RCARE: RNA Sequence Comparison and Annotation for RNA Editing

RESIC: A Tool for Comprehensive Adenosine to Inosine RNA Editing Site Identification and Classification

Adenosine to inosine (A-to-I) RNA editing, the most prevalent type of RNA editing in metazoans, is carried out by adenosine deaminases (ADARs) in double-stranded RNA regions. Several computational approaches have been recently developed to identify A-to-I RNA editing sites from sequencing data, each addressing a particular issue. Here, we present RNA Editing Sites Identification and Classification (RESIC), an efficient pipeline that combines several approaches for the detection and classification of RNA editing sites. The pipeline can be used for all organisms and can use any number of RNA-sequencing datasets as input. RESIC provides (1) the detection of editing sites in both repetitive and non-repetitive genomic regions; (2) the identification of hyper-edited regions; and (3) optional exclusion of polymorphism sites to increase reliability, based on DNA, and ADAR-mutant RNA sequencing datasets, or SNP databases. We demonstrate the utility of RESIC by applying it to human, successfully overlapping and extending the list of known putative editing sites. We further tested changes in the patterns of A-to-I RNA editing, and RNA abundance of ADAR enzymes, following SARS-CoV-2 infection in human cell lines. Our results suggest that upon SARS-CoV-2 infection, compared to mock, the number of hyper editing sites is increased, and in agreement, the activity of ADAR1, which catalyzes hyper-editing, is enhanced. These results imply the involvement of A-to-I RNA editing in conceiving the unpredicted phenotype of COVID-19 disease. RESIC code is open-source and is easily extendable.

A-to-I RNA Editing in Cancer: From Evaluating the Editing Level to Exploring the Editing Effects

As an important regulatory mechanism at the posttranscriptional level in metazoans, adenosine deaminase acting on RNA (ADAR)-induced A-to-I RNA editing modification of double-stranded RNA has been widely detected and reported. Editing may lead to non-synonymous amino acid mutations, RNA secondary structure alterations, pre-mRNA processing changes, and microRNA-mRNA redirection, thereby affecting multiple cellular processes and functions. In recent years, researchers have successfully developed several bioinformatics software tools and pipelines to identify RNA editing sites. However, there are still no widely accepted editing site standards due to the variety of parallel optimization and RNA high-seq protocols and programs. It is also challenging to identify RNA editing by normal protocols in tumor samples due to the high DNA mutation rate. Numerous RNA editing sites have been reported to be located in non-coding regions and can affect the biosynthesis of ncRNAs, including miRNAs and circular RNAs. Predicting the function of RNA editing sites located in non-coding regions and ncRNAs is significantly difficult. In this review, we aim to provide a better understanding of bioinformatics strategies for human cancer A-to-I RNA editing identification and briefly discuss recent advances in related areas, such as the oncogenic and tumor suppressive effects of RNA editing.

Bioinformatics Resources for RNA Editing

RNA editing is a widespread co/posttranscriptional mechanism affecting primary RNAs by specific nucleotide modifications, which plays relevant roles in molecular processes including regulation of gene expression and/or processing of noncoding RNAs (ncRNAs). In recent years, the detection of editing sites has been greatly improved through the availability of high-throughput RNA sequencing technologies. Several pipelines, employing various read mappers and variant callers with a wide range of adjustable parameters, are currently available for the detection of RNA editing events. Hereafter, we describe some of the most recent and popular tools and provide guidelines for the detection of RNA editing in massive transcriptome data.

Transcriptional frameshifts contribute to protein allergenicity

Transcription infidelity (TI) is a mechanism that increases RNA and protein diversity. We found that single-base omissions (i.e., gaps) occurred at significantly higher rates in the RNA of highly allergenic legumes. Transcripts from peanut, soybean, sesame, and mite allergens contained a higher density of gaps than those of nonallergens. Allergen transcripts translate into proteins with a cationic carboxy terminus depleted in hydrophobic residues. In mice, recombinant TI variants of the peanut allergen Ara h 2, but not the canonical allergen itself, induced, without adjuvant, the production of anaphylactogenic specific IgE (sIgE), binding to linear epitopes on both canonical and TI segments of the TI variants. The removal of cationic proteins from bovine lactoserum markedly reduced its capacity to induce sIgE. In peanut-allergic children, the sIgE reactivity was directed toward both canonical and TI segments of Ara h 2 variants. We discovered 2 peanut allergens, which we believe to be previously unreported, because of their RNA-DNA divergence gap patterns and TI peptide amino acid composition. Finally, we showed that the sIgE of children with IgE-negative milk allergy targeted cationic proteins in lactoserum. We propose that it is not the canonical allergens, but their TI variants, that initiate sIgE isotype switching, while both canonical and TI variants elicit clinical allergic reactions.

Systematic Nucleotide Exchange Analysis of ESTs From the Human Cancer Genome Project Report: Origins of 347 Unknown ESTs Indicate Putative Transcription of Non-Coding Genomic Regions

Expressed sequence tags (ESTs) provide an imprint of cellular RNA diversity irrespectively of sequence homology with template genomes. NCBI databases include many unknown RNAs from various normal and cancer cells. These are usually ignored assuming sequencing artefacts or contamination due to their lack of sequence homology with template DNA. Here, we report genomic origins of 347 ESTs previously assumed artefacts/unknown, from the FAPESP/LICR Human Cancer Genome Project. EST template detection uses systematic nucleotide exchange analyses called swinger transformations. Systematic nucleotide exchanges replace systematically particular nucleotides with different nucleotides. Among 347 unknown ESTs, 51 ESTs match mitogenome transcription, 17 and 2 ESTs are from nuclear chromosome non-coding regions, and uncharacterized nuclear genes. Identified ESTs mapped on 205 protein-coding genes, 10 genes had swinger RNAs in several biosamples. Whole cell transcriptome searches for 17 ESTs mapping on non-coding regions confirmed their transcription. The 10 swinger-transcribed genes identified more than once associate with cancer induction and progression, suggesting swinger transformation occurs mainly in highly transcribed genes. Swinger transformation is a unique method to identify noncanonical RNAs obtained from NGS, which identifies putative ncRNA transcribed regions. Results suggest that swinger transcription occurs in highly active genes in normal and genetically unstable cancer cells.

Elucidating the editome: bioinformatics approaches for RNA editing detection

RNA editing is a widespread co/posttranscriptional mechanism affecting primary RNAs by specific nucleotide modifications, which plays relevant roles in molecular processes including regulation of gene expression and/or the processing of noncoding RNAs. In recent years, the detection of editing sites has been improved through the availability of high-throughput RNA sequencing (RNA-Seq) technologies. Accurate bioinformatics pipelines are essential for the analysis of next-generation sequencing (NGS) data to ensure the correct identification of edited sites. Several pipelines, using various read mappers and variant callers with a wide range of adjustable parameters, are available for the detection of RNA editing events. In this review, we discuss some of the most recent and popular tools and provide guidelines for RNA-Seq data generation and analysis for the detection of RNA editing in massive transcriptome data. Using simulated and real data sets, we provide an overview of their behavior, emphasizing the fact that the RNA editing detection in NGS data sets remains a challenging task.

Transcripts with systematic nucleotide deletion of 1-12 nucleotide in human mitochondrion suggest potential non-canonical transcription

Raw transcriptomic data contain numerous RNA reads whose homology with template DNA doesn't match canonical transcription. Transcriptome analyses usually ignore such noncanonical RNA reads. Here, analyses search for noncanonical mitochondrial RNAs systematically deleting 1 to 12 nucleotides after each transcribed nucleotide triplet, producing deletion-RNAs (delRNAs). We detected delRNAs in the human whole cell and purified mitochondrial transcriptomes, and in Genbank's human EST database corresponding to systematic deletions of 1 to 12 nucleotides after each transcribed trinucleotide. DelRNAs detected in both transcriptomes mapped along with 55.63% of the EST delRNAs. A bias exists for delRNAs covering identical mitogenomic regions in both transcriptomic and EST datasets. Among 227 delRNAs detected in these 3 datasets, 81.1% and 8.4% of delRNAs were mapped on mitochondrial coding and hypervariable region 2 of dloop. Del-transcription analyses of GenBank's EST database confirm observations from whole cell and purified mitochondrial transcriptomes, eliminating the possibility that detected delRNAs are false positives matches, cytosolic DNA/RNA nuclear contamination or sequencing artefacts. These detected delRNAs are enriched in frameshift-inducing homopolymers and are poor in frameshift-preventing circular code codons (a set of 20 codons which regulate reading frame detection, over- and underrepresented in coding and other frames of genes, respectively) suggesting a motif-based regulation of non-canonical transcription. These findings show that rare non-canonical transcripts exist. Such non canonical del-transcription does increases mitochondrial coding potential and non-coding regulation of intracellular mechanisms, and could explain the dark DNA conundrum.

Computational approaches for detection and quantification of A-to-I RNA-editing

Adenosine deaminases that act on RNA (ADARs) catalyze adenosine-to-inosine (A-to-I) RNA editing in double-stranded RNA. Such editing is important for protection against false activation of the immune system, but also confers plasticity on the transcriptome by generating several versions of a transcript from a single genomic locus. Recently, great efforts were made in developing computational methods for detecting editing events directly from RNA-sequencing (RNA-seq) data. These efforts have led to an improved understanding of the makeup of the editome in various genomes. Here we review recent advances in editing detection based on the data available to the researcher, with emphasis on the principles underlying the various methods and the limitations they were designed to overcome. We also discuss the available various methods for analyzing and quantifying editing levels. This review collects and organizes the available approaches for analyzing RNA editing and discuss the current status of the different A-to-I detection methods with possible directions for extending these approaches.

REDO: RNA Editing Detection in Plant Organelles Based on Variant Calling Results

RNA editing is a post-transcriptional or cotranscriptional process that changes the sequence of the precursor transcript by substitutions, insertions, or deletions. Almost all of the land plants undergo RNA editing in organelles (plastids and mitochondria). Although several software tools have been developed to identify RNA editing events, there has been a great challenge to distinguish true RNA editing events from genome variation, sequencing errors, and other factors. Here we introduce REDO, a comprehensive application tool for identifying RNA editing events in plant organelles based on variant call format files from RNA-sequencing data. REDO is a suite of Perl scripts that illustrate a bunch of attributes of RNA editing events in figures and tables. REDO can also detect RNA editing events in multiple samples simultaneously and identify the significant differential proportion of RNA editing loci. Comparing with similar tools, such as REDItools, REDO runs faster with higher accuracy, and more specificity at the cost of slightly lower sensitivity. Moreover, REDO annotates each RNA editing site in RNAs, whereas REDItools reports only possible RNA editing sites in genome, which need additional steps to obtain RNA editing profiles for RNAs. Overall, REDO can identify potential RNA editing sites easily and provide several functions such as detailed annotations, statistics, figures, and significantly differential proportion of RNA editing sites among different samples.

Genome-wide analysis of differential RNA editing in epilepsy

The recoding of genetic information through RNA editing contributes to proteomic diversity, but the extent and significance of RNA editing in disease is poorly understood. In particular, few studies have investigated the relationship between RNA editing and disease at a genome-wide level. Here, we developed a framework for the genome-wide detection of RNA sites that are differentially edited in disease. Using RNA-sequencing data from 100 hippocampi from mice with epilepsy (pilocarpine–temporal lobe epilepsy model) and 100 healthy control hippocampi, we identified 256 RNA sites (overlapping with 87 genes) that were significantly differentially edited between epileptic cases and controls. The degree of differential RNA editing in epileptic mice correlated with frequency of seizures, and the set of genes differentially RNA-edited between case and control mice were enriched for functional terms highly relevant to epilepsy, including “neuron projection” and “seizures.” Genes with differential RNA editing were preferentially enriched for genes with a genetic association to epilepsy. Indeed, we found that they are significantly enriched for genes that harbor nonsynonymous de novo mutations in patients with epileptic encephalopathy and for common susceptibility variants associated with generalized epilepsy. These analyses reveal a functional convergence between genes that are differentially RNA-edited in acquired symptomatic epilepsy and those that contribute risk for genetic epilepsy. Taken together, our results suggest a potential role for RNA editing in the epileptic hippocampus in the occurrence and severity of epileptic seizures.

RNA Editing Deficiency in Neurodegeneration

The molecular process of RNA editing allows changes in RNA transcripts that increase genomic diversity. These highly conserved RNA editing events are catalyzed by a group of enzymes known as adenosine deaminases acting on double-stranded RNA (ADARs). ADARs are necessary for normal development, they bind to over thousands of genes, impact millions of editing sites, and target critical components of the central nervous system (CNS) such as glutamate receptors, serotonin receptors, and potassium channels. Dysfunctional ADARs are known to cause alterations in CNS protein products and therefore play a role in chronic or acute neurodegenerative and psychiatric diseases as well as CNS cancer. Here, we review how RNA editing deficiency impacts CNS function and summarize its role during disease pathogenesis.

RED: A Java-MySQL Software for Identifying and Visualizing RNA Editing Sites Using Rule-Based and Statistical Filters

RNA editing is one of the post- or co-transcriptional processes that can lead to amino acid substitutions in protein sequences, alternative pre-mRNA splicing, and changes in gene expression levels. Although several methods have been suggested to identify RNA editing sites, there remains challenges to be addressed in distinguishing true RNA editing sites from its counterparts on genome and technical artifacts. In addition, there lacks a software framework to identify and visualize potential RNA editing sites. Here, we presented a software - 'RED' (RNA Editing sites Detector) - for the identification of RNA editing sites by integrating multiple rule-based and statistical filters. The potential RNA editing sites can be visualized at the genome and the site levels by graphical user interface (GUI). To improve performance, we used MySQL database management system (DBMS) for high-throughput data storage and query. We demonstrated the validity and utility of RED by identifying the presence and absence of C→U RNA-editing sites experimentally validated, in comparison with REDItools, a command line tool to perform high-throughput investigation of RNA editing. In an analysis of a sample data-set with 28 experimentally validated C→U RNA editing sites, RED had sensitivity and specificity of 0.64 and 0.5. In comparison, REDItools had a better sensitivity (0.75) but similar specificity (0.5). RED is an easy-to-use, platform-independent Java-based software, and can be applied to RNA-seq data without or with DNA sequencing data. The package is freely available under the GPLv3 license at http://github.com/REDetector/RED or https://sourceforge.net/projects/redetector.