RED-ML: a novel, effective RNA editing detection method based on machine learning

Unveiling RNA Editing by ADAR and APOBEC Protein Gene Families

RNA editing is a crucial post-transcriptional modification that alters the transcriptome and proteome and affects many cellular processes, including splicing, microRNA specificity, stability of RNA molecules, and protein structure. Enzymes from the adenosine deaminase acting on RNA (ADAR) and apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) protein families mediate RNA editing and can alter a variety of non-coding and coding RNAs, including all regions of mRNA molecules, leading to tumor development and progression. This review provides novel insights into the potential use of RNA editing parameters, such as editing levels, expression of ADAR and APOBEC genes, and specifically edited genes, as biomarkers for cancer progression, distinguishing it from previous studies that focused on isolated aspects of RNA editing mechanisms. The methodological section offers clues to accelerate high-throughput analysis of RNA or DNA sequencing data for the identification of RNA editing events.

REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events

A-to-I ribonucleic acid (RNA) editing detection is still a challenging task. Current bioinformatics tools rely on empirical filters and whole genome sequencing or whole exome sequencing data to remove background noise, sequencing errors, and artifacts. Sometimes they make use of cumbersome and time-consuming computational procedures. Here, we present REDInet, a temporal convolutional network-based deep learning algorithm, to profile RNA editing in human RNA sequencing (RNAseq) data. It has been trained on REDIportal RNA editing sites, the largest collection of human A-to-I changes from >8000 RNAseq data of the genotype-tissue expression project. REDInet can classify editing events with high accuracy harnessing RNAseq nucleotide frequencies of 101-base windows without the need for coupled genomic data.

Consistent RNA expression and RNA modification patterns in cardiotoxicity induced by Matrine and Evodiamine

Recent research has demonstrated the efficacy of traditional Chinese medicine (TCM) and its active compounds in combating cancer, leading to an increasing utilization of TCM as adjunctive therapy in clinical oncology. However, the optimal dosage of TCM remains unclear, and excessive use may result in cardiotoxicity, which poses a significant health concern for patients undergoing systemic treatment. Therefore, elucidating the underlying mechanisms of cytotoxicity induced by TCM can provide valuable insights for clinical management. In this study, we employed a comprehensive bioinformatics analysis to present sequencing data obtained from AC16 myocardial cells treated with two bioactive derived from botanical drugs: Matrine and Evodiamine. We aim to investigate the dysregulated signaling pathways associated with cardiotoxicity induced by these compounds. Based on our sequencing results, we observed consistent patterns of gene expression and epitranscriptome regulation (m6A and A-to-I modifications) across various drugs-treated AC16 cells when analyzed using KEGG pathway enrichment and gene ontology analyses. Furthermore, m6A writers VIRMA and A-to-I writers ADARB1 is consistent target of Evodiamine and Matrine. In general, our findings suggest that different Chinese botanical drugs induced cardiotoxicity may share common therapeutic strategies.

ncRNA Editing: Functional Characterization and Computational Resources

Non-coding RNAs (ncRNAs) play crucial roles in gene expression regulation, translation, and disease development, including cancer. They are classified by size in short and long non-coding RNAs. This chapter focuses on the functional implications of adenosine-to-inosine (A-to-I) RNA editing in both short (e.g., miRNAs) and long ncRNAs. RNA editing dynamically alters the sequence and structure of primary transcripts, impacting ncRNA biogenesis and function. Notable findings include the role of miRNA editing in promoting glioblastoma invasiveness, characterizing RNA editing hotspots across cancers, and its implications in thyroid cancer and ischemia. This chapter also highlights bioinformatics resources and next-generation sequencing (NGS) technologies that enable comprehensive ncRNAome studies and genome-wide RNA editing detection. Dysregulation of RNA editing machinery has been linked to various human diseases, emphasizing the potential of RNA editing as a biomarker and therapeutic target. This overview integrates current knowledge and computational tools for studying ncRNA editing, providing insights into its biological significance and clinical applications.

Sappanone A alleviates metabolic dysfunction-associated steatohepatitis by decreasing hepatocyte lipotoxicity via targeting Mup3 in mice

BACKGROUND AND PURPOSE

Metabolic dysfunction-associated steatohepatitis (MASH) is an inflammatory lipotoxic disorder marked by hepatic steatosis, hepatocyte damage, inflammation, and varying stages of fibrosis. Sappanone A (SA), a flavonoid, exhibits anti-inflammatory and hepatoprotection activities. Nevertheless, the effects of SA on MASH remain ambiguous. We evaluated the effects of SA on hepatocyte lipotoxicity, inflammation, and fibrosis conditions in MASH mice, as well as the underlying mechanisms.

METHODS

A conventional murine MASH model fed a methionine-choline-deficient (MCD) diet was utilized to assess the role of SA on MASH in vivo. Drug target prediction and liver transcriptomics were employed to elucidate the potential actions of SA. AML12 cells were applied to further explore the effects and mechanisms of SA in vitro.

RESULTS

The in silico prediction indicated that SA could modulate inflammation, insulin resistance, lipid metabolism, and collagen catabolic process. Treating with SA dose-dependently lessened the elevated levels of serum ALT and AST in mice with diet-triggered MASH, and high-dose SA treatment exhibited a similar effect to silymarin. Additionally, SA treatment significantly reduced lipid deposition, inflammation, and fibrosis subjected to metabolic stress in a dose-dependent manner. Besides, SA mitigated palmitate-triggered lipotoxicity in hepatocytes. Liver transcriptomics further confirmed the aforementioned findings. Of note, mRNA-sequencing analysis and molecular biology experiments demonstrated that SA statistically up-regulated the hepatic expression of major urinary protein 3 (Mup3), thereby facilitating lipid transportation and inhibiting lipotoxicity. Furthermore, Mup3 knockdown in hepatocytes significantly abolished the hepatoprotection provided by SA.

CONCLUSION

SA alleviates MASH by decreasing lipid accumulation and lipotoxicity in hepatocytes, at least partially by targeting Mup3, and subsequently blocks MASH process. Therefore, SA could be a promising hepatoprotective agent in the context of MASH.

DEMINING: A deep learning model embedded framework to distinguish RNA editing from DNA mutations in RNA sequencing data

Precise calling of promiscuous adenosine-to-inosine RNA editing sites from transcriptomic datasets is hindered by DNA mutations and sequencing/mapping errors. Here, we present a stepwise computational framework, called DEMINING, to distinguish RNA editing and DNA mutations directly from RNA sequencing datasets, with an embedded deep learning model named DeepDDR. After transfer learning, DEMINING can also classify RNA editing sites and DNA mutations from non-primate sequencing samples. When applied in samples from acute myeloid leukemia patients, DEMINING uncovers previously underappreciated DNA mutation and RNA editing sites; some associated with the upregulated expression of host genes or the production of neoantigens.

Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification

Inosine is a nucleotide resulting from the deamination of adenosine in RNA. This chemical modification process, known as RNA editing, is typically mediated by a family of double-stranded RNA binding proteins named Adenosine Deaminase Acting on dsRNA (ADAR). While the presence of ADAR orthologs has been traced throughout the evolution of metazoans, the existence and extension of RNA editing have been characterized in a more limited number of animals so far. Undoubtedly, ADAR-mediated RNA editing plays a vital role in physiology, organismal development and disease, making the understanding of the evolutionary conservation of this phenomenon pivotal to a deep characterization of relevant biological processes. However, the lack of direct high-throughput methods to reveal RNA modifications at single nucleotide resolution limited an extended investigation of RNA editing. Nowadays, these methods have been developed, and appropriate bioinformatic pipelines are required to fully exploit this data, which can complement existing approaches to detect ADAR editing. Here, we review the current literature on the "bioinformatics for inosine" subject and we discuss future research avenues in the field.

Recommendations for detection, validation, and evaluation of RNA editing events in cardiovascular and neurological/neurodegenerative diseases

RNA editing, a common and potentially highly functional form of RNA modification, encompasses two different RNA modifications, namely adenosine to inosine (A-to-I) and cytidine to uridine (C-to-U) editing. As inosines are interpreted as guanosines by the cellular machinery, both A-to-I and C-to-U editing change the nucleotide sequence of the RNA. Editing events in coding sequences have the potential to change the amino acid sequence of proteins, whereas editing events in noncoding RNAs can, for example, affect microRNA target binding. With advancing RNA sequencing technology, more RNA editing events are being discovered, studied, and reported. However, RNA editing events are still often overlooked or discarded as sequence read quality defects. With this position paper, we aim to provide guidelines and recommendations for the detection, validation, and follow-up experiments to study RNA editing, taking examples from the fields of cardiovascular and brain disease. We discuss all steps, from sample collection, storage, and preparation, to different strategies for RNA sequencing and editing-sensitive data analysis strategies, to validation and follow-up experiments, as well as potential pitfalls and gaps in the available technologies. This paper may be used as an experimental guideline for RNA editing studies in any disease context.

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.

Benchmarking RNA Editing Detection Tools

RNA, like DNA and proteins, can undergo modifications. To date, over 170 RNA modifications have been identified, leading to the emergence of a new research area known as epitranscriptomics. RNA editing is the most frequent RNA modification in mammalian transcriptomes, and two types have been identified: (1) the most frequent, adenosine to inosine (A-to-I); and (2) the less frequent, cysteine to uracil (C-to-U) RNA editing. Unlike other epitranscriptomic marks, RNA editing can be readily detected from RNA sequencing (RNA-seq) data without any chemical conversions of RNA before sequencing library preparation. Furthermore, analyzing RNA editing patterns from transcriptomic data provides an additional layer of information about the epitranscriptome. As the significance of epitranscriptomics, particularly RNA editing, gains recognition in various fields of biology and medicine, there is a growing interest in detecting RNA editing sites (RES) by analyzing RNA-seq data. To cope with this increased interest, several bioinformatic tools are available. However, each tool has its advantages and disadvantages, which makes the choice of the most appropriate tool for bench scientists and clinicians difficult. Here, we have benchmarked bioinformatic tools to detect RES from RNA-seq data. We provide a comprehensive view of each tool and its performance using previously published RNA-seq data to suggest recommendations on the most appropriate for utilization in future studies.

Lessons from discovery of true ADAR RNA editing sites in a human cell line

BACKGROUND

Conversion or editing of adenosine (A) into inosine (I) catalyzed by specialized cellular enzymes represents one of the most common post-transcriptional RNA modifications with emerging connection to disease. A-to-I conversions can happen at specific sites and lead to increase in proteome diversity and changes in RNA stability, splicing, and regulation. Such sites can be detected as adenine-to-guanine sequence changes by next-generation RNA sequencing which resulted in millions reported sites from multiple genome-wide surveys. Nonetheless, the lack of extensive independent validation in such endeavors, which is critical considering the relatively high error rate of next-generation sequencing, leads to lingering questions about the validity of the current compendiums of the editing sites and conclusions based on them.

RESULTS

Strikingly, we found that the current analytical methods suffer from very high false positive rates and that a significant fraction of sites in the public databases cannot be validated. In this work, we present potential solutions to these problems and provide a comprehensive and extensively validated list of A-to-I editing sites in a human cancer cell line. Our findings demonstrate that most of true A-to-I editing sites in a human cancer cell line are located in the non-coding transcripts, the so-called RNA 'dark matter'. On the other hand, many ADAR editing events occurring in exons of human protein-coding mRNAs, including those that can recode the transcriptome, represent false positives and need to be interpreted with caution. Nonetheless, yet undiscovered authentic ADAR sites that increase the diversity of human proteome exist and warrant further identification.

CONCLUSIONS

Accurate identification of human ADAR sites remains a challenging problem, particularly for the sites in exons of protein-coding mRNAs. As a result, genome-wide surveys of ADAR editome must still be accompanied by extensive Sanger validation efforts. However, given the vast number of unknown human ADAR sites, there is a need for further developments of the analytical techniques, potentially those that are based on deep learning solutions, in order to provide a quick and reliable identification of the editome in any sample.

A novel computational method enables RNA editome profiling during human hematopoiesis from scRNA-seq data

RNA editing is a post-transcriptional modification with a cell-specific manner and important biological implications. Although single-cell RNA-seq (scRNA-seq) is an effective method for studying cellular heterogeneity, it is difficult to detect and study RNA editing events from scRNA-seq data because of the low sequencing coverage. To overcome this, we develop a computational method to systematically identify RNA editing sites of cell types from scRNA-seq data. To demonstrate its effectiveness, we apply it to scRNA-seq data of human hematopoietic stem/progenitor cells (HSPCs) with an annotated lineage differentiation relationship according to previous research and study the impacts of RNA editing on hematopoiesis. The dynamic editing patterns reveal the relevance of RNA editing on different HSPCs. For example, four microRNA (miRNA) target sites on 3' UTR of EIF2AK2 are edited across all HSPC populations, which may abolish the miRNA-mediated inhibition of EIF2AK2. Elevated EIF2AK2 may thus activate the integrated stress response (ISR) pathway to initiate global translational attenuation as a protective mechanism to maintain cellular homeostasis during HSPCs' differentiation. Besides, our findings also indicate that RNA editing plays an essential role in the coordination of lineage commitment and self-renewal of hematopoietic stem cells (HSCs). Taken together, we demonstrate the capacity of scRNA-seq data to exploit RNA editing events of cell types, and find that RNA editing may exert multiple modules of regulation in hematopoietic processes.

ADAR1-mediated RNA editing promotes B cell lymphomagenesis

Diffuse large B cell lymphoma (DLBCL) is one of the most common types of aggressive lymphoid malignancies. Here, we explore the contribution of RNA editing to DLBCL pathogenesis. We observed that DNA mutations and RNA editing events are often mutually exclusive, suggesting that tumors can modulate pathway outcomes by altering sequences at either the genomic or the transcriptomic level. RNA editing targets transcripts within known disease-driving pathways such as apoptosis, p53 and NF-κB signaling, as well as the RIG-I-like pathway. In this context, we show that ADAR1-mediated editing within MAVS transcript positively correlates with MAVS protein expression levels, associating with increased interferon/NF-κB signaling and T cell exhaustion. Finally, using targeted RNA base editing tools to restore editing within MAVS 3'UTR in ADAR1-deficient cells, we demonstrate that editing is likely to be causal to an increase in downstream signaling in the absence of activation by canonical nucleic acid receptor sensing.

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.

RNA Modification in Inflammatory Bowel Diseases

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder characterized by damage to the intestinal mucosa, which is caused by a combination of factors. These include genetic and epigenetic alterations, environmental influence, microorganism interactions, and immune conditions. Some populations with IBD show a cancer-prone phenotype. Recent studies have provided insight into the involvement of RNA modifications in the specific pathogenesis of IBD through regulation of RNA biology in epithelial and immune cells. Studies of several RNA modification-targeting reagents have shown preferable outcomes in patients with colitis. Here, we note a new awareness of RNA modification in the targeting of IBD and related diseases, which will contribute to early diagnosis, disease monitoring, and possible control by innovative therapeutic approaches.

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.

ADAR1-Dependent RNA Editing Promotes MET and iPSC Reprogramming by Alleviating ER Stress

RNA editing of adenosine to inosine (A to I) is catalyzed by ADAR1 and dramatically alters the cellular transcriptome, although its functional roles in somatic cell reprogramming are largely unexplored. Here, we show that loss of ADAR1-mediated A-to-I editing disrupts mesenchymal-to-epithelial transition (MET) during induced pluripotent stem cell (iPSC) reprogramming and impedes acquisition of induced pluripotency. Using chemical and genetic approaches, we show that absence of ADAR1-dependent RNA editing induces aberrant innate immune responses through the double-stranded RNA (dsRNA) sensor MDA5, unleashing endoplasmic reticulum (ER) stress and hindering epithelial fate acquisition. We found that A-to-I editing impedes MDA5 sensing and sequestration of dsRNAs encoding membrane proteins, which promote ER homeostasis by activating the PERK-dependent unfolded protein response pathway to consequently facilitate MET. This study therefore establishes a critical role for ADAR1 and its A-to-I editing activity during cell fate transitions and delineates a key regulatory layer underlying MET to control efficient reprogramming.

Investigating RNA editing in deep transcriptome datasets with REDItools and REDIportal

RNA editing is a widespread post-transcriptional mechanism able to modify transcripts through insertions/deletions or base substitutions. It is prominent in mammals, in which millions of adenosines are deaminated to inosines by members of the ADAR family of enzymes. A-to-I RNA editing has a plethora of biological functions, but its detection in large-scale transcriptome datasets is still an unsolved computational task. To this aim, we developed REDItools, the first software package devoted to the RNA editing profiling in RNA-sequencing (RNAseq) data. It has been successfully used in human transcriptomes, proving the tissue and cell type specificity of RNA editing as well as its pervasive nature. Outcomes from large-scale REDItools analyses on human RNAseq data have been collected in our specialized REDIportal database, containing more than 4.5 million events. Here we describe in detail two bioinformatic procedures based on our computational resources, REDItools and REDIportal. In the first procedure, we outline a workflow to detect RNA editing in the human cell line NA12878, for which transcriptome and whole genome data are available. In the second procedure, we show how to identify dysregulated editing at specific recoding sites in post-mortem brain samples of Huntington disease donors. On a 64-bit computer running Linux with ≥32 GB of random-access memory (RAM), both procedures should take ~76 h, using 4 to 24 cores. Our protocols have been designed to investigate RNA editing in different organisms with available transcriptomic and/or genomic reads. Scripts to complete both procedures and a docker image are available at https://github.com/BioinfoUNIBA/REDItools.

ADAR1 mediated regulation of neural crest derived melanocytes and Schwann cell development

The neural crest gives rise to numerous cell types, dysfunction of which contributes to many disorders. Here, we report that adenosine deaminase acting on RNA (ADAR1), responsible for adenosine-to-inosine editing of RNA, is required for regulating the development of two neural crest derivatives: melanocytes and Schwann cells. Neural crest specific conditional deletion of Adar1 in mice leads to global depigmentation and absence of myelin from peripheral nerves, resulting from alterations in melanocyte survival and differentiation of Schwann cells, respectively. Upregulation of interferon stimulated genes precedes these defects, which are associated with the triggering of a signature resembling response to injury in peripheral nerves. Simultaneous extinction of MDA5, a key sensor of unedited RNA, rescues both melanocytes and myelin defects in vitro, suggesting that ADAR1 safeguards neural crest derivatives from aberrant MDA5-mediated interferon production. We thus extend the landscape of ADAR1 function to the fields of neural crest development and disease.

Cell lineage inference from SNP and scRNA-Seq data

Several recent studies focus on the inference of developmental and response trajectories from single cell RNA-Seq (scRNA-Seq) data. A number of computational methods, often referred to as pseudo-time ordering, have been developed for this task. Recently, CRISPR has also been used to reconstruct lineage trees by inserting random mutations. However, both approaches suffer from drawbacks that limit their use. Here, we develop a method to detect significant, cell type specific, sequence mutations from scRNA-Seq data. We show that only a few mutations are enough for reconstructing good branching models. Integrating these mutations with expression data further improves the accuracy of the reconstructed models. As we show, the majority of mutations we identify are likely RNA editing events indicating that such information can be used to distinguish cell types.

CirGRDB: a database for the genome-wide deciphering circadian genes and regulators

Circadian rhythms govern various kinds of physiological and behavioral functions of the living organisms, and disruptions of the rhythms are highly detrimental to health. Although several databases have been built for circadian genes, a resource for comprehensive post-transcriptional regulatory information of circadian RNAs and expression patterns of disease-related circadian RNAs is still lacking. Here, we developed CirGRDB (http://cirgrdb.biols.ac.cn) by integrating more than 4936 genome-wide assays, with the aim of fulfilling the growing need to understand the rhythms of life. CirGRDB presents a friendly web interface that allows users to search and browse temporal expression patterns of interested genes in 37 human/mouse tissues or cell lines, and three clinical disorders including sleep disorder, aging and tumor. More importantly, eight kinds of potential transcriptional and post-transcriptional regulators involved in the rhythmic expression of the specific genes, including transcription factors, histone modifications, chromatin accessibility, enhancer RNAs, miRNAs, RNA-binding proteins, RNA editing and RNA methylation, can also be retrieved. Furthermore, a regulatory network could be generated based on the regulatory information. In summary, CirGRDB offers a useful repository for exploring disease-related circadian RNAs, and deciphering the transcriptional and post-transcriptional regulation of circadian rhythms.

CREDO: Highly confident disease-relevant A-to-I RNA-editing discovery in breast cancer

Adenosine-to-Inosine (A-to-I) RNA editing is the most prevalent post-transcriptional modification of RNA molecules. Researchers have attempted to find reliable RNA editing using next generation sequencing (NGS) data. However, most of these attempts suffered from a high rate of false positives, and they did not consider the clinical relevance of the identified RNA editing, for example, in disease progression. We devised an effective RNA-editing discovery pipeline called CREDO, which includes novel statistical filtering modules based on integration of DNA- and RNA-seq data from matched tumor-normal tissues. CREDO was compared with three other RNA-editing discovery pipelines and found to give significantly fewer false positives. Application of CREDO to breast cancer data from the Cancer Genome Atlas (TCGA) project discovered highly confident RNA editing with clinical relevance to cancer progression in terms of patient survival. RNA-editing detection using DNA- and RNA-seq data from matched tumor-normal tissues should be more routinely performed as multiple omics data are becoming commonly available from each patient sample. We believe CREDO is an effective and reliable tool for this problem.

Single-nucleotide variants in human RNA: RNA editing and beyond

Through analysis of paired high-throughput DNA-Seq and RNA-Seq data, researchers quickly recognized that RNA-Seq can be used for more than just gene expression quantification. The alternative applications of RNA-Seq data are abundant, and we are particularly interested in its usefulness for detecting single-nucleotide variants, which arise from RNA editing, genomic variants and other RNA modifications. A stunning discovery made from RNA-Seq analyses is the unexpectedly high prevalence of RNA-editing events, many of which cannot be explained by known RNA-editing mechanisms. Over the past 6-7 years, substantial efforts have been made to maximize the potential of RNA-Seq data. In this review we describe the controversial history of mining RNA-editing events from RNA-Seq data and the corresponding development of methodologies to identify, predict, assess the quality of and catalog RNA-editing events as well as genomic variants.

Detection and Application of RNA Editing in Cancer

RNA editing is the process which happened in the post-transcriptional stage that the genetic information contained in an RNA molecule will be changed. RNA editing has been found to be related with many cancers, so through identifying RNA editing sites, we can find useful information on the process of carcinogenesis. In this review, we will discuss the main types of RNA editing and their role in cancers, as well as the current detection methods of RNA editing and the challenges we should overcome.

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.

Maximizing the Utility of Cancer Transcriptomic Data

Transcriptomic profiling has been applied to large numbers of cancer samples, by large-scale consortia, including The Cancer Genome Atlas, International Cancer Genome Consortium, and Cancer Cell Line Encyclopedia. Advances in mining cancer transcriptomic data enable us to understand the endless complexity of the cancer transcriptome and thereby to discover new biomarkers and therapeutic targets. In this paper, we review computational resources for deep mining of transcriptomic data to identify, quantify, and determine the functional effects and clinical utility of transcriptomic events, including noncoding RNAs, post-transcriptional regulation, exogenous RNAs, and transcribed genetic variants. These approaches can be applied to other complex diseases, thereby greatly leveraging the impact of this work.

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.

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features

BACKGROUND

Promoter is an important sequence regulation element, which is in charge of gene transcription initiation. In prokaryotes, σ70 promoters regulate the transcription of most genes. The promoter recognition has been a crucial part of gene structure recognition. It's also the core issue of constructing gene transcriptional regulation network. With the successfully completion of genome sequencing from an increasing number of microbe species, the accurate identification of σ70 promoter regions in DNA sequence is not easy.

RESULTS

In order to improve the prediction accuracy of sigma70 promoters in prokaryote, a promoter recognition model 70ProPred was established. In this work, two sequence-based features, including position-specific trinucleotide propensity based on single-stranded characteristic (PSTNPss) and electron-ion potential values for trinucleotides (PseEIIP), were assessed to build the best prediction model. It was found that 79 features of PSTNPSS combined with 64 features of PseEIIP obtained the best performance for sigma70 promoter identification, with a promising accuracy and the Matthews correlation coefficient (MCC) at 95.56% and 0.90, respectively.

CONCLUSION

The jackknife tests showed that 70ProPred outperforms the existing sigma70 promoter prediction approaches in terms of accuracy and stability. Additionally, this approach can also be extended to predict promoters of other species. In order to facilitate experimental biologists, an online web server for the proposed method was established, which is freely available at http://server.malab.cn/70ProPred/ .

Genome-wide identification and analysis of A-to-I RNA editing events in bovine by transcriptome sequencing

RNA editing increases the diversity of the transcriptome and proteome. Adenosine-to-inosine (A-to-I) editing is the predominant type of RNA editing in mammals and it is catalyzed by the adenosine deaminases acting on RNA (ADARs) family. Here, we used a largescale computational analysis of transcriptomic data from brain, heart, colon, lung, spleen, kidney, testes, skeletal muscle and liver, from three adult animals in order to identify RNA editing sites in bovine. We developed a computational pipeline and used a rigorous strategy to identify novel editing sites from RNA-Seq data in the absence of corresponding DNA sequence information. Our methods take into account sequencing errors, mapping bias, as well as biological replication to reduce the probability of obtaining a false-positive result. We conducted a detailed characterization of sequence and structural features related to novel candidate sites and found 1,600 novel canonical A-to-I editing sites in the nine bovine tissues analyzed. Results show that these sites 1) occur frequently in clusters and short interspersed nuclear elements (SINE) repeats, 2) have a preference for guanines depletion/enrichment in the flanking 5′/3′ nucleotide, 3) occur less often in coding sequences than other regions of the genome, and 4) have low evolutionary conservation. Further, we found that a positive correlation exists between expression of ADAR family members and tissue-specific RNA editing. Most of the genes with predicted A-to-I editing in each tissue were significantly enriched in biological terms relevant to the function of the corresponding tissue. Lastly, the results highlight the importance of the RNA editome in nervous system regulation. The present study extends the list of RNA editing sites in bovine and provides pipelines that may be used to investigate the editome in other organisms.

RED-ML: a novel, effective RNA editing detection method based on machine learning

With the advancement of second generation sequencing techniques, our ability to detect and quantify RNA editing on a global scale has been vastly improved. As a result, RNA editing is now being studied under a growing number of biological conditions so that its biochemical mechanisms and functional roles can be further understood. However, a major barrier that prevents RNA editing from being a routine RNA-seq analysis, similar to gene expression and splicing analysis, for example, is the lack of user-friendly and effective computational tools. Based on years of experience of analyzing RNA editing using diverse RNA-seq datasets, we have developed a software tool, RED-ML: RNA Editing Detection based on Machine learning (pronounced as "red ML"). The input to RED-ML can be as simple as a single BAM file, while it can also take advantage of matched genomic variant information when available. The output not only contains detected RNA editing sites, but also a confidence score to facilitate downstream filtering. We have carefully designed validation experiments and performed extensive comparison and analysis to show the efficiency and effectiveness of RED-ML under different conditions, and it can accurately detect novel RNA editing sites without relying on curated RNA editing databases. We have also made this tool freely available via GitHub . We have developed a highly accurate, speedy and general-purpose tool for RNA editing detection using RNA-seq data. With the availability of RED-ML, it is now possible to conveniently make RNA editing a routine analysis of RNA-seq. We believe this can greatly benefit the RNA editing research community and has profound impact to accelerate our understanding of this intriguing posttranscriptional modification process.