Special Issue "RNA Target Prediction Methods"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (30 June 2019).

Special Issue Editors

Dr. Stefania Bortoluzzi
E-Mail Website
Guest Editor
Department of Molecular Medicine, University of Padova, 35122 Padova, Italy
Interests: cancer genomics and transciptomics; bioinformatics; systems biology; microRNAs; circular RNAs
Dr. Enrico Gaffo
E-Mail Website
Guest Editor
Department of Molecular Medicine, University of Padova, 35122 Padua, Italy
Interests: bioinformatics; transcriptomics; regulatory networks; circular RNAs; small RNAs; exosomes

Special Issue Information

Beyond their informational roles, RNAs play a wide range of functions in a variety of cellular processes and in disease mechanisms, mediated by the recognition and binding of target molecules. By base-pairing with DNA or other RNAs, RNAs regulate an array of processes through direct interactions, such as those involving the high pleiotropic microRNAs, and via competition or decoy functions, also in complex networks. Moreover, key to the function of many RNAs is their binding to proteins by sequence or folding motifs. Thousands of noncoding RNAs have been uncovered in the NGS era and the recent findings on circular RNAs further expanded the field. However, for many of these RNAs, the functions are still unknown, calling for an improved definition of RNAs’ interactions by experimental studies, high-throughput methods, and bioinformatic approaches. Far from being an easy task, the prediction of RNA targets represents a turning point in most research projects, since it allows the prioritization of RNAs for functional experimental study. A series of computational and methodological challenges are set by the complexity of RNA biology.

This Special Issue is dedicated to all the aspects of RNA target prediction with a focus on, but not limited to, computational methods, software, and resources. We welcome submissions of reviews, research articles, short communications, and “concept papers”.

Prof. Stefania Bortoluzzi
Dr. Enrico Gaffo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • RNA function prediction
  • RNA–RNA interactions
  • microRNA targeting
  • RNA–protein binding
  • RNA fold
  • RNA motifs
  • circular RNAs
  • bioinformatics
  • high-throughput data

Published Papers (2 papers)

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Research

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Article
Prediction of Long Non-Coding RNAs Based on Deep Learning
Genes 2019, 10(4), 273; https://doi.org/10.3390/genes10040273 - 03 Apr 2019
Cited by 7 | Viewed by 1825
Abstract
With the rapid development of high-throughput sequencing technology, a large number of transcript sequences have been discovered, and how to identify long non-coding RNAs (lncRNAs) from transcripts is a challenging task. The identification and inclusion of lncRNAs not only can more clearly help [...] Read more.
With the rapid development of high-throughput sequencing technology, a large number of transcript sequences have been discovered, and how to identify long non-coding RNAs (lncRNAs) from transcripts is a challenging task. The identification and inclusion of lncRNAs not only can more clearly help us to understand life activities themselves, but can also help humans further explore and study the disease at the molecular level. At present, the detection of lncRNAs mainly includes two forms of calculation and experiment. Due to the limitations of bio sequencing technology and ineluctable errors in sequencing processes, the detection effect of these methods is not very satisfactory. In this paper, we constructed a deep-learning model to effectively distinguish lncRNAs from mRNAs. We used k-mer embedding vectors obtained through training the GloVe algorithm as input features and set up the deep learning framework to include a bidirectional long short-term memory model (BLSTM) layer and a convolutional neural network (CNN) layer with three additional hidden layers. By testing our model, we have found that it obtained the best values of 97.9%, 96.4% and 99.0% in F1score, accuracy and auROC, respectively, which showed better classification performance than the traditional PLEK, CNCI and CPC methods for identifying lncRNAs. We hope that our model will provide effective help in distinguishing mature mRNAs from lncRNAs, and become a potential tool to help humans understand and detect the diseases associated with lncRNAs. Full article
(This article belongs to the Special Issue RNA Target Prediction Methods)
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Review

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Review
Integration of Bioinformatic Predictions and Experimental Data to Identify circRNA-miRNA Associations
Genes 2019, 10(9), 642; https://doi.org/10.3390/genes10090642 - 24 Aug 2019
Cited by 24 | Viewed by 1660
Abstract
Circular RNAs (circRNAs) have recently emerged as a novel class of transcripts, characterized by covalently linked 3′–5′ ends that result in the so-called backsplice junction. During the last few years, thousands of circRNAs have been identified in different organisms. Yet, despite their role [...] Read more.
Circular RNAs (circRNAs) have recently emerged as a novel class of transcripts, characterized by covalently linked 3′–5′ ends that result in the so-called backsplice junction. During the last few years, thousands of circRNAs have been identified in different organisms. Yet, despite their role as disease biomarker started to emerge, depicting their function remains challenging. Different studies have shown that certain circRNAs act as miRNA sponges, but any attempt to generalize from the single case to the “circ-ome” has failed so far. In this review, we explore the potential to define miRNA “sponging” as a more general function of circRNAs and describe the different approaches to predict miRNA response elements (MREs) in known or novel circRNA sequences. Moreover, we discuss how experiments based on Ago2-IP and experimentally validated miRNA:target duplexes can be used to either prioritize or validate putative miRNA-circRNA associations. Full article
(This article belongs to the Special Issue RNA Target Prediction Methods)
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