diabetes is now well documented, there is emerging evidence indicating that other classes of ncRNAs are also participating in different aspects of islet physiology. The aim of this article will be to provide a comprehensive and updated view of the studies carried out in human samples and rodent models over the past 15 years on the role of ncRNAs in the control of α- and β-cell development and function and to highlight the recent discoveries in the field. We not only describe the role of ncRNAs in the control of insulin and glucagon secretion but also address the contribution of these regulatory molecules in the proliferation and survival of islet cells under physiological and pathological conditions. It is now well established that most cells release part of their ncRNAs inside small extracellular vesicles, allowing the delivery of genetic material to neighboring or distantly located target cells. The role of these secreted RNAs in cell-to-cell communication between β-cells and other metabolic tissues as well as their potential use as diabetes biomarkers will be discussed. © 2020 American Physiological Society. Compr Physiol 10:893-932, 2020.

Applications of Machine Learning in miRNA Discovery and Target Prediction

MicroRNA (miRNA) is a small non-coding molecule that is involved in gene regulation and RNA silencing by complementary on their targets. Experimental methods for target prediction can be time-consuming and expensive. Thus, the application of the computational approach is implicated to enlighten these complications with experimental studies. However, there is still a need for an optimized approach in miRNA biology. Therefore, machine learning (ML) would initiate a new era of research in miRNA biology towards potential diseases biomarker. In this article, we described the application of ML approaches in miRNA discovery and target prediction with functions and future prospective. The implementation of a new era of computational methodologies in this direction would initiate further advanced levels of discoveries in miRNA.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

BACKGROUND

Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.

OBJECTIVE

The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA's roles in cellular processes and drugs design, briefly.

METHOD

In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.

RESULTS

The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.

CONCLUSION

ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

Computational Resources for Prediction and Analysis of Functional miRNA and Their Targetome

microRNAs are evolutionarily conserved, endogenously produced, noncoding RNAs (ncRNAs) of approximately 19-24 nucleotides (nts) in length known to exhibit gene silencing of complementary target sequence. Their deregulated expression is reported in various disease conditions and thus has therapeutic implications. In the last decade, various computational resources are published in this field. In this chapter, we have reviewed bioinformatics resources, i.e., miRNA-centered databases, algorithms, and tools to predict miRNA targets. First section has enlisted more than 75 databases, which mainly covers information regarding miRNA registries, targets, disease associations, differential expression, interactions with other noncoding RNAs, and all-in-one resources. In the algorithms section, we have compiled about 140 algorithms from eight subcategories, viz. for the prediction of precursor (pre-) and mature miRNAs. These algorithms are developed on various sequence, structure, and thermodynamic based features incorporated into different machine learning techniques (MLTs). In addition, computational identification of miRNAs from high-throughput next generation sequencing (NGS) data and their variants, viz. isomiRs, differential expression, miR-SNPs, and functional annotation, are discussed. Prediction and analysis of miRNAs and their associated targets are also evaluated under miR-targets section providing knowledge regarding novel miRNA targets and complex host-pathogen interactions. In conclusion, we have provided comprehensive review of in silico resources published in miRNA research to help scientific community be updated and choose the appropriate tool according to their needs.

In Silico Prediction of RNA Secondary Structure

The secondary structure of an RNA molecule represents the base-pairing interactions within the molecule and fundamentally determines its overall structure. In this chapter, we overview the main approaches and existing tools for predicting RNA secondary structures, as well as methods for identifying noncoding RNAs from genomic sequences or RNA sequencing data. We then focus on the identification of a well-known class of small noncoding RNAs, namely microRNAs, which play very important roles in many biological processes through regulating post-transcriptionally the expression of genes and which dysregulation has been shown to be involved in several human diseases.

A naïve Bayesian classifier for identifying plant microRNAs

MicroRNAs (miRNAs) are important regulatory molecules in eukaryotic organisms. Existing methods for the identification of mature miRNA sequences in plants rely extensively on the search for stem-loop structures, leading to high false negative rates. Here, we describe a probabilistic method for ranking putative plant miRNAs using a naïve Bayes classifier and its publicly available implementation. We use a number of properties to construct the classifier, including sequence length, number of observations, existence of detectable predicted miRNA* sequences, the distribution of nearby reads and mapping multiplicity. We apply the method to small RNA sequence data from soybean, peach, Arabidopsis and rice and provide experimental validation of several predictions in soybean. The approach performs well overall and strongly enriches for known miRNAs over other types of sequences. By utilizing a Bayesian approach to rank putative miRNAs, our method is able to score miRNAs that would be eliminated by other methods, such as those that have low counts or lack detectable miRNA* sequences. As a result, we are able to detect several soybean miRNA candidates, including some that are 24 nucleotides long, a class that is almost universally eliminated by other methods.

Bioinformatic tools for microRNA dissection

Recently, microRNAs (miRNAs) have emerged as important elements of gene regulatory networks. MiRNAs are endogenous single-stranded non-coding RNAs (~22-nt long) that regulate gene expression at the post-transcriptional level. Through pairing with mRNA, miRNAs can down-regulate gene expression by inhibiting translation or stimulating mRNA degradation. In some cases they can also up-regulate the expression of a target gene. MiRNAs influence a variety of cellular pathways that range from development to carcinogenesis. The involvement of miRNAs in several human diseases, particularly cancer, makes them potential diagnostic and prognostic biomarkers. Recent technological advances, especially high-throughput sequencing, have led to an exponential growth in the generation of miRNA-related data. A number of bioinformatic tools and databases have been devised to manage this growing body of data. We analyze 129 miRNA tools that are being used in diverse areas of miRNA research, to assist investigators in choosing the most appropriate tools for their needs.

Small RNAs in Plant Responses to Abiotic Stresses: Regulatory Roles and Study Methods

To survive under abiotic stresses in the environment, plants trigger a reprogramming of gene expression, by transcriptional regulation or translational regulation, to turn on protective mechanisms. The current focus of research on how plants cope with abiotic stresses has transitioned from transcriptomic analyses to small RNA investigations. In this review, we have summarized and evaluated the current methodologies used in the identification and validation of small RNAs and their targets, in the context of plant responses to abiotic stresses.

Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential

BACKGROUND

Development of human cancer can proceed through the accumulation of different genetic changes affecting the structure and function of the genome. Combined analyses of molecular data at multiple levels, such as DNA copy-number alteration, mRNA and miRNA expression, can clarify biological functions and pathways deregulated in cancer. The integrative methods that are used to investigate these data involve different fields, including biology, bioinformatics, and statistics.

RESULTS

These methodologies are presented in this review, and their implementation in breast cancer is discussed with a focus on integration strategies. We report current applications, recent studies and interesting results leading to the identification of candidate biomarkers for diagnosis, prognosis, and therapy in breast cancer by using both individual and combined analyses.

CONCLUSION

This review presents a state of art of the role of different technologies in breast cancer based on the integration of genetics and epigenetics, and shares some issues related to the new opportunities and challenges offered by the application of such integrative approaches.

MicroRNAs: New Biomarkers for Diagnosis, Prognosis, Therapy Prediction and Therapeutic Tools for Breast Cancer

Dysregulation of microRNAs (miRNAs) is involved in the initiation and progression of several human cancers, including breast cancer (BC), as strong evidence has been found that miRNAs can act as oncogenes or tumor suppressor genes. This review presents the state of the art on the role of miRNAs in the diagnosis, prognosis, and therapy of BC. Based on the results obtained in the last decade, some miRNAs are emerging as biomarkers of BC for diagnosis (i.e., miR-9, miR-10b, and miR-17-5p), prognosis (i.e., miR-148a and miR-335), and prediction of therapeutic outcomes (i.e., miR-30c, miR-187, and miR-339-5p) and have important roles in the control of BC hallmark functions such as invasion, metastasis, proliferation, resting death, apoptosis, and genomic instability. Other miRNAs are of interest as new, easily accessible, affordable, non-invasive tools for the personalized management of patients with BC because they are circulating in body fluids (e.g., miR-155 and miR-210). In particular, circulating multiple miRNA profiles are showing better diagnostic and prognostic performance as well as better sensitivity than individual miRNAs in BC. New miRNA-based drugs are also promising therapy for BC (e.g., miR-9, miR-21, miR34a, miR145, and miR150), and other miRNAs are showing a fundamental role in modulation of the response to other non-miRNA treatments, being able to increase their efficacy (e.g., miR-21, miR34a, miR195, miR200c, and miR203 in combination with chemotherapy).

MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology

We address the problem of predicting the position of a miRNA duplex on a microRNA hairpin via the development and application of a novel SVM-based methodology. Our method combines a unique problem representation and an unbiased optimization protocol to learn from mirBase19.0 an accurate predictive model, termed MiRduplexSVM. This is the first model that provides precise information about all four ends of the miRNA duplex. We show that (a) our method outperforms four state-of-the-art tools, namely MaturePred, MiRPara, MatureBayes, MiRdup as well as a Simple Geometric Locator when applied on the same training datasets employed for each tool and evaluated on a common blind test set. (b) In all comparisons, MiRduplexSVM shows superior performance, achieving up to a 60% increase in prediction accuracy for mammalian hairpins and can generalize very well on plant hairpins, without any special optimization. (c) The tool has a number of important applications such as the ability to accurately predict the miRNA or the miRNA*, given the opposite strand of a duplex. Its performance on this task is superior to the 2nts overhang rule commonly used in computational studies and similar to that of a comparative genomic approach, without the need for prior knowledge or the complexity of performing multiple alignments. Finally, it is able to evaluate novel, potential miRNAs found either computationally or experimentally. In relation with recent confidence evaluation methods used in miRBase, MiRduplexSVM was successful in identifying high confidence potential miRNAs.

miRBoost: boosting support vector machines for microRNA precursor classification

Identification of microRNAs (miRNAs) is an important step toward understanding post-transcriptional gene regulation and miRNA-related pathology. Difficulties in identifying miRNAs through experimental techniques combined with the huge amount of data from new sequencing technologies have made in silico discrimination of bona fide miRNA precursors from non-miRNA hairpin-like structures an important topic in bioinformatics. Among various techniques developed for this classification problem, machine learning approaches have proved to be the most promising. However these approaches require the use of training data, which is problematic due to an imbalance in the number of miRNAs (positive data) and non-miRNAs (negative data), which leads to a degradation of their performance. In order to address this issue, we present an ensemble method that uses a boosting technique with support vector machine components to deal with imbalanced training data. Classification is performed following a feature selection on 187 novel and existing features. The algorithm, miRBoost, performed better in comparison with state-of-the-art methods on imbalanced human and cross-species data. It also showed the highest ability among the tested methods for discovering novel miRNA precursors. In addition, miRBoost was over 1400 times faster than the second most accurate tool tested and was significantly faster than most of the other tools. miRBoost thus provides a good compromise between prediction efficiency and execution time, making it highly suitable for use in genome-wide miRNA precursor prediction. The software miRBoost is available on our web server http://EvryRNA.ibisc.univ-evry.fr.

Ab initio human miRNA and 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 ).

Direct updating of an RNA base-pairing probability matrix with marginal probability constraints

A base-pairing probability matrix (BPPM) stores the probabilities for every possible base pair in an RNA sequence and has been used in many algorithms in RNA informatics (e.g., RNA secondary structure prediction and motif search). In this study, we propose a novel algorithm to perform iterative updates of a given BPPM, satisfying marginal probability constraints that are (approximately) given by recently developed biochemical experiments, such as SHAPE, PAR, and FragSeq. The method is easily implemented and is applicable to common models for RNA secondary structures, such as energy-based or machine-learning-based models. In this article, we focus mainly on the details of the algorithms, although preliminary computational experiments will also be presented.

A Review of Computational Tools in microRNA Discovery

Since microRNAs (miRNAs) were discovered, their impact on regulating various biological activities has been a surprising and exciting field. Knowing the entire repertoire of these small molecules is the first step to gain a better understanding of their function. High throughput discovery tools such as next-generation sequencing significantly increased the number of known miRNAs in different organisms in recent years. However, the process of being able to accurately identify miRNAs is still a complex and difficult task, requiring the integration of experimental approaches with computational methods. A number of prediction algorithms based on characteristics of miRNA molecules have been developed to identify new miRNA species. Different approaches have certain strengths and weaknesses and in this review, we aim to summarize several commonly used tools in metazoan miRNA discovery.

Prediction of conserved precursors of miRNAs and their mature forms by integrating position-specific structural features

MicroRNA (miRNA) precursor hairpins have a unique secondary structure, nucleotide length, and nucleotide content that are in most cases evolutionarily conserved. The aim of this study was to utilize position-specific features of miRNA hairpins to improve their identification. To this end, we defined the evolutionary and structurally conserved features in each position of miRNA hairpins with heuristically derived values, which were successfully integrated using a probabilistic framework. Our method, miRRim2, can not only accurately detect miRNA hairpins, but infer the location of a mature miRNA sequence. To evaluate the accuracy of miRRim2, we designed a cross validation test in which the whole human genome was used for evaluation. miRRim2 could more accurately detect miRNA hairpins than the other computational predictions that had been performed on the human genome, and detect the position of the 5′-end of mature miRNAs with sensitivity and positive predictive value (PPV) above 0.4. To further evaluate miRRim2 on independent data, we applied it to the Ciona intestinalis genome. Our method detected 47 known miRNA hairpins among top 115 candidates, and pinpointed the 5′-end of mature miRNAs with sensitivity and PPV about 0.4. When our results were compared with deep-sequencing reads of small RNA libraries from Ciona intestinalis cells, we found several candidates in which the predicted mature miRNAs were in good accordance with deep-sequencing results.

Cloning and expression of porcine Dicer and the impact of developmental stage and culture conditions on MicroRNA expression in porcine embryos

MicroRNA (miRNA) is a class of small, single-stranded ribonucleic acids that regulate gene expression post-transcriptionally and are involved in somatic cell, germ cell, and embryonic development. As the enzyme responsible for producing mature miRNA, Dicer is crucial to miRNA production. Characterization of Dicer and its expression at the nucleotide level, as well as the identification of miRNA expression in reproductive tissues, have yet to be reported for the domestic pig (Sus scrofa), a species important for disease modeling, biomedical research, and food production. In this study we determined the primary cDNA sequence of porcine Dicer (pDicer), confirmed its expression in porcine oocytes and early stage embryos, and evaluated the expression of specific miRNA during early embryonic development and between in vivo (IVO) and in vitro (IVF) produced embryos. Total cellular RNA (tcRNA) was isolated and subjected to end point RT-PCR, subcloning, and sequencing. The pDicer coding sequence was found to be highly conserved, and phylogenetic analysis showed that pDicer is more highly conserved to human Dicer (hDicer) than the mouse homolog. Expression of pDicer mRNA was detected in oocytes and in IVO produced blastocyst embryos. Two RT-PCR procedures were conducted to identify and quantitate miRNA expressed in metaphase II oocytes (MII) and embryos. RT-PCR array was conducted using primers designed for human miRNA, and 86 putative porcine miRNA in MII and early embryos were detected. Fewer miRNAs were detected in 8-cell (8C) embryos compared to MII and blastocysts (B) (P=0.026 and P<0.0001, respectively). Twenty-one miRNA (of 88 examined) were differentially expressed between MII and 8C, 8C and B, or MII and B. Transcripts targeted by the differentially expressed miRNA were enriched in gene ontology (GO) categories associated with cellular development and differentiation. Further, we evaluated the effects of IVF culture on the expression of specific miRNA at the blastocyst stage. Quantitative RT-PCR was conducted on blastocyst tcRNA isolated from individual IVO and IVF produced embryos for miR-18a, -21, and -24. Only the expression level of miR-24 differed due to culture conditions, with lower levels detected in the IVO embryos. These data show that pDicer and miRNA are present in porcine oocytes and embryos. In addition, specific miRNA levels are altered due to stage of embryonic development and, in the case of miR-24, due to culture conditions, making this miRNA a candidate for screening of embryo quality. Additional studies characterizing Dicer and miRNA expression during early embryonic development from IVO and IVF sources are required to further examine and evaluate the use of miRNA as a marker for embryo quality.

Integrated sequence-structure motifs suffice to identify microRNA precursors

Background

Upwards of 1200 miRNA loci have hitherto been annotated in the human genome. The specific features defining a miRNA precursor and deciding its recognition and subsequent processing are not yet exhaustively described and miRNA loci can thus not be computationally identified with sufficient confidence.

Results

We rendered pre-miRNA and non-pre-miRNA hairpins as strings of integrated sequence-structure information, and used the software Teiresias to identify sequence-structure motifs (ss-motifs) of variable length in these data sets. Using only ss-motifs as features in a Support Vector Machine (SVM) algorithm for pre-miRNA identification achieved 99.2% specificity and 97.6% sensitivity on a human test data set, which is comparable to previously published algorithms employing combinations of sequence-structure and additional features. Further analysis of the ss-motif information contents revealed strongly significant deviations from those of the respective training sets, revealing important potential clues as to how the sequence and structural information of RNA hairpins are utilized by the miRNA processing apparatus.

Conclusion

Integrated sequence-structure motifs of variable length apparently capture nearly all information required to distinguish miRNA precursors from other stem-loop structures.

A fast ab-initio method for predicting miRNA precursors in genomes

miRNAs are small non coding RNA structures which play important roles in biological processes. Finding miRNA precursors in genomes is therefore an important task, where computational methods are required. The goal of these methods is to select potential pre-miRNAs which could be validated by experimental methods. With the new generation of sequencing techniques, it is important to have fast algorithms that are able to treat whole genomes in acceptable times. We developed an algorithm based on an original method where an approximation of miRNA hairpins are first searched, before reconstituting the pre-miRNA structure. The approximation step allows a substantial decrease in the number of possibilities and thus the time required for searching. Our method was tested on different genomic sequences, and was compared with CID-miRNA, miRPara and VMir. It gives in almost all cases better sensitivity and selectivity. It is faster than CID-miRNA, miRPara and VMir: it takes ≈ 30 s to process a 1 MB sequence, when VMir takes 30 min, miRPara takes 20 h and CID-miRNA takes 55 h. We present here a fast ab-initio algorithm for searching for pre-miRNA precursors in genomes, called miRNAFold. miRNAFold is available at http://EvryRNA.ibisc.univ-evry.fr/.

Finding cancer-associated miRNAs: methods and tools

Changes in the structure and/or the expression of protein coding genes were thought to be the major cause of cancer for many decades. The recent discovery of non-coding RNA (ncRNA) transcripts (i.e., microRNAs) suggests that the molecular biology of cancer is far more complex. MicroRNAs (miRNAs) have been under investigation due to their involvement in carcinogenesis, often taking up roles of tumor suppressors or oncogenes. Due to the slow nature of experimental identification of miRNA genes, computational procedures have been applied as a valuable complement to cloning. Numerous computational tools, implemented to recognize the features of miRNA biogenesis, have resulted in the prediction of novel miRNA genes. Computational approaches provide clues as to which are the dominant features that characterize these regulatory units and furthermore act by narrowing down the search space making experimental verification faster and cheaper. In combination with large scale, high throughput methods, such as deep sequencing, computational methods have aided in the discovery of putative molecular signatures of miRNA deregulation in human tumors. This review focuses on existing computational methods for identifying miRNA genes, provides an overview of the methodology undertaken by these tools, and underlies their contribution towards unraveling the role of miRNAs in cancer.

Evaluation of online miRNA resources for biomedical applications

MicroRNAs (miRNAs) are endogenous single-stranded, 22-nt (nucleotide) RNAs which complement mRNA to initiate post-transcriptional regulation. This review presents updates and evaluations of the public domain resources available for miRNA identification and target prediction toward their utilization in the biomedical research approach. This study discusses the basic principles of miRNA computational studies based on the nature and mechanism of action of miRNAs. Furthermore, we have explored fifty-nine current online miRNA tools that can be categorized into three classes in this paper: (i) miRNA identification; (ii) miRNA target prediction; and (iii) specialized miRNA tools.

Computational identification of miRNAs involved in cancer

Changes in the structure and/or the expression of protein-coding genes were thought to be the major cause of cancer for many decades. However, the recent discovery of non-coding RNA (ncRNA) transcripts suggests that the molecular biology of cancer is far more complex. MicroRNAs (miRNAs) are key players of the family of ncRNAs and they have been under extensive investigation because of their involvement in carcinogenesis, often taking up roles of tumor suppressors or oncogenes. Owing to the slow nature of experimental identification of miRNA genes, computational procedures have been applied as a valuable complement to cloning. Numerous computational tools, implemented to recognize the characteristic features of miRNA biogenesis, have resulted in the prediction of multiple novel miRNA genes. Computational approaches provide valuable clues as to which are the dominant features that characterize these regulatory units and furthermore act by narrowing down the search space making experimental verification faster and significantly cheaper. Moreover, in combination with large-scale, high-throughput methods, such as deep sequencing and tilling arrays, computational methods have aided in the discovery of putative molecular signatures of miRNA deregulation in human tumors. This chapter focuses on existing computational methods for identifying miRNA genes, provides an overview of the methodology undertaken by these tools, and underlies their contribution toward unraveling the role of miRNAs in cancer.

Genome-wide approaches in the study of microRNA biology

MicroRNAs (miRNAs), a class of ∼21-23 nucleotide long non-coding RNAs (ncRNAs), have critical roles in diverse biological processes that encompass development, proliferation, apoptosis, stress response, and fat metabolism. miRNAs recognize their target mRNA transcripts by partial sequence complementarity and collectively have been estimated to regulate the majority of human genes. Consequently, misregulation of miRNAs or disruption of their target sites in genes has been implicated in a variety of human diseases ranging from cancer metastasis to neurological disorders. With the development and availability of genomic technologies and computational approaches, the field of miRNA biology has advanced tremendously over the last decade. Here we review the genome-wide approaches that have allowed for the discovery of new miRNAs, the characterization of their targets, and a systems-level view of their impact.

Computational and experimental identification of mirtrons in Drosophila melanogaster and Caenorhabditis elegans

Mirtrons are intronic hairpin substrates of the dicing machinery that generate functional microRNAs. In this study, we describe experimental assays that defined the essential requirements for entry of introns into the mirtron pathway. These data informed a bioinformatic screen that effectively identified functional mirtrons from the Drosophila melanogaster transcriptome. These included 17 known and six confident novel mirtrons among the top 51 candidates, and additional candidates had limited read evidence in available small RNA data. Our computational model also proved effective on Caenorhabditis elegans, for which the identification of 14 cloned mirtrons among the top 22 candidates more than tripled the number of validated mirtrons in this species. A few low-scoring introns generated mirtron-like read patterns from atypical RNA structures, but their paucity suggests that relatively few such loci were not captured by our model. Unexpectedly, we uncovered examples of clustered mirtrons in both fly and worm genomes, including a <8-kb region in C. elegans harboring eight distinct mirtrons. Altogether, we demonstrate that discovery of functional mirtrons, unlike canonical miRNAs, is amenable to computational methods independent of evolutionary constraint.

MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features

Background

MicroRNAs (simply miRNAs) are derived from larger hairpin RNA precursors and play essential regular roles in both animals and plants. A number of computational methods for miRNA genes finding have been proposed in the past decade, yet the problem is far from being tackled, especially when considering the imbalance issue of known miRNAs and unidentified miRNAs, and the pre-miRNAs with multi-loops or higher minimum free energy (MFE). This paper presents a new computational approach, miRenSVM, for finding miRNA genes. Aiming at better prediction performance, an ensemble support vector machine (SVM) classifier is established to deal with the imbalance issue, and multi-loop features are included for identifying those pre-miRNAs with multi-loops.

Results

We collected a representative dataset, which contains 697 real miRNA precursors identified by experimental procedure and other computational methods, and 5428 pseudo ones from several datasets. Experiments showed that our miRenSVM achieved a 96.5% specificity and a 93.05% sensitivity on the dataset. Compared with the state-of-the-art approaches, miRenSVM obtained better prediction results. We also applied our method to predict 14 Homo sapiens pre-miRNAs and 13 Anopheles gambiae pre-miRNAs that first appeared in miRBase13.0, MiRenSVM got a 100% prediction rate. Furthermore, performance evaluation was conducted over 27 additional species in miRBase13.0, and 92.84% (4863/5238) animal pre-miRNAs were correctly identified by miRenSVM.

Conclusion

MiRenSVM is an ensemble support vector machine (SVM) classification system for better detecting miRNA genes, especially those with multi-loop secondary structure.

A framework for automated enrichment of functionally significant inverted repeats in whole genomes

BACKGROUND

RNA transcripts from genomic sequences showing dyad symmetry typically adopt hairpin-like, cloverleaf, or similar structures that act as recognition sites for proteins. Such structures often are the precursors of non-coding RNA (ncRNA) sequences like microRNA (miRNA) and small-interfering RNA (siRNA) that have recently garnered more functional significance than in the past. Genomic DNA contains hundreds of thousands of such inverted repeats (IRs) with varying degrees of symmetry. But by collecting statistically significant information from a known set of ncRNA, we can sort these IRs into those that are likely to be functional.

RESULTS

A novel method was developed to scan genomic DNA for partially symmetric inverted repeats and the resulting set was further refined to match miRNA precursors (pre-miRNA) with respect to their density of symmetry, statistical probability of the symmetry, length of stems in the predicted hairpin secondary structure, and the GC content of the stems. This method was applied on the Arabidopsis thaliana genome and validated against the set of 190 known Arabidopsis pre-miRNA in the miRBase database. A preliminary scan for IRs identified 186 of the known pre-miRNA but with 714700 pre-miRNA candidates. This large number of IRs was further refined to 483908 candidates with 183 pre-miRNA identified and further still to 165371 candidates with 171 pre-miRNA identified (i.e. with 90% of the known pre-miRNA retained).

CONCLUSIONS

165371 candidates for potentially functional miRNA is still too large a set to warrant wet lab analyses, such as northern blotting, on all of them. Hence additional filters are needed to further refine the number of candidates while still retaining most of the known miRNA. These include detection of promoters and terminators, homology analyses, location of candidate relative to coding regions, and better secondary structure prediction algorithms. The software developed is designed to easily accommodate such additional filters with a minimal experience in Perl.

Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

Background

MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, ab initio approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these ab initio approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention.

Results

This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G²DE) based classifier. The G²DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set.

Conclusion

Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G²DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G²DE based predictor.

Sequence and structure analysis of noncoding RNAs

Noncoding RNAs (ncRNAs) are increasingly recognized as important functional molecules in the cell. Here we give a short overview of fundamental computational techniques to analyze ncRNAs that can help us better understand their function. Topics covered include prediction of secondary structure from the primary sequence, prediction of consensus structures for homologous sequences, search for homologous sequences in databases using sequence and structure comparisons, annotation of tRNAs, rRNAs, snoRNAs, and microRNAs, de novo prediction of novel ncRNAs, and prediction of RNA/RNA interactions including miRNA target prediction.

Prediction of novel microRNA genes in cancer-associated genomic regions--a combined computational and experimental approach

The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of these studies miRNA gene predictions are rarely supported by experimental evidence and prediction accuracy remains uncertain. In this work we present a new computational tool (SSCprofiler) utilizing a probabilistic method based on Profile Hidden Markov Models to predict novel miRNA precursors. Via the simultaneous integration of biological features such as sequence, structure and conservation, SSCprofiler achieves a performance accuracy of 88.95% sensitivity and 84.16% specificity on a large set of human miRNA genes. The trained classifier is used to identify novel miRNA gene candidates located within cancer-associated genomic regions and rank the resulting predictions using expression information from a full genome tiling array. Finally, four of the top scoring predictions are verified experimentally using northern blot analysis. Our work combines both analytical and experimental techniques to show that SSCprofiler is a highly accurate tool which can be used to identify novel miRNA gene candidates in the human genome. SSCprofiler is freely available as a web service at http://www.imbb.forth.gr/SSCprofiler.html.

Novel method for high-throughput colony PCR screening in nanoliter-reactors

We introduce a technology for the rapid identification and sequencing of conserved DNA elements employing a novel suspension array based on nanoliter (nl)-reactors made from alginate. The reactors have a volume of 35 nl and serve as reaction compartments during monoseptic growth of microbial library clones, colony lysis, thermocycling and screening for sequence motifs via semi-quantitative fluorescence analyses. nl-Reactors were kept in suspension during all high-throughput steps which allowed performing the protocol in a highly space-effective fashion and at negligible expenses of consumables and reagents. As a first application, 11 high-quality microsatellites for polymorphism studies in cassava were isolated and sequenced out of a library of 20,000 clones in 2 days. The technology is widely scalable and we envision that throughputs for nl-reactor based screenings can be increased up to 100,000 and more samples per day thereby efficiently complementing protocols based on established deep-sequencing technologies.

HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models

Background

MicroRNAs (miRNAs) are small non-coding single-stranded RNAs (20–23 nts) that are known to act as post-transcriptional and translational regulators of gene expression. Although, they were initially overlooked, their role in many important biological processes, such as development, cell differentiation, and cancer has been established in recent times. In spite of their biological significance, the identification of miRNA genes in newly sequenced organisms is still based, to a large degree, on extensive use of evolutionary conservation, which is not always available.

Results

We have developed HHMMiR, a novel approach for de novo miRNA hairpin prediction in the absence of evolutionary conservation. Our method implements a Hierarchical Hidden Markov Model (HHMM) that utilizes region-based structural as well as sequence information of miRNA precursors. We first established a template for the structure of a typical miRNA hairpin by summarizing data from publicly available databases. We then used this template to develop the HHMM topology.

Conclusion

Our algorithm achieved average sensitivity of 84% and specificity of 88%, on 10-fold cross-validation of human miRNA precursor data. We also show that this model, trained on human sequences, works well on hairpins from other vertebrate as well as invertebrate species. Furthermore, the human trained model was able to correctly classify ~97% of plant miRNA precursors. The success of this approach in such a diverse set of species indicates that sequence conservation is not necessary for miRNA prediction. This may lead to efficient prediction of miRNA genes in virtually any organism.

Genome-wide searching with base-pairing kernel functions for noncoding RNAs: computational and expression analysis of snoRNA families in Caenorhabditis elegans

Despite the accumulating research on noncoding RNAs (ncRNAs), it is likely that we are seeing only the tip of the iceberg regarding our understanding of the functions and the regulatory roles served by ncRNAs in cellular metabolism, pathogenesis and host-pathogen interactions. Therefore, more powerful computational and experimental tools for analyzing ncRNAs need to be developed. To this end, we propose novel kernel functions, called base-pairing profile local alignment (BPLA) kernels, for analyzing functional ncRNA sequences using support vector machines (SVMs). We extend the local alignment kernels for amino acid sequences in order to handle RNA sequences by using STRAL's; scoring function, which takes into account sequence similarities as well as upstream and downstream base-pairing probabilities, thus enabling us to model secondary structures of RNA sequences. As a test of the performance of BPLA kernels, we applied our kernels to the problem of discriminating members of an RNA family from nonmembers using SVMs. The results indicated that the discrimination ability of our kernels is stronger than that of other existing methods. Furthermore, we demonstrated the applicability of our kernels to the problem of genome-wide search of snoRNA families in the Caenorhabditis elegans genome, and confirmed that the expression is valid in 14 out of 48 of our predicted candidates by using qRT-PCR. Finally, highly expressed six candidates were identified as the original target regions by DNA sequencing.

Non-coding RNA prediction and verification in Saccharomyces cerevisiae

Non-coding RNA (ncRNA) play an important and varied role in cellular function. A significant amount of research has been devoted to computational prediction of these genes from genomic sequence, but the ability to do so has remained elusive due to a lack of apparent genomic features. In this work, thermodynamic stability of ncRNA structural elements, as summarized in a Z-score, is used to predict ncRNA in the yeast Saccharomyces cerevisiae. This analysis was coupled with comparative genomics to search for ncRNA genes on chromosome six of S. cerevisiae and S. bayanus. Sets of positive and negative control genes were evaluated to determine the efficacy of thermodynamic stability for discriminating ncRNA from background sequence. The effect of window sizes and step sizes on the sensitivity of ncRNA identification was also explored. Non-coding RNA gene candidates, common to both S. cerevisiae and S. bayanus, were verified using northern blot analysis, rapid amplification of cDNA ends (RACE), and publicly available cDNA library data. Four ncRNA transcripts are well supported by experimental data (RUF10, RUF11, RUF12, RUF13), while one additional putative ncRNA transcript is well supported but the data are not entirely conclusive. Six candidates appear to be structural elements in 5′ or 3′ untranslated regions of annotated protein-coding genes. This work shows that thermodynamic stability, coupled with comparative genomics, can be used to predict ncRNA with significant structural elements.

Recent advances in DNA sequence technology have made it possible to sequence entire genomes. Once a genome is sequenced, it becomes necessary to identify the set of genes and other functional elements within the genome. This is particularly challenging as much of the genomic sequence does not appear to perform any function and is loosely referred to as “junk.” Identifying functional elements among the “junk” is difficult. Experimental methods have been developed for this purpose but they are time-consuming, expensive, and often provide an incomplete picture. Thus, it is important to develop the ability to identify these functional elements using computational methods. Protein-coding genes are relatively easy to identify computationally, but other categories of functional elements present a significantly greater challenge. In this work, we used a computational approach to identify genes that do not encode for a protein but rather function as an RNA molecule. We then used experimental methods to verify our predictions and thereby validate the computational method.

Software.ncrna.org: web servers for analyses of RNA sequences

We present web servers for analysis of non-coding RNA sequences on the basis of their secondary structures. Software tools for structural multiple sequence alignments, structural pairwise sequence alignments and structural motif findings are available from the integrated web server and the individual stand-alone web servers. The servers are located at http://software.ncrna.org, along with the information for the evaluation and downloading. This website is freely available to all users and there is no login requirement.

miRNAminer: a tool for homologous microRNA gene search

BACKGROUND

MicroRNAs (miRNAs), present in most metazoans, are small non-coding RNAs that control gene expression by negatively regulating translation through binding to the 3'UTR of mRNA transcripts. Previously, experimental and computational methods were used to construct miRNA gene repositories agreeing with careful submission guidelines.

RESULTS

An algorithm we developed - miRNAminer - is used for homologous conserved miRNA gene search in several animal species. Given a search query, candidate homologs from different species are tested for their known miRNA properties, such as secondary structure, energy and alignment and conservation, in order to asses their fidelity. When applying miRNAminer on seven mammalian species we identified several hundreds of high-confidence homologous miRNAs increasing the total collection of (miRbase) miRNAs, in these species, by more than 50%. miRNAminer uses stringent criteria and exhibits high sensitivity and specificity.

CONCLUSION

We present - miRNAminer - the first web-server for homologous miRNA gene search in animals. miRNAminer can be used to identify conserved homolog miRNA genes and can also be used prior to depositing miRNAs in public databases. miRNAminer is available at http://pag.csail.mit.edu/mirnaminer.