Predicting RNA hyper-editing with a novel tool when unambiguous alignment is impossible

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.