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

PlantC2U: deep learning of cross-species sequence landscapes predicts plastid C-to-U RNA editing in plants

In plants, C-to-U RNA editing mainly occurs in plastid and mitochondrial transcripts, which contributes to a complex transcriptional regulatory network. More evidence reveals that RNA editing plays critical roles in plant growth and development. However, accurate detection of RNA editing sites using transcriptome sequencing data alone is still challenging. In the present study, we develop PlantC2U, which is a convolutional neural network, to predict plastid C-to-U RNA editing based on the genomic sequence. PlantC2U achieves >95% sensitivity and 99% specificity, which outperforms the PREPACT tool, random forests, and support vector machines. PlantC2U not only further checks RNA editing sites from transcriptome data to reduce possible false positives, but also assesses the effect of different mutations on C-to-U RNA editing based on the flanking sequences. Moreover, we found the patterns of tissue-specific RNA editing in the mangrove plant Kandelia obovata, and observed reduced C-to-U RNA editing rates in the cold stress response of K. obovata, suggesting their potential regulatory roles in plant stress adaptation. In addition, we present RNAeditDB, available online at https://jasonxu.shinyapps.io/RNAeditDB/. Together, PlantC2U and RNAeditDB will help researchers explore the RNA editing events in plants and thus will be of broad utility for the plant research community.

A Bibliometric Study for Plant RNA Editing Research: Trends and Future Challenges

RNA editing is a post-transcriptional process that introduces changes in RNA sequences encoded by nuclear, mitochondrial, or plastid genomes. To understand the research progress of plant RNA editing, we comprehensively analyze the articles on plant RNA editing from 2001 to 2022 through bibliometric methods. Nucleic Acids Research, Plant Journal and Plant cell are the journals that deserve attention with their high production, total local citation scores (TLCS), and h-indexes. The USA, China, and Germany are the top three countries with highly productive publications. Ulm University, Cornell University, and Chinese Acad Sci are excellent cooperative institutions with a high level of influence in the field, and KNOOP V and TAKENAKA M are good partnership. Plant RNA editing researches concentrate on the subject categories of Biochemistry & Molecular Biology, Plant Sciences, Genetics & Heredity, etc. Plant mitochondria, genome editing and messenger-RNA may be the research hotspots in the future. The main plant RNA editing research tools are JACUSA, SPRINT, and REDO, and the main databases are REDIdb, PED, and dbRES. At present, the research streams are (1) RNA editing sites; (2) Pentapeptide repeat protein (PPR) involved in RNA editing; (3) RNA editing factors. Overall, this article summarizes the research overview of plant RNA editing until 2022 and provides theoretical implications for its possible future directions.

RNA Editing in Chloroplast: Advancements and Opportunities

Many eukaryotic and prokaryotic organisms employ RNA editing (insertion, deletion, or conversion) as a post-transcriptional modification mechanism. RNA editing events are common in these organelles of plants and have gained particular attention due to their role in the development and growth of plants, as well as their ability to cope with abiotic stress. Owing to rapid developments in sequencing technologies and data analysis methods, such editing sites are being accurately predicted, and many factors that influence RNA editing are being discovered. The mechanism and role of the pentatricopeptide repeat protein family of proteins in RNA editing are being uncovered with the growing realization of accessory proteins that might help these proteins. This review will discuss the role and type of RNA editing events in plants with an emphasis on chloroplast RNA editing, involved factors, gaps in knowledge, and future outlooks.

Discovering RNA Editing Events in Fungi

RNA editing is an important posttranscriptional process that alters the genetic information of RNA encoded by genomic DNA. Adenosine-to-inosine (A-to-I) editing is the most prevalent type of RNA editing in animal kingdom, catalyzed by adenosine deaminases acting on RNA (ADARs). Recently, genome-wide A-to-I RNA editing is discovered in fungi, involving adenosine deamination mechanisms distinct from animals. Aiming to draw more attention to RNA editing in fungi, here we discuss the considerations for deep sequencing data preparation and the available various methods for detecting RNA editing, with a special emphasis on their usability for fungal RNA editing detection. We describe computational protocols for the identification of candidate RNA editing sites in fungi by using two software packages REDItools and RES-Scanner with RNA sequencing (RNA-Seq) and genomic DNA sequencing (DNA-Seq) data.

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.

RNA editing in plants: A comprehensive survey of bioinformatics tools and databases

RNA editing is a widespread epitranscriptomic mechanism by which primary RNAs are specifically modified through insertions/deletions or nucleotide substitutions. In plants, RNA editing occurs in organelles (plastids and mitochondria), involves the cytosine to uridine modification (rarely uridine to cytosine) within protein-coding and non-protein-coding regions of RNAs and affects organelle biogenesis, adaptation to environmental changes and signal transduction. High-throughput sequencing technologies have dramatically improved the detection of RNA editing sites at genomic scale. Consequently, different bioinformatics resources have been released to discovery and/or collect novel events. Here, we review and describe the state-of-the-art bioinformatics tools devoted to the characterization of RNA editing in plant organelles with the aim to improve our knowledge about this fascinating but yet under investigated process.

An Evolutionary Landscape of A-to-I RNA Editome across Metazoan Species

Adenosine-to-inosine (A-to-I) editing is widespread across the kingdom Metazoa. However, for the lack of comprehensive analysis in nonmodel animals, the evolutionary history of A-to-I editing remains largely unexplored. Here, we detect high-confidence editing sites using clustering and conservation strategies based on RNA sequencing data alone, without using single-nucleotide polymorphism information or genome sequencing data from the same sample. We thereby unveil the first evolutionary landscape of A-to-I editing maps across 20 metazoan species (from worm to human), providing unprecedented evidence on how the editing mechanism gradually expands its territory and increases its influence along the history of evolution. Our result revealed that highly clustered and conserved editing sites tended to have a higher editing level and a higher magnitude of the ADAR motif. The ratio of the frequencies of nonsynonymous editing to that of synonymous editing remarkably increased with increasing the conservation level of A-to-I editing. These results thus suggest potentially functional benefit of highly clustered and conserved editing sites. In addition, spatiotemporal dynamics analyses reveal a conserved enrichment of editing and ADAR expression in the central nervous system throughout more than 300 Myr of divergent evolution in complex animals and the comparability of editing patterns between invertebrates and between vertebrates during development. This study provides evolutionary and dynamic aspects of A-to-I editome across metazoan species, expanding this important but understudied class of nongenomically encoded events for comprehensive characterization.

SPRINT: an SNP-free toolkit for identifying RNA editing sites

Motivation

RNA editing generates post-transcriptional sequence alterations. Detection of RNA editing sites (RESs) typically requires the filtering of SNVs called from RNA-seq data using an SNP database, an obstacle that is difficult to overcome for most organisms.

Results

Here, we present a novel method named SPRINT that identifies RESs without the need to filter out SNPs. SPRINT also integrates the detection of hyper RESs from remapped reads, and has been fully automated to any RNA-seq data with reference genome sequence available. We have rigorously validated SPRINT’s effectiveness in detecting RESs using RNA-seq data of samples in which genes encoding RNA editing enzymes are knock down or over-expressed, and have also demonstrated its superiority over current methods. We have applied SPRINT to investigate RNA editing across tissues and species, and also in the development of mouse embryonic central nervous system. A web resource (http://sprint.tianlab.cn) of RESs identified by SPRINT has been constructed.

Availability and implementation

The software and related data are available at http://sprint.tianlab.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Long Non-coding RNAs in Endothelial Biology

In recent years, the role of RNA has expanded to the extent that protein-coding RNAs are now the minority with a variety of non-coding RNAs (ncRNAs) now comprising the majority of RNAs in higher organisms. A major contributor to this shift in understanding is RNA sequencing (RNA-seq), which allows a largely unconstrained method for monitoring the status of RNA from whole organisms down to a single cell. This observational power presents both challenges and new opportunities, which require specialized bioinformatics tools to extract knowledge from the data and the ability to reuse data for multiple studies. In this review, we summarize the current status of long non-coding RNA (lncRNA) research in endothelial biology. Then, we will cover computational methods for identifying, annotating, and characterizing lncRNAs in the heart, especially endothelial cells.

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.