RED-ML: a novel, effective RNA editing detection method based on machine learning

Heng Xiong; Dongbing Liu; Qiye Li; Mengyue Lei; Liqin Xu; Liang Wu; Zongji Wang; Shancheng Ren; Wangsheng Li; Min Xia; Lihua Lu; Haorong Lu; Yong Hou; Shida Zhu; Xin Liu; Yinghao Sun; Jian Wang; Huanming Yang; Kui Wu; Xun Xu; Leo J. Lee

doi:10.1093/gigascience/gix012

RED-ML: a novel, effective RNA editing detection method based on machine learning

Heng Xiong, Dongbing Liu, Qiye Li, Mengyue Lei, Liqin Xu, Liang Wu, Zongji Wang, Shancheng Ren, Wangsheng Li, Min Xia, Lihua Lu, Haorong Lu, Yong Hou, Shida Zhu, Xin Liu, Yinghao Sun, Jian Wang, Huanming Yang, Kui Wu, Xun XuLeo J. Lee^*

^*Corresponding author for this work

Genome Research and Molecular Bio Medicine

19 Citations (Scopus)

81 Downloads (Pure)

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.

Original language	English
Article number	gix012
Journal	GigaScience
Volume	6
Issue number	5
Pages (from-to)	1-8
Number of pages	8
ISSN	2047-217X
DOIs	https://doi.org/10.1093/gigascience/gix012
Publication status	Published - May 2017

Keywords

A-to-I editing
machine learning
posttranscriptional modification
RNA editing
RNA-seq

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Xiong_2017_RED-MLFinal published version, 482 KBLicence: CC BY

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title = "RED-ML: a novel, effective RNA editing detection method based on machine learning",

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 . 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.",

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AU - Xiong, Heng

AU - Liu, Dongbing

AU - Li, Qiye

AU - Lei, Mengyue

AU - Xu, Liqin

AU - Wu, Liang

AU - Wang, Zongji

AU - Ren, Shancheng

AU - Li, Wangsheng

AU - Xia, Min

AU - Lu, Lihua

AU - Lu, Haorong

AU - Hou, Yong

AU - Zhu, Shida

AU - Liu, Xin

AU - Sun, Yinghao

AU - Wang, Jian

AU - Yang, Huanming

AU - Wu, Kui

AU - Xu, Xun

AU - Lee, Leo J.

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KW - A-to-I editing

KW - machine learning

KW - posttranscriptional modification

KW - RNA editing

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RED-ML: a novel, effective RNA editing detection method based on machine learning

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