Online resources for miRNA analysis

Objective: This review aims to provide a brief introduction to each major category of available tools and algorithms for microRNA (miRNA) research, as well as to present some of the most widely used or promising representative applications. Methods: Only tools offering a fully functional web interface have been included, excluding implementations requiring deployment in local servers or workstations. Furthermore, we have specifically evaluated implementations focusing on Homo sapiens or on mammals used extensively in in vivo research, such as mice and rats. Results: We present an overview of databases and repositories of miRNA sequences and expression, a commentary on miRNA target prediction algorithms, tools for miRNA functional investigation, and online pipelines for the analysis of high throughput experiments. Examples and case studies are provided at the end of the manuscript, which can hopefully contribute in elucidating the utility of these implementations to basic and applied research. Conclusions: Computational tools and algorithms play a significant role in miRNA-related research, supporting equally basic and applied research efforts. However, numerous challenges still remain to be answered by the relevant research community. © 2013 The Canadian Society of Clinical Chemists

Functional analysis of miRNAs using the DIANA tools online suite

microRNAs (miRNAs) are central regulators of gene expression. They are actively studied for their involvement in numerous physiological and pathological conditions but also as diagnostic biomarkers or promising therapeutic targets. The increased complexity of the miRNA interactomes hinders straightforward interpretation of miRNA expression differences between states and conditions. To this end, functional analysis web servers process and combine experimental and in silico data, enabling researchers to uncover targeted pathways and transcriptional mechanisms that are hidden within numerous interactions and vast expression datasets. DIANA-tools (www.microrna.gr) is a web server hosting state-of-the-art utilities and databases for miRNA functional investigation. Available utilities cover a wide scope of different needs and research scenarios, rendering DIANA website a one-stop-shop for miRNA analyses. The most commonly utilized databases and algorithms include DIANA-microT-CDS, DIANA-TarBase v7.0, DIANA-lncBase v2.0, DIANA-miRGen v3.0, DIANA-miRPath v3.0, and DIANA-mirExTra v2.0. In the presented protocol, we will utilize different online tools in order to explore miRNA functions and to identify probable targets of interest for downstream analyses and wet lab experiments. The combined use of different applications from the DIANA suite can shed light to numerous different aspects of miRNA regulation and regulatory function, without the necessity for extensive bioinformatics expertise or computational infrastructure. © Springer Science+Business Media New York 2017

SVM Based Prediction of Bacterial Transcription Start Sites

Identifying bacterial promoters is the key to understanding gene expression. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). Knowing the TSS position, one can predict promoter positions to within a few base pairs, and vice versa. As a route to promoter identification, we formally address the problem of TSS prediction, drawing on the RegulonDB database of known (mapped) Escherichia coli TSS locations. The accepted method of finding promoters (and therefore TSSs) is to use position weight matrices (PWMs). We use an alternative approach based on sup-port vector machines (SVMs). In particular, we quantify performance of several SVM models versus a PWM approach, using area under the detection-error tradeoff (DET) curve as a performance metric. SVM models are shown to out-perform the PWM at TSS prediction, and to substantially reduce numbers of false positives, which are the bane of this problem

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

Identifying pri-miRNA transcription start sites

MicroRNAs (miRNAs) are small non-coding RNAs that can regulate gene expression playing vital role in nearly all biological pathways. Even though miRNAs have been intensely studied for more than two decades, information regarding miRNA transcription regulation remains limited. The rapid cleavage of primary miRNA transcripts (pri-miRNAs) by Drosha in the nucleus hinders their identification with conventional RNA-seq approaches. Identifying the transcription start site (TSS) of miRNAs will enable genome-wide identification of their expression regulators, including transcription factors (TFs), other non-coding RNAs (ncRNAs) and epigenetic modifiers, providing significant breakthroughs in understanding the mechanisms underlying miRNA expression in development and disease. Here we present a protocol that utilizes microTSS, a versatile computational framework for accurate and single-nucleotide resolution miRNA TSS predictions as well as miRGen, a database of miRNA gene TSSs coupled with genome-wide maps of TF binding sites. © 2018, Springer Science+Business Media, LLC, part of Springer Nature

DeepTSS: multi-branch convolutional neural network for transcription start site identification from CAGE data

Background: The widespread usage of Cap Analysis of Gene Expression (CAGE) has led to numerous breakthroughs in understanding the transcription mechanisms. Recent evidence in the literature, however, suggests that CAGE suffers from transcriptional and technical noise. Regardless of the sample quality, there is a significant number of CAGE peaks that are not associated with transcription initiation events. This type of signal is typically attributed to technical noise and more frequently to random five-prime capping or transcription bioproducts. Thus, the need for computational methods emerges, that can accurately increase the signal-to-noise ratio in CAGE data, resulting in error-free transcription start site (TSS) annotation and quantification of regulatory region usage. In this study, we present DeepTSS, a novel computational method for processing CAGE samples, that combines genomic signal processing (GSP), structural DNA features, evolutionary conservation evidence and raw DNA sequence with Deep Learning (DL) to provide single-nucleotide TSS predictions with unprecedented levels of performance. Results: To evaluate DeepTSS, we utilized experimental data, protein-coding gene annotations and computationally-derived genome segmentations by chromatin states. DeepTSS was found to outperform existing algorithms on all benchmarks, achieving 98% precision and 96% sensitivity (accuracy 95.4%) on the protein-coding gene strategy, with 96.66% of its positive predictions overlapping active chromatin, 98.27% and 92.04% co-localized with at least one transcription factor and H3K4me3 peak. Conclusions: CAGE is a key protocol in deciphering the language of transcription, however, as every experimental protocol, it suffers from biological and technical noise that can severely affect downstream analyses. DeepTSS is a novel DL-based method for effectively removing noisy CAGE signal. In contrast to existing software, DeepTSS does not require feature selection since the embedded convolutional layers can readily identify patterns and only utilize the important ones for the classification task. This study highlights the key role that DL can play in Molecular Biology, by removing the inherent flaws of experimental protocols, that form the backbone of contemporary research. Here, we show how DeepTSS can unleash the full potential of an already popular and mature method such as CAGE, and push the boundaries of coding and non-coding gene expression regulator research even further. © 2022, The Author(s)

Manatee: detection and quantification of small non-coding RNAs from next-generation sequencing data

Small non-coding RNAs (sncRNAs) play important roles in health and disease. Next Generation Sequencing (NGS) technologies are considered as the most powerful and versatile methodologies to explore small RNA (sRNA) transcriptomes in diverse experimental and clinical studies. Small RNA-Seq (sRNA-Seq) data analysis proved to be challenging due to non-unique genomic origin, short length, and abundant post-transcriptional modifications of sRNA species. Here, we present Manatee, an algorithm for the quantification of sRNA classes and the detection of novel expressed non-coding loci. Manatee combines prior annotation of sRNAs with reliable alignment density information and extensive rescue of usually neglected multimapped reads to provide accurate transcriptome-wide sRNA expression quantification. Comparison of Manatee against state-of-the-art implementations using real and simulated data demonstrates its high accuracy across diverse sRNA classes. Manatee also goes beyond common pipelines by identifying and quantifying expression from unannotated loci and microRNA isoforms (isomiRs). It is user-friendly, can be easily incorporated in pipelines, and provides a simplified output suitable for direct usage in downstream analyses and functional studies. © 2020, The Author(s)

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)

DIANA-miTED: A microRNA tissue expression database

microRNAs (miRNAs) are short (∼23nt) single-stranded non-coding RNAs that act as potent post-transcriptional gene expression regulators. Information about miRNA expression and distribution across cell types and tissues is crucial to the understanding of their function and for their translational use as biomarkers or therapeutic targets. DIANA-miTED is the most comprehensive and systematic collection of miRNA expression values derived from the analysis of 15 183 raw human small RNA-Seq (sRNA-Seq) datasets from the Sequence Read Archive (SRA) and The Cancer Genome Atlas (TCGA). Metadata quality maximizes the utility of expression atlases, therefore we manually curated SRA and TCGA-derived information to deliver a comprehensive and standardized set, incorporating in total 199 tissues, 82 anatomical sublocations, 267 cell lines and 261 diseases. miTED offers rich instant visualizations of the expression and sample distributions of requested data across variables, as well as study-wide diagrams and graphs enabling efficient content exploration. Queries also generate links towards state-of-the-art miRNA functional resources, deeming miTED an ideal starting point for expression retrieval, exploration, comparison, and downstream analysis, without requiring bioinformatics support or expertise. DIANA-miTED is freely available at http://www.microrna.gr/mited. © 2022 The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research