Special Issue "Bioinformatics Softwares and Databases for Non-Coding RNA Research"

A special issue of Non-Coding RNA (ISSN 2311-553X).

Deadline for manuscript submissions: closed (31 December 2016)

Special Issue Editors

Guest Editor
Prof. Dr. Jian-Hua Yang

Biotechnology Research Center, Sun Yat-sen University, Guangzhou 510275, China
Website | E-Mail
Interests: bioinformatics, computational RNomics, lncRNA, microRNA, ceRNA, RNA-binding protein, CLIP-Seq, cancer genomics, regulatory network
Guest Editor
Prof. Dr. Liang-Hu Qu

State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
Website | E-Mail
Interests: RNomics, RNA biology, ncRNA, miRNA, snoRNA,

Special Issue Information

Dear Colleagues,

Eukaryotic genomes encode thousands of small and long non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), PIWI-interacting RNAs (piRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs) and pseudogenes. These RNA molecules are emerging as important regulatory molecules in developmental, physiological, and pathological processes. Recent advances in high-throughput next-generation sequencing technologies have provided new biological insights on the complex regulatory mechanisms of ncRNAs as well as enabled the detection and profiling of known and novel ncRNAs at unprecedented sensitivity and depth. Bioinformatics softwares and databases provide powerful ways to integrate massive datasets that produced by high-throughput experiments to identify novel ncRNAs and explore the expression, evolution, modification, regulatory network, structure and function of diverse ncRNAs, and finally to link these ncRNAs to a variety of diseases, such as cancer, metabolic disorders and neurological diseases .

This special issue of Non-Coding RNA journal will be devoted to original papers and reviews about bioinformatics softwares and resources for non-coding RNA research.  This issue emphasis on various types of ncRNAs, such as miRNAs, lncRNAs, circRNAs, eRNAs, piRNAs, snoRNAs, endogenous siRNAs, regulatory small RNAs, pseudogenes, tRNA/snoRNA-derived RNA fragments, etc. The scope  of this special issue includes, but is not limited to, the following subjects:

  • novel databases or resources of non-coding RNAs
  • new softwares or pipelines for identifying novel ncRNAs from next-generation sequencing data or genomic sequences
  • predicting the functional targets and interacting RNA-Binding Proteins (RBPs) of ncRNAs
  • constructing regulatory networks of ncRNAs and inferring their function
  • decoding ncRNA structurome from high-throughput sequencing data
  • computational analysis of ncRNA modification
  • computational identification of disease-related ncRNAs

Prof. JianHua Yang
Guest Editor

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. Non-Coding RNA is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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

  • ncRNA
  • miRNA
  • lncRNA
  • database
  • software
  • pipeline
  • genome-wide sequencing

Published Papers (12 papers)

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Research

Jump to: Review

Open AccessArticle Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach
by , , and
Non-Coding RNA 2017, 3(2), 20; doi:10.3390/ncrna3020020
Received: 15 January 2017 / Revised: 28 March 2017 / Accepted: 10 April 2017 / Published: 19 April 2017
PDF Full-text (3867 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In the era of network medicine, pathway analysis methods play a central role in the prediction of phenotype from high throughput experiments. In this paper, we present a network-based systems biology approach capable of extracting disease-perturbed subpathways within pathway networks in connection with
[...] Read more.
In the era of network medicine, pathway analysis methods play a central role in the prediction of phenotype from high throughput experiments. In this paper, we present a network-based systems biology approach capable of extracting disease-perturbed subpathways within pathway networks in connection with expression data taken from The Cancer Genome Atlas (TCGA). Our system extends pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The framework enables the extraction, visualization, and analysis of statistically significant disease-specific subpathways through an easy to use web interface. Our analysis shows that the methodology is able to fill the gap in current techniques, allowing a more comprehensive analysis of the phenomena underlying disease states. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
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Open AccessArticle Present Scenario of Long Non-Coding RNAs in Plants
by , , , and
Non-Coding RNA 2017, 3(2), 16; doi:10.3390/ncrna3020016
Received: 31 December 2016 / Revised: 3 March 2017 / Accepted: 20 March 2017 / Published: 24 March 2017
Cited by 1 | PDF Full-text (265 KB) | HTML Full-text | XML Full-text
Abstract
Small non-coding RNAs have been extensively studied in plants over the last decade. In contrast, genome-wide identification of plant long non-coding RNAs (lncRNAs) has recently gained momentum. LncRNAs are now being recognized as important players in gene regulation, and their potent regulatory roles
[...] Read more.
Small non-coding RNAs have been extensively studied in plants over the last decade. In contrast, genome-wide identification of plant long non-coding RNAs (lncRNAs) has recently gained momentum. LncRNAs are now being recognized as important players in gene regulation, and their potent regulatory roles are being studied comprehensively in eukaryotes. LncRNAs were first reported in humans in 1992. Since then, research in animals, particularly in humans, has rapidly progressed, and a vast amount of data has been generated, collected, and organized using computational approaches. Additionally, numerous studies have been conducted to understand the roles of these long RNA species in several diseases. However, the status of lncRNA investigation in plants lags behind that in animals (especially humans). Efforts are being made in this direction using computational tools and high-throughput sequencing technologies, such as the lncRNA microarray technique, RNA-sequencing (RNA-seq), RNA capture sequencing, (RNA CaptureSeq), etc. Given the current scenario, significant amounts of data have been produced regarding plant lncRNAs, and this amount is likely to increase in the subsequent years. In this review we have documented brief information about lncRNAs and their status of research in plants, along with the plant-specific resources/databases for information retrieval on lncRNAs. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
Open AccessArticle Computational Characterization of ncRNA Fragments in Various Tissues of the Brassica rapa Plant
by , and
Non-Coding RNA 2017, 3(2), 17; doi:10.3390/ncrna3020017
Received: 30 January 2017 / Revised: 17 March 2017 / Accepted: 20 March 2017 / Published: 24 March 2017
PDF Full-text (2899 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Recently, a novel type of non-coding RNA (ncRNA), known as ncRNA fragments or ncRFs, has been characterised in various organisms, including plants. The biogenesis mechanism, function and abundance of ncRFs stemming from various ncRNAs are poorly understood, especially in plants. In this work,
[...] Read more.
Recently, a novel type of non-coding RNA (ncRNA), known as ncRNA fragments or ncRFs, has been characterised in various organisms, including plants. The biogenesis mechanism, function and abundance of ncRFs stemming from various ncRNAs are poorly understood, especially in plants. In this work, we have computationally analysed the composition of ncRNAs and the fragments that derive from them in various tissues of Brassica rapa plants, including leaves, meristem tissue, pollen, unfertilized and fertilized ova, embryo and endosperm. Detailed analysis of transfer RNA (tRNA) fragments (tRFs), ribosomal RNA (rRNA) fragments (rRFs), small nucleolar RNA (snoRNA) fragments (snoRFs) and small nuclear RNA (snRNA) fragments (snRFs) showed a predominance of tRFs, with the 26 nucleotides (nt) fraction being the largest. Mapping ncRF reads to full-length mature ncRNAs showed a strong bias for one or both termini. tRFs mapped predominantly to the 5′ end, whereas snRFs mapped to the 3′ end, suggesting that there may be specific biogenesis and retention mechanisms. In the case of tRFs, specific isoacceptors were enriched, including tRNAGly(UCC) and tRFAsp(GUC). The analysis showed that the processing of 26-nt tRF5′ occurred by cleavage at the last unpaired nucleotide of the loop between the D arm and the anticodon arm. Further support for the functionality of ncRFs comes from the analysis of binding between ncRFs and their potential targets. A higher average percentage of binding at the first half of fragments was observed, with the highest percentage being at 2–6 nt. To summarise, our analysis showed that ncRFs in B. rapa are abundantly produced in a tissue-specific manner, with bias toward a terminus, the bias toward the size of generated fragments and the bias toward the targeting of specific biological processes. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
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Open AccessArticle MicroRNA MultiTool: A Software for Identifying Modified and Unmodified Human microRNA Using Mass Spectrometry
by , and
Non-Coding RNA 2017, 3(1), 13; doi:10.3390/ncrna3010013
Received: 31 December 2016 / Revised: 10 March 2017 / Accepted: 10 March 2017 / Published: 16 March 2017
PDF Full-text (3003 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
microRNA (miRNA) are short endogenous non-coding RNA that play a crucial role in post-transcriptional gene regulation and have been implicated in the initiation and progression of 160+ human diseases. Excellent analytical methods have been developed for the measurement of miRNA by mass spectrometry.
[...] Read more.
microRNA (miRNA) are short endogenous non-coding RNA that play a crucial role in post-transcriptional gene regulation and have been implicated in the initiation and progression of 160+ human diseases. Excellent analytical methods have been developed for the measurement of miRNA by mass spectrometry. However, interpretation of mass spectrometric data has been an incapacitating bottleneck in miRNA identification. This study details the development of MicroRNA MultiTool, a software for the identification of miRNA from mass spectrometric data. The software includes capabilities such as miRNA search and mass calculator, modified miRNA mass calculator, and miRNA fragment search. MicroRNA MultiTool bridges the gap between experimental data and identification of miRNA by providing a rapid means of mass spectrometric data interpretation. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
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Open AccessArticle PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants
by , , , , , , , and
Non-Coding RNA 2017, 3(1), 11; doi:10.3390/ncrna3010011
Received: 29 December 2016 / Revised: 19 February 2017 / Accepted: 24 February 2017 / Published: 4 March 2017
PDF Full-text (2086 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play
[...] Read more.
Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM). We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane (Saccharum spp.) and in maize (Zea mays). From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
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Open AccessArticle CirComPara: A Multi‐Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA‐seq Data
by , , and
Non-Coding RNA 2017, 3(1), 8; doi:10.3390/ncrna3010008
Received: 22 December 2016 / Accepted: 27 January 2017 / Published: 10 February 2017
PDF Full-text (2210 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Circular RNAs (circRNAs) are generated by backsplicing of immature RNA forming covalently closed loops of intron/exon RNA molecules. Pervasiveness, evolutionary conservation, massive and regulated expression, and posttranscriptional regulatory roles of circRNAs in eukaryotes have been appreciated and described only recently. Moreover, being easily
[...] Read more.
Circular RNAs (circRNAs) are generated by backsplicing of immature RNA forming covalently closed loops of intron/exon RNA molecules. Pervasiveness, evolutionary conservation, massive and regulated expression, and posttranscriptional regulatory roles of circRNAs in eukaryotes have been appreciated and described only recently. Moreover, being easily detectable disease markers, circRNAs undoubtedly represent a molecular class with high bearing on molecular pathobiology. CircRNAs can be detected from RNAseq data using appropriate computational methods to identify the sequence reads spanning backsplice junctions that do not colinearly map to the reference genome. To this end, several programs were developed and critical assessment of various strategies and tools suggested the combination of at least two methods as good practice to guarantee robust circRNA detection. Here,we present CirComPara (http://github.com/egaffo/CirComPara), an automated bioinformatics pipeline, to detect, quantify and annotate circRNAs from RNAseq data using in parallel four different methods for backsplice identification. CirComPara also provides quantification of linear RNAs and gene expression, ultimately comparing and correlating circRNA and gene/transcript expression level. We applied our method to RNAseqdata of monocyte and macrophage samples in relation to haploinsufficiency of the RNAbinding splicing factor Quaking (QKI). The biological relevance of the results, in terms of number, types and variations of circRNAs expressed, illustrates CirComPara potential to enlarge the knowledge of the transcriptome, adding details on the circRNAome, and facilitating further computational and experimental studies. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
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Open AccessArticle oncoNcRNA: A Web Portal for Exploring the Non-Coding RNAs with Oncogenic Potentials in Human Cancers
by , , , and
Non-Coding RNA 2017, 3(1), 7; doi:10.3390/ncrna3010007
Received: 30 November 2016 / Revised: 12 January 2017 / Accepted: 20 January 2017 / Published: 7 February 2017
PDF Full-text (1990 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Non-coding RNAs (ncRNAs) have been shown to contribute to tumorigenesis and progression. However, the functions of the majority of ncRNAs remain unclear. Through integrating published large-scale somatic copy number alterations (SCNAs) data from various human cancer types, we have developed oncoNcRNA, a user-friendly
[...] Read more.
Non-coding RNAs (ncRNAs) have been shown to contribute to tumorigenesis and progression. However, the functions of the majority of ncRNAs remain unclear. Through integrating published large-scale somatic copy number alterations (SCNAs) data from various human cancer types, we have developed oncoNcRNA, a user-friendly web portal to explore ncRNAs with oncogenic potential in human cancers. The portal characterizes the SCNAs of over 58,000 long non-coding RNAs (lncRNAs), 34,000 piwi-interacting RNAs (piRNAs), 2700 microRNAs (miRNAs), 600 transfer RNAs (tRNAs) and 400 small nucleolar RNAs (snoRNAs) in 64 human cancer types. It enables researchers to rapidly and intuitively analyze the oncogenic potential of ncRNAs of interest. Indeed, we have discovered a large number of ncRNAs which are frequently amplified or deleted within and across tumor types. Moreover, we built a web-based tool, Correlations, to explore the relationships between gene expression and copy number from ~10,000 tumor samples in 36 cancer types identified by The Cancer Genome Atlas (TCGA). oncoNcRNA is a valuable tool for investigating the function and clinical relevance of ncRNAs in human cancers. oncoNcRNA is freely available at http://rna.sysu.edu.cn/onconcrna/. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
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Review

Jump to: Research

Open AccessReview Identification of RNA Polymerase III-Transcribed SINEs at Single-Locus Resolution from RNA Sequencing Data
by and
Non-Coding RNA 2017, 3(1), 15; doi:10.3390/ncrna3010015
Received: 23 December 2016 / Revised: 27 February 2017 / Accepted: 14 March 2017 / Published: 21 March 2017
PDF Full-text (2123 KB) | HTML Full-text | XML Full-text
Abstract
Short Interspersed Element (SINE) retrotransposons are one of the most abundant DNA repeat elements in the human genome. They have been found to impact the expression of protein-coding genes, but the possible roles in cell physiology of their noncoding RNAs, generated by RNA
[...] Read more.
Short Interspersed Element (SINE) retrotransposons are one of the most abundant DNA repeat elements in the human genome. They have been found to impact the expression of protein-coding genes, but the possible roles in cell physiology of their noncoding RNAs, generated by RNA polymerase (Pol) III, are just starting to be elucidated. For this reason, Short Interspersed Element (SINE) expression profiling is becoming mandatory to obtain a comprehensive picture of their regulatory roles. However, their repeated nature and frequent location within Pol II-transcribed genes represent a serious obstacle to the identification and quantification of genuine, Pol III-derived SINE transcripts at single-locus resolution on a genomic scale. Among the recent Next Generation Sequencing technologies, only RNA sequencing (RNA-Seq) holds the potential to solve these issues, even though both technical and biological matters need to be taken into account. A bioinformatic pipeline has been recently set up that, by exploiting RNA-seq features and knowledge of SINE transcription mechanisms, allows for easy identification and profiling of transcriptionally active genomic loci which are a source of genuine Pol III SINE transcripts. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
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Open AccessReview RNA Biomarkers: Frontier of Precision Medicine for Cancer
by , , , , , and
Non-Coding RNA 2017, 3(1), 9; doi:10.3390/ncrna3010009
Received: 30 December 2016 / Accepted: 13 February 2017 / Published: 20 February 2017
PDF Full-text (2557 KB) | HTML Full-text | XML Full-text
Abstract
As an essential part of central dogma, RNA delivers genetic and regulatory information and reflects cellular states. Based on high‐throughput sequencing technologies, cumulating data show that various RNA molecules are able to serve as biomarkers for the diagnosis and prognosis of various diseases,
[...] Read more.
As an essential part of central dogma, RNA delivers genetic and regulatory information and reflects cellular states. Based on high‐throughput sequencing technologies, cumulating data show that various RNA molecules are able to serve as biomarkers for the diagnosis and prognosis of various diseases, for instance, cancer. In particular, detectable in various bio‐fluids, such as serum, saliva and urine, extracellular RNAs (exRNAs) are emerging as non‐invasive biomarkers for earlier cancer diagnosis, tumor progression monitor, and prediction of therapy response. In this review, we summarize the latest studies on various types of RNA biomarkers, especially extracellular RNAs, in cancer diagnosis and prognosis, and illustrate several well‐known RNA biomarkers of clinical utility. In addition, we describe and discuss general procedures and issues in investigating exRNA biomarkers, and perspectives on utility of exRNAs in precision medicine. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
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Open AccessReview A Brief Review of RNA-Protein Interaction Database Resources
by , , and
Non-Coding RNA 2017, 3(1), 6; doi:10.3390/ncrna3010006
Received: 27 November 2016 / Accepted: 23 January 2017 / Published: 27 January 2017
PDF Full-text (182 KB) | HTML Full-text | XML Full-text
Abstract
RNA-protein interactions play critical roles in various biological processes. By collecting and analyzing the RNA-protein interactions and binding sites from experiments and predictions, RNA-protein interaction databases have become an essential resource for the exploration of the transcriptional and post-transcriptional regulatory network. Here, we
[...] Read more.
RNA-protein interactions play critical roles in various biological processes. By collecting and analyzing the RNA-protein interactions and binding sites from experiments and predictions, RNA-protein interaction databases have become an essential resource for the exploration of the transcriptional and post-transcriptional regulatory network. Here, we briefly review several widely used RNA-protein interaction database resources developed in recent years to provide a guide of these databases. The content and major functions in databases are presented. The brief description of database helps users to quickly choose the database containing information they interested. In short, these RNA-protein interaction database resources are continually updated, but the current state shows the efforts to identify and analyze the large amount of RNA-protein interactions. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
Open AccessReview Computational Approaches to tRNA-Derived Small RNAs
by , , , and
Non-Coding RNA 2017, 3(1), 2; doi:10.3390/ncrna3010002
Received: 23 November 2016 / Revised: 21 December 2016 / Accepted: 26 December 2016 / Published: 4 January 2017
PDF Full-text (1253 KB) | HTML Full-text | XML Full-text
Abstract
tRNA-derived small RNAs (tDRs) are a group of small, non-coding RNAs derived from transfer RNAs (tRNAs). They can be classified as tRNA halves and tRNA-derived small RNA fragments (tRFs). Accumulating experimental evidence suggests their functional roles in cells and in various biological processes.
[...] Read more.
tRNA-derived small RNAs (tDRs) are a group of small, non-coding RNAs derived from transfer RNAs (tRNAs). They can be classified as tRNA halves and tRNA-derived small RNA fragments (tRFs). Accumulating experimental evidence suggests their functional roles in cells and in various biological processes. Advances in next-generation sequencing (NGS) techniques allow a large amount of small RNA deep-sequencing data to be generated. To investigate tDRs from these data, software to identify tDRs and databases to retrieve or manage tDR data have been devised. In this review, we summarized the tools and databases for tDR identification and collection, with the aim of helping researchers choose the best tools for their analysis and inspiring the invention or improvement of tools in the field. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
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Open AccessReview Recent Advances in Identification of RNA Modifications
by and
Non-Coding RNA 2017, 3(1), 1; doi:10.3390/ncrna3010001
Received: 22 November 2016 / Revised: 19 December 2016 / Accepted: 23 December 2016 / Published: 28 December 2016
PDF Full-text (191 KB) | HTML Full-text | XML Full-text
Abstract
RNA modifications are involved in a broad spectrum of biological and physiological processes. To reveal the functions of RNA modifications, it is important to accurately predict their positions. Although high-throughput experimental techniques have been proposed, they are cost-ineffective. As good complements of experiments,
[...] Read more.
RNA modifications are involved in a broad spectrum of biological and physiological processes. To reveal the functions of RNA modifications, it is important to accurately predict their positions. Although high-throughput experimental techniques have been proposed, they are cost-ineffective. As good complements of experiments, many computational methods have been proposed to predict RNA modification sites in recent years. In this review, we will summarize the existing computational approaches directed at predicting RNA modification sites. We will also discuss the challenges and future perspectives in developing reliable methods for predicting RNA modification sites. Full article
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
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