Corrigendum: Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning
- 1State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Shaanxi, China
- 2Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Shaanxi, China
- 3College of Information Engineering, Northwest A&F University, Shaanxi, China
by Song, J., Zhai, J., Bian, E., Song, Y., Yu, J., and Ma, C. (2018). Front. Plant Sci. 9:519. doi: 10.3389/fpls.2018.00519
In the original article, there was an error, the word “reversible” is misleading. A correction has been made to the Abstract and the Introduction, paragraph 2.
Though high-throughput experimental technologies have been developed and applied to profile m5C modifications under certain conditions, transcriptome-wide studies of m5C modifications are still hindered by the dynamic nature of m5C and the lack of computational prediction methods.
Second, because of the dynamic nature of m5C (Wang and He, 2014), existing high-throughput sequencing technologies can only capture a snapshot of RNA modifications under certain experimental conditions, and cover just a small fraction of the whole transcriptome of a given sample (Zhou et al., 2016), resulting in the generation of significant numbers of false negatives (non-detected true m5C modifications).
The authors apologize for the mistake. This error does not change the scientific conclusions of the article in any way. The original article has been updated.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Keywords: AUC, Epitranscriptome, machine learning, RNA modification, RNA 5-methylcytosine
Citation: Song J, Zhai J, Bian E, Song Y, Yu J and Ma C (2018) Corrigendum: Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning. Front. Plant Sci. 9:1762. doi: 10.3389/fpls.2018.01762
Received: 17 October 2018; Accepted: 13 November 2018;
Published: 30 November 2018.
Edited by:Giovanni Nigita, The Ohio State University, United States
Reviewed by:Salvatore Alaimo, Università degli Studi di Catania, Italy
Copyright © 2018 Song, Zhai, Bian, Song, Yu and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Chuang Ma, firstname.lastname@example.org
†These authors have contributed equally to this work