Literature DB >> 29360500

iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC.

Pengmian Feng1, Hui Yang2, Hui Ding2, Hao Lin3, Wei Chen4, Kuo-Chen Chou5.   

Abstract

N6-methyladenine (6mA) is one kind of post-replication modification (PTM or PTRM) occurring in a wide range of DNA sequences. Accurate identification of its sites will be very helpful for revealing the biological functions of 6mA, but it is time-consuming and expensive to determine them by experiments alone. Unfortunately, so far, no bioinformatics tool is available to do so. To fill in such an empty area, we have proposed a novel predictor called iDNA6mA-PseKNC that is established by incorporating nucleotide physicochemical properties into Pseudo K-tuple Nucleotide Composition (PseKNC). It has been observed via rigorous cross-validations that the predictor's sensitivity (Sn), specificity (Sp), accuracy (Acc), and stability (MCC) are 93%, 100%, 96%, and 0.93, respectively. For the convenience of most experimental scientists, a user-friendly web server for iDNA6mA-PseKNC has been established at http://lin-group.cn/server/iDNA6mA-PseKNC, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  General PseKNC; Intuitive metrics; Lingering density; N(6)-methyladenine; Nucleotide physicochemical properties; PTMs

Mesh:

Substances:

Year:  2018        PMID: 29360500     DOI: 10.1016/j.ygeno.2018.01.005

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  62 in total

1.  iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou's 5-step rule.

Authors:  Nguyen Quoc Khanh Le
Journal:  Mol Genet Genomics       Date:  2019-05-04       Impact factor: 3.291

2.  iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.

Authors:  Yaser Daanial Khan; Nouman Rasool; Waqar Hussain; Sher Afzal Khan; Kuo-Chen Chou
Journal:  Mol Biol Rep       Date:  2018-10-11       Impact factor: 2.316

3.  Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC.

Authors:  Ahmad Hassan Butt; Nouman Rasool; Yaser Daanial Khan
Journal:  Mol Biol Rep       Date:  2018-09-20       Impact factor: 2.316

Review 4.  Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs.

Authors:  Qiu-Xing Jiang
Journal:  Med Chem       Date:  2019       Impact factor: 2.745

5.  Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.

Authors:  Fuyi Li; Yanan Wang; Chen Li; Tatiana T Marquez-Lago; André Leier; Neil D Rawlings; Gholamreza Haffari; Jerico Revote; Tatsuya Akutsu; Kuo-Chen Chou; Anthony W Purcell; Robert N Pike; Geoffrey I Webb; A Ian Smith; Trevor Lithgow; Roger J Daly; James C Whisstock; Jiangning Song
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

6.  Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

Authors:  Fuyi Li; Chen Li; Tatiana T Marquez-Lago; André Leier; Tatsuya Akutsu; Anthony W Purcell; A Ian Smith; Trevor Lithgow; Roger J Daly; Jiangning Song; Kuo-Chen Chou
Journal:  Bioinformatics       Date:  2018-12-15       Impact factor: 6.937

Review 7.  Some illuminating remarks on molecular genetics and genomics as well as drug development.

Authors:  Kuo-Chen Chou
Journal:  Mol Genet Genomics       Date:  2020-01-01       Impact factor: 3.291

8.  Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning.

Authors:  Haodong Xu; Peilin Jia; Zhongming Zhao
Journal:  Brief Bioinform       Date:  2021-05-20       Impact factor: 11.622

9.  6mA-Finder: a novel online tool for predicting DNA N6-methyladenine sites in genomes.

Authors:  Haodong Xu; Ruifeng Hu; Peilin Jia; Zhongming Zhao
Journal:  Bioinformatics       Date:  2020-05-01       Impact factor: 6.937

10.  i6mA-DNCP: Computational Identification of DNA N6-Methyladenine Sites in the Rice Genome Using Optimized Dinucleotide-Based Features.

Authors:  Liang Kong; Lichao Zhang
Journal:  Genes (Basel)       Date:  2019-10-20       Impact factor: 4.096

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