Comprehensive overview and assessment of computational prediction of microRNA targets in animals
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paper_title: RNAi: a potential new class of therapeutic for human genetic disease

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Dominant negative genetic disorders, in which a mutant allele of a gene causes disease in the presence of a second, normal copy, have been challenging since there is no cure and treatments are only to alleviate the symptoms. Current therapies involving pharmacological and biological drugs are not suitable to target mutant genes selectively due to structural indifference of the normal variant of their targets from the disease-causing mutant ones. In instances when the target contains single nucleotide polymorphism (SNP), whether it is an enzyme or structural or receptor protein are not ideal for treatment using conventional drugs due to their lack of selectivity. Therefore, there is a need to develop new approaches to accelerate targeting these previously inaccessible targets by classical therapeutics. Although there is a cooling trend by the pharmaceutical industry for the potential of RNA interference (RNAi), RNAi and other RNA targeting drugs (antisense, ribozyme, etc.) still hold their promise as the only drugs that provide an opportunity to target genes with SNP mutations found in dominant negative disorders, genes specific to pathogenic tumor cells, and genes that are critical for mediating the pathology of various other diseases. Because of its exquisite specificity and potency, RNAi has attracted a considerable interest as a new class of therapeutic for genetic diseases including amyotrophic lateral sclerosis, Huntington’s disease (HD), Alzheimer’s disease (AD), Parkinson’s disease (PD), spinocerebellar ataxia, dominant muscular dystrophies, and cancer. In this review, progress and challenges in developing RNAi therapeutics for genetic diseases will be discussed.
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paper_title: myMIR: a genome-wide microRNA targets identification and annotation tool

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miRNA target genes prediction represents a crucial step in miRNAs functional characterization. In this context, the challenging issue remains predictions accuracy and recognition of false positive results. In this article myMIR, a web based system for increasing reliability of miRNAs predicted targets lists, is presented. myMIR implements an integrated pipeline for computing ranked miRNA::target lists and provides annotations for narrowing them down. The system relies on knowledge base data, suitably integrated in order to extend the functional characterization of targeted genes to miRNAs, by highlighting the search on over-represented annotation terms. Validation results show a dramatic reduction in the quantity of predictions and an increase in the sensitivity, when compared to other methods. This improves the predictions accuracy and allows the formulation of novel hypotheses on miRNAs functional involvement.
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paper_title: Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach

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MicroRNAs (miRNAs) are small non-coding RNAs which play a key role in the post-transcriptional regulation of many genes. Elucidating miRNA-regulated gene networks is crucial for the understanding of mechanisms and functions of miRNAs in many biological processes, such as cell proliferation, development, differentiation and cell homeostasis, as well as in many types of human tumors. To this aim, we have recently presented the biclustering method HOCCLUS2, for the discovery of miRNA regulatory networks. Experiments on predicted interactions revealed that the statistical and biological consistency of the obtained networks is negatively affected by the poor reliability of the output of miRNA target prediction algorithms. Recently, some learning approaches have been proposed to learn to combine the outputs of distinct prediction algorithms and improve their accuracy. However, the application of classical supervised learning algorithms presents two challenges: i) the presence of only positive examples in datasets of experimentally verified interactions and ii) unbalanced number of labeled and unlabeled examples. We present a learning algorithm that learns to combine the score returned by several prediction algorithms, by exploiting information conveyed by (only positively labeled/) validated and unlabeled examples of interactions. To face the two related challenges, we resort to a semi-supervised ensemble learning setting. Results obtained using miRTarBase as the set of labeled (positive) interactions and mirDIP as the set of unlabeled interactions show a significant improvement, over competitive approaches, in the quality of the predictions. This solution also improves the effectiveness of HOCCLUS2 in discovering biologically realistic miRNA:mRNA regulatory networks from large-scale prediction data. Using the miR-17-92 gene cluster family as a reference system and comparing results with previous experiments, we find a large increase in the number of significantly enriched biclusters in pathways, consistent with miR-17-92 functions. The proposed approach proves to be fundamental for the computational discovery of miRNA regulatory networks from large-scale predictions. This paves the way to the systematic application of HOCCLUS2 for a comprehensive reconstruction of all the possible multiple interactions established by miRNAs in regulating the expression of gene networks, which would be otherwise impossible to reconstruct by considering only experimentally validated interactions.
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paper_title: Identification of Drosophila MicroRNA Targets

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MicroRNAs (miRNAs) are short RNA molecules that regulate gene expression by binding to target messenger RNAs and by controlling protein production or causing RNA cleavage. To date, functions have been assigned to only a few of the hundreds of identified miRNAs, in part because of the difficulty in identifying their targets. The short length of miRNAs and the fact that their complementarity to target sequences is imperfect mean that target identification in animal genomes is not possible by standard sequence comparison methods. Here we screen conserved 3′ UTR sequences from the Drosophila melanogaster genome for potential miRNA targets. The screening procedure combines a sequence search with an evaluation of the predicted miRNA–target heteroduplex structures and energies. We show that this approach successfully identifies the five previously validated let-7, lin-4, and bantam targets from a large database and predict new targets for Drosophila miRNAs. Our target predictions reveal striking clusters of functionally related targets among the top predictions for specific miRNAs. These include Notch target genes for miR-7, proapoptotic genes for the miR-2 family, and enzymes from a metabolic pathway for miR-277. We experimentally verified three predicted targets each for miR-7 and the miR-2 family, doubling the number of validated targets for animal miRNAs. Statistical analysis indicates that the best single predicted target sites are at the border of significance; thus, target predictions should be considered as tentative until experimentally validated. We identify features shared by all validated targets that can be used to evaluate target predictions for animal miRNAs. Our initial evaluation and experimental validation of target predictions suggest functions for two miRNAs. For others, the screen suggests plausible functions, such as a role for miR-277 as a metabolic switch controlling amino acid catabolism. Cross-genome comparison proved essential, as it allows reduction of the sequence search space. Improvements in genome annotation and increased availability of cDNA sequences from other genomes will allow more sensitive screens. An increase in the number of confirmed targets is expected to reveal general structural features that can be used to improve their detection. While the screen is likely to miss some targets, our study shows that valid targets can be identified from sequence alone.
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paper_title: Desperately seeking microRNA targets

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MicroRNAs (miRNAs) suppress gene expression by inhibiting translation, promoting mRNA decay or both. Each miRNA may regulate hundreds of genes to control the cell's response to developmental and other environmental cues. The best way to understand the function of a miRNA is to identify the genes that it regulates. Target gene identification is challenging because miRNAs bind to their target mRNAs by partial complementarity over a short sequence, suppression of an individual target gene is often small, and the rules of targeting are not completely understood. Here we review computational and experimental approaches to the identification of miRNA-regulated genes. The examination of changes in gene expression that occur when miRNA expression is altered and biochemical isolation of miRNA-associated transcripts complement target prediction algorithms. Bioinformatic analysis of over-represented pathways and nodes in protein-DNA interactomes formed from experimental candidate miRNA gene target lists can focus attention on biologically significant target genes.
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paper_title: MicroRNA and cancer chemoprevention.

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MicroRNAs (miRNA) are a group of naturally occurring, small, noncoding, and single-strand RNA molecules that regulate gene expression at the posttranscriptional and translational levels. By controlling the expression of oncogenic and tumor suppressor proteins, miRNAs are believed to play an important role in pathologic processes associated with malignant progression including tumor cell proliferation, apoptosis, differentiation, angiogenesis, invasion, and metastasis. However, relatively few studies have investigated the influence of chemopreventive agents on miRNA expression and their regulation of target genes. Given the significance of miRNAs in modulating gene expression, such research can provide insight into the pleiotropic biologic effects that chemopreventive agents often display and a deeper understanding of their mechanism of action to inhibit carcinogenesis. In addition, miRNAs can provide useful biomarkers for assessing antineoplastic activity of these agents in preclinical and clinical observations. In this review, we summarize recent publications that highlight a potentially important role of miRNAs in cancer chemoprevention research.
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paper_title: MicroRNA therapeutics

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MicroRNAs (miRNAs) provide new therapeutic targets for many diseases, while their myriad roles in development and cellular processes make them fascinating to study. We still do not fully understand the molecular mechanisms by which miRNAs regulate gene expression nor do we know the complete repertoire of mRNAs each miRNA regulates. However, recent progress in the development of effective strategies to block miRNAs suggests that anti-miRNA drugs may soon be used in the clinic.
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paper_title: Computational identification of microRNA targets

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Recent experiments have shown that the genomes of organisms such as worm, fly, human and mouse encode hundreds of microRNA genes. Many of these microRNAs are thought to regulate the translational expression of other genes by binding to partially complementary sites in messenger RNAs. Phenotypic and expression analysis suggest an important role of microRNAs during development. Therefore, it is of fundamental importance to identify microRNA targets. However, no experimental or computational high-throughput method for target site identification in animals has been published yet. Our main result is a new computational method which is designed to identify microRNA target sites. This method recovers with high specificity known microRNA target sites which previously have been defined experimentally. Based on these results, we present a simple model for the mechanism of microRNA target site recognition. Our model incorporates both kinetic and thermodynamic components of target recognition. When we applied our method to a set of 74 Drosophila melanogaster microRNAs, searching 3' UTR sequences of a predefined set of fly mRNAs for target sites which were evolutionary conserved between Drosophila melanogaster and Drosophila pseudoobscura, we found that a number of key developmental body patterning genes such as hairy and fushi-tarazu are likely to be translationally regulated by microRNAs.
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paper_title: MicroRNA-33 and the SREBP host genes cooperate to control cholesterol homeostasis.

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Proper coordination of cholesterol biosynthesis and trafficking is essential to human health. The sterol regulatory element-binding proteins (SREBPs) are key transcription regulators of genes involved in cholesterol biosynthesis and uptake. We show here that microRNAs (miR-33a/b) embedded within introns of the SREBP genes target the adenosine triphosphate-binding cassette transporter A1 (ABCA1), an important regulator of high-density lipoprotein (HDL) synthesis and reverse cholesterol transport, for posttranscriptional repression. Antisense inhibition of miR-33 in mouse and human cell lines causes up-regulation of ABCA1 expression and increased cholesterol efflux, and injection of mice on a western-type diet with locked nucleic acid-antisense oligonucleotides results in elevated plasma HDL. Our findings indicate that miR-33 acts in concert with the SREBP host genes to control cholesterol homeostasis and suggest that miR-33 may represent a therapeutic target for ameliorating cardiometabolic diseases.
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paper_title: Prediction of Plant MicroRNA Targets

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We predict regulatory targets for 14 Arabidopsis microRNAs (miRNAs) by identifying mRNAs with near complementarity. Complementary sites within predicted targets are conserved in rice. Of the 49 predicted targets, 34 are members of transcription factor gene families involved in developmental patterning or cell differentiation. The near-perfect complementarity between plant miRNAs and their targets suggests that many plant miRNAs act similarly to small interfering RNAs and direct mRNA cleavage. The targeting of developmental transcription factors suggests that many plant miRNAs function during cellular differentiation to clear key regulatory transcripts from daughter cell lineages.
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paper_title: RNA Interference and Cancer Therapy

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Since its discovery in 1998, RNA interference (RNAi) has revolutionized basic and clinical research. Small RNAs, including small interfering RNA (siRNA), short hairpin RNA (shRNA) and microRNA (miRNA), mediate RNAi effects through either cleavage-dependent or cleavage-independent RNA inducible silencing complex (RISC) effector processes. As a result of its efficacy and potential, RNAi has been elevated to the status of "blockbuster therapeutic" alongside recombinant protein and monoclonal antibody. RNAi has already contributed to our understanding of neoplasia and has great promise for anti-cancer therapeutics, particularly so for personalized cancer therapy. Despite this potential, several hurdles have to be overcome for successful development of RNAi-based pharmaceuticals. This review will discuss the potential for, challenges to, and the current status of RNAi-based cancer therapeutics.
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paper_title: Analysis of microRNA-target interactions across diverse cancer types

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Analysis of data from The Cancer Genome Atlas generates a pan-cancer network of 143 recurrent miRNA-target relationships. The identified miRNAs were frequently regulated by genetic and epigenetic alterations in cancer. The work also reveals that some miRNAs might coordinately regulate cancer pathways, such as miR-29 regulation of TET1 and TDG mRNAs, encoding components from the active DNA demethylation pathway.
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paper_title: Emerging RNA-based drugs: siRNAs, microRNAs and derivates.

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An emerging new category of therapeutic agents based on ribonucleic acid has emerged and shown very promising in vitro, animal and pre-clinical results, known as small interfering RNAs (siRNAs), microRNAs mimics (miRNA mimics) and their derivates. siRNAs are small RNA molecules that promote potent and specific silencing of mutant, exogenous or aberrant genes through a mechanism known as RNA interference. These agents have called special attention to medicine since they have been used to experimentally treat a series of neurological conditions with distinct etiologies such as prion, viral, bacterial, fungal, genetic disorders and others. siRNAs have also been tested in other scenarios such as: control of anxiety, alcohol consumption, drug-receptor blockage and inhibition of pain signaling. Although in a much earlier stage, miRNAs mimics, anti-miRs and small activating RNAs (saRNAs) also promise novel therapeutic approaches to control gene expression. In this review we intend to introduce clinicians and medical researchers to the most recent advances in the world of siRNA- and miRNA-mediated gene control, its history, applications in cells, animals and humans, delivery methods (an yet unsolved hurdle), current status and possible applications in future clinical practice.
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paper_title: Developing MicroRNA Therapeutics

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Rarely a new research area has gotten such an overwhelming amount of attention as have microRNAs. Although several basic questions regarding their biological principles still remain to be answered, many specific characteristics of microRNAs in combination with compelling therapeutic efficacy data and a clear involvement in human disease have triggered the biotechnology community to start exploring the possibilities of viewing microRNAs as therapeutic entities. This review serves to provide some general insight into some of the current microRNAs targets, how one goes from the initial bench discovery to actually developing a therapeutically useful modality, and will briefly summarize the current patent landscape and the companies that have started to explore microRNAs as the next drug target.
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paper_title: Therapeutic Silencing of MicroRNA-122 in Primates with Chronic Hepatitis C Virus Infection

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The liver-expressed microRNA-122 (miR-122) is essential for hepatitis C virus (HCV) RNA accumulation in cultured liver cells, but its potential as a target for antiviral intervention has not been assessed. We found that treatment of chronically infected chimpanzees with a locked nucleic acid (LNA)-modified oligonucleotide (SPC3649) complementary to miR-122 leads to long-lasting suppression of HCV viremia, with no evidence of viral resistance or side effects in the treated animals. Furthermore, transcriptome and histological analyses of liver biopsies demonstrated derepression of target mRNAs with miR-122 seed sites, down-regulation of interferon-regulated genes, and improvement of HCV-induced liver pathology. The prolonged virological response to SPC3649 treatment without HCV rebound holds promise of a new antiviral therapy with a high barrier to resistance.
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paper_title: MicroRNA target prediction by expression analysis of host genes

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MicroRNAs (miRNAs) are small noncoding RNAs that control gene expression by inducing RNA cleavage or translational inhibition. Most human miRNAs are intragenic and are transcribed as part of their hosting transcription units. We hypothesized that the expression profiles of miRNA host genes and of their targets are inversely correlated and devised a novel procedure, HOCTAR (host gene oppositely correlated targets), which ranks predicted miRNA target genes based on their anti-correlated expression behavior relative to their respective miRNA host genes. HOCTAR is the first tool for systematic miRNA target prediction that utilizes the same set of microarray experiments to monitor the expression of both miRNAs (through their host genes) and candidate targets. We applied the procedure to 178 human intragenic miRNAs and found that it performs better than currently available prediction softwares in pinpointing previously validated miRNA targets. The high-scoring HOCTAR predicted targets were enriched in Gene Ontology categories, which were consistent with previously published data, as in the case of miR-106b and miR-93. By means of overexpression and loss-of-function assays, we also demonstrated that HOCTAR is efficient in predicting novel miRNA targets and we identified, by microarray and qRT-PCR procedures, 34 and 28 novel targets for miR-26b and miR-98, respectively. Overall, we believe that the use of HOCTAR significantly reduces the number of candidate miRNA targets to be tested compared to the procedures based solely on target sequence recognition. Finally, our data further confirm that miRNAs have a significant impact on the mRNA levels of most of their targets.
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paper_title: Lost in translation: an assessment and perspective for computational microRNA target identification

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MicroRNAs (miRNAs) are a class of short endogenously expressed RNA molecules that regulate gene expression by binding directly to the messenger RNA of protein coding genes. They have been found to confer a novel layer of genetic regulation in a wide range of biological processes. Computational miRNA target prediction remains one of the key means used to decipher the role of miRNAs in development and disease. Here we introduce the basic idea behind the experimental identification of miRNA targets and present some of the most widely used computational miRNA target identification programs. The review includes an assessment of the prediction quality of these programs and their combinations. Contact: p.alexiou@fleming.gr Supplementary information: Supplementary data are available at Bioinformatics online.
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paper_title: Update on the development of microRNA and siRNA molecules as regulators of cell physiology.

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RNA interference (RNAi) is one of the most significant recent breakthroughs in biomedical sciences. In 2006, Drs. Fire and Mello were awarded the Nobel Price for Physiology or Medicine for their discovery of gene silencing by double-stranded RNA. Basic scientists have used RNAi as a tool to study gene regulation, signal transduction and disease mechanisms, while preclinical drug development has gained from its use in target validation and lead optimization. RNAi has also shown promise in therapeutic applications, and several synthetic RNA molecules have entered clinical trials. The family of short regulatory RNA molecules, including small interfering RNAs (siRNAs) and micro-RNAs (miRNAs), offers many possibilities for the innovative mind. When conventional small molecule inhibitors cannot be used, RNAi technology offers the possibility for sequence-specific targeting and subsequent target gene knockdown. Currently the major challenges related to RNAi -based drug development include delivery, off-target effects, activation of the immune system and RNA degradation. Although many of the expectations related to drug development have not been met thus far, these physiologically important molecules are used in several applications. This review summarizes recent patent applications concerning micro-RNA biology. Despite the somewhat unclear intellectual property right (IPR) status for RNAi, there are many possibilities for new inventions, and much remains to be learned from the physiology behind gene regulation by short RNA molecules.
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paper_title: A Pattern-Based Method for the Identification of MicroRNA Binding Sites and Their Corresponding Heteroduplexes

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Summary We present rna22 , a method for identifying microRNA binding sites and their corresponding heteroduplexes. Rna22 does not rely upon cross-species conservation, is resilient to noise, and, unlike previous methods, it first finds putative microRNA binding sites in the sequence of interest, then identifies the targeting microRNA. Computationally, we show that rna22 identifies most of the currently known heteroduplexes. Experimentally, with luciferase assays, we demonstrate average repressions of 30% or more for 168 of 226 tested targets. The analysis suggests that some microRNAs may have as many as a few thousand targets, and that between 74% and 92% of the gene transcripts in four model genomes are likely under microRNA control through their untranslated and amino acid coding regions. We also extended the method's key idea to a low-error microRNA-precursor-discovery scheme; our studies suggest that the number of microRNA precursors in mammalian genomes likely ranges in the tens of thousands.
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paper_title: RNAi-based therapeutic strategies for metabolic disease

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RNA interference (RNAi) is a robust gene silencing mechanism that degrades mRNAs complementary to the antisense strands of double-stranded, short interfering RNAs (siRNAs). As a therapeutic strategy, RNAi has an advantage over small-molecule drugs, as virtually all genes are susceptible to targeting by siRNA molecules. This advantage is, however, counterbalanced by the daunting challenge of achieving safe, effective delivery of oligonucleotides to specific tissues in vivo. Lipid-based carriers of siRNA therapeutics can now target the liver in metabolic diseases and are being assessed in clinical trials for the treatment of hypercholesterolemia. For this indication, a chemically modified oligonucleotide that targets endogenous small RNA modulators of gene expression (microRNAs) is also under investigation in clinical trials. Emerging 'self-delivery' siRNAs that are covalently linked to lipophilic moieties show promise for the future development of therapies. Besides the liver, inflammation of the adipose tissue in patients with obesity and type 2 diabetes mellitus may be an attractive target for siRNA therapeutics. Administration of siRNAs encapsulated within glucan microspheres can silence genes in inflammatory phagocytic cells, as can certain lipid-based carriers of siRNA. New technologies that combine siRNA molecules with antibodies or other targeting molecules also appear encouraging. Although still at an early stage, the emergence of RNAi-based therapeutics has the potential to markedly influence our clinical future.
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paper_title: A guide through present computational approaches for the identification of mammalian microRNA targets

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Computational microRNA (miRNA) target prediction is a field in flux. Here we present a guide through five widely used mammalian target prediction programs. We include an analysis of the performance of these individual programs and of various combinations of these programs. For this analysis we compiled several benchmark data sets of experimentally supported miRNA-target gene interactions. Based on the results, we provide a discussion on the status of target prediction and also suggest a stepwise approach toward predicting and selecting miRNA targets for experimental testing.
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paper_title: MicroRNAs and neurodegeneration: role and impact.

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Neurodegenerative diseases are typically late-onset, progressive disorders that affect neural function and integrity. Although most attention has been focused on the genetic underpinnings of familial disease, mechanisms are likely to be shared with more predominant sporadic forms, which can be influenced by age, environment, and genetic inputs. Previous work has largely addressed the roles of select protein-coding genes; however, disease pathogenesis is complicated and can be modulated through not just protein-coding genes, but also regulatory mechanisms mediated by the exploding world of small non-coding RNAs. Here, we focus on emerging roles of miRNAs in age-associated events impacting long-term brain integrity and neurodegenerative disease.
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paper_title: Role of microRNA in the pathogenesis of malignant lymphoma.

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MicroRNA (miRNA) are non-coding regulatory RNA usually consisting of 20-24 nucleotides. Over the past decade, increases and decreases in miRNA expression have been shown to associate with various types of disease, including cancer. The first two known miRNA aberrations resulted from altered expression of DLEU2 and C13orf25 in hematological malignancies. DLEU2, which encodes miR-15a and miR-16-1, was discovered from 13q14 deletion in chronic lymphocytic leukemia, while C13orf25, which encodes six mature miRNA (miR-17, miR-18, miR-19a, miR-19b, miR-20a and miR-92a), was identified from 13q31 amplification in aggressive B-cell lymphomas. These miRNA were downregulated or upregulated in accordance with genomic deletion or amplification, which suggests that they contribute to tumorigenesis through altered regulation of target oncogenes or tumor suppressors. Consistent with that idea, miR-15a/16-1 is known to regulate Bcl2 in chronic lymphocytic leukemia, and miR-17-92 regulates the tumor suppressors p21, Pten and Bim in aggressive B-cell lymphomas. Dysregulation of other miRNA, including miR-21, miR-29, miR-150 and miR-155, have also been shown to play crucial roles in the pathogenesis of aggressive transformed, high-grade and refractory lymphomas. Addition of miRNA dysregulation to the original genetic events likely enhances tumorigenicity of malignant lymphoma through activation of one or more signaling pathways.
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paper_title: TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support

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As the relevant literature and the number of experiments increase at a super linear rate, databases that curate and collect experimentally verified microRNA (miRNA) targets have gradually emerged. These databases attempt to provide efficient access to this wealth of experimental data, which is scattered in thousands of manuscripts. Aim of TarBase 6.0 (http://www.microrna.gr/tarbase) is to face this challenge by providing a significant increase of available miRNA targets derived from all contemporary experimental techniques (gene specific and high-throughput), while incorporating a powerful set of tools in a user-friendly interface. TarBase 6.0 hosts detailed information for each miRNA–gene interaction, ranging from miRNA- and gene-related facts to information specific to their interaction, the experimental validation methodologies and their outcomes. All database entries are enriched with function-related data, as well as general information derived from external databases such as UniProt, Ensembl and RefSeq. DIANA microT miRNA target prediction scores and the relevant prediction details are available for each interaction. TarBase 6.0 hosts the largest collection of manually curated experimentally validated miRNA–gene interactions (more than 65 000 targets), presenting a 16.5–175-fold increase over other available manually curated databases.
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paper_title: miRCancer: a microRNA–cancer association database constructed by text mining on literature

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Motivation: Research interests in microRNAs have increased rapidly in the past decade. Many studies have showed that microRNAs have close relationships with various human cancers, and they potentially could be used as cancer indicators in diagnosis or as a suppressor for treatment purposes. There are several databases that contain microRNA–cancer associations predicted by computational methods but few from empirical results. Despite the fact that abundant experiments investigating microRNA expressions in cancer cells have been carried out, the results have remain scattered in the literature. We propose to extract microRNA–cancer associations by text mining and store them in a database called miRCancer. Results: The text mining is based on 75 rules we have constructed, which represent the common sentence structures typically used to state microRNA expressions in cancers. The microRNA–cancer association database, miRCancer, is updated regularly by running the text mining algorithm against PubMed. All miRNA–cancer associations are confirmed manually after automatic extraction. miRCancer currently documents 878 relationships between 236 microRNAs and 79 human cancers through the processing of426 000 published articles. Availability: miRCancer is freely available on the web at http://mircan
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paper_title: A microRNA component of the p53 tumour suppressor network

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A global decrease in microRNA (miRNA) levels is often observed in human cancers, indicating that small RNAs may have an intrinsic function in tumour suppression. To identify miRNA components of tumour suppressor pathways, we compared miRNA expression profiles of wild-type and p53-deficient cells. Here we describe a family of miRNAs, miR-34a-c, whose expression reflected p53 status. Genes encoding miRNAs in the miR-34 family are direct transcriptional targets of p53, whose induction by DNA damage and oncogenic stress depends on p53 both in vitro and in vivo. Ectopic expression of miR-34 induces cell cycle arrest in both primary and tumour-derived cell lines, which is consistent with the observed ability of miR-34 to downregulate a programme of genes promoting cell cycle progression. The p53 network suppresses tumour formation through the coordinated activation of multiple transcriptional targets, and miR-34 may act in concert with other effectors to inhibit inappropriate cell proliferation.
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paper_title: Widespread changes in protein synthesis induced by microRNAs

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Animal microRNAs (miRNAs) regulate gene expression by inhibiting translation and/or by inducing degradation of target messenger RNAs. It is unknown how much translational control is exerted by miRNAs on a genome-wide scale. We used a new proteomic approach to measure changes in synthesis of several thousand proteins in response to miRNA transfection or endogenous miRNA knockdown. In parallel, we quantified mRNA levels using microarrays. Here we show that a single miRNA can repress the production of hundreds of proteins, but that this repression is typically relatively mild. A number of known features of the miRNA-binding site such as the seed sequence also govern repression of human protein synthesis, and we report additional target sequence characteristics. We demonstrate that, in addition to downregulating mRNA levels, miRNAs also directly repress translation of hundreds of genes. Finally, our data suggest that a miRNA can, by direct or indirect effects, tune protein synthesis from thousands of genes.
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paper_title: Small noncoding RNAs: biogenesis, function, and emerging significance in toxicology.

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In recent years, the discovery of small ncRNAs (noncoding RNAs) has unveiled a slew of powerful riboregulators of gene expression. So far, many different types of small ncRNAs have been described. Of these, miRNAs (microRNAs), siRNAs (small interfering RNAs), and piRNAs (Piwi-interacting RNAs) have been studied in more detail. A significant fraction of genes in most organisms and tissues is targets of these small ncRNAs. Because these tiny RNAs are turning out to be important regulators of gene and genome expression, their aberrant expression profiles are expected to be associated with cellular dysfunction and disease. In fact, an ever-increasing number of studies have implicated miRNAs and siRNAs in human health and disease ranging from metabolic disorders to diseases of various organ systems as well as various forms of cancer. Nevertheless, despite the flurry of research on these small ncRNAs, many aspects of their biology still remain to be understood. The following discussion focuses on some aspects of the biogenesis and function of small ncRNAs with major emphasis on miRNAs since these are the most widespread endogenous small ncRNAs that have been called "micromanagers" of gene expression. Their emerging significance in toxicology is also discussed.
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paper_title: miR2Disease: a manually curated database for microRNA deregulation in human disease

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‘miR2Disease’, a manually curated database, aims at providing a comprehensive resource of microRNA deregulation in various human diseases. The current version of miR2Disease documents 1939 curated relationships between 299 human microRNAs and 94 human diseases by reviewing more than 600 published papers. Around one-seventh of the microRNA–disease relationships represent the pathogenic roles of deregulated microRNA in human disease. Each entry in the miR2Disease contains detailed information on a microRNA–disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA–disease relationship, an expression pattern of the microRNA, the detection method for microRNA expression, experimentally verified target gene(s) of the microRNA and a literature reference. miR2Disease provides a user-friendly interface for a convenient retrieval of each entry by microRNA ID, disease name, or target gene. In addition, miR2Disease offers a submission page that allows researchers to submit established microRNA–disease relationships that are not documented. Once approved by the submission review committee, the submitted records will be included in the database. miR2Disease is freely available at http://www.miR2Disease.org.
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paper_title: Global analysis of cellular protein translation by pulsed SILAC

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Current methods for system-wide gene expression analysis detect changes in mRNA abundance, but neglect regulation at the level of translation. Pulse labeling with stable isotopes has been used to measure protein turnover rates, but this does not directly provide information about translation rates. Here, we developed pulsed stable isotope labeling by amino acids in cell culture (pSILAC) with two heavy isotope labels to directly quantify protein translation on a proteome-wide scale. We applied the method to cellular iron homeostasis as a model system and demonstrate that it can confidently identify proteins that are translationally regulated by iron availability.
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paper_title: NCBI GEO: archive for functional genomics data sets—update

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The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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paper_title: Small RNAs in development and disease

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MicroRNAs (miRNAs) and short interfering RNAs (siRNAs) are classes of regulatory small RNA molecules, ranging from 18 to 24 nucleotides in length, whose roles in development and disease are becoming increasingly recognized. They function by altering the stability or translational efficiency of messenger RNAs (mRNAs) with which they share sequence complementarity, and are predicted to affect up to one-third of all human genes. Computer algorithms and microarray data estimate the presence of nearly 1000 human miRNAs, and direct examination of candidate miRNAs has validated their involvement in various cancers, disorders of neuronal development, cardiac hypertrophy, and skin diseases such as psoriasis. This article reviews the history of miRNA and siRNA discovery, key aspects of their biogenesis and mechanism of action, and known connections to human health, with an emphasis on their roles in skin development and disease. Learning objectives After completing this learning activity, participants should be able to summarize the relevance of microRNAs in development and disease, explain the molecular steps of how small RNAs regulate their targets within the human cell, and discuss the role of small RNAs in the diagnosis and treatment of disease.
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paper_title: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

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AbstractmirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.
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paper_title: miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions

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MicroRNAs (miRNAs) are small non-coding RNA molecules capable of negatively regulating gene expression to control many cellular mechanisms. The miRTarBase database (http://mirtarbase.mbc.nctu.edu.tw/) provides the most current and comprehensive information of experimentally validated miRNA-target interactions. The database was launched in 2010 with data sources for >100 published studies in the identification of miRNA targets, molecular networks of miRNA targets and systems biology, and the current release (2013, version 4) includes significant expansions and enhancements over the initial release (2010, version 1). This article reports the current status of and recent improvements to the database, including (i) a 14-fold increase to miRNA-target interaction entries, (ii) a miRNA-target network, (iii) expression profile of miRNA and its target gene, (iv) miRNA target-associated diseases and (v) additional utilities including an upgrade reminder and an error reporting/user feedback system.
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paper_title: Transcripts Targeted by the MicroRNA-16 Family Cooperatively Regulate Cell Cycle Progression

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microRNAs (miRNAs) regulate mRNA translation and mRNA decay in plants and animals (49). Hundreds of human miRNAs are now known (4-6, 15, 19). In animals, miRNAs regulate thousands of genes with spatial and temporal specificity, helping to ensure the accuracy of gene expression programs (17, 38, 47). Understanding the precise biological functions of animal miRNAs will require the identification of their multiple targets and the pathways that they control. ::: ::: Animal miRNAs generally share limited sequence complementarity with their targets. miRNA target recognition involves complementary base pairing of the target with the 5′ end (positions 1 to 8) of the miRNA guide strand seed region. However, the extent of seed region complementarity is not precisely determined and can be modified by 3′ pairing (8). Computational methods have been used to predict human miRNA targets (31, 34, 37, 45, 52). Most predicted miRNA target recognition sites lie in 3′ untranslated regions (3′UTRs), although coding region sites (CDS) may also be used (8, 36). Current estimates are that 30% or more of human mRNAs are regulated by miRNAs (36). ::: ::: While thousands of miRNA targets have been predicted, relatively few have been experimentally validated. Available methods for validation are laborious and not easily amenable to high-throughput methodologies (4). Since a single miRNA can regulate hundreds of targets, the biological pathways regulated by miRNAs are not always obvious from an examination of their targets. There is a clear need for high-throughput, low-cost methods to experimentally determine miRNA targets, validate computational predictions, and decipher miRNA function. ::: ::: One method to experimentally identify miRNA targets and their functions is microarray analysis (50). Although miRNAs may silence their targets via translational blocking (16), they also regulate target transcript levels. miRNAs in transfected cells down-regulate hundreds of mRNAs detectable by microarray profiling (38). These down-regulated transcripts have expression patterns that are complementary to that of the introduced miRNA and are also highly enriched within their 3′UTRs with hexamer, heptamer, and octamer motifs complementary to miRNA seed regions. This regulation resembles the “off-target” silencing of imperfectly matched targets by small interfering RNAs (siRNAs) (28, 29). Thus, both miRNAs and siRNAs can target partially complementary transcripts for degradation, resulting in transcript changes that can be monitored using microarrays. In fact, changes in transcript levels due to miRNA activity have been observed directly in vivo. The let-7 and lin-4 miRNAs trigger the degradation of their target mRNAs (2). Also, the depletion of miRNAs in mice and zebrafish led to the up-regulation of target mRNAs that were measured on microarrays (18, 35). ::: ::: A potential advantage of using microarrays to analyze miRNA targets is the utility of expression profiles for predicting gene function (25). In this study, we have explored the use of miRNA expression profiles to analyze miRNA targets and functions. Included in our analysis are several miRNAs reported to have a role in cancer (9-12, 14, 21, 22, 27, 32, 40, 41). We use this approach to show that a family of miRNAs sharing seed region identity with miRNA-16 (miR-16) negatively regulates cell cycle progression from G0/G1 to S.
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paper_title: miRecords: an integrated resource for microRNA–target interactions

paper_content:

MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes’ expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA–target interactions. The Validated Targets component of this resource hosts a large, high-quality manually curated database of experimentally validated miRNA–target interactions with systematic documentation of experimental support for each interaction. The current release of this database includes 1135 records of validated miRNA–target interactions between 301 miRNAs and 902 target genes in seven animal species. The Predicted Targets component of miRecords stores predicted miRNA targets produced by 11 established miRNA target prediction programs. miRecords is expected to serve as a useful resource not only for experimental miRNA researchers, but also for informatics scientists developing the next-generation miRNA target prediction programs. The miRecords is available at http:// miRecords.umn.edu/miRecords.
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paper_title: miRmap: Comprehensive prediction of microRNA target repression strength

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MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA ‘seed’ region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may use thermodynamic, evolutionary, probabilistic or sequence-based features. We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan. miRmap is freely available from http://cegg.unige.ch/mirmap.
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paper_title: Searching the coding region for microRNA targets.

paper_content:

Finding microRNA targets in the coding region is difficult due to the overwhelming signal encoding the amino acid sequence. Here, we introduce an algorithm (called PACCMIT-CDS) that finds potential microRNA targets within coding sequences by searching for conserved motifs that are complementary to the microRNA seed region and also overrepresented in comparison with a background model preserving both codon usage and amino acid sequence. Precision and sensitivity of PACCMIT-CDS are evaluated using PAR-CLIP and proteomics data sets. Thanks to the properly constructed background, the new algorithm achieves a lower rate of false positives and better ranking of predictions than do currently available algorithms, which were designed to find microRNA targets within 3' UTRs.
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paper_title: Assessing the Utility of Thermodynamic Features for microRNA Target Prediction under Relaxed Seed and No Conservation Requirements

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BACKGROUND ::: Many computational microRNA target prediction tools are focused on several key features, including complementarity to 5'seed of miRNAs and evolutionary conservation. While these features allow for successful target identification, not all miRNA target sites are conserved and adhere to canonical seed complementarity. Several studies have propagated the use of energy features of mRNA:miRNA duplexes as an alternative feature. However, different independent evaluations reported conflicting results on the reliability of energy-based predictions. Here, we reassess the usefulness of energy features for mammalian target prediction, aiming to relax or eliminate the need for perfect seed matches and conservation requirement. ::: ::: ::: METHODOLOGY/PRINCIPAL FINDINGS ::: We detect significant differences of energy features at experimentally supported human miRNA target sites and at genome-wide sites of AGO protein interaction. This trend is confirmed on datasets that assay the effect of miRNAs on mRNA and protein expression changes, and a simple linear regression model leads to significant correlation of predicted versus observed expression change. Compared to 6-mer seed matches as baseline, application of our energy-based model leads to ∼3-5-fold enrichment on highly down-regulated targets, and allows for prediction of strictly imperfect targets with enrichment above baseline. ::: ::: ::: CONCLUSIONS/SIGNIFICANCE ::: In conclusion, our results indicate significant promise for energy-based miRNA target prediction that includes a broader range of targets without having to use conservation or impose stringent seed match rules.
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paper_title: Identification of Drosophila MicroRNA Targets

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MicroRNAs (miRNAs) are short RNA molecules that regulate gene expression by binding to target messenger RNAs and by controlling protein production or causing RNA cleavage. To date, functions have been assigned to only a few of the hundreds of identified miRNAs, in part because of the difficulty in identifying their targets. The short length of miRNAs and the fact that their complementarity to target sequences is imperfect mean that target identification in animal genomes is not possible by standard sequence comparison methods. Here we screen conserved 3′ UTR sequences from the Drosophila melanogaster genome for potential miRNA targets. The screening procedure combines a sequence search with an evaluation of the predicted miRNA–target heteroduplex structures and energies. We show that this approach successfully identifies the five previously validated let-7, lin-4, and bantam targets from a large database and predict new targets for Drosophila miRNAs. Our target predictions reveal striking clusters of functionally related targets among the top predictions for specific miRNAs. These include Notch target genes for miR-7, proapoptotic genes for the miR-2 family, and enzymes from a metabolic pathway for miR-277. We experimentally verified three predicted targets each for miR-7 and the miR-2 family, doubling the number of validated targets for animal miRNAs. Statistical analysis indicates that the best single predicted target sites are at the border of significance; thus, target predictions should be considered as tentative until experimentally validated. We identify features shared by all validated targets that can be used to evaluate target predictions for animal miRNAs. Our initial evaluation and experimental validation of target predictions suggest functions for two miRNAs. For others, the screen suggests plausible functions, such as a role for miR-277 as a metabolic switch controlling amino acid catabolism. Cross-genome comparison proved essential, as it allows reduction of the sequence search space. Improvements in genome annotation and increased availability of cDNA sequences from other genomes will allow more sensitive screens. An increase in the number of confirmed targets is expected to reveal general structural features that can be used to improve their detection. While the screen is likely to miss some targets, our study shows that valid targets can be identified from sequence alone.
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paper_title: Prediction and verification of microRNA targets by MovingTargets, a highly adaptable prediction method

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BackgroundMicroRNAs (miRNAs) mediate a form of translational regulation in animals. Hundreds of animal miRNAs have been identified, but only a few of their targets are known. Prediction of miRNA targets for translational regulation is challenging, since the interaction with the target mRNA usually occurs via incomplete and interrupted base pairing. Moreover, the rules that govern such interactions are incompletely defined.ResultsMovingTargets is a software program that allows a researcher to predict a set of miRNA targets that satisfy an adjustable set of biological constraints. We used MovingTargets to identify a high-likelihood set of 83 miRNA targets in Drosophila, all of which adhere to strict biological constraints. We tested and verified 3 of these predictions in cultured cells, including a target for the Drosophila let-7 homolog. In addition, we utilized the flexibility of MovingTargets by relaxing the biological constraints to identify and validate miRNAs targeting tramtrack, a gene also known to be subject to translational control dependent on the RNA binding protein Musashi.ConclusionMovingTargets is a flexible tool for the accurate prediction of miRNA targets in Drosophila. MovingTargets can be used to conduct a genome-wide search of miRNA targets using all Drosophila miRNAs and potential targets, or it can be used to conduct a focused search for miRNAs targeting a specific gene. In addition, the values for a set of biological constraints used to define a miRNA target are adjustable, allowing the software to incorporate the rules used to characterize a miRNA target as these rules are experimentally determined and interpreted.
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paper_title: DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and bibliographic miRNA to disease association

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microRNAs (miRNAs) are small endogenous RNA molecules that are implicated in many biological processes through post-transcriptional regulation of gene expression. The DIANA-microT Web server provides a user-friendly interface for comprehensive computational analysis of miRNA targets in human and mouse. The server has now been extended to support predictions for two widely studied species: Drosophila melanogaster and Caenorhabditis elegans. In the updated version, the Web server enables the association of miRNAs to diseases through bibliographic analysis and provides insights for the potential involvement of miRNAs in biological processes. The nomenclature used to describe mature miRNAs along different miRBase versions has been extensively analyzed, and the naming history of each miRNA has been extracted. This enables the identification of miRNA publications regardless of possible nomenclature changes. User interaction has been further refined allowing users to save results that they wish to analyze further. A connection to the UCSC genome browser is now provided, enabling users to easily preview predicted binding sites in comparison to a wide array of genomic tracks, such as single nucleotide polymorphisms. The Web server is publicly accessible in www.microrna.gr/microT-v4.
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paper_title: DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows

paper_content:

MicroRNAs (miRNAs) are small endogenous RNA molecules that regulate gene expression through mRNA degradation and/or translation repression, affecting many biological processes. DIANA-microT web server (http://www.microrna.gr/webServer) is dedicated to miRNA target prediction/functional analysis, and it is being widely used from the scientific community, since its initial launch in 2009. DIANA-microT v5.0, the new version of the microT server, has been significantly enhanced with an improved target prediction algorithm, DIANAmicroT-CDS. It has been updated to incorporate miRBase version 18 and Ensembl version 69. The in silico-predicted miRNA–gene interactions in Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans exceed 11 million in total. The web server was completely redesigned, to host a series of sophisticated workflows, which can be used directly from the on-line web interface, enabling users without the necessary bioinformatics infrastructure to perform advanced multi-step functional miRNA analyses. For instance, one available pipeline performs miRNA target prediction using different thresholds and meta-analysis statistics, followed by pathway enrichment analysis. DIANAmicroT web server v5.0 also supports a complete integration with the Taverna Workflow Management System (WMS), using the in-house developed DIANA-Taverna Plug-in. This plug-in provides ready-to-use modules for miRNA target prediction and functional analysis, which can be used to form advanced high-throughput analysis pipelines.
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paper_title: Animal MicroRNAs Confer Robustness to Gene Expression and Have a Significant Impact on 3′UTR Evolution

paper_content:

MicroRNAs are small noncoding RNAs that serve as posttranscriptional regulators of gene expression in higher eukaryotes. Their widespread and important role in animals is highlighted by recent estimates that 20%-30% of all genes are microRNA targets. Here, we report that a large set of genes involved in basic cellular processes avoid microRNA regulation due to short 3'UTRs that are specifically depleted of microRNA binding sites. For individual microRNAs, we find that coexpressed genes avoid microRNA sites, whereas target genes and microRNAs are preferentially expressed in neighboring tissues. This mutually exclusive expression argues that microRNAs confer accuracy to developmental gene-expression programs, thus ensuring tissue identity and supporting cell-lineage decisions.
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paper_title: Vienna RNA secondary structure server

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The Vienna RNA secondary structure server provides a web interface to the most frequently used functions of the Vienna RNA software package for the analysis of RNA secondary structures. It currently offers prediction of secondary structure from a single sequence, prediction of the consensus secondary structure for a set of aligned sequences and the design of sequences that will fold into a predefined structure. All three services can be accessed via the Vienna RNA web server at http://rna.tbi.univie.ac.at/.
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paper_title: The role of site accessibility in microRNA target recognition

paper_content:

MicroRNAs are key regulators of gene expression, but the precise mechanisms underlying their interaction with their mRNA targets are still poorly understood. Here, we systematically investigate the role of target-site accessibility, as determined by base-pairing interactions within the mRNA, in microRNA target recognition. We experimentally show that mutations diminishing target accessibility substantially reduce microRNA-mediated translational repression, with effects comparable to those of mutations that disrupt sequence complementarity. We devise a parameter-free model for microRNA-target interaction that computes the difference between the free energy gained from the formation of the microRNA-target duplex and the energetic cost of unpairing the target to make it accessible to the microRNA. This model explains the variability in our experiments, predicts validated targets more accurately than existing algorithms, and shows that genomes accommodate site accessibility by preferentially positioning targets in highly accessible regions. Our study thus demonstrates that target accessibility is a critical factor in microRNA function.
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paper_title: Potent effect of target structure on microRNA function

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MicroRNAs (miRNAs) are small noncoding RNAs that repress protein synthesis by binding to target messenger RNAs. We investigated the effect of target secondary structure on the efficacy of repression by miRNAs. Using structures predicted by the Sfold program, we model the interaction between an miRNA and a target as a two-step hybridization reaction: nucleation at an accessible target site followed by hybrid elongation to disrupt local target secondary structure and form the complete miRNA-target duplex. This model accurately accounts for the sensitivity to repression by let-7 of various mutant forms of the Caenorhabditis elegans lin-41 3¢ untranslated region and for other experimentally tested miRNA-target interactions in C. elegans and Drosophila melanogaster. These findings indicate a potent effect of target structure on target recognition by miRNAs and establish a structure-based framework for genome-wide identification of animal miRNA targets.
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paper_title: Predicting and validating microRNA targets

paper_content:

Given that microRNAs select their targets by nucleotide base-pairing, it follows that it should be possible to find microRNA targets computationally. There has been considerable progress, but assessing success and biological significance requires a move into the 'wet' lab.
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paper_title: HuMiTar: A sequence-based method for prediction of human microRNA targets

paper_content:

BackgroundMicroRNAs (miRs) are small noncoding RNAs that bind to complementary/partially complementary sites in the 3' untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate, high-throughput identification of physiologically active miR targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the miR's seed region and implementation of cross-species conservation of the target site, and machine learning (ML) methods that explore patterns that contrast true and false miR-mRNA duplexes. However, in the case of the traditional methods research shows that some seed region matches that are conserved are false positives and that some of the experimentally validated target sites are not conserved.ResultsWe present HuMiTar, a computational method for identifying common targets of miRs, which is based on a scoring function that considers base-pairing for both seed and non-seed positions for human miR-mRNA duplexes. Our design shows that certain non-seed miR nucleotides, such as 14, 18, 13, 11, and 17, are characterized by a strong bias towards formation of Watson-Crick pairing. We contrasted HuMiTar with several representative competing methods on two sets of human miR targets and a set of ten glioblastoma oncogenes. Comparison with the two best performing traditional methods, PicTar and TargetScanS, and a representative ML method that considers the non-seed positions, NBmiRTar, shows that HuMiTar predictions include majority of the predictions of the other three methods. At the same time, the proposed method is also capable of finding more true positive targets as a trade-off for an increased number of predictions. Genome-wide predictions show that the proposed method is characterized by 1.99 signal-to-noise ratio and linear, with respect to the length of the mRNA sequence, computational complexity. The ROC analysis shows that HuMiTar obtains results comparable with PicTar, which are characterized by high true positive rates that are coupled with moderate values of false positive rates.ConclusionThe proposed HuMiTar method constitutes a step towards providing an efficient model for studying translational gene regulation by miRs.
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paper_title: Prediction of Plant MicroRNA Targets

paper_content:

We predict regulatory targets for 14 Arabidopsis microRNAs (miRNAs) by identifying mRNAs with near complementarity. Complementary sites within predicted targets are conserved in rice. Of the 49 predicted targets, 34 are members of transcription factor gene families involved in developmental patterning or cell differentiation. The near-perfect complementarity between plant miRNAs and their targets suggests that many plant miRNAs act similarly to small interfering RNAs and direct mRNA cleavage. The targeting of developmental transcription factors suggests that many plant miRNAs function during cellular differentiation to clear key regulatory transcripts from daughter cell lineages.
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paper_title: microRNA Target Predictions across Seven Drosophila Species and Comparison to Mammalian Targets

paper_content:

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.
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paper_title: Mfold web server for nucleic acid folding and hybridization prediction

paper_content:

The abbreviated name,‘mfold web server’,describes a number of closely related software applications available on the World Wide Web (WWW) for the prediction of the secondary structure of single stranded nucleic acids. The objective of this web server is to provide easy access to RNA and DNA folding and hybridization software to the scientific community at large. By making use of universally available web GUIs (Graphical User Interfaces),the server circumvents the problem of portability of this software. Detailed output,in the form of structure plots with or without reliability information,single strand frequency plots and ‘energy dot plots’, are available for the folding of single sequences. A variety of ‘bulk’ servers give less information,but in a shorter time and for up to hundreds of sequences at once. The portal for the mfold web server is http://www.bioinfo.rpi.edu/applications/ mfold. This URL will be referred to as ‘MFOLDROOT’.
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paper_title: MicroRNA Targets - How to predict?

paper_content:

A number of web tools are available for the prediction and identification of target microRNAs (miRNAs). The choice, availability, validity and selection of an optimal yet appropriate tool are a challenge for the design of high throughput assays with promising miRNA targets. The current trends and challenges for target microRNAs (miRNAs) prediction, identification and selection is described in this review.
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paper_title: Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms

paper_content:

We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs' 5' end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3' UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method's specificity. The initial seeding step determines an algorithm's sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost's weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps. (TargetBoost is available as a Web tool from http://www.interagon.com/demo/.).
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paper_title: MultiMiTar: A Novel Multi Objective Optimization based miRNA-Target Prediction Method

paper_content:

Background ::: Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. ::: Methodology/Principal Finding ::: In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. ::: Conclusions/Significance ::: MultiMiTar is found to achieve much higher Matthew’s correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from −0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.
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paper_title: The majority of endogenous microRNA targets within Alu elements avoid the microRNA machinery

paper_content:

Motivation: The massive spread of repetitive elements in the human genome presents a substantial challenge to the organism, as such elements may accidentally contain seemingly functional motifs. A striking example is offered by the roughly one million copies of Alu repeats in the genome, of which ~0.5% reside within genes’ untranslated regions (UTRs), presenting ~30 000 novel potential targets for highly conserved microRNAs (miRNAs). Here, we examine the functionality of miRNA targets within Alu elements in 3′UTRs in the human genome. ::: ::: Results: Using a comprehensive dataset of miRNA overexpression assays, we show that mRNAs with miRNA targets within Alus are significantly less responsive to the miRNA effects compared with mRNAs that have the same targets outside Alus. Using Ago2-binding mRNA profiling, we confirm that the miRNA machinery avoids miRNA targets within Alus, as opposed to the highly efficient binding of targets outside Alus. We propose three features that prevent potential miRNA sites within Alus from being recognized by the miRNA machinery: (i) Alu repeats that contain miRNA targets and genuine functional miRNA targets appear to reside in distinct mutually exclusive territories within 3′UTRs; (ii) Alus have tight secondary structure that may limit access to the miRNA machinery; and (iii) A-to-I editing of Alu-derived mRNA sequences may divert miRNA targets. The combination of these features is proposed to allow toleration of Alu insertions into mRNAs. Nonetheless, a subset of miRNA targets within Alus appears not to possess any of the aforementioned features, and thus may represent cases where Alu insertion in the genome has introduced novel functional miRNA targets. ::: ::: Contact: moshe.oren@weizmann.ac.il or Pilpel@weizmann.ac.il ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.
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paper_title: Accurate microRNA target prediction correlates with protein repression levels

paper_content:

BackgroundMicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease.ResultsDIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction.ConclusionRecently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT
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paper_title: Target gene expression levels and competition between transfected and endogenous microRNAs are strong confounding factors in microRNA high-throughput experiments

paper_content:

BackgroundMicroRNA (miRNA) target genes tend to have relatively long and conserved 3' untranslated regions (UTRs), but to what degree these characteristics contribute to miRNA targeting is poorly understood. Different high-throughput experiments have, for example, shown that miRNAs preferentially regulate genes with both short and long 3' UTRs and that target site conservation is both important and irrelevant for miRNA targeting.ResultsWe have analyzed several gene context-dependent features, including 3' UTR length, 3' UTR conservation, and messenger RNA (mRNA) expression levels, reported to have conflicting influence on miRNA regulation. By taking into account confounding factors such as technology-dependent experimental bias and competition between transfected and endogenous miRNAs, we show that two factors - target gene expression and competition - could explain most of the previously reported experimental differences. Moreover, we find that these and other target site-independent features explain about the same amount of variation in target gene expression as the target site-dependent features included in the TargetScan model.ConclusionsOur results show that it is important to consider confounding factors when interpreting miRNA high throughput experiments and urge special caution when using microarray data to compare average regulatory effects between groups of genes that have different average gene expression levels.
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paper_title: Sfold web server for statistical folding and rational design of nucleic acids

paper_content:

TheSfoldwebserverprovidesuser-friendlyaccessto Sfold, a recently developed nucleic acid folding software package, via the World Wide Web (WWW). The software is based on a new statistical sampling paradigm for the prediction of RNA secondary structure. One of the main objectives of this software is to offer computational tools for the rational design of RNAtargetingnucleicacids,whichincludesmallinterfering RNAs (siRNAs), antisense oligonucleotides and trans-cleaving ribozymes for gene knock-down studies. The methodology for siRNA design is based on a combination of RNA target accessibility prediction, siRNA duplex thermodynamic properties and empirical design rules. Our approach to target accessibility evaluation is an original extension of the underlying RNA folding algorithm to account for the likely existence of a population of structures for the target mRNA. In addition to the application modules Sirna, Soligo and Sribo for siRNAs, antisense oligos and ribozymes, respectively, the moduleSrna offers comprehensive features for statistical representation of sampledstructures.Detailedoutputin both graphical andtextformatsisavailableforallmodules.TheSfold server is available at http://sfold.wadsworth.org and http://www.bioinfo.rpi.edu/applications/sfold.
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paper_title: Structural Protein Descriptors in 1-Dimension and their Sequence-Based Predictions

paper_content:

The last few decades observed an increasing interest in development and application of 1-dimensional (1D) descriptors of protein structure. These descriptors project 3D structural features onto 1D strings of residue-wise structural assignments. They cover a wide-range of structural aspects including conformation of the backbone, burying depth/solvent exposure and flexibility of residues, and inter-chain residue-residue contacts. We perform first-of-its-kind comprehensive comparative review of the existing 1D structural descriptors. We define, review and categorize ten structural descriptors and we also describe, summarize and contrast over eighty computational models that are used to predict these descriptors from the protein sequences. We show that the majority of the recent sequence-based predictors utilize machine learning models, with the most popular being neural networks, support vector machines, hidden Markov models, and support vector and linear regressions. These methods provide high-throughput predictions and most of them are accessible to a non-expert user via web servers and/or stand-alone software packages. We empirically evaluate several recent sequence-based predictors of secondary structure, disorder, and solvent accessibility descriptors using a benchmark set based on CASP8 targets. Our analysis shows that the secondary structure can be predicted with over 80% accuracy and segment overlap (SOV), disorder with over 0.9 AUC, 0.6 Matthews Correlation Coefficient (MCC), and 75% SOV, and relative solvent accessibility with PCC of 0.7 and MCC of 0.6 (0.86 when homology is used). We demonstrate that the secondary structure predicted from sequence without the use of homology modeling is as good as the structure extracted from the 3D folds predicted by top-performing template-based methods.
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paper_title: Identifying Mammalian MicroRNA Targets Based on Supervised Distance Metric Learning

paper_content:

MicroRNAs (miRNAs) have been emerged as a novel class of endogenous posttranscriptional regulators in a variety of animal and plant species. One challenge facing miRNA research is to accurately identify the target mRNAs, because of the very limited sequence complementarity between miRNAs and their target sites, and the scarcity of experimentally validated targets to guide accurate prediction. In this paper, we propose a new method called SuperMirTar that exploits super vised distance learning to predict miRNA targets. Specifically, we use the experimentally supported miRNA-mRNA pairs as a training set to learn a distance metric function that minimizes the distances between miRNAs and mRNAs with validated interactions, then use the learned function to calculate the distances of test miRNA-mRNA interactions, and those with smaller distances than a predefined threshold are regarded as true interactions. We carry out performance comparison between the proposed approach and seven existing methods on independent datasets; the results show that our method achieves superior performance and can effectively narrow the gap between the number of predicted miRNA targets and the number of experimentally validated ones.
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paper_title: AU-Rich-Element-Mediated Upregulation of Translation by FXR1 and Argonaute 2

paper_content:

Summary A U- r ich e lements (AREs), present in mRNA 3′-UTRs, are potent posttranscriptional regulatory signals that can rapidly effect changes in mRNA stability and translation, thereby dramatically altering gene expression with clinical and developmental consequences. In human cell lines, the TNFα ARE enhances translation relative to mRNA levels upon serum starvation, which induces cell-cycle arrest. An in vivo crosslinking-coupled affinity purification method was developed to isolate ARE-associated complexes from activated versus basal translation conditions. We surprisingly found two microRNP-related proteins, f ragile- X -mental-retardation- r elated protein 1 (FXR1) and A r go naute 2 (AGO2), that associate with the ARE exclusively during translation activation. Through tethering and shRNA-knockdown experiments, we provide direct evidence for the translation activation function of both FXR1 and AGO2 and demonstrate their interdependence for upregulation. This novel cell-growth-dependent translation activation role for FXR1 and AGO2 allows new insights into ARE-mediated signaling and connects two important posttranscriptional regulatory systems in an unexpected way.
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paper_title: Fast and effective prediction of microRNA/target duplexes

paper_content:

MicroRNAs (miRNAs) are short RNAs that post-transcriptionally regulate the expression of target genes by binding to the target mRNAs. Although a large number of animal miRNAs has been defined, only a few targets are known. In contrast to plant miRNAs, which usually bind nearly perfectly to their targets, animal miRNAs bind less tightly, with a few nucleotides being unbound, thus producing more complex secondary structures of miRNA/target duplexes. Here, we present a program, RNA-hybrid, that predicts multiple potential binding sites of miRNAs in large target RNAs. In general, the program finds the energetically most favorable hybridization sites of a small RNA in a large RNA. Intramolecular hybridizations, that is, base pairings between target nucleotides or between miRNA nucleotides are not allowed. For large targets, the time complexity of the algorithm is linear in the target length, allowing many long targets to be searched in a short time. Statistical significance of predicted targets is assessed with an extreme value statistics of length normalized minimum free energies, a Poisson approximation of multiple binding sites, and the calculation of effective numbers of orthologous targets in comparative studies of multiple organisms. We applied our method to the prediction of Drosophila miRNA targets in 3'UTRs and coding sequence. RNAhybrid, with its accompanying programs RNAcalibrate and RNAeffective, is available for download and as a Web tool on the Bielefeld Bioinformatics Server (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/).
---
paper_title: Efficient use of accessibility in microRNA target prediction

paper_content:

Considering accessibility of the 3′UTR is believed to increase the precision of microRNA target predictions. We show that, contrary to common belief, ranking by the hybridization energy or by the sum of the opening and hybridization energies, used in currently available algorithms, is not an efficient way to rank predictions. Instead, we describe an algorithm which also considers only the accessible binding sites but which ranks predictions according to over-representation. When compared with experimentally validated and refuted targets in the fruit fly and human, our algorithm shows a remarkable improvement in precision while significantly reducing the computational cost in comparison with other free energy based methods. In the human genome, our algorithm has at least twice higher precision than other methods with their default parameters. In the fruit fly, we find five times more validated targets among the top 500 predictions than other methods with their default parameters. Furthermore, using a common statistical framework we demonstrate explicitly the advantages of using the canonical ensemble instead of using the minimum free energy structure alone. We also find that ‘naive’ global folding sometimes outperforms the local folding approach.
---
paper_title: miRcode: a map of putative microRNA target sites in the long non-coding transcriptome

paper_content:

Summary: Although small non-coding RNAs, such as microRNAs, have well-established functions in the cell, long non-coding RNAs (lncRNAs) have only recently started to emerge as abundant regulators of cell physiology, and their functions may be diverse. A small number of studies describe interactions between small and lncRNAs, with lncRNAs acting either as inhibitory decoys or as regulatory targets of microRNAs, but such interactions are still poorly explored. To facilitate the study of microRNA–lncRNA interactions, we implemented miRcode: a comprehensive searchable map of putative microRNA target sites across the complete GENCODE annotated transcriptome, including 10 419 lncRNA genes in the current version. ::: ::: Availability: http://www.mircode.org ::: ::: Contact: es.ug@nossral.kire ::: ::: Supplementary Information: Supplementary data are available at Bioinformatics online.
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paper_title: doRiNA: a database of RNA interactions in post-transcriptional regulation

paper_content:

In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a posttranscriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code. Within doRiNA, we are systematically curating, storing and integrating binding site data for RBPs and miRNAs. Users are free to take a target (mRNA) or regulator (RBP and/or miRNA) centric view on the data. We have implemented a database framework with short query response times for complex searches (e.g. asking for all targets of a particular combination of regulators). All search results can be browsed, inspected and analyzed in conjunction with a huge selection of other genome-wide data, because our database is directly linked to a local copy of the UCSC genome browser. At the time of writing, doRiNA encompasses RBP data for the human, mouse and worm genomes. For computational miRNA target site predictions, we provide an update of PicTar predictions.
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paper_title: Improving performance of mammalian microRNA target prediction

paper_content:

BackgroundMicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing. Constructing a richer and more reliable training data set and developing an algorithm that properly exploits this data set would be the key to improve the performance current prediction algorithms.ResultsA comprehensive training data set is constructed for mammalian miRNAs with its positive targets obtained from the most up-to-date miRNA target depository called miRecords and its negative targets derived from 20 microarray data. A new algorithm SVMicrO is developed, which assumes a 2-stage structure including a site support vector machine (SVM) followed by a UTR-SVM. SVMicrO makes prediction based on 21 optimal site features and 18 optimal UTR features, selected by training from a comprehensive collection of 113 site and 30 UTR features. Comprehensive evaluation of SVMicrO performance has been carried out on the training data, proteomics data, and immunoprecipitation (IP) pull-down data. Comparisons with some popular algorithms demonstrate consistent improvements in prediction specificity, sensitivity and precision in all tested cases. All the related materials including source code and genome-wide prediction of human targets are available at http://compgenomics.utsa.edu/svmicro.html.ConclusionsA 2-stage SVM based new miRNA target prediction algorithm called SVMicrO is developed. SVMicrO is shown to be able to achieve robust performance. It holds the promise to achieve continuing improvement whenever better training data that contain additional verified or high confidence positive targets and properly selected negative targets are available.
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paper_title: A Pattern-Based Method for the Identification of MicroRNA Binding Sites and Their Corresponding Heteroduplexes

paper_content:

Summary We present rna22 , a method for identifying microRNA binding sites and their corresponding heteroduplexes. Rna22 does not rely upon cross-species conservation, is resilient to noise, and, unlike previous methods, it first finds putative microRNA binding sites in the sequence of interest, then identifies the targeting microRNA. Computationally, we show that rna22 identifies most of the currently known heteroduplexes. Experimentally, with luciferase assays, we demonstrate average repressions of 30% or more for 168 of 226 tested targets. The analysis suggests that some microRNAs may have as many as a few thousand targets, and that between 74% and 92% of the gene transcripts in four model genomes are likely under microRNA control through their untranslated and amino acid coding regions. We also extended the method's key idea to a low-error microRNA-precursor-discovery scheme; our studies suggest that the number of microRNA precursors in mammalian genomes likely ranges in the tens of thousands.
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paper_title: MicroTar: predicting microRNA targets from RNA duplexes

paper_content:

BackgroundThe accurate prediction of a comprehensive set of messenger RNAs (targets) regulated by animal microRNAs (miRNAs) remains an open problem. In particular, the prediction of targets that do not possess evolutionarily conserved complementarity to their miRNA regulators is not adequately addressed by current tools.ResultsWe have developed MicroTar, an animal miRNA target prediction tool based on miRNA-target complementarity and thermodynamic data. The algorithm uses predicted free energies of unbound mRNA and putative mRNA-miRNA heterodimers, implicitly addressing the accessibility of the mRNA 3' untranslated region. MicroTar does not rely on evolutionary conservation to discern functional targets, and is able to predict both conserved and non-conserved targets. MicroTar source code and predictions are accessible at http://tiger.dbs.nus.edu.sg/microtar/, where both serial and parallel versions of the program can be downloaded under an open-source licence.ConclusionMicroTar achieves better sensitivity than previously reported predictions when tested on three distinct datasets of experimentally-verified miRNA-target interactions in C. elegans, Drosophila, and mouse.
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paper_title: Analysis of CDS-located miRNA target sites suggests that they can effectively inhibit translation

paper_content:

Most of what is presently known about how miRNAs regulate gene expression comes from studies that characterized the regulatory effect of miRNA binding sites located in the 3' untranslated regions (UTR) of mRNAs. In recent years, there has been increasing evidence that miRNAs also bind in the coding region (CDS), but the implication of these interactions remains obscure because they have a smaller impact on mRNA stability compared with miRNA-target interactions that involve 3' UTRs. Here we show that miRNA-complementary sites that are located in both CDS and 3'-UTRs are under selection pressure and share the same sequence and structure properties. Analyzing recently published data of ribosome-protected fragment profiles upon miRNA transfection from the perspective of the location of miRNA-complementary sites, we find that sites located in the CDS are most potent in inhibiting translation, while sites located in the 3' UTR are more efficient at triggering mRNA degradation. Our study suggests that miRNAs may combine targeting of CDS and 3' UTR to flexibly tune the time scale and magnitude of their post-transcriptional regulatory effects.
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paper_title: DINAMelt web server for nucleic acid melting prediction

paper_content:

The DINAMelt web server simulates the melting of one or two single-stranded nucleic acids in solution. The goal is to predict not just a melting temperature for a hybridized pair of nucleic acids, but entire equilibrium melting profiles as a function of temperature. The two molecules are not required to be complementary, nor must the two strand concentrations be equal. Competition among different molecular species is automatically taken into account. Calculations consider not only the heterodimer, but also the two possible homodimers, as well as the folding of each single-stranded molecule. For each of these five molecular species, free energies are computed by summing Boltzmann factors over every possible hybridized or folded state. For temperatures within a user-specified range, calculations predict species mole fractions together with the free energy, enthalpy, entropy and heat capacity of the ensemble. Ultraviolet (UV) absorbance at 260 nm is simulated using published extinction coefficients and computed base pair probabilities. All results are available as text files and plots are provided for species concentrations, heat capacity and UV absorbance versus temperature. This server is connected to an active research program and should evolve as new theory and software are developed. The server URL is http://www.bioinfo. rpi.edu/applications/hybrid/.
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paper_title: Pattern recognition in bioinformatics

paper_content:

Pattern recognition is concerned with the development of systems that learn to solve a given problem using a set of example instances, each represented by a number of features. These problems include clustering, the grouping of similar instances; classification, the task of assigning a discrete label to a given instance; and dimensionality reduction, combining or selecting features to arrive at a more useful representation. The use of statistical pattern recognition algorithms in bioinformatics is pervasive. Classification and clustering are often applied to high-throughput measurement data arising from microarray, mass spectrometry and next-generation sequencing experiments for selecting markers, predicting phenotype and grouping objects or genes. Less explicitly, classification is at the core of a wide range of tools such as predictors of genes, protein function, functional or genetic interactions, etc., and used extensively in systems biology. A course on pattern recognition (or machine learning) should therefore be at the core of any bioinformatics education program. In this review, we discuss the main elements of a pattern recognition course, based on material developed for courses taught at the BSc, MSc and PhD levels to an audience of bioinformaticians, computer scientists and life scientists. We pay attention to common problems and pitfalls encountered in applications and in interpretation of the results obtained.
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paper_title: Mammalian microRNAs predominantly act to decrease target mRNA levels

paper_content:

Summary MicroRNAs (miRNAs) are endogenous ∼22-nucleotide RNAs that play important gene-regulatory roles by pairing to the mRNAs of protein-coding genes to direct their repression. Repression of these regulatory targets leads to decreased translational efficiency and/or decreased mRNA levels, but the relative contributions of these two outcomes have been largely unknown, particularly for endogenous targets expressed at low-to-moderate levels. Here, we use ribosome profiling to measure the overall effects on protein production and compare these to simultaneously measured effects on mRNA levels. For both ectopic and endogenous miRNA regulatory interactions, lowered mRNA levels account for most (≥84%) of the decreased protein production. These results show that changes in mRNA levels closely reflect the impact of miRNAs on gene expression and indicate that destabilization of target mRNAs is the predominant reason for reduced protein output.
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paper_title: Identification of Drosophila MicroRNA Targets

paper_content:

MicroRNAs (miRNAs) are short RNA molecules that regulate gene expression by binding to target messenger RNAs and by controlling protein production or causing RNA cleavage. To date, functions have been assigned to only a few of the hundreds of identified miRNAs, in part because of the difficulty in identifying their targets. The short length of miRNAs and the fact that their complementarity to target sequences is imperfect mean that target identification in animal genomes is not possible by standard sequence comparison methods. Here we screen conserved 3′ UTR sequences from the Drosophila melanogaster genome for potential miRNA targets. The screening procedure combines a sequence search with an evaluation of the predicted miRNA–target heteroduplex structures and energies. We show that this approach successfully identifies the five previously validated let-7, lin-4, and bantam targets from a large database and predict new targets for Drosophila miRNAs. Our target predictions reveal striking clusters of functionally related targets among the top predictions for specific miRNAs. These include Notch target genes for miR-7, proapoptotic genes for the miR-2 family, and enzymes from a metabolic pathway for miR-277. We experimentally verified three predicted targets each for miR-7 and the miR-2 family, doubling the number of validated targets for animal miRNAs. Statistical analysis indicates that the best single predicted target sites are at the border of significance; thus, target predictions should be considered as tentative until experimentally validated. We identify features shared by all validated targets that can be used to evaluate target predictions for animal miRNAs. Our initial evaluation and experimental validation of target predictions suggest functions for two miRNAs. For others, the screen suggests plausible functions, such as a role for miR-277 as a metabolic switch controlling amino acid catabolism. Cross-genome comparison proved essential, as it allows reduction of the sequence search space. Improvements in genome annotation and increased availability of cDNA sequences from other genomes will allow more sensitive screens. An increase in the number of confirmed targets is expected to reveal general structural features that can be used to improve their detection. While the screen is likely to miss some targets, our study shows that valid targets can be identified from sequence alone.
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paper_title: HuMiTar: A sequence-based method for prediction of human microRNA targets

paper_content:

BackgroundMicroRNAs (miRs) are small noncoding RNAs that bind to complementary/partially complementary sites in the 3' untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate, high-throughput identification of physiologically active miR targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the miR's seed region and implementation of cross-species conservation of the target site, and machine learning (ML) methods that explore patterns that contrast true and false miR-mRNA duplexes. However, in the case of the traditional methods research shows that some seed region matches that are conserved are false positives and that some of the experimentally validated target sites are not conserved.ResultsWe present HuMiTar, a computational method for identifying common targets of miRs, which is based on a scoring function that considers base-pairing for both seed and non-seed positions for human miR-mRNA duplexes. Our design shows that certain non-seed miR nucleotides, such as 14, 18, 13, 11, and 17, are characterized by a strong bias towards formation of Watson-Crick pairing. We contrasted HuMiTar with several representative competing methods on two sets of human miR targets and a set of ten glioblastoma oncogenes. Comparison with the two best performing traditional methods, PicTar and TargetScanS, and a representative ML method that considers the non-seed positions, NBmiRTar, shows that HuMiTar predictions include majority of the predictions of the other three methods. At the same time, the proposed method is also capable of finding more true positive targets as a trade-off for an increased number of predictions. Genome-wide predictions show that the proposed method is characterized by 1.99 signal-to-noise ratio and linear, with respect to the length of the mRNA sequence, computational complexity. The ROC analysis shows that HuMiTar obtains results comparable with PicTar, which are characterized by high true positive rates that are coupled with moderate values of false positive rates.ConclusionThe proposed HuMiTar method constitutes a step towards providing an efficient model for studying translational gene regulation by miRs.
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paper_title: A combined computational-experimental approach predicts human microRNA targets.

paper_content:

A new paradigm of gene expression regulation has emerged recently with the discovery of microRNAs (miRNAs). Most, if not all, miRNAs are thought to control gene expression, mostly by base pairing with miRNA-recognition elements (MREs) found in their messenger RNA (mRNA) targets. Although a large number of human miRNAs have been reported, many of their mRNA targets remain unknown. Here we used a combined bioinformatics and experimental approach to identify important rules governing miRNA-MRE recognition that allow prediction of human miRNA targets. We describe a computational program, "DIANA-microT", that identifies mRNA targets for animal miRNAs and predicts mRNA targets, bearing single MREs, for human and mouse miRNAs.
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paper_title: microRNA Target Predictions across Seven Drosophila Species and Comparison to Mammalian Targets

paper_content:

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.
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paper_title: TargetSpy: a supervised machine learning approach for microRNA target prediction

paper_content:

BackgroundVirtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites.ResultsWe developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences.In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms.ConclusionOnly a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org.
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paper_title: Fast and effective prediction of microRNA/target duplexes

paper_content:

MicroRNAs (miRNAs) are short RNAs that post-transcriptionally regulate the expression of target genes by binding to the target mRNAs. Although a large number of animal miRNAs has been defined, only a few targets are known. In contrast to plant miRNAs, which usually bind nearly perfectly to their targets, animal miRNAs bind less tightly, with a few nucleotides being unbound, thus producing more complex secondary structures of miRNA/target duplexes. Here, we present a program, RNA-hybrid, that predicts multiple potential binding sites of miRNAs in large target RNAs. In general, the program finds the energetically most favorable hybridization sites of a small RNA in a large RNA. Intramolecular hybridizations, that is, base pairings between target nucleotides or between miRNA nucleotides are not allowed. For large targets, the time complexity of the algorithm is linear in the target length, allowing many long targets to be searched in a short time. Statistical significance of predicted targets is assessed with an extreme value statistics of length normalized minimum free energies, a Poisson approximation of multiple binding sites, and the calculation of effective numbers of orthologous targets in comparative studies of multiple organisms. We applied our method to the prediction of Drosophila miRNA targets in 3'UTRs and coding sequence. RNAhybrid, with its accompanying programs RNAcalibrate and RNAeffective, is available for download and as a Web tool on the Bielefeld Bioinformatics Server (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/).
---
paper_title: Efficient use of accessibility in microRNA target prediction

paper_content:

Considering accessibility of the 3′UTR is believed to increase the precision of microRNA target predictions. We show that, contrary to common belief, ranking by the hybridization energy or by the sum of the opening and hybridization energies, used in currently available algorithms, is not an efficient way to rank predictions. Instead, we describe an algorithm which also considers only the accessible binding sites but which ranks predictions according to over-representation. When compared with experimentally validated and refuted targets in the fruit fly and human, our algorithm shows a remarkable improvement in precision while significantly reducing the computational cost in comparison with other free energy based methods. In the human genome, our algorithm has at least twice higher precision than other methods with their default parameters. In the fruit fly, we find five times more validated targets among the top 500 predictions than other methods with their default parameters. Furthermore, using a common statistical framework we demonstrate explicitly the advantages of using the canonical ensemble instead of using the minimum free energy structure alone. We also find that ‘naive’ global folding sometimes outperforms the local folding approach.
---
paper_title: MTar: a computational microRNA target prediction architecture for human transcriptome

paper_content:

BackgroundMicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.ResultsWe present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone.ConclusionMTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.
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paper_title: Improving performance of mammalian microRNA target prediction

paper_content:

BackgroundMicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing. Constructing a richer and more reliable training data set and developing an algorithm that properly exploits this data set would be the key to improve the performance current prediction algorithms.ResultsA comprehensive training data set is constructed for mammalian miRNAs with its positive targets obtained from the most up-to-date miRNA target depository called miRecords and its negative targets derived from 20 microarray data. A new algorithm SVMicrO is developed, which assumes a 2-stage structure including a site support vector machine (SVM) followed by a UTR-SVM. SVMicrO makes prediction based on 21 optimal site features and 18 optimal UTR features, selected by training from a comprehensive collection of 113 site and 30 UTR features. Comprehensive evaluation of SVMicrO performance has been carried out on the training data, proteomics data, and immunoprecipitation (IP) pull-down data. Comparisons with some popular algorithms demonstrate consistent improvements in prediction specificity, sensitivity and precision in all tested cases. All the related materials including source code and genome-wide prediction of human targets are available at http://compgenomics.utsa.edu/svmicro.html.ConclusionsA 2-stage SVM based new miRNA target prediction algorithm called SVMicrO is developed. SVMicrO is shown to be able to achieve robust performance. It holds the promise to achieve continuing improvement whenever better training data that contain additional verified or high confidence positive targets and properly selected negative targets are available.
---
paper_title: HuMiTar: A sequence-based method for prediction of human microRNA targets

paper_content:

BackgroundMicroRNAs (miRs) are small noncoding RNAs that bind to complementary/partially complementary sites in the 3' untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate, high-throughput identification of physiologically active miR targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the miR's seed region and implementation of cross-species conservation of the target site, and machine learning (ML) methods that explore patterns that contrast true and false miR-mRNA duplexes. However, in the case of the traditional methods research shows that some seed region matches that are conserved are false positives and that some of the experimentally validated target sites are not conserved.ResultsWe present HuMiTar, a computational method for identifying common targets of miRs, which is based on a scoring function that considers base-pairing for both seed and non-seed positions for human miR-mRNA duplexes. Our design shows that certain non-seed miR nucleotides, such as 14, 18, 13, 11, and 17, are characterized by a strong bias towards formation of Watson-Crick pairing. We contrasted HuMiTar with several representative competing methods on two sets of human miR targets and a set of ten glioblastoma oncogenes. Comparison with the two best performing traditional methods, PicTar and TargetScanS, and a representative ML method that considers the non-seed positions, NBmiRTar, shows that HuMiTar predictions include majority of the predictions of the other three methods. At the same time, the proposed method is also capable of finding more true positive targets as a trade-off for an increased number of predictions. Genome-wide predictions show that the proposed method is characterized by 1.99 signal-to-noise ratio and linear, with respect to the length of the mRNA sequence, computational complexity. The ROC analysis shows that HuMiTar obtains results comparable with PicTar, which are characterized by high true positive rates that are coupled with moderate values of false positive rates.ConclusionThe proposed HuMiTar method constitutes a step towards providing an efficient model for studying translational gene regulation by miRs.
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paper_title: miTarget: microRNA target gene prediction using a support vector machine

paper_content:

BackgroundMicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes.ResultsWe implemented miTarget, a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features. The latter features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set obtained from the literature, compared with previous tools. We predicted significant functions for human miR-1, miR-124a, and miR-373 using Gene Ontology (GO) analysis and revealed the importance of pairing at positions 4, 5, and 6 in the 5' region of a miRNA from a feature selection experiment. We also provide a web interface for the program.ConclusionmiTarget is a reliable miRNA target gene prediction tool and is a successful application of an SVM classifier. Compared with previous tools, its predictions are meaningful by GO analysis and its performance can be improved given more training examples.
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paper_title: MultiMiTar: A Novel Multi Objective Optimization based miRNA-Target Prediction Method

paper_content:

Background ::: Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. ::: Methodology/Principal Finding ::: In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. ::: Conclusions/Significance ::: MultiMiTar is found to achieve much higher Matthew’s correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from −0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.
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paper_title: NCBI GEO: archive for functional genomics data sets—update

paper_content:

The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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paper_title: TarBase: A comprehensive database of experimentally supported animal microRNA targets

paper_content:

MicroRNAs (miRNAs) are ~22-nt RNA segments that are involved in the regulation of protein expression primarily by binding to one or more target sites on an mRNA transcript and inhibiting translation. MicroRNAs are likely to factor into multiple developmental pathways, multiple mechanisms of gene regulation, and underlie an array of inherited disease processes and phenotypic determinants. Several computational programs exist to predict miRNA targets in mammals, fruit flies, worms, and plants. However, to date, there is no systematic collection and description of miRNA targets with experimental support. We describe a database, TarBase, which houses a manually curated collection of experimentally tested miRNA targets, in human/mouse, fruit fly, worm, and zebrafish, distinguishing between those that tested positive and those that tested negative. Each positive target site is described by the miRNA that binds it, the gene in which it occurs, the nature of the experiments that were conducted to test it, the sufficiency of the site to induce translational repression and/or cleavage, and the paper from which all these data were extracted. Additionally, the database is functionally linked to several other useful databases such as Gene Ontology (GO) and UCSC Genome Browser. TarBase reveals significantly more experimentally supported targets than even recent reviews claim, thereby providing a comprehensive data set from which to assess features of miRNA targeting that will be useful for the next generation of target prediction programs. TarBase can be accessed at http://www.diana.pcbi.upenn.edu/tarbase.
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paper_title: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

paper_content:

AbstractmirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.
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paper_title: TargetSpy: a supervised machine learning approach for microRNA target prediction

paper_content:

BackgroundVirtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites.ResultsWe developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences.In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms.ConclusionOnly a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org.
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paper_title: MTar: a computational microRNA target prediction architecture for human transcriptome

paper_content:

BackgroundMicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.ResultsWe present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone.ConclusionMTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.
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paper_title: Searching the coding region for microRNA targets.

paper_content:

Finding microRNA targets in the coding region is difficult due to the overwhelming signal encoding the amino acid sequence. Here, we introduce an algorithm (called PACCMIT-CDS) that finds potential microRNA targets within coding sequences by searching for conserved motifs that are complementary to the microRNA seed region and also overrepresented in comparison with a background model preserving both codon usage and amino acid sequence. Precision and sensitivity of PACCMIT-CDS are evaluated using PAR-CLIP and proteomics data sets. Thanks to the properly constructed background, the new algorithm achieves a lower rate of false positives and better ranking of predictions than do currently available algorithms, which were designed to find microRNA targets within 3' UTRs.
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paper_title: The microRNA.org resource: targets and expression

paper_content:

MicroRNA.org (http://www.microrna.org) is a comprehensive resource of microRNA target predictions and expression profiles. Target predictions are based on a development of the miRanda algorithm which incorporates current biological knowledge on target rules and on the use of an up-to-date compendium of mammalian microRNAs. MicroRNA expression profiles are derived from a comprehensive sequencing project of a large set of mammalian tissues and cell lines of normal and disease origin. Using an improved graphical interface, a user can explore (i) the set of genes that are potentially regulated by a particular microRNA, (ii) the implied cooperativity of multiple microRNAs on a particular mRNA and (iii) microRNA expression profiles in various tissues. To facilitate future updates and development, the microRNA.org database structure and software architecture is flexibly designed to incorporate new expression and target discoveries. The web resource provides users with functional information about the growing number of microRNAs and their interaction with target genes in many species and facilitates novel discoveries in microRNA gene regulation.
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paper_title: Inference of miRNA targets using evolutionary conservation and pathway analysis

paper_content:

BackgroundMicroRNAs have emerged as important regulatory genes in a variety of cellular processes and, in recent years, hundreds of such genes have been discovered in animals. In contrast, functional annotations are available only for a very small fraction of these miRNAs, and even in these cases only partially.ResultsWe developed a general Bayesian method for the inference of miRNA target sites, in which, for each miRNA, we explicitly model the evolution of orthologous target sites in a set of related species. Using this method we predict target sites for all known miRNAs in flies, worms, fish, and mammals. By comparing our predictions in fly with a reference set of experimentally tested miRNA-mRNA interactions we show that our general method performs at least as well as the most accurate methods available to date, including ones specifically tailored for target prediction in fly. An important novel feature of our model is that it explicitly infers the phylogenetic distribution of functional target sites, independently for each miRNA. This allows us to infer species-specific and clade-specific miRNA targeting. We also show that, in long human 3' UTRs, miRNA target sites occur preferentially near the start and near the end of the 3' UTR.To characterize miRNA function beyond the predicted lists of targets we further present a method to infer significant associations between the sets of targets predicted for individual miRNAs and specific biochemical pathways, in particular those of the KEGG pathway database. We show that this approach retrieves several known functional miRNA-mRNA associations, and predicts novel functions for known miRNAs in cell growth and in development.ConclusionWe have presented a Bayesian target prediction algorithm without any tunable parameters, that can be applied to sequences from any clade of species. The algorithm automatically infers the phylogenetic distribution of functional sites for each miRNA, and assigns a posterior probability to each putative target site. The results presented here indicate that our general method achieves very good performance in predicting miRNA target sites, providing at the same time insights into the evolution of target sites for individual miRNAs. Moreover, by combining our predictions with pathway analysis, we propose functions of specific miRNAs in nervous system development, inter-cellular communication and cell growth. The complete target site predictions as well as the miRNA/pathway associations are accessible on the ElMMo web server.
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paper_title: DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows

paper_content:

MicroRNAs (miRNAs) are small endogenous RNA molecules that regulate gene expression through mRNA degradation and/or translation repression, affecting many biological processes. DIANA-microT web server (http://www.microrna.gr/webServer) is dedicated to miRNA target prediction/functional analysis, and it is being widely used from the scientific community, since its initial launch in 2009. DIANA-microT v5.0, the new version of the microT server, has been significantly enhanced with an improved target prediction algorithm, DIANAmicroT-CDS. It has been updated to incorporate miRBase version 18 and Ensembl version 69. The in silico-predicted miRNA–gene interactions in Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans exceed 11 million in total. The web server was completely redesigned, to host a series of sophisticated workflows, which can be used directly from the on-line web interface, enabling users without the necessary bioinformatics infrastructure to perform advanced multi-step functional miRNA analyses. For instance, one available pipeline performs miRNA target prediction using different thresholds and meta-analysis statistics, followed by pathway enrichment analysis. DIANAmicroT web server v5.0 also supports a complete integration with the Taverna Workflow Management System (WMS), using the in-house developed DIANA-Taverna Plug-in. This plug-in provides ready-to-use modules for miRNA target prediction and functional analysis, which can be used to form advanced high-throughput analysis pipelines.
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paper_title: MicroInspector: a web tool for detection of miRNA binding sites in an RNA sequence

paper_content:

Regulationofpost-transcriptionalgeneexpressionby microRNAs(miRNA)hassofarbeenvalidatedforonly a few mRNA targets. Based on the large number of miRNA genes and the possibility that one miRNA might influence gene expression of several targets simultaneously, the quantity of ribo-regulated genes is expected to be much higher. Here, we describe the web tool MicroInspector that will analyse a userdefined RNA sequence, which is typically an mRNA or a part of an mRNA, for the occurrence of binding sites for known and registered miRNAs. The program allows variation of temperature, the setting of energy values as well as the selection of different miRNA databasestoidentifymiRNA-bindingsitesofdifferent strength. MicroInspector could spot the correct sites for miRNA-interaction in known target mRNAs. Using other mRNAs, for which such an interaction has not yet been described, we discovered frequently potential miRNA binding sites of similar quality, which can now be analysed experimentally. The MicroInspector program is easy to use and does not require specific computer skills. The service can be accessed via the MicroInspector web server at http://www.imbb. forth.gr/microinspector.
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paper_title: Animal MicroRNAs Confer Robustness to Gene Expression and Have a Significant Impact on 3′UTR Evolution

paper_content:

MicroRNAs are small noncoding RNAs that serve as posttranscriptional regulators of gene expression in higher eukaryotes. Their widespread and important role in animals is highlighted by recent estimates that 20%-30% of all genes are microRNA targets. Here, we report that a large set of genes involved in basic cellular processes avoid microRNA regulation due to short 3'UTRs that are specifically depleted of microRNA binding sites. For individual microRNAs, we find that coexpressed genes avoid microRNA sites, whereas target genes and microRNAs are preferentially expressed in neighboring tissues. This mutually exclusive expression argues that microRNAs confer accuracy to developmental gene-expression programs, thus ensuring tissue identity and supporting cell-lineage decisions.
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paper_title: The role of site accessibility in microRNA target recognition

paper_content:

MicroRNAs are key regulators of gene expression, but the precise mechanisms underlying their interaction with their mRNA targets are still poorly understood. Here, we systematically investigate the role of target-site accessibility, as determined by base-pairing interactions within the mRNA, in microRNA target recognition. We experimentally show that mutations diminishing target accessibility substantially reduce microRNA-mediated translational repression, with effects comparable to those of mutations that disrupt sequence complementarity. We devise a parameter-free model for microRNA-target interaction that computes the difference between the free energy gained from the formation of the microRNA-target duplex and the energetic cost of unpairing the target to make it accessible to the microRNA. This model explains the variability in our experiments, predicts validated targets more accurately than existing algorithms, and shows that genomes accommodate site accessibility by preferentially positioning targets in highly accessible regions. Our study thus demonstrates that target accessibility is a critical factor in microRNA function.
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paper_title: Potent effect of target structure on microRNA function

paper_content:

MicroRNAs (miRNAs) are small noncoding RNAs that repress protein synthesis by binding to target messenger RNAs. We investigated the effect of target secondary structure on the efficacy of repression by miRNAs. Using structures predicted by the Sfold program, we model the interaction between an miRNA and a target as a two-step hybridization reaction: nucleation at an accessible target site followed by hybrid elongation to disrupt local target secondary structure and form the complete miRNA-target duplex. This model accurately accounts for the sensitivity to repression by let-7 of various mutant forms of the Caenorhabditis elegans lin-41 3¢ untranslated region and for other experimentally tested miRNA-target interactions in C. elegans and Drosophila melanogaster. These findings indicate a potent effect of target structure on target recognition by miRNAs and establish a structure-based framework for genome-wide identification of animal miRNA targets.
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paper_title: Systematic discovery of regulatory motifs in human promoters and 3′ UTRs by comparison of several mammals

paper_content:

Comprehensive identification of all functional elements encoded in the human genome is a fundamental need in biomedical research. Here, we present a comparative analysis of the human, mouse, rat and dog genomes to create a systematic catalogue of common regulatory motifs in promoters and 3' untranslated regions (3' UTRs). The promoter analysis yields 174 candidate motifs, including most previously known transcription-factor binding sites and 105 new motifs. The 3'-UTR analysis yields 106 motifs likely to be involved in post-transcriptional regulation. Nearly one-half are associated with microRNAs (miRNAs), leading to the discovery of many new miRNA genes and their likely target genes. Our results suggest that previous estimates of the number of human miRNA genes were low, and that miRNAs regulate at least 20% of human genes. The overall results provide a systematic view of gene regulation in the human, which will be refined as additional mammalian genomes become available.
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paper_title: HuMiTar: A sequence-based method for prediction of human microRNA targets

paper_content:

BackgroundMicroRNAs (miRs) are small noncoding RNAs that bind to complementary/partially complementary sites in the 3' untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate, high-throughput identification of physiologically active miR targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the miR's seed region and implementation of cross-species conservation of the target site, and machine learning (ML) methods that explore patterns that contrast true and false miR-mRNA duplexes. However, in the case of the traditional methods research shows that some seed region matches that are conserved are false positives and that some of the experimentally validated target sites are not conserved.ResultsWe present HuMiTar, a computational method for identifying common targets of miRs, which is based on a scoring function that considers base-pairing for both seed and non-seed positions for human miR-mRNA duplexes. Our design shows that certain non-seed miR nucleotides, such as 14, 18, 13, 11, and 17, are characterized by a strong bias towards formation of Watson-Crick pairing. We contrasted HuMiTar with several representative competing methods on two sets of human miR targets and a set of ten glioblastoma oncogenes. Comparison with the two best performing traditional methods, PicTar and TargetScanS, and a representative ML method that considers the non-seed positions, NBmiRTar, shows that HuMiTar predictions include majority of the predictions of the other three methods. At the same time, the proposed method is also capable of finding more true positive targets as a trade-off for an increased number of predictions. Genome-wide predictions show that the proposed method is characterized by 1.99 signal-to-noise ratio and linear, with respect to the length of the mRNA sequence, computational complexity. The ROC analysis shows that HuMiTar obtains results comparable with PicTar, which are characterized by high true positive rates that are coupled with moderate values of false positive rates.ConclusionThe proposed HuMiTar method constitutes a step towards providing an efficient model for studying translational gene regulation by miRs.
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paper_title: microRNA Target Predictions across Seven Drosophila Species and Comparison to Mammalian Targets

paper_content:

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.
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paper_title: Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms

paper_content:

We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs' 5' end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3' UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method's specificity. The initial seeding step determines an algorithm's sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost's weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps. (TargetBoost is available as a Web tool from http://www.interagon.com/demo/.).
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paper_title: MultiMiTar: A Novel Multi Objective Optimization based miRNA-Target Prediction Method

paper_content:

Background ::: Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. ::: Methodology/Principal Finding ::: In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. ::: Conclusions/Significance ::: MultiMiTar is found to achieve much higher Matthew’s correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from −0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.
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paper_title: The Impact of miRNA Target Sites in Coding Sequences and in 3′UTRs

paper_content:

Animal miRNAs are a large class of small regulatory RNAs that are known to directly and negatively regulate the expression of a large fraction of all protein encoding genes. The identification and characterization of miRNA targets is thus a fundamental problem in biology. miRNAs regulate target genes by binding to 3′ untranslated regions (3′UTRs) of target mRNAs, and multiple binding sites for the same miRNA in 3′UTRs can strongly enhance the degree of regulation. Recent experiments have demonstrated that a large fraction of miRNA binding sites reside in coding sequences. Overall, miRNA binding sites in coding regions were shown to mediate smaller regulation than 3′UTR binding. However, possible interactions between target sites in coding sequences and 3′UTRs have not been studied. Using transcriptomics and proteomics data of ten miRNA mis-expression experiments as well as transcriptome-wide experimentally identified miRNA target sites, we found that mRNA and protein expression of genes containing target sites both in coding regions and 3′UTRs were in general mildly but significantly more regulated than those containing target sites in 3′UTRs only. These effects were stronger for conserved target sites of length 7–8 nt in coding regions compared to non-conserved sites. Combined with our other finding that miRNA target sites in coding regions are under negative selection, our results shed light on the functional importance of miRNA targeting in coding regions.
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paper_title: AU-Rich-Element-Mediated Upregulation of Translation by FXR1 and Argonaute 2

paper_content:

Summary A U- r ich e lements (AREs), present in mRNA 3′-UTRs, are potent posttranscriptional regulatory signals that can rapidly effect changes in mRNA stability and translation, thereby dramatically altering gene expression with clinical and developmental consequences. In human cell lines, the TNFα ARE enhances translation relative to mRNA levels upon serum starvation, which induces cell-cycle arrest. An in vivo crosslinking-coupled affinity purification method was developed to isolate ARE-associated complexes from activated versus basal translation conditions. We surprisingly found two microRNP-related proteins, f ragile- X -mental-retardation- r elated protein 1 (FXR1) and A r go naute 2 (AGO2), that associate with the ARE exclusively during translation activation. Through tethering and shRNA-knockdown experiments, we provide direct evidence for the translation activation function of both FXR1 and AGO2 and demonstrate their interdependence for upregulation. This novel cell-growth-dependent translation activation role for FXR1 and AGO2 allows new insights into ARE-mediated signaling and connects two important posttranscriptional regulatory systems in an unexpected way.
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paper_title: Fast and effective prediction of microRNA/target duplexes

paper_content:

MicroRNAs (miRNAs) are short RNAs that post-transcriptionally regulate the expression of target genes by binding to the target mRNAs. Although a large number of animal miRNAs has been defined, only a few targets are known. In contrast to plant miRNAs, which usually bind nearly perfectly to their targets, animal miRNAs bind less tightly, with a few nucleotides being unbound, thus producing more complex secondary structures of miRNA/target duplexes. Here, we present a program, RNA-hybrid, that predicts multiple potential binding sites of miRNAs in large target RNAs. In general, the program finds the energetically most favorable hybridization sites of a small RNA in a large RNA. Intramolecular hybridizations, that is, base pairings between target nucleotides or between miRNA nucleotides are not allowed. For large targets, the time complexity of the algorithm is linear in the target length, allowing many long targets to be searched in a short time. Statistical significance of predicted targets is assessed with an extreme value statistics of length normalized minimum free energies, a Poisson approximation of multiple binding sites, and the calculation of effective numbers of orthologous targets in comparative studies of multiple organisms. We applied our method to the prediction of Drosophila miRNA targets in 3'UTRs and coding sequence. RNAhybrid, with its accompanying programs RNAcalibrate and RNAeffective, is available for download and as a Web tool on the Bielefeld Bioinformatics Server (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/).
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paper_title: RepTar: a database of predicted cellular targets of host and viral miRNAs

paper_content:

Computational identification of putative microRNA (miRNA) targets is an important step towards elucidating miRNA functions. Several miRNA target-prediction algorithms have been developed followed by publicly available databases of these predictions. Here we present a new database offering miRNA target predictions of several binding types, identified by our recently developed modular algorithm RepTar. RepTar is based on identification of repetitive elements in 3′-UTRs and is independent of both evolutionary conservation and conventional binding patterns (i.e. Watson–Crick pairing of ‘seed’ regions). The modularity of RepTar enables the prediction of targets with conventional seed sites as well as rarer targets with non-conventional sites, such as sites with seed wobbles (G-U pairing in the seed region), 3′-compensatory sites and the newly discovered centered sites. Furthermore, RepTar’s independence of conservation enables the prediction of cellular targets of the less evolutionarily conserved viral miRNAs. Thus, the RepTar database contains genome-wide predictions of human and mouse miRNAs as well as predictions of cellular targets of human and mouse viral miRNAs. These predictions are presented in a user-friendly database, which allows browsing through the putative sites as well as conducting simple and advanced queries including data intersections of various types. The RepTar database is available at http://reptar.ekmd.huji.ac.il.
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paper_title: miRcode: a map of putative microRNA target sites in the long non-coding transcriptome

paper_content:

Summary: Although small non-coding RNAs, such as microRNAs, have well-established functions in the cell, long non-coding RNAs (lncRNAs) have only recently started to emerge as abundant regulators of cell physiology, and their functions may be diverse. A small number of studies describe interactions between small and lncRNAs, with lncRNAs acting either as inhibitory decoys or as regulatory targets of microRNAs, but such interactions are still poorly explored. To facilitate the study of microRNA–lncRNA interactions, we implemented miRcode: a comprehensive searchable map of putative microRNA target sites across the complete GENCODE annotated transcriptome, including 10 419 lncRNA genes in the current version. ::: ::: Availability: http://www.mircode.org ::: ::: Contact: es.ug@nossral.kire ::: ::: Supplementary Information: Supplementary data are available at Bioinformatics online.
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paper_title: MTar: a computational microRNA target prediction architecture for human transcriptome

paper_content:

BackgroundMicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.ResultsWe present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone.ConclusionMTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.
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paper_title: A Pattern-Based Method for the Identification of MicroRNA Binding Sites and Their Corresponding Heteroduplexes

paper_content:

Summary We present rna22 , a method for identifying microRNA binding sites and their corresponding heteroduplexes. Rna22 does not rely upon cross-species conservation, is resilient to noise, and, unlike previous methods, it first finds putative microRNA binding sites in the sequence of interest, then identifies the targeting microRNA. Computationally, we show that rna22 identifies most of the currently known heteroduplexes. Experimentally, with luciferase assays, we demonstrate average repressions of 30% or more for 168 of 226 tested targets. The analysis suggests that some microRNAs may have as many as a few thousand targets, and that between 74% and 92% of the gene transcripts in four model genomes are likely under microRNA control through their untranslated and amino acid coding regions. We also extended the method's key idea to a low-error microRNA-precursor-discovery scheme; our studies suggest that the number of microRNA precursors in mammalian genomes likely ranges in the tens of thousands.
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paper_title: MicroTar: predicting microRNA targets from RNA duplexes

paper_content:

BackgroundThe accurate prediction of a comprehensive set of messenger RNAs (targets) regulated by animal microRNAs (miRNAs) remains an open problem. In particular, the prediction of targets that do not possess evolutionarily conserved complementarity to their miRNA regulators is not adequately addressed by current tools.ResultsWe have developed MicroTar, an animal miRNA target prediction tool based on miRNA-target complementarity and thermodynamic data. The algorithm uses predicted free energies of unbound mRNA and putative mRNA-miRNA heterodimers, implicitly addressing the accessibility of the mRNA 3' untranslated region. MicroTar does not rely on evolutionary conservation to discern functional targets, and is able to predict both conserved and non-conserved targets. MicroTar source code and predictions are accessible at http://tiger.dbs.nus.edu.sg/microtar/, where both serial and parallel versions of the program can be downloaded under an open-source licence.ConclusionMicroTar achieves better sensitivity than previously reported predictions when tested on three distinct datasets of experimentally-verified miRNA-target interactions in C. elegans, Drosophila, and mouse.
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paper_title: DIANA-microT web server: elucidating microRNA functions through target prediction

paper_content:

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.
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paper_title: miRmap: Comprehensive prediction of microRNA target repression strength

paper_content:

MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA ‘seed’ region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may use thermodynamic, evolutionary, probabilistic or sequence-based features. We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan. miRmap is freely available from http://cegg.unige.ch/mirmap.
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paper_title: Inference of miRNA targets using evolutionary conservation and pathway analysis

paper_content:

BackgroundMicroRNAs have emerged as important regulatory genes in a variety of cellular processes and, in recent years, hundreds of such genes have been discovered in animals. In contrast, functional annotations are available only for a very small fraction of these miRNAs, and even in these cases only partially.ResultsWe developed a general Bayesian method for the inference of miRNA target sites, in which, for each miRNA, we explicitly model the evolution of orthologous target sites in a set of related species. Using this method we predict target sites for all known miRNAs in flies, worms, fish, and mammals. By comparing our predictions in fly with a reference set of experimentally tested miRNA-mRNA interactions we show that our general method performs at least as well as the most accurate methods available to date, including ones specifically tailored for target prediction in fly. An important novel feature of our model is that it explicitly infers the phylogenetic distribution of functional target sites, independently for each miRNA. This allows us to infer species-specific and clade-specific miRNA targeting. We also show that, in long human 3' UTRs, miRNA target sites occur preferentially near the start and near the end of the 3' UTR.To characterize miRNA function beyond the predicted lists of targets we further present a method to infer significant associations between the sets of targets predicted for individual miRNAs and specific biochemical pathways, in particular those of the KEGG pathway database. We show that this approach retrieves several known functional miRNA-mRNA associations, and predicts novel functions for known miRNAs in cell growth and in development.ConclusionWe have presented a Bayesian target prediction algorithm without any tunable parameters, that can be applied to sequences from any clade of species. The algorithm automatically infers the phylogenetic distribution of functional sites for each miRNA, and assigns a posterior probability to each putative target site. The results presented here indicate that our general method achieves very good performance in predicting miRNA target sites, providing at the same time insights into the evolution of target sites for individual miRNAs. Moreover, by combining our predictions with pathway analysis, we propose functions of specific miRNAs in nervous system development, inter-cellular communication and cell growth. The complete target site predictions as well as the miRNA/pathway associations are accessible on the ElMMo web server.
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paper_title: DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows

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MicroRNAs (miRNAs) are small endogenous RNA molecules that regulate gene expression through mRNA degradation and/or translation repression, affecting many biological processes. DIANA-microT web server (http://www.microrna.gr/webServer) is dedicated to miRNA target prediction/functional analysis, and it is being widely used from the scientific community, since its initial launch in 2009. DIANA-microT v5.0, the new version of the microT server, has been significantly enhanced with an improved target prediction algorithm, DIANAmicroT-CDS. It has been updated to incorporate miRBase version 18 and Ensembl version 69. The in silico-predicted miRNA–gene interactions in Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans exceed 11 million in total. The web server was completely redesigned, to host a series of sophisticated workflows, which can be used directly from the on-line web interface, enabling users without the necessary bioinformatics infrastructure to perform advanced multi-step functional miRNA analyses. For instance, one available pipeline performs miRNA target prediction using different thresholds and meta-analysis statistics, followed by pathway enrichment analysis. DIANAmicroT web server v5.0 also supports a complete integration with the Taverna Workflow Management System (WMS), using the in-house developed DIANA-Taverna Plug-in. This plug-in provides ready-to-use modules for miRNA target prediction and functional analysis, which can be used to form advanced high-throughput analysis pipelines.
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paper_title: microRNA Target Predictions across Seven Drosophila Species and Comparison to Mammalian Targets

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microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.
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paper_title: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

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AbstractmirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.
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paper_title: miRmap: Comprehensive prediction of microRNA target repression strength

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MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA ‘seed’ region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may use thermodynamic, evolutionary, probabilistic or sequence-based features. We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan. miRmap is freely available from http://cegg.unige.ch/mirmap.
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paper_title: Specificity of microRNA target selection in translational repression

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MicroRNAs (miRNAs) are a class of noncoding RNAs found in organisms as evolutionarily distant as plants and mammals, yet most of the mRNAs they regulate are unknown. Here we show that the ability of an miRNA to translationally repress a target mRNA is largely dictated by the free energy of binding of the first eight nucleotides in the 5′ region of the miRNA. However, G:U wobble base-pairing in this region interferes with activity beyond that predicted on the basis of thermodynamic stability. Furthermore, an mRNA can be simultaneously repressed by more than one miRNA species. The level of repression achieved is dependent on both the amount of mRNA and the amount of available miRNA complexes. Thus, predicted miRNA:mRNA interactions must be viewed in the context of other potential interactions and cellular conditions.
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paper_title: microRNA target predictions in animals

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In recent years, microRNAs (miRNAs) have emerged as a major class of regulatory genes, present in most metazoans and important for a diverse range of biological functions. Because experimental identification of miRNA targets is difficult, there has been an explosion of computational target predictions. Although the initial round of predictions resulted in very diverse results, subsequent computational and experimental analyses suggested that at least a certain class of conserved miRNA targets can be confidently predicted and that this class of targets is large, covering, for example, at least 30% of all human genes when considering about 60 conserved vertebrate miRNA gene families. Most recent approaches have also shown that there are correlations between domains of miRNA expression and mRNA levels of their targets. Our understanding of miRNA function is still extremely limited, but it may be that by integrating mRNA and miRNA sequence and expression data with other comparative genomic data, we will be able to gain global and yet specific insights into the function and evolution of a broad layer of post-transcriptional control.
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paper_title: miRNA-target prediction based on transcriptional regulation

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BackgroundmicroRNAs (miRNAs) are tiny endogenous RNAs that have been discovered in animals and plants, and direct the post-transcriptional regulation of target mRNAs for degradation or translational repression via binding to the 3'UTRs and the coding exons. To gain insight into the biological role of miRNAs, it is essential to identify the full repertoire of mRNA targets (target genes). A number of computer programs have been developed for miRNA-target prediction. These programs essentially focus on potential binding sites in 3'UTRs, which are recognized by miRNAs according to specific base-pairing rules.ResultsHere, we introduce a novel method for miRNA-target prediction that is entirely independent of existing approaches. The method is based on the hypothesis that transcription of a miRNA and its target genes tend to be co-regulated by common transcription factors. This hypothesis predicts the frequent occurrence of common cis-elements between promoters of a miRNA and its target genes. That is, our proposed method first identifies putative cis-elements in a promoter of a given miRNA, and then identifies genes that contain common putative cis-elements in their promoters. In this paper, we show that a significant number of common cis-elements occur in ~28% of experimentally supported human miRNA-target data. Moreover, we show that the prediction of human miRNA-targets based on our method is statistically significant. Further, we discuss the random incidence of common cis-elements, their consensus sequences, and the advantages and disadvantages of our method.ConclusionsThis is the first report indicating prevalence of transcriptional regulation of a miRNA and its target genes by common transcription factors and the predictive ability of miRNA-targets based on this property.
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paper_title: Lost in translation: an assessment and perspective for computational microRNA target identification

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MicroRNAs (miRNAs) are a class of short endogenously expressed RNA molecules that regulate gene expression by binding directly to the messenger RNA of protein coding genes. They have been found to confer a novel layer of genetic regulation in a wide range of biological processes. Computational miRNA target prediction remains one of the key means used to decipher the role of miRNAs in development and disease. Here we introduce the basic idea behind the experimental identification of miRNA targets and present some of the most widely used computational miRNA target identification programs. The review includes an assessment of the prediction quality of these programs and their combinations. Contact: p.alexiou@fleming.gr Supplementary information: Supplementary data are available at Bioinformatics online.
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paper_title: A guide through present computational approaches for the identification of mammalian microRNA targets

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Computational microRNA (miRNA) target prediction is a field in flux. Here we present a guide through five widely used mammalian target prediction programs. We include an analysis of the performance of these individual programs and of various combinations of these programs. For this analysis we compiled several benchmark data sets of experimentally supported miRNA-target gene interactions. Based on the results, we provide a discussion on the status of target prediction and also suggest a stepwise approach toward predicting and selecting miRNA targets for experimental testing.
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Title: Comprehensive Overview and Assessment of Computational Prediction of microRNA Targets in Animals

Section 1: Introduction
Description 1: Introduce the importance and role of microRNAs in gene regulation and their significance in biological processes and diseases. Detail the growing interest and development in the field of miRNA research and therapeutics, as well as the need for miRNA target prediction.

Section 2: Benchmark Data Sets
Description 2: Describe the databases of experimentally validated and curated miRNA targets and the methods used to develop benchmark data sets. Explain the specific data sets used for evaluation in the study.

Section 3: Considered miRNA Target Predictors
Description 3: List and discuss several representative predictors selected for empirical evaluation. Explain the criteria for selecting these methods and their types of predictive models.

Section 4: Predictive Methodologies and Mechanistic Basis of miRNA-mRNA Interaction
Description 4: Summarize the different types of predictive models (heuristic and empirical) and the mechanistic details (such as complementarity, site accessibility, and evolutionary conservation) that these methods use to predict miRNA targets.

Section 5: Criteria for Empirical Evaluation
Description 5: Outline the comprehensive set of evaluation measures used to assess the predictions of miRNA:target duplexes and miRNA-gene pairs. Explain the methods used for statistical significance and measures such as MCC, SNR, AUC, etc.

Section 6: Evaluation Protocols
Description 6: Summarize benchmark data sets and evaluation procedures used to develop and test the predictors. Discuss the details about training and test data sets and methods used to generate predicted outcomes.

Section 7: Usability and Impact of miRNA Target Predictors
Description 7: Discuss various aspects related to the ease of use, scope of predictions, input and output formats, and the ability to predict novel miRNAs. Analyze the impact/popularity based on inclusion in prior reviews and citation counts.

Section 8: Empirical Evaluation of Representative miRNA Target Predictors
Description 8: Present the empirical evaluation of seven representative target predictors. Include comparative results based on different benchmark data sets, measuring predictive quality at both duplex and gene levels.

Section 9: Discussion
Description 9: Provide insights from the empirical assessment, including practical recommendations for end users to select appropriate methods. Discuss areas where further improvements are possible and future directions for developing new predictors.

Section 10: Conclusion
Description 10: Summarize the key findings and contributions of the review. Highlight the significance of computational miRNA target predictors and the necessity for new methods focusing on high-throughput predictions and improved performance.