|Table of Contents|

Identification of m6A site based on reduction ofnucleotide physical-chemical properties significance(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2019年02期
Page:
199-
Research Field:
Publishing date:

Info

Title:
Identification of m6A site based on reduction ofnucleotide physical-chemical properties significance
Author(s):
Zhang Ming12Xu Yan1Chen Tao1Wang Changbao1Yu Dongjun2
1.School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang 212003,China; 2.School of Computer Science and Engineering,Nanjing University of Scienceand Technology,Nanjing 210094,China
Keywords:
heuristic reduction algorithm classifier ensemble pseudo dinucleotide composition auto variance and cross covariance
PACS:
TP18
DOI:
10.14177/j.cnki.32-1397n.2019.43.02.012
Abstract:
In order to improve the prediction accuracy of N6-methyladenosine(m6A)sites on the ribonucleic acid(RNA)sequence,a novel K-fold selection heuristic reduction algorithm is developed here based on the significance measurement of the nucleotide physical-chemical properties.The final predictor is constructed with the classifier ensemble,the K base classifiers are obtained by combining the support vector machine(SVM)with reduction subsets,and the reduction algorithm is used to remove the redundant physical-chemical properties and re-encode the RNA sample sequence. Finally,the jackknife test on the benchmark dataset verifies the pseudo dinucleotide composition analysis and auto variance and cross covariance transformation respectively,which are two typical representative algorithms based on nucleotide physical-chemical properties. The experimental results show that the proposed algorithm can significantly improve the overall prediction performance of m6A sites.

References:

[1] Yue Y,Liu J,He C. RNA N6-methyladenosine methylation in post-transcriptional gene expression regulation[J]. Genes & Development,2015,29:1343-55.
[2]Dominissini D,Moshitch-Moshkovitz S,Salmon-Divon M,et al. Transcriptome-wide mapping of N6-methyladenosine by m6A-seq based on immunocapturing and massively parallel sequencing[J]. Nature Protocols,2013,8:176-89.
[3]Chen W,Feng P,Ding H,et al. iRNA-methyl:identifying N6-methyladenosine sites using pseudo nucleotide composition[J]. Analytical Biochemistry,2015,490:26-33.
[4]Liu Z,Xiao X,Yu D J,et al. pRNAm-PC:Predicting N6-methyladenosine sites in RNA sequences via physical-chemical properties[J]. Analytical Biochemistry,2015,497:60-67.
[5]Schwartz S,Agarwala S D,Mumbach M R,et al. High-resolution mapping reveals a conserved,widespread,dynamic mRNA methylation program in yeast meiosis[J]. Cell,2013,155:1409-1421.
[6]Jia G,Fu Y,Zhao X,et al. N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO[J]. Nature Chemical Biology,2011(7):885-891.
[7]Yang H,Zheng Y,Li T W,et al. Methionine adenosyltransferase 2B,HuR,and sirtuin 1 protein cross-talk impacts on the effect of resveratrol on apoptosis and growth in liver cancer cells[J]. Journal of Biological Chemistry,2013,288:23161-23170.
[8]Blanco S,Frye M. Role of RNA methyltransferases in tissue renewal and pathology. Current opinion in cell biology[J]. Current Opinion in Cell Biology,2014,31:1-7.
[9]Zhang M,Sun J W,Liu Z,et al. Improving N6-methyladenosine site prediction with heuristic selection of nucleotide physical-chemical properties[J]. Analytical Biochemistry,2016(508):104-113.
[10]Benítez-Caballero M J,Medina J,Ramírez-Poussa E. Attribute reduction in rough set theory and formal concept analysis. Rough sets[C]//IJCRS 2017. Olsztyn,Poland:Springer,2017:513-525.
[11]王宇,杨志荣,杨习贝.决策粗糙集属性约简:一种局部视角方法[J]. 南京理工大学学报,2003,27(5):630-635.
Wang Yu,Yang Zhirong,Yang Xibei. Local attribute reduction approach based on decision-theoretic rough set[J]. Journal of Nanjing University of Science and Technology,2003,27(5):630-635.
[12]徐菲菲,雷景生,毕忠勤,等. 大数据环境下多决策表的区间值全局近似约简[J]. 软件学报,2014,25(9):2119-2135.
Xu Feifei,Lei Jingsheng,Bi Zhongqin,et al. Approaches to approximate reduction with interval-valued multi-decision tables in big data[J]. Journal of Software,2014,25(9):2119-2135.
[13]Jing Y,Li T,Fujita H,et al. An incremental attribute reduction approach based on knowledge granularity with a multi-granulation view[J]. Information Sciences,2017,411:23-28.
[14]郜法启,於东军,沈红斌.基于分类器集成的跨膜蛋白两亲螺旋区域位置预测[J]. 南京理工大学学报,2016,40(4):431-437.
Gao Faqi,Yu Dongjun,Shen Hongbin. Prediction of amphipathic helices in transmembrane proteins by using ensembled classifier[J]. Journal of Nanjing University of Science and Technology,2016,40(4):431-437.
[15]Liu B,Liu F,Fang L,et al. RepRNA:a web server for generating various feature vectors of RNA sequences[J]. Molecular Genetics and Genomics,2016,291(1):473-481.
[16]Chang C C,Lin C J. LIBSVM:A library for support vector machines[J]. Acm Transactions on Intelligent Systems & Technology,2011,2(3):389-396.
[17]Wang G Y,Yu H. Monotonic uncertainty measures for attribute reduction in probabilistic rough set model[J]. International Journal of Approximate Reasoning,2015,59:41-67.
[18]Li H,Li D,Zhai Y,et al. A novel attribute reduction approach for multi-label data based on rough set theory[J]. Information Sciences,2016,367:827-847.
[19]Chen J,Lin Y,Lin G,et al. The relationship between attribute reducts in rough sets and minimal vertex covers of graphs[J]. Information Sciences,2015,325:87-97.
[20]Zhao Y,Yao Y Y. Attribute reduction in decision-theoretic rough set models[J]. Information Sciences,2008,178(17):3356-3373.

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Last Update: 2019-04-26