RED-ML: a novel, effective RNA editing detection method based on machine learning.
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.
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Heng
H
Xiong
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Dongbing
D
Liu
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Qiye
Q
Li
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Mengyue
M
Lei
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Liqin
L
Xu
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Liang
L
Wu
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Zongji
Z
Wang
Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
Shancheng
S
Ren
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Wangsheng
W
Li
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Min
M
Xia
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Lihua
L
Lu
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Haorong
H
Lu
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.|Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
Yong
Y
Hou
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.|Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
Shida
S
Zhu
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Xin
X
Liu
Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
Yinghao
Y
Sun
BGI-Shenzhen, Shenzhen 518083, China.|James D. Watson Institute of Genome Sciences, Hangzhou 310058, China.
Jian
J
Wang
BGI-Shenzhen, Shenzhen 518083, China.|James D. Watson Institute of Genome Sciences, Hangzhou 310058, China.
Huanming
H
Yang
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.|Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
Kui
K
Wu
BGI-Shenzhen, Shenzhen 518083, China.|China National GeneBank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
Xun
X
Xu
BGI-Shenzhen, Shenzhen 518083, China.|Department of Electrical and Computer Engineering, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3G4, Canada.
Leo J
LJ
Lee
D006801:Humans; D000069550:Machine Learning; D008297:Male; D017393:RNA Editing; D017423:Sequence Analysis, RNA; D012984:Software
A-to-I editing; RNA editing; RNA-seq; machine learning; posttranscriptional modification