RED-ML: a novel, effective RNA editing detection method based on machine learning
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Documents
- Xiong_2017_RED-ML
Final published version, 482 KB, PDF document
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.
Original language | English |
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Article number | gix012 |
Journal | GigaScience |
Volume | 6 |
Issue number | 5 |
Pages (from-to) | 1-8 |
Number of pages | 8 |
ISSN | 2047-217X |
DOIs |
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Publication status | Published - May 2017 |
- A-to-I editing, machine learning, posttranscriptional modification, RNA editing, RNA-seq
Research areas
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