Abstract
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 <https://github.com/BGIRED/RED-ML>. 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.
Highlights
RNA editing provides a dynamic and flexible means to alter the sequence of RNA transcripts during development and in a celltype specific manner
Based on years of experience of analyzing RNA editing using diverse RNA-seq datasets, we have developed a software tool, RNA editing detection (RED)-machine learning (ML): RNA Editing Detection based on Machine learning
Envisioning that deep, high-throughput RNA sequencing will keep acting as a driving force of RNA editing research, we have developed a fast, high performance, and user-friendly RED tool based on machine learning (ML) to better serve the community and advance the field
Summary
RNA editing provides a dynamic and flexible means to alter the sequence of RNA transcripts during development and in a celltype specific manner. A common theme of many previous RNA editing detection (RED) methods, including our own [17, 25], is to apply a series of carefully tuned filters to combat different types of errors affecting RED, such as sequencing artifacts, mapping errors, contamination from genomic variants, etc., in addition to the possible use of a second read alignment program [26]. While highly effective, these hard filters are difficult to adjust, tend to work well only under specific conditions, and cannot be modified to achieve different trade-offs between sensitivity and specificity
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