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Review
. 2024 Jul 29;15(8):996.
doi: 10.3390/genes15080996.

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

Enrico Bortoletto  1 , Umberto Rosani  1
Affiliations
Review

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

Enrico Bortoletto et al. Genes (Basel). .

Abstract

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.

Keywords: A-to-I editing; ADAR; RNA editing; bioinformatics; inosine.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Timeline of RNA Editing Sites (RES) detection. The Figure reports the most important events in the history of advancing in RNA editing site detection, namely: inosine discovery in tRNA (1965, [14]), the discovery of the protein responsible for RNA editing (1987–1988, [17,18,19]), the first example of edited ion channel (1991, [21]), the development of a first biochemical method to detect inosine (1997, [23]), the development of a first bioinformatic pipeline for the reliable identification of RES (2012, [24]), the creation of the first RES database (2014, [25]), a first tool for hyper-editing detection (2014, [26]) and a first machine learning based tool for RES detection (2016, [27]).
Figure 2
Figure 2
FASTQ files preprocessing for the detection of RNA Editing Sites (RES). In the upper part, the picture depicts the four most essential steps in preprocessing FASTQ files for RES detection and some available tools for each step. In the lower part of the figure, a more detailed description of the bioinformatic pipeline steps is presented.
Figure 3
Figure 3
Strategies used to trace RNA Editing Sites (RES). This figure reports the most common strategies employed to trace RNA editing sites. In the case of Illumina sequencing data, in addition to the approach described based on REDItools nomenclature (Known panel (a), DNA-RNA panel (b) and De Novo panel (c)), an indicative bar showing the risk of false positive rate associated with the different approaches is depicted. Moreover, the most innovative methods still under development for the trace of RES are shown (the Machine learning and long-read approaches, panel (d) and panel (e), respectively).
Figure 4
Figure 4
Main filtering criteria used to select genuine RNA Editing Sites (RES). The picture illustrates the main parameters used to filter the RES. The parameters include the overall coverage at RES, the number of reads displaying the RES, the associated mapping quality value, the frequency and position of the RES and the base quality values. Low quality values are, as examples, 15 or “)”, good quality ones are 30 or “F”. In addition, two strong filters are represented by the relative position of the identified RES compared to other putative RES or different types of mismatches.
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