Front. Plant Sci. Frontiers in Plant Science Front. Plant Sci. 1664-462X Frontiers Media S.A. 10.3389/fpls.2018.01762 Plant Science Correction Corrigendum: Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning Song Jie 1 2 † Zhai Jingjing 1 † Bian Enze 3 † Song Yujia 3 Yu Jiantao 3 Ma Chuang 1 2 * 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

Edited by: Giovanni Nigita, The Ohio State University, United States

Reviewed by: Salvatore Alaimo, Università degli Studi di Catania, Italy

 *Correspondence: Chuang Ma chuangma2006@gmail.com

This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Plant Science

†These authors have contributed equally to this work

 30 11 2018 2018 9 1762 17 10 2018 13 11 2018 Copyright © 2018 Song, Zhai, Bian, Song, Yu and Ma. 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.

 A corrigendum on Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning 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 AUC Epitranscriptome machine learning RNA modification RNA 5-methylcytosine

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.

 References Wang X. He C. (2014). Dynamic RNA modifications in posttranscriptional regulation. Mol. Cell 56, 5–12. 10.1016/j.molcel.2014.09.00125280100 Zhou Y. Zeng P. Li Y. H. Zhang Z. Cui Q. (2016). SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features. Nucleic Acids Res. 44:e91. 10.1093/nar/gkw10426896799