Literature DB >> 27550167

AthMethPre: a web server for the prediction and query of mRNA m6A sites in Arabidopsis thaliana.

Shunian Xiang1, Zhangming Yan1, Ke Liu2, Yaou Zhang3, Zhirong Sun1.   

Abstract

N6-Methyladenosine (m6A) is the most prevalent and abundant modification in mRNA that has been linked to many key biological processes. High-throughput experiments have generated m6A-peaks across the transcriptome of A. thaliana, but the specific methylated sites were not assigned, which impedes the understanding of m6A functions in plants. Therefore, computational prediction of mRNA m6A sites becomes emergently important. Here, we present a method to predict the m6A sites for A. thaliana mRNA sequence(s). To predict the m6A sites of an mRNA sequence, we employed the support vector machine to build a classifier using the features of the positional flanking nucleotide sequence and position-independent k-mer nucleotide spectrum. Our method achieved good performance and was applied to a web server to provide service for the prediction of A. thaliana m6A sites. The server also provides a comprehensive database of predicted transcriptome-wide m6A sites and curated m6A-seq peaks from the literature for query and visualization. The AthMethPre web server is the first web server that provides a user-friendly tool for the prediction and query of A. thaliana mRNA m6A sites, which is freely accessible for public use at .

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Year:  2016        PMID: 27550167     DOI: 10.1039/c6mb00536e

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  17 in total

1.  WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach.

Authors:  Kunqi Chen; Zhen Wei; Qing Zhang; Xiangyu Wu; Rong Rong; Zhiliang Lu; Jionglong Su; João Pedro de Magalhães; Daniel J Rigden; Jia Meng
Journal:  Nucleic Acids Res       Date:  2019-04-23       Impact factor: 16.971

2.  GlyinsRNA: a webserver for predicting glycosylation sites on small RNAs.

Authors:  Chunmei Cui; Xiaobin Wu; Yuan Zhou
Journal:  RNA Biol       Date:  2021-09-24       Impact factor: 4.766

3.  WITMSG: Large-scale Prediction of Human Intronic m6A RNA Methylation Sites from Sequence and Genomic Features.

Authors:  Lian Liu; Xiujuan Lei; Jia Meng; Zhen Wei
Journal:  Curr Genomics       Date:  2020-01       Impact factor: 2.236

4.  HLMethy: a machine learning-based model to identify the hidden labels of m6A candidates.

Authors:  Ze Liu; Wei Dong; WenJie Luo; Wei Jiang; QuanWu Li; ZiLi He
Journal:  Plant Mol Biol       Date:  2019-11-13       Impact factor: 4.076

5.  HSM6AP: a high-precision predictor for the Homo sapiens N6-methyladenosine (m^6 A) based on multiple weights and feature stitching.

Authors:  Jing Li; Shida He; Fei Guo; Quan Zou
Journal:  RNA Biol       Date:  2021-02-12       Impact factor: 4.652

6.  CNNLSTMac4CPred: A Hybrid Model for N4-Acetylcytidine Prediction.

Authors:  Guiyang Zhang; Wei Luo; Jianyi Lyu; Zu-Guo Yu; Guohua Huang
Journal:  Interdiscip Sci       Date:  2022-02-01       Impact factor: 2.233

Review 7.  Recent Advances in Identification of RNA Modifications.

Authors:  Wei Chen; Hao Lin
Journal:  Noncoding RNA       Date:  2016-12-28

8.  BERMP: a cross-species classifier for predicting m6A sites by integrating a deep learning algorithm and a random forest approach.

Authors:  Yu Huang; Ningning He; Yu Chen; Zhen Chen; Lei Li
Journal:  Int J Biol Sci       Date:  2018-09-07       Impact factor: 6.580

9.  M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species.

Authors:  Xiaoli Qiang; Huangrong Chen; Xiucai Ye; Ran Su; Leyi Wei
Journal:  Front Genet       Date:  2018-10-25       Impact factor: 4.599

Review 10.  Epigenetics: Roles and therapeutic implications of non-coding RNA modifications in human cancers.

Authors:  Dawei Rong; Guangshun Sun; Fan Wu; Ye Cheng; Guoqiang Sun; Wei Jiang; Xiao Li; Yi Zhong; Liangliang Wu; Chuanyong Zhang; Weiwei Tang; Xuehao Wang
Journal:  Mol Ther Nucleic Acids       Date:  2021-05-01       Impact factor: 8.886

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