Literature DB >> 25596338

iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition.

Zi Liu1, Xuan Xiao2, Wang-Ren Qiu3, Kuo-Chen Chou4.   

Abstract

Predominantly occurring on cytosine, DNA methylation is a process by which cells can modify their DNAs to change the expression of gene products. It plays very important roles in life development but also in forming nearly all types of cancer. Therefore, knowledge of DNA methylation sites is significant for both basic research and drug development. Given an uncharacterized DNA sequence containing many cytosine residues, which one can be methylated and which one cannot? With the avalanche of DNA sequences generated during the postgenomic age, it is highly desired to develop computational methods for accurately identifying the methylation sites in DNA. Using the trinucleotide composition, pseudo amino acid components, and a dataset-optimizing technique, we have developed a new predictor called "iDNA-Methyl" that has achieved remarkably higher success rates in identifying the DNA methylation sites than the existing predictors. A user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iDNA-Methyl, where users can easily get their desired results. We anticipate that the web-server predictor will become a very useful high-throughput tool for basic research and drug development and that the novel approach and technique can also be used to investigate many other DNA-related problems and genome analysis.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  3→1 Codon conversion; DNA methylation; Neighborhood cleaning rule; Pseudo amino acid components; Synthetic minority oversampling technique; Target–jackknife cross-validation

Mesh:

Substances:

Year:  2015        PMID: 25596338     DOI: 10.1016/j.ab.2014.12.009

Source DB:  PubMed          Journal:  Anal Biochem        ISSN: 0003-2697            Impact factor:   3.365


  56 in total

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2.  iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.

Authors:  Muhammad Kabir; Maqsood Hayat
Journal:  Mol Genet Genomics       Date:  2015-08-30       Impact factor: 3.291

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Journal:  Mol Genet Genomics       Date:  2015-06-18       Impact factor: 3.291

4.  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

5.  Comparative analysis of housekeeping and tissue-selective genes in human based on network topologies and biological properties.

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Journal:  Mol Genet Genomics       Date:  2016-02-20       Impact factor: 3.291

6.  Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences.

Authors:  Bin Liu; Fule Liu; Xiaolong Wang; Junjie Chen; Longyun Fang; Kuo-Chen Chou
Journal:  Nucleic Acids Res       Date:  2015-05-09       Impact factor: 16.971

7.  Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types.

Authors:  Weizhong Lin; Dong Xu
Journal:  Bioinformatics       Date:  2016-08-26       Impact factor: 6.937

8.  Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.

Authors:  Khurshid Ahmad; Muhammad Waris; Maqsood Hayat
Journal:  J Membr Biol       Date:  2016-01-08       Impact factor: 1.843

9.  LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec.

Authors:  Sho Tsukiyama; Md Mehedi Hasan; Satoshi Fujii; Hiroyuki Kurata
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

10.  CUE: CpG impUtation ensemble for DNA methylation levels across the human methylation450 (HM450) and EPIC (HM850) BeadChip platforms.

Authors:  Gang Li; Laura Raffield; Mark Logue; Mark W Miller; Hudson P Santos; T Michael O'Shea; Rebecca C Fry; Yun Li
Journal:  Epigenetics       Date:  2020-10-04       Impact factor: 4.528

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