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MicroRNA target site identification by integrating sequence and binding information.

ABSTRACT: High-throughput sequencing has opened numerous possibilities for the identification of regulatory RNA-binding events. Cross-linking and immunoprecipitation of Argonaute proteins can pinpoint a microRNA (miRNA) target site within tens of bases but leaves the identity of the miRNA unresolved. A flexible computational framework, microMUMMIE, integrates sequence with cross-linking features and reliably identifies the miRNA family involved in each binding event. It considerably outperforms sequence-only approaches and quantifies the prevalence of noncanonical binding modes.

SUBMITTER: Majoros WH

PROVIDER: S-EPMC3818907 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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MicroRNA target site identification by integrating sequence and binding information.

Majoros William H WH Lekprasert Parawee P Mukherjee Neelanjan N Skalsky Rebecca L RL Corcoran David L DL Cullen Bryan R BR Ohler Uwe U

Nature methods 20130526 7

High-throughput sequencing has opened numerous possibilities for the identification of regulatory RNA-binding events. Cross-linking and immunoprecipitation of Argonaute proteins can pinpoint a microRNA (miRNA) target site within tens of bases but leaves the identity of the miRNA unresolved. A flexible computational framework, microMUMMIE, integrates sequence with cross-linking features and reliably identifies the miRNA family involved in each binding event. It considerably outperforms sequence-o ...[more]

PMID: 23708386

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MicroRNA Target Site Identification by Integrating Sequence and Binding Information

Project description:High-throughput sequencing has opened numerous possibilities for the identification of regulatory RNA-binding events. Cross-linking and immunoprecipitation of Argonaute protein members can pinpoint microRNA target sites within tens of bases, but leaves the identity of the microRNA unresolved. A flexible computational framework that integrates sequence with cross-linking features reliably identifies the microRNA family involved in each binding event, considerably outperforms sequence-only approaches, and quantifies the prevalence of noncanonical binding modes. Ago2 (Argonaute 2) PAR-CLIP and RNA deep sequencing of Epstein-Barr virus B95.8-infected Lymphoblastoid Cell Lines (LCLs)

2013-05-25 | E-GEOD-46611 | biostudies-arrayexpress

MicroRNA Target Site Identification by Integrating Sequence and Binding Information

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Genome-wide identification of hypoxia-inducible factor binding sites and target genes by a probabilistic model integrating transcription-profiling data and in silico binding site prediction.

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A reexamination of information theory-based methods for DNA-binding site identification.

Project description:BackgroundSearching for transcription factor binding sites in genome sequences is still an open problem in bioinformatics. Despite substantial progress, search methods based on information theory remain a standard in the field, even though the full validity of their underlying assumptions has only been tested in artificial settings. Here we use newly available data on transcription factors from different bacterial genomes to make a more thorough assessment of information theory-based search methods.ResultsOur results reveal that conventional benchmarking against artificial sequence data leads frequently to overestimation of search efficiency. In addition, we find that sequence information by itself is often inadequate and therefore must be complemented by other cues, such as curvature, in real genomes. Furthermore, results on skewed genomes show that methods integrating skew information, such as Relative Entropy, are not effective because their assumptions may not hold in real genomes. The evidence suggests that binding sites tend to evolve towards genomic skew, rather than against it, and to maintain their information content through increased conservation. Based on these results, we identify several misconceptions on information theory as applied to binding sites, such as negative entropy, and we propose a revised paradigm to explain the observed results.ConclusionWe conclude that, among information theory-based methods, the most unassuming search methods perform, on average, better than any other alternatives, since heuristic corrections to these methods are prone to fail when working on real data. A reexamination of information content in binding sites reveals that information content is a compound measure of search and binding affinity requirements, a fact that has important repercussions for our understanding of binding site evolution.

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Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism.

Project description:DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations.

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Identification of DNA-binding proteins using support vector machine with sequence information.

Project description:DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.

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Functional microRNA binding site variants.

Project description:Germline single nucleotide polymorphisms are one of the most common genetic variations. Polymorphisms that cause nonsynonymous mutations in gene coding regions are known to cause serious deleterious downstream effects. However, even polymorphisms in noncoding regions can have profound functional consequences by disrupting essential regulatory sites. Specifically, polymorphisms that alter microRNA binding sites can disrupt the regulation of hallmark biological pathways implicated in tumorigenesis and tumor progression. Many of these microRNA-associated polymorphisms (miR-SNPs) have recently been shown to be important biomarkers of cancer risk, prognosis, and treatment outcomes. This review will summarize the functional impact of key miR-SNPs and define a subset of miR-SNPs that may be clinically useful prognostic or predictive biomarkers.

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A non-canonical plant microRNA target site.

Project description:Plant microRNAs (miRNAs) typically form near-perfect duplexes with their targets and mediate mRNA cleavage. Here, we describe an unconventional miRNA target of miR398 in Arabidopsis, an mRNA encoding the blue copper-binding protein (BCBP). BCBP mRNA carries an miR398 complementary site in its 5'-untranslated region (UTR) with a bulge of six nucleotides opposite to the 5' region of the miRNA. Despite the disruption of a target site region thought to be especially critical for function, BCBP mRNAs are cleaved by ARGONAUTE1 between nucleotides 10th and 11th, opposite to the miRNA, like conventional plant target sites. Levels of BCBP mRNAs are inversely correlated to levels of miR398 in mutants lacking the miRNA, or transgenic plants overexpressing it. Introducing two mutations that disrupt the miRNA complementarity around the cleavage site renders the target cleavage-resistant. The BCBP site functions outside of the context of the BCBP mRNA and does not depend on 5'-UTR location. Reducing the bulge does not interfere with miR398-mediated regulation and completely removing it increases the efficiency of the slicing. Analysis of degradome data and target predictions revealed that the miR398-BCBP interaction seems to be rather unique. Nevertheless, our results imply that functional target sites with non-perfect pairings in the 5' region of an ancient conserved miRNA exist in plants.

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Accurate microRNA Target Prediction Using Detailed Binding Site Accessibility and Machine Learning on Proteomics Data.

Project description:MicroRNAs (miRNAs) are a class of small regulatory genes regulating gene expression by targeting messenger RNA. Though computational methods for miRNA target prediction are the prevailing means to analyze their function, they still miss a large fraction of the targeted genes and additionally predict a large number of false positives. Here we introduce a novel algorithm called DIANA-microT-ANN which combines multiple novel target site features through an artificial neural network (ANN) and is trained using recently published high-throughput data measuring the change of protein levels after miRNA overexpression, providing positive and negative targeting examples. The features characterizing each miRNA recognition element include binding structure, conservation level, and a specific profile of structural accessibility. The ANN is trained to integrate the features of each recognition element along the 3'untranslated region into a targeting score, reproducing the relative repression fold change of the protein. Tested on two different sets the algorithm outperforms other widely used algorithms and also predicts a significant number of unique and reliable targets not predicted by the other methods. For 542 human miRNAs DIANA-microT-ANN predicts 120000 targets not provided by TargetScan 5.0. The algorithm is freely available at http://microrna.gr/microT-ANN.

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