RDDSVM: accurate prediction of A-to-I RNA editing sites from sequence using support vector machines

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.

Harnessing ADAR-Mediated Site-Specific RNA Editing in Immune-Related Disease: Prediction and Therapeutic Implications

ADAR (Adenosine Deaminases Acting on RNA) proteins are a group of enzymes that play a vital role in RNA editing by converting adenosine to inosine in RNAs. This process is a frequent post-transcriptional event observed in metazoan transcripts. Recent studies indicate widespread dysregulation of ADAR-mediated RNA editing across many immune-related diseases, such as human cancer. We comprehensively review ADARs' function as pattern recognizers and their capability to contribute to mediating immune-related pathways. We also highlight the potential role of site-specific RNA editing in maintaining homeostasis and its relationship to various diseases, such as human cancers. More importantly, we summarize the latest cutting-edge computational approaches and data resources for predicting and analyzing RNA editing sites. Lastly, we cover the recent advancement in site-directed ADAR editing tool development. This review presents an up-to-date overview of ADAR-mediated RNA editing, how site-specific RNA editing could potentially impact disease pathology, and how they could be harnessed for therapeutic applications.

ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species

A-to-I editing is the most prevalent RNA editing event, which refers to the change of adenosine (A) bases to inosine (I) bases in double-stranded RNAs. Several studies have revealed that A-to-I editing can regulate cellular processes and is associated with various human diseases. Therefore, accurate identification of A-to-I editing sites is crucial for understanding RNA-level (i.e. transcriptional) modifications and their potential roles in molecular functions. To date, various computational approaches for A-to-I editing site identification have been developed; however, their performance is still unsatisfactory and needs further improvement. In this study, we developed a novel stacked-ensemble learning model, ATTIC (A-To-I ediTing predICtor), to accurately identify A-to-I editing sites across three species, including Homo sapiens, Mus musculus and Drosophila melanogaster. We first comprehensively evaluated 37 RNA sequence-derived features combined with 14 popular machine learning algorithms. Then, we selected the optimal base models to build a series of stacked ensemble models. The final ATTIC framework was developed based on the optimal models improved by the feature selection strategy for specific species. Extensive cross-validation and independent tests illustrate that ATTIC outperforms state-of-the-art tools for predicting A-to-I editing sites. We also developed a web server for ATTIC, which is publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/ATTIC/. We anticipate that ATTIC can be utilized as a useful tool to accelerate the identification of A-to-I RNA editing events and help characterize their roles in post-transcriptional regulation.

Effect of salinity on ccmfn gene RNA editing of mitochondria in wild barley and uncommon types of RNA editing

The primary function of mitochondria is cellular respiration and energy production. Cytochrome C complex is an essential complex that transports electrons in the respiratory chain between complex III and complex IV. One of this complex's main subunits is CcmFN, which is believed to be crucial for holocytochrome assembly. In wild-type plant Hordeum vulgare subsp. spontaneum, four ccmfn cDNAs are subjected to high salt stress (500 mM salinity), 0 h (or control) (GenBank accession no. ON764850), after 2 h (GenBank accession no. ON7648515), after 12 h (GenBank accession no. ON764852), and after 24 h (GenBank accession no. ON764853) and mtDNA of ccmfn gene (GenBank accession no. ON764854). Using raw data from RNA-seq, 47 sites with nucleotide and amino acid modifications were detected. There were ten different RNA editing types, with most of them are C to U. Unusual editing types in plants have also been found, such as A to C, C to A, A to G, A to U, T to A, T to C, C to G, G to C, and T to G. High levels of editing were observed in control as well as treatments of salinity stress. Amino acid changes were found in 43 sites; nearly all showed hydrophilic to hydrophilic alterations. Only C749 showed regulation under salinity stress.