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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<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/gigascience/gix012">doi:10.1093/gigascience/gix012</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28328004">pmid:28328004</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5467039/">pmcid:PMC5467039</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nzwwqj2xe5b2nmh6b6k2gzfjwa">fatcat:nzwwqj2xe5b2nmh6b6k2gzfjwa</a> </span>
more »... 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
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