Literature DB >> 31805335

i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome.

Md Mehedi Hasan1, Balachandran Manavalan2, Mst Shamima Khatun3, Hiroyuki Kurata4.   

Abstract

One of the most important epigenetic modifications is N4-methylcytosine, which regulates many biological processes including DNA replication and chromosome stability. Identification of N4-methylcytosine sites is pivotal to understand specific biological functions. Herein, we developed the first bioinformatics tool called i4mC-ROSE for identifying N4-methylcytosine sites in the genomes of Fragaria vesca and Rosa chinensis in the Rosaceae, which utilizes a random forest classifier with six encoding methods that cover various aspects of DNA sequence information. The i4mC-ROSE predictor achieves area under the curve scores of 0.883 and 0.889 for the two genomes during cross-validation. Moreover, the i4mC-ROSE outperforms other classifiers tested in this study when objectively evaluated on the independent datasets. The proposed i4mC-ROSE tool can serve users' demand for the prediction of 4mC sites in the Rosaceae genome. The i4mC-ROSE predictor and utilized datasets are publicly accessible at http://kurata14.bio.kyutech.ac.jp/i4mC-ROSE/.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  DNA methylation; Linear regression; Machine learning; N4-methylcytosine site; Sequence encoding

Mesh:

Substances:

Year:  2019        PMID: 31805335     DOI: 10.1016/j.ijbiomac.2019.12.009

Source DB:  PubMed          Journal:  Int J Biol Macromol        ISSN: 0141-8130            Impact factor:   6.953


  24 in total

1.  Computational prediction of species-specific yeast DNA replication origin via iterative feature representation.

Authors:  Balachandran Manavalan; Shaherin Basith; Tae Hwan Shin; Gwang Lee
Journal:  Brief Bioinform       Date:  2021-07-20       Impact factor: 11.622

2.  Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction.

Authors:  Ying Liang; Yanan Wu; Zequn Zhang; Niannian Liu; Jun Peng; Jianjun Tang
Journal:  BMC Bioinformatics       Date:  2022-06-29       Impact factor: 3.307

3.  Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy.

Authors:  Md Mehedi Hasan; Sho Tsukiyama; Jae Youl Cho; Hiroyuki Kurata; Md Ashad Alam; Xiaowen Liu; Balachandran Manavalan; Hong-Wen Deng
Journal:  Mol Ther       Date:  2022-05-06       Impact factor: 12.910

4.  Extremely-randomized-tree-based Prediction of N6-Methyladenosine Sites in Saccharomyces cerevisiae.

Authors:  Rajiv G Govindaraj; Sathiyamoorthy Subramaniyam; Balachandran Manavalan
Journal:  Curr Genomics       Date:  2020-01       Impact factor: 2.236

5.  i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation.

Authors:  Md Mehedi Hasan; Balachandran Manavalan; Watshara Shoombuatong; Mst Shamima Khatun; Hiroyuki Kurata
Journal:  Plant Mol Biol       Date:  2020-03-05       Impact factor: 4.076

6.  iDNA-MT: Identification DNA Modification Sites in Multiple Species by Using Multi-Task Learning Based a Neural Network Tool.

Authors:  Xiao Yang; Xiucai Ye; Xuehong Li; Lesong Wei
Journal:  Front Genet       Date:  2021-03-31       Impact factor: 4.599

7.  A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.

Authors:  Chowdhury Rafeed Rahman; Ruhul Amin; Swakkhar Shatabda; Md Sadrul Islam Toaha
Journal:  Sci Rep       Date:  2021-05-14       Impact factor: 4.379

8.  i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes.

Authors:  Md Mehedi Hasan; Balachandran Manavalan; Watshara Shoombuatong; Mst Shamima Khatun; Hiroyuki Kurata
Journal:  Comput Struct Biotechnol J       Date:  2020-04-08       Impact factor: 7.271

9.  i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning.

Authors:  Yanjuan Li; Zhengnan Zhao; Zhixia Teng
Journal:  Biomed Res Int       Date:  2021-05-29       Impact factor: 3.411

10.  Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features.

Authors:  Zhixun Zhao; Xiaocai Zhang; Fang Chen; Liang Fang; Jinyan Li
Journal:  BMC Genomics       Date:  2020-09-11       Impact factor: 3.969

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