Annotation Of Non-coding RNAs Research Articles

Computational approaches and challenges for identification and annotation of non-coding RNAs using RNA-Seq.

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNASeq applications have evolved in conjunction with sequence technology and bioinformatic tool advances. In this review, we explained various computational resources, tools, and bioinformatics analyses advancement for small and large non-coding RNAs. These include non-coding RNAs (ncRNAs) such as piwi, micro, circular, and long ncRNAs. In addition, this article discusses future challenges, single-cell level sequencing for non-coding RNAs, and advantages of using long-read sequencing to annotate lncRNAs.

NcFANs v2.0: an integrative platform for functional annotation of non-coding RNAs.

Increasing evidence proves the essential regulatory roles of non-coding RNAs (ncRNAs) in biological processes. However, characterizing the specific functions of ncRNAs remains a challenging task, owing to the intensive consumption of the experimental approaches. Here, we present an online platform ncFANs v2.0 that is a significantly enhanced version of our previous ncFANs to provide multiple computational methods for ncRNA functional annotation. Specifically, ncFANs v2.0 was updated to embed three functional modules, including ncFANs-NET, ncFANs-eLnc and ncFANs-CHIP. ncFANs-NET is a new module designed for data-free functional annotation based on four kinds of pre-built networks, including the co-expression network, co-methylation network, long non-coding RNA (lncRNA)-centric regulatory network and random forest-based network. ncFANs-eLnc enables the one-stop identification of enhancer-derived lncRNAs from the de novo assembled transcriptome based on the user-defined or our pre-annotated enhancers. Moreover, ncFANs-CHIP inherits the original functions for microarray data-based functional annotation and supports more chip types. We believe that our ncFANs v2.0 carries sufficient convenience and practicability for biological researchers and facilitates unraveling the regulatory mechanisms of ncRNAs. The ncFANs v2.0 server is freely available at http://bioinfo.org/ncfans or http://ncfans.gene.ac.

LncMachine: a machine learning algorithm for long noncoding RNA annotation in plants.

Following the elucidation of the critical roles they play in numerous important biological processes, long noncoding RNAs (lncRNAs) have gained vast attention in recent years. Manual annotation of lncRNAs is restricted by known gene annotations and is prone to false prediction due to the incompleteness of available data. However, with the advent of high-throughput sequencing technologies, a magnitude of high-quality data has become available for annotation, especially for plant species such as wheat. Here, we compared prediction accuracies of several machine learning algorithms using a 10-fold cross-validation. This study includes a comprehensive feature selection step to refine irrelevant and repeated features. We present a crop-specific, alignment-free coding potential prediction tool, LncMachine, that performs at higher prediction accuracies than the currently available popular tools (CPC2, CPAT, and CNIT) when used with the Random Forest algorithm. Further, LncMachine with Random Forest performed well on human and mouse data, with an average accuracy of 92.67%. LncMachine only requires either a FASTA file or a TAB separated CSV file containing features as input files. LncMachine can deploy several user-provided algorithms in real time and therefore be effortlessly applied to a wide range of studies.

FANTOM enters 20th year: expansion of transcriptomic atlases and functional annotation of non-coding RNAs.

The Functional ANnoTation Of the Mammalian genome (FANTOM) Consortium has continued to provide extensive resources in the pursuit of understanding the transcriptome, and transcriptional regulation, of mammalian genomes for the last 20 years. To share these resources with the research community, the FANTOM web-interfaces and databases are being regularly updated, enhanced and expanded with new data types. In recent years, the FANTOM Consortium's efforts have been mainly focused on creating new non-coding RNA datasets and resources. The existing FANTOM5 human and mouse miRNA atlas was supplemented with rat, dog, and chicken datasets. The sixth (latest) edition of the FANTOM project was launched to assess the function of human long non-coding RNAs (lncRNAs). From its creation until 2020, FANTOM6 has contributed to the research community a large dataset generated from the knock-down of 285 lncRNAs in human dermal fibroblasts; this is followed with extensive expression profiling and cellular phenotyping. Other updates to the FANTOM resource includes the reprocessing of the miRNA and promoter atlases of human, mouse and chicken with the latest reference genome assemblies. To facilitate the use and accessibility of all above resources we further enhanced FANTOM data viewers and web interfaces. The updated FANTOM web resource is publicly available at https://fantom.gsc.riken.jp/.

NONCODEV6: an updated database dedicated to long non-coding RNA annotation in both animals and plants.

NONCODE (http://www.noncode.org/) is a comprehensive database of collection and annotation of noncoding RNAs, especially long non-coding RNAs (lncRNAs) in animals. NONCODEV6 is dedicated to providing the full scope of lncRNAs across plants and animals. The number of lncRNAs in NONCODEV6 has increased from 548 640 to 644 510 since the last update in 2017. The number of human lncRNAs has increased from 172 216 to 173 112. The number of mouse lncRNAs increased from 131 697 to 131 974. The number of plant lncRNAs is 94 697. The relationship between lncRNAs in human and cancer were updated with transcriptome sequencing profiles. Three important new features were also introduced in NONCODEV6: (i) updated human lncRNA-disease relationships, especially cancer; (ii) lncRNA annotations with tissue expression profiles and predicted function in five common plants; iii) lncRNAs conservation annotation at transcript level for 23 plant species. NONCODEV6 is accessible through http://www.noncode.org/.

Exploring Non-Coding RNAs in RNAcentral.

Non-coding RNAs are essential for all life and carry out a wide range of functions. Information about these molecules is distributed across dozens of specialized resources. RNAcentral is a database of non-coding RNA sequences that provides a unified access point to non-coding RNA annotations from >40 member databases and helps provide insight into the function of these RNAs. This article describes different ways of accessing the data, including searching the website and retrieving the data programmatically over web APIs and a public database. We also demonstrate an example Galaxy workflow for using RNAcentral for RNA-seq differential expression analysis. RNAcentral is available at https://rnacentral.org. © 2020 The Authors. Basic Protocol 1: Viewing RNAcentral sequence reports Basic Protocol 2: Using RNAcentral text search to explore ncRNA sequences Basic Protocol 3: Using RNAcentral sequence search Basic Protocol 4: Using RNAcentral FTP archive Support Protocol 1: Using web APIs for programmatic data access Support Protocol 2: Using public Postgres database to export large datasets Support Protocol 3: Analyze non-coding RNA in RNA-seq datasets using RNAcentral and Galaxy.

Exploring the overlap between rheumatoid arthritis susceptibility loci and long non-coding RNA annotations.

Whilst susceptibility variants for many complex diseases, such as rheumatoid arthritis (RA), have been well characterised, the mechanism by which risk is mediated is still unclear for many loci. This is especially true for the majority of variants that do not affect protein-coding regions. lncRNA represent a group of molecules that have been shown to be enriched amongst variants associated with RA and other complex diseases, compared to random variants. In order to establish to what degree direct disruption of lncRNA may represent a potential mechanism for mediating RA susceptibility, we chose to further explore this overlap. By testing the ability of annotated features to improve a model of disease susceptibility, we were able to demonstrate a local enrichment of enhancers from immune-relevant cell types amongst RA susceptibility variants (log2 enrichment 3.40). This was not possible for lncRNA annotations in general, however a small, but significant enrichment was observed for immune-enriched lncRNA (log2 enrichment 0.867002). This enrichment was no longer apparent when the model was conditioned on immune-relevant enhancers (log2 enrichment -0.372734), suggesting that direct disruption of lncRNA sequence, independent of enhancer disruption, does not represent a major mechanism by which susceptibility to complex diseases is mediated. Furthermore, we demonstrated that, in keeping with general lncRNA characteristics, immune-enriched lncRNA are expressed at low levels that may not be amenable to functional characterisation.

Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA.

The study of RNA expression is the fastest growing area of genomic research. However, despite the dramatic increase in the number of sequenced transcriptomes, we still do not have accurate estimates of the number and expression levels of non-coding RNA genes. Non-coding transcripts are often overlooked due to incomplete genome annotation. In this study, we use annotation-independent detection of RNA reads generated using a reverse transcriptase with low structure bias to identify non-coding RNA. Transcripts between 20 and 500 nucleotides were filtered and crosschecked with non-coding RNA annotations revealing 111 non-annotated non-coding RNAs expressed in different cell lines and tissues. Inspecting the sequence and structural features of these transcripts indicated that 60% of these transcripts correspond to new snoRNA and tRNA-like genes. The identified genes exhibited features of their respective families in terms of structure, expression, conservation and response to depletion of interacting proteins. Together, our data reveal a new group of RNA that are difficult to detect using standard gene prediction and RNA sequencing techniques, suggesting that reliance on actual gene annotation and sequencing techniques distorts the perceived architecture of the human transcriptome.

NONCODEV5: a comprehensive annotation database for long non-coding RNAs.

NONCODE (http://www.bioinfo.org/noncode/) is a systematic database that is dedicated to presenting the most complete collection and annotation of non-coding RNAs (ncRNAs), especially long non-coding RNAs (lncRNAs). Since NONCODE 2016 was released two years ago, the amount of novel identified ncRNAs has been enlarged by the reduced cost of next-generation sequencing, which has produced an explosion of newly identified data. The third-generation sequencing revolution has also offered longer and more accurate annotations. Moreover, accumulating evidence confirmed by biological experiments has provided more comprehensive knowledge of lncRNA functions. The ncRNA data set was expanded by collecting newly identified ncRNAs from literature published over the past two years and integration of the latest versions of RefSeq and Ensembl. Additionally, pig was included in the database for the first time, bringing the total number of species to 17. The number of lncRNAs in NONCODEv5 increased from 527 336 to 548 640. NONCODEv5 also introduced three important new features: (i) human lncRNA–disease relationships and single nucleotide polymorphism-lncRNA–disease relationships were constructed; (ii) human exosome lncRNA expression profiles were displayed; (iii) the RNA secondary structures of NONCODE human transcripts were predicted. NONCODEv5 is also accessible through http://www.noncode.org/.

Abstract P278: Identification of Differentially Expressed Aortic Genes in Brown Norway Introgressed Chromosome 2 Segments into Hypertensive Dahl Salt Sensitive Rats

Background: Rat chromosome (RNO) 2 introgression from normotensive Brown Norway (BN) rats into hypertensive Dahl salt sensitive (SS) background (consomic SB2) reduced vascular inflammation. We hypothesized that the BN-RNO2 contains genes that reduce vascular inflammation, which could be identified using microRNA (miRNA) and total RNA expression profiling in aorta of congenic rats containing different portions of BN-RNO2 on the SS background. Methods and Results: Twelve-to-13-week-old male SS rats and congenic rats containing the distal portion of BN-RNO2 (SB2a), the middle segment (SB2b) and the proximal segment (SB2e) on the SS background, fed a normal-salt diet, were studied. Systolic blood pressure (SBP) was measured by telemetry. SBP was lower in SB2a and SB2b but not SB2e compared to SS (125±3, 127±6, 138±4 vs 146±2 mm Hg, P <0.05). Total RNA was extracted from aorta and used to construct libraries for small and total RNA sequencing using Illumina HiSeq-2500. The bioinformatics pipeline included: FastQC for quality control, STAR for genome alignment to Rattus norvegicus release-86, mirdeep2 for miRNA annotation and counting, Htseq-count for mRNA and long non-coding RNA annotation and counting; R for differential expression analysis. Differentially expressed miRNAs and genes (mRNA and non-coding RNA) were identified in SB2a vs SS (miRNAs: 3 up and 2 down, genes: 1 up and 3 down), SB2b vs SS (miRNAs: 2 up and 3 down, genes: 67 up and 112 down) and SB2e vs SS (miRNAs: 29 up and 25 down, genes: 12 up and 35 down), with FDR<0.05. Differentially expressed genes encoded within different BN-RNO2 congenic portions were identified in SB2a vs SS (2 down), SB2b vs SS (14 up and 18 down) and SB2e vs SS (1 down). Conclusions and Perspectives: Differentially expressed BN-RNO2 encoded genes were identified in aorta of congenic SB2a, SB2b and SB2e rats. Whether these genes play a role in inflammation or vascular injury remains to be determined.

Abstract P279: Mapping of Chromosome 2 Differentially Expressed Aortic Genes Linked to Vascular Inflammation Using Congenic Rats Fed a High-salt Diet

Background: Three congenic rat strains (SB2a, SB2b and SB2e) were created by chromosome (Chr) 2 fragment introgression from normotensive Brown Norway (BN) rats into hypertensive Dahl salt sensitive (SS) background. SB2a and SB2b rats fed a normal-salt diet presented reduced blood pressure (BP) and inflammation when compared to SS rats. We hypothesized that BN-Chr2 contains antihypertensive and anti-inflammatory genes that could prevent high-salt diet (HSD)-induced BP elevation and vascular injury in SB2a and SB2b rats. These genes will be identified using microRNA (miRNA) and total RNA expression profiling analysis in aorta of congenic rats fed a HSD. Methods and Results: Four-to-6 week-old male SS, SS, SB2a and SB2b rats were fed a HSD (4% NaCl) for 8 weeks or until they developed a stroke as manifested by seizures. Systolic blood pressure (SBP) was measured by telemetry. Systolic BP was higher in SB2b but not SB2a when compared to SS (185±8, 167±7 vs 168±5 mm Hg). Total RNA was extracted from aorta and used to construct libraries for small and total RNA sequencing using Illumina HiSeq-2500. The bioinformatics pipeline included: FastQC for quality control, STAR for genome (Rattus norvegicus, release-86) alignment, mirdeep2 for miRNA annotation and counting, Htseq-count for mRNA and long non-coding RNA annotation and counting; R for differential expression analysis. Differentially expressed miRNAs and genes (mRNAs and non-coding RNAs) were identified in SB2a vs SS (miRNAs: 11 up and 10 down; genes: 92 up and 91 down) and in SB2b vs SS (miRNAs: 3 up and 2 down; genes: 10 up and 13 down) with FDR<0.05. Differentially expressed genes encoded within different BN-Chr2 congenic portions were identified in SB2a vs SS (genes: 7 up and 2 down) and SB2b vs SS (genes: 6 up and 4 down). Conclusions and Perspectives: Differentially expressed BN-Chr2 encoded genes were identified in aorta of congenic SB2a and SB2b rats fed HSD. Whether these genes play a role in HSD-induced BP elevation and vascular inflammation remains to be determined.

Using the NONCODE Database Resource.

NONCODE is a comprehensive database that aims to present the most complete collection and annotation of non-coding RNAs, especially long non-coding RNAs (lncRNA genes), and thus NONCODE is essential to modern biological and medical research. Scientists are producing a flood of new data from which new lncRNA genes and lncRNA-disease relationships are continually being identified. NONCODE assimilates such information from a wide variety of sources including published articles, RNA-seq data, micro-array data and databases on genetic variation (dbSNP) and genome-wide associations (GWAS). NONCODE organizes all this information and makes it freely available to the public via the Internet. The NONCODE protocol provides step-by-step instructions on how to browse and search lncRNA information such as sequence, expression, and disease relationships, how to use the tools for functional prediction, species conservation assays, blast analysis, identifier conversion, and, finally, how to submit sequences to identify lncRNA genes. As of Dec 2016, NONCODE has cataloged 487,851 lncRNA genes sequenced from 16 species. © 2017 by John Wiley & Sons, Inc.

In Silico identification and annotation of non-coding RNAs by RNA-seq and De Novo assembly of the transcriptome of Tomato Fruits.

The complexity of the tomato (Solanum lycopersicum) transcriptome has not yet been fully elucidated. To gain insights into the diversity and features of coding and non-coding RNA molecules of tomato fruits, we generated strand-specific libraries from berries of two tomato cultivars grown in two open-field conditions with different soil type. Following high-throughput Illumina RNA-sequencing (RNA-seq), more than 90% of the reads (over one billion, derived from twelve dataset) were aligned to the tomato reference genome. We report a comprehensive analysis of the transcriptome, improved with 39,095 transcripts, which reveals previously unannotated novel transcripts, natural antisense transcripts, long non-coding RNAs and alternative splicing variants. In addition, we investigated the sequence variants between the cultivars under investigation to highlight their genetic difference. Our strand-specific analysis allowed us to expand the current tomato transcriptome annotation and it is the first to reveal the complexity of the poly-adenylated RNA world in tomato. Moreover, our work demonstrates the usefulness of strand specific RNA-seq approach for the transcriptome-based genome annotation and provides a resource valuable for further functional studies.

FEELnc: a tool for long non-coding RNA annotation and its application to the dog transcriptome.

Whole transcriptome sequencing (RNA-seq) has become a standard for cataloguing and monitoring RNA populations. One of the main bottlenecks, however, is to correctly identify the different classes of RNAs among the plethora of reconstructed transcripts, particularly those that will be translated (mRNAs) from the class of long non-coding RNAs (lncRNAs). Here, we present FEELnc (FlExible Extraction of LncRNAs), an alignment-free program that accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-the-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE data sets. The program also provides specific modules that enable the user to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to identify lncRNAs even in the absence of a training set of non-coding RNAs. We used FEELnc on a real data set comprising 20 canine RNA-seq samples produced by the European LUPA consortium to substantially expand the canine genome annotation to include 10 374 novel lncRNAs and 58 640 mRNA transcripts. FEELnc moves beyond conventional coding potential classifiers by providing a standardized and complete solution for annotating lncRNAs and is freely available at https://github.com/tderrien/FEELnc.

Automated detection of ncRNAs in the draft genome sequence of a colonial tunicate: the carpet sea squirt Didemnum vexillum.

BackgroundThe colonial ascidian Didemnum vexillum, sea carpet squirt, is not only a key marine organism to study morphological ancestral patterns of chordates evolution but it is also of great ecological importance due to its status as a major invasive species. Non-coding RNAs, in particular microRNAs (miRNAs), are important regulatory genes that impact development and environmental adaptation. Beyond miRNAs, not much in known about tunicate ncRNAs.ResultsWe provide here a comprehensive homology-based annotation of non-coding RNAs in the recently sequenced genome of D. vexillum. To this end we employed a combination of several computational approaches, including blast searches with a wide range of parameters, and secondary structured centered survey with infernal. The resulting candidate set was curated extensively to produce a high-quality ncRNA annotation of the first draft of the D. vexillum genome. It comprises 57 miRNA families, 4 families of ribosomal RNAs, 22 isoacceptor classes of tRNAs (of which more than 72 % of loci are pseudogenes), 13 snRNAs, 12 snoRNAs, and 1 other RNA family. Additionally, 21 families of mitochondrial tRNAs and 2 of mitochondrial ribosomal RNAs and 1 long non-coding RNA.ConclusionsThe comprehensive annotation of the D. vexillum non-coding RNAs provides a starting point towards a better understanding of the restructuring of the small RNA system in ascidians. Furthermore it provides a valuable research for efforts to establish detailed non-coding RNA annotations for other recently published and recently sequences in tunicate genomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2934-5) contains supplementary material, which is available to authorized users.

Functional annotation of the vlinc class of non-coding RNAs using systems biology approach.

Functionality of the non-coding transcripts encoded by the human genome is the coveted goal of the modern genomics research. While commonly relied on the classical methods of forward genetics, integration of different genomics datasets in a global Systems Biology fashion presents a more productive avenue of achieving this very complex aim. Here we report application of a Systems Biology-based approach to dissect functionality of a newly identified vast class of very long intergenic non-coding (vlinc) RNAs. Using highly quantitative FANTOM5 CAGE dataset, we show that these RNAs could be grouped into 1542 novel human genes based on analysis of insulators that we show here indeed function as genomic barrier elements. We show that vlincRNAs genes likely function in cis to activate nearby genes. This effect while most pronounced in closely spaced vlincRNA–gene pairs can be detected over relatively large genomic distances. Furthermore, we identified 101 vlincRNA genes likely involved in early embryogenesis based on patterns of their expression and regulation. We also found another 109 such genes potentially involved in cellular functions also happening at early stages of development such as proliferation, migration and apoptosis. Overall, we show that Systems Biology-based methods have great promise for functional annotation of non-coding RNAs.

NONCODEv4: Annotation of Noncoding RNAs with Emphasis on Long Noncoding RNAs.

The rapid development of high-throughput sequencing technologies and bioinformatics algorithms now enables detection and profiling of a large number of noncoding transcripts. Long noncoding RNAs (lncRNAs), which are longer than 200 nucleotides, are accumulating with important roles involved in biological processes and tissue physiology. In this chapter, we describe the use of NONCODEv4, a database that provide a comprehensive catalog of noncoding RNAs with particularly detailed annotations for lncRNAs. NONCODEv4 stores more than half million transcripts, of which more than 200,000 are lncRNAs. NONCODEv4 raises the concept of lncRNA genes and explores their expression and functions based on public transcriptome data. NONCODEv4 also integrated a series of online tools and have a web interface easy to use. NONCODEv4 is available at http://www.noncode.org/ http://www.bioinfo.org/ noncode.

NONCODE 2016: an informative and valuable data source of long non-coding RNAs.

NONCODE (http://www.bioinfo.org/noncode/) is an interactive database that aims to present the most complete collection and annotation of non-coding RNAs, especially long non-coding RNAs (lncRNAs). The recently reduced cost of RNA sequencing has produced an explosion of newly identified data. Revolutionary third-generation sequencing methods have also contributed to more accurate annotations. Accumulative experimental data also provides more comprehensive knowledge of lncRNA functions. In this update, NONCODE has added six new species, bringing the total to 16 species altogether. The lncRNAs in NONCODE have increased from 210 831 to 527,336. For human and mouse, the lncRNA numbers are 167,150 and 130,558, respectively. NONCODE 2016 has also introduced three important new features: (i) conservation annotation; (ii) the relationships between lncRNAs and diseases; and (iii) an interface to choose high-quality datasets through predicted scores, literature support and long-read sequencing method support. NONCODE is also accessible through http://www.noncode.org/.

A Machine Learning Approach for Accurate Annotation of Noncoding RNAs.

Searching genomes to locate noncoding RNA genes with known secondary structure is an important problem in bioinformatics. In general, the secondary structure of a searched noncoding RNA is defined with a structure model constructed from the structural alignment of a set of sequences from its family. Computing the optimal alignment between a sequence and a structure model is the core part of an algorithm that can search genomes for noncoding RNAs. In practice, a single structure model may not be sufficient to capture all crucial features important for a noncoding RNA family. In this paper, we develop a novel machine learning approach that can efficiently search genomes for noncoding RNAs with high accuracy. During the search procedure, a sequence segment in the searched genome sequence is processed and a feature vector is extracted to represent it. Based on the feature vector, a classifier is used to determine whether the sequence segment is the searched ncRNA or not. Our testing results show that this approach is able to efficiently capture crucial features of a noncoding RNA family. Compared with existing search tools, it significantly improves the accuracy of genome annotation.

Spatially conserved regulatory elements identified within human and mouse Cd247 gene using high-throughput sequencing data from the ENCODE project.

The Cd247 gene encodes for a transmembrane protein important for the expression and assembly of TCR/CD3 complex on the surface of T lymphocytes. Down-regulation of CD247 has functional consequences in systemic autoimmunity and has been shown to be associated with Type 1 Diabetes in NOD mouse. In this study, we have utilized the wealth of high-throughput sequencing data produced during the Encyclopedia of DNA Elements (ENCODE) project to identify spatially conserved regulatory elements within the Cd247 gene from human and mouse. We show the presence of two transcription factor binding sites, supported by histone marks and ChIP-seq data, that specifically have features of an enhancer and a promoter, respectively. We also identified a putative long non-coding RNA from the characteristically long first intron of the Cd247 gene. The long non-coding RNA annotation is supported by manual annotations from the GENCODE project in human and our expression quantification analysis performed in NOD and B6 mice using qRT-PCR. Furthermore, 17 of the 23 SNPs already known to be implicated with T1D were observed within the long non-coding RNA region in mouse. The spatially conserved regulatory elements identified in this study have the potential to enrich our understanding of the role of Cd247 gene in autoimmune diabetes.