iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition

被引:183
作者
Qiu, Wang-Ren [1 ,2 ,3 ,4 ]
Jiang, Shi-Yu [3 ]
Xu, Zhao-Chun [3 ]
Xiao, Xuan [3 ,4 ]
Chou, Kuo-Chen [4 ,5 ,6 ]
机构
[1] Univ Missouri, Dept Comp Sci, Columbia, MO USA
[2] Univ Missouri, Bond Life Sci Ctr, Columbia, MO USA
[3] Jingdezhen Ceram Inst, Comp Dept, Jingdezhen, Peoples R China
[4] Gordon Life Sci Inst, Boston, MA 02478 USA
[5] Univ Elect Sci & Technol China, Ctr Informat Biol, Chengdu, Peoples R China
[6] King Abdulaziz Univ, CEGMR, Jeddah, Saudi Arabia
关键词
RNA 5-methylcytosine sites; pseudo dinucleotide composition; physical-chemical property matrix; auto/cross-covariance; web-server; AMINO-ACID-COMPOSITION; MEMBRANE-PROTEIN TYPES; LYSINE SUCCINYLATION SITES; LABEL LEARNING CLASSIFIER; SEQUENCE-BASED PREDICTOR; PHYSICOCHEMICAL PROPERTIES; SUBCELLULAR-LOCALIZATION; ENSEMBLE CLASSIFIER; RECOMBINATION SPOTS; WEB-SERVER;
D O I
10.18632/oncotarget.17104
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Occurring at cytosine (C) of RNA, 5-methylcytosine (m(5)C) is an important post-transcriptional modification (PTCM). The modification plays significant roles in biological processes by regulating RNA metabolism in both eukaryotes and prokaryotes. It may also, however, cause cancers and other major diseases. Given an uncharacterized RNA sequence that contains many C residues, can we identify which one of them can be of m5C modification, and which one cannot? It is no doubt a crucial problem, particularly with the explosive growth of RNA sequences in the postgenomic age. Unfortunately, so far no user-friendly web-server whatsoever has been developed to address such a problem. To meet the increasingly high demand from most experimental scientists working in the area of drug development, we have developed a new predictor called iRNAm5C-PseDNC by incorporating ten types of physical-chemical properties into pseudo dinucleotide composition via the auto/cross-covariance approach. Rigorous jackknife tests show that its anticipated accuracy is quite high. For most experimental scientists' convenience, a user-friendly web-server for the predictor has been provided at http://www.jci-bioinfo.cn/iRNAm5C-PseDNC along with a step-by-step user guide, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the approach presented here can also be used to deal with many other problems in genome analysis.
引用
收藏
页码:41178 / 41188
页数:11
相关论文
共 106 条
[1]   Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition [J].
Ahmad, Khurshid ;
Waris, Muhammad ;
Hayat, Maqsood .
JOURNAL OF MEMBRANE BIOLOGY, 2016, 249 (03) :293-304
[2]   Classification of membrane protein types using Voting Feature Interval in combination with Chou's Pseudo Amino Acid Composition [J].
Ali, Farman ;
Hayat, Maqsood .
JOURNAL OF THEORETICAL BIOLOGY, 2015, 384 :78-83
[3]  
ALTHAUS IW, 1993, J BIOL CHEM, V268, P14875
[4]   KINETIC-STUDIES WITH THE NONNUCLEOSIDE HIV-1 REVERSE-TRANSCRIPTASE INHIBITOR-U-88204E [J].
ALTHAUS, IW ;
CHOU, JJ ;
GONZALES, AJ ;
DEIBEL, MR ;
CHOU, KC ;
KEZDY, FJ ;
ROMERO, DL ;
PALMER, JR ;
THOMAS, RC ;
ARISTOFF, PA ;
TARPLEY, WG ;
REUSSER, F .
BIOCHEMISTRY, 1993, 32 (26) :6548-6554
[5]  
[Anonymous], MED CHEM
[6]  
[Anonymous], MED CHEM
[7]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[8]   Conservation of tRNA and rRNA 5-methylcytosine in the kingdom Plantae [J].
Burgess, Alice Louise ;
David, Rakesh ;
Searle, Iain Robert .
BMC PLANT BIOLOGY, 2015, 15
[9]   Prediction of linear B-cell epitopes using amino acid pair antigenicity scale [J].
Chen, J. ;
Liu, H. ;
Yang, J. ;
Chou, K.-C. .
AMINO ACIDS, 2007, 33 (03) :423-428
[10]   dRHP-PseRA: detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation [J].
Chen, Junjie ;
Long, Ren ;
Wang, Xiao-long ;
Liu, Bin ;
Chou, Kuo-Chen .
SCIENTIFIC REPORTS, 2016, 6