DIANA-tarbase and DIANA suite tools: Studying experimentally supported microRNA targets

microRNAs (miRNAs) are short non-coding RNAs (~22 nts) present in animals, plants, and viruses. They are considered central post-transcriptional regulators of gene expression and are key components in a great number of physiological and pathological conditions. The accurate characterization of their targets is considered essential to a series of applications and basic or applied research settings.DIANA-TarBase (http://www.microrna.gr/tarbase)was initially launched in 2006. It is a reference repository indexing experimentally derived miRNA-gene interactions in different cell types, tissues, and conditions across numerous species. This unit focuses on the study of experimentally supported miRNA-gene interactions, as well as their functional interpretation through the use of available tools in the DIANAsuite (http://www.microrna.gr). The proposed use-case scenarios are presented in protocols, describing how to utilize the DIANA-TarBase database and DIANA-microT-CDS server and perform miRNA-targeted pathway analysis with DIANA-miRPath-v3. All analyses are directly invoked or initiated from DIANA-TarBase. © 2016 by John Wiley & Sons, Inc

Characterizing miRNA–lncRNA Interplay

Long noncoding RNAs (lncRNAs) are noncoding transcripts, usually longer than 200 nt, that constitute one of the largest and significantly heterogeneous RNA families. The annotation of lncRNAs and the characterization of their function is a constantly evolving field. LncRNA interplay with microRNAs (miRNAs) is thoroughly studied in several physiological and disease states. miRNAs are small noncoding RNAs (~22 nt) that posttranscriptionally regulate the expression of protein coding genes, through mRNA target cleavage, degradation or direct translational suppression. miRNAs can affect lncRNA half-life by promoting their degradation, or lncRNAs can act as miRNA “sponges,” reducing miRNA regulatory effect on target mRNAs. This chapter outlines the miRNA–lncRNA interplay and provides hands-on methodologies for experimentally supported and in silico-guided analyses. The proposed techniques are a valuable asset to further understand lncRNA functions and can be appropriately adapted to become the backbone for further downstream analyses. © 2021, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

microCLIP super learning framework uncovers functional transcriptome-wide miRNA interactions

Argonaute crosslinking and immunoprecipitation (CLIP) experiments are the most widely used high-throughput methodologies for miRNA targetome characterization. The analysis of Photoactivatable Ribonucleoside-Enhanced (PAR) CLIP methodology focuses on sequence clusters containing T-to-C conversions. Here, we demonstrate for the first time that the non-T-to-C clusters, frequently observed in PAR-CLIP experiments, exhibit functional miRNA-binding events and strong RNA accessibility. This discovery is based on the analysis of an extensive compendium of bona fide miRNA-binding events, and is further supported by numerous miRNA perturbation experiments and structural sequencing data. The incorporation of these previously neglected clusters yields an average of 14% increase in miRNA-target interactions per PAR-CLIP library. Our findings are integrated in microCLIP (www.microrna.gr/microCLIP), a cutting-edge framework that combines deep learning classifiers under a super learning scheme. The increased performance of microCLIP in CLIP-Seq-guided detection of miRNA interactions, uncovers previously elusive regulatory events and miRNA-controlled pathways. © 2018, The Author(s)

Neuronal ELAVL proteins utilize AUF-1 as a co-partner to induce neuron-specific alternative splicing of APP

Aβ peptide that accumulates in Alzheimer's disease brain, derives from proteolytic processing of the amyloid precursor protein (APP) that exists in three main isoforms derived by alternative splicing. The isoform APP695, lacking exons 7 and 8, is predominately expressed in neurons and abnormal neuronal splicing of APP has been observed in the brain of patients with Alzheimer's disease. Herein, we demonstrate that expression of the neuronal members of the ELAVL protein family (nELAVLs) correlate with APP695 levels in vitro and in vivo. Moreover, we provide evidence that nELAVLs regulate the production of APP695; by using a series of reporters we show that concurrent binding of nELAVLs to sequences located both upstream and downstream of exon 7 is required for its skipping, whereas nELAVL-binding to a highly conserved U-rich sequence upstream of exon 8, is sufficient for its exclusion. Finally, we report that nELAVLs block APP exon 7 or 8 definition by reducing the binding of the essential splicing factor U2AF65, an effect facilitated by the concurrent binding of AUF-1. Our study provides new insights into the regulation of APP pre-mRNA processing, supports the role for nELAVLs as neuron-specific splicing regulators and reveals a novel function of AUF1 in alternative splicing. © The Author(s) 2017

Computational Challenges and -omics Approaches for the Identification of microRNAs and Targets

From very early on, computational methods have been deemed as key players in microRNA (miRNA) research. Currently, there are numerous implemented approaches that can be used to support miRNA-related studies by identifying their coding and noncoding targets, their expression, their regulators, as well as by inferring their underlying regulatory networks. This chapter aims to provide an overview of the available state-of-the-art tools, web servers, and databases utilized in miRNA research. The selected tools cover an extensive scope ranging from miRNA annotation, promoter identification, and transcription to the identification of miRNA-controlled pathways. Significant attention has been paid to present and explain standard and novel -omics approaches that can be used to support, complement, or even substitute their in silico counterparts. Importantly, current open challenges, limitations, and good experimental practices followed in the field are comprehensively explored. © 2017 Elsevier Inc. All rights reserved

BUFET: Boosting the unbiased miRNA functional enrichment analysis using bitsets

Background: A group of miRNAs can regulate a biological process by targeting genes involved in the process. The unbiased miRNA functional enrichment analysis is the most precise in silico approach to predict the biological processes that may be regulated by a given miRNA group. However, it is computationally intensive and significantly more expensive than its alternatives. Results: We introduce BUFET, a new approach to significantly reduce the time required for the execution of the unbiased miRNA functional enrichment analysis. It derives its strength from the utilization of efficient bitset-based methods and parallel computation techniques. Conclusions:BUFET outperforms the state-of-the-art implementation, in regard to computational efficiency, in all scenarios (both single- and multi-core), being, in some cases, more than one order of magnitude faster. © 2017 The Author(s)

DIANA-LncBase: Experimentally verified and computationally predicted microRNA targets on long non-coding RNAs

Recently, the attention of the research community has been focused on long non-coding RNAs (IncRNAs) and their physiological/pathological implications. As the number of experiments increase in a rapid rate and transcriptional units are better annotated, databases indexing IncRNA properties and function gradually become essential tools to this process. Aim of DIANA-LncBase (www. microrna.gr/LncBase) is to reinforce researchers' attempts and unravel microRNA (miRNA)-IncRNA putative functional interactions. This study provides, for the first time, a comprehensive annotation of miRNA targets on IncRNAs. DIANA-LncBase hosts transcriptome-wide experimentally verified and computationally predicted miRNA recognition elements (MREs) on human and mouse IncRNAs. The analysis performed includes an integration of most of the available IncRNA resources, relevant high-throughput HITS-CLIP and PAR-CLIP experimental data as well as state-of-the-art in silico target predictions. The experimentally supported entries available in DIANA-LncBase correspond to >5000 interactions, while the computationally predicted interactions exceed 10 million. DIANA-LncBase hosts detailed information for each miRNA-IncRNA pair, such as external links, graphic plots of transcripts' genomic location, representation of the binding sites, IncRNA tissue expression as well as MREs conservation and prediction scores. © The Author(s) 2012

microTSS: Accurate microRNA transcription start site identification reveals a significant number of divergent pri-miRNAs

A large fraction of microRNAs (miRNAs) are derived from intergenic non-coding loci and the identification of their promoters remains 'elusive'. Here, we present microTSS, a machine-learning algorithm that provides highly accurate, single-nucleotide resolution predictions for intergenic miRNA transcription start sites (TSSs). MicroTSS integrates high-resolution RNA-sequencing data with active transcription marks derived from chromatin immunoprecipitation and DNase-sequencing to enable the characterization of tissue-specific promoters. MicroTSS is validated with a specifically designed Drosha-null/conditional-null mouse model, generated using the conditional by inversion (COIN) methodology. Analyses of global run-on sequencing data revealed numerous pri-miRNAs in human and mouse either originating from divergent transcription at promoters of active genes or partially overlapping with annotated long non-coding RNAs. MicroTSS is readily applicable to any cell or tissue samples and constitutes the missing part towards integrating the regulation of miRNA transcription into the modelling of tissue-specific regulatory networks. © 2014 Macmillan Publishers Limited. All rights reserved

DIANA-LncBase v3: Indexing experimentally supported miRNA targets on non-coding transcripts

DIANA-LncBase v3.0 (www.microrna.gr/LncBase) is a reference repository with experimentally supported miRNA targets on non-coding transcripts. Its third version provides approximately half a million entries, corresponding to ∼240 000 unique tissue and cell type specific miRNA-lncRNA pairs. This compilation of interactions is derived from the manual curation of publications and the analysis of >300 high-throughput datasets. miRNA targets are supported by 14 experimental methodologies, applied to 243 distinct cell types and tissues in human and mouse. The largest part of the database is highly confident, AGO-CLIP-derived miRNA-binding events. LncBase v3.0 is the first relevant database to employ a robust CLIP-Seq-guided algorithm, microCLIP framework, to analyze 236 AGO-CLIP-Seq libraries and catalogue ∼370 000 miRNA binding events. The database was redesigned from the ground up, providing new functionalities. Known short variant information, on >67,000 experimentally supported target sites and lncRNA expression profiles in different cellular compartments are catered to users. Interactive visualization plots, portraying correlations of miRNA-lncRNA pairs, as well as lncRNA expression profiles in a wide range of cell types and tissues, are presented for the first time through a dedicated page. LncBase v3.0 constitutes a valuable asset for ncRNA research, providing new insights to the understanding of the still widely unexplored lncRNA functions. © 2019 The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research

DIANA-miRPath v3.0: Deciphering microRNA function with experimental support

The functional characterization of miRNAs is still an open challenge. Here, we present DIANA-miRPath v3.0 (http://www.microrna.gr/miRPathv3) an online software suite dedicated to the assessment of miRNA regulatory roles and the identification of controlled pathways. The new miRPath web server renders possible the functional annotation of one or more miRNAs using standard (hypergeometric distributions), unbiased empirical distributions and/or meta-analysis statistics. DIANA-miRPath v3.0 database and functionality have been significantly extended to support all analyses for KEGGmolecular pathways, as well as multiple slices of Gene Ontology (GO) in seven species (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Caenorhabditis elegans, Gallus gallus and Danio rerio). Importantly, more than 600 000 experimentally supported miRNA targets from DIANA-TarBase v7.0 have been incorporated into the new schema. Users of DIANA-miRPath v3.0 can harness this wealth of information and substitute or combine the available in silico predicted targets from DIANA-microTCDS and/or TargetScan v6.2 with high quality experimentally supported interactions. A unique feature of DIANA-miRPath v3.0 is its redesigned Reverse Search module, which enables users to identify and visualize miRNAs significantly controlling selected pathways or belonging to specific GO categories based on in silico or experimental data. DIANA-miRPath v3.0 is freely available to all users without any login requirement. © 2015 The Author(s)