Bioinformatic Approaches for Identification of A-to-I Editing Sites

Proteome Diversification by RNA Editing

RNA editing is an RNA modification that alters the RNA sequence relative to its genomic blueprint. The most common type of RNA editing is A-to-I editing by double-stranded RNA-specific adenosine deaminase (ADAR) enzymes. Editing of a protein-coding region within the RNA molecule may result in non-synonymous substitutions, leading to a modified protein product. These editing sites, also known as "recoding" sites, contribute to the complexity and diversification of the proteome. Recent computational transcriptomic studies have identified thousands of recoding sites in multiple species, many of which are conserved within (but not usually across) lineages and have functional and evolutionary importance. In this chapter we describe the recoding phenomenon across species, consider its potential utility for diversity and adaptation, and discuss its evolution.

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.

A-to-I RNA editing contributes to the persistence of predicted damaging mutations in populations

Adenosine-to-inosine (A-to-I) RNA editing is a very common co-/posttranscriptional modification that can lead to A-to-G changes at the RNA level and compensate for G-to-A genomic changes to a certain extent. It has been shown that each healthy individual can carry dozens of missense variants predicted to be severely deleterious. Why strongly detrimental variants are preserved in a population and not eliminated by negative natural selection remains mostly unclear. Here, we ask if RNA editing correlates with the burden of deleterious A/G polymorphisms in a population. Integrating genome and transcriptome sequencing data from 447 human lymphoblastoid cell lines, we show that nonsynonymous editing activities (prevalence/level) are negatively correlated with the deleteriousness of A-to-G genomic changes and positively correlated with that of G-to-A genomic changes within the population. We find a significantly negative correlation between nonsynonymous editing activities and allele frequency of A within the population. This negative editing-allele frequency correlation is particularly strong when editing sites are located in highly important genes/loci. Examinations of deleterious missense variants from the 1000 Genomes Project further show a significantly higher proportion of rare missense mutations for G-to-A changes than for other types of changes. The proportion for G-to-A changes increases with increasing deleterious effects of the changes. Moreover, the deleteriousness of G-to-A changes is significantly positively correlated with the percentage of editing enzyme binding motifs at the variants. Overall, we show that nonsynonymous editing is associated with the increased burden of G-to-A missense mutations in healthy individuals, expanding RNA editing in pathogenomics studies.

Mapping the dsRNA World

Long double-stranded RNAs (dsRNAs) are abundantly expressed in animals, in which they frequently occur in introns and 3' untranslated regions of mRNAs. Functions of long, cellular dsRNAs are poorly understood, although deficiencies in adenosine deaminases that act on RNA, or ADARs, promote their recognition as viral dsRNA and an aberrant immune response. Diverse dsRNA-binding proteins bind cellular dsRNAs, hinting at additional roles. Understanding these roles is facilitated by mapping the genomic locations that express dsRNA in various tissues and organisms. ADAR editing provides a signature of dsRNA structure in cellular transcripts. In this review, we detail approaches to map ADAR editing sites and dsRNAs genome-wide, with particular focus on high-throughput sequencing methods and considerations for their successful application to the detection of editing sites and dsRNAs.

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.

Controlling the Editor: The Many Roles of RNA-Binding Proteins in Regulating A-to-I RNA Editing

RNA editing is a cellular process used to expand and diversify the RNA transcripts produced from a generally immutable genome. In animals, the most prevalent type of RNA editing is adenosine (A) to inosine (I) deamination catalyzed by the ADAR family. Throughout development, A-to-I editing levels increase while ADAR expression is constant, suggesting cellular mechanisms to regulate A-to-I editing exist. Furthermore, in several disease states, ADAR expression levels are similar to the normal state, but A-to-I editing levels are altered. Therefore, understanding how these enzymes are regulated in normal tissues and misregulated in disease states is of profound importance. This chapter will both discuss how to identify A-to-I editing sites across the transcriptome and explore the mechanisms that regulate ADAR editing activity, with particular focus on the diverse types of RNA-binding proteins implicated in regulating A-to-I editing in vivo.