iRNA-3typeA

iRNA-3typeA: Identifying Three Types of Modification at RNA’s Adenosine Sites. RNA modifications are additions of chemical groups to nucleotides or their local structural changes. Knowledge about the occurrence sites of these modifications is essential for in-depth understanding of the biological functions and mechanisms and for treating some genomic diseases as well. With the avalanche of RNA sequences generated in the post-genomic age, many computational methods have been proposed for identifying various types of RNA modifications one by one. However, so far no method whatsoever has been developed for simultaneously identifying several different types of RNA modifications. To address such a challenge, we developed a predictor called “iRNA-3typeA,” by which we can simultaneously identify the occurrence sites of the following three most frequently observed modifications in RNA: (1) N1-methyladenosine (m1A), (2) N6-methyladenosine (m6A), and (3) adenosine to inosine (A-to-I). It has been shown via rigorous cross-validations for the RNA sequences from Homo sapiens and Mus musculus transcriptomes that the success rates achieved by the powerful new predictor are quite high. For the convenience of broad experimental scientists, a user-friendly web server for iRNA-3typeA has been established at http://lin-group.cn/server/iRNA-3typeA/. It is anticipated that iRNA-3typeA may become a useful high throughput tool for genome analysis.


References in zbMATH (referenced in 16 articles )

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  1. Adilina, Sheikh; Farid, Dewan Md; Shatabda, Swakkhar: Effective DNA binding protein prediction by using key features via Chou’s general PseAAC (2019)
  2. Ahmad, Jamal; Hayat, Maqsood: MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components (2019)
  3. Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
  4. Lu, Fuhua; Zhu, Maoshu; Lin, Ying; Zhong, Hongbin; Cai, Lei; He, Lin; Chou, Kuo-Chen: The preliminary efficacy evaluation of the CTLA-4-ig treatment against lupus nephritis through \textitin-silico analyses (2019)
  5. Pan, Yi; Wang, Shiyuan; Zhang, Qi; Lu, Qianzi; Su, Dongqing; Zuo, Yongchun; Yang, Lei: Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions (2019)
  6. Tahir, Muhammad; Tayara, Hilal; Chong, Kil To: iRNA-PseKNC(2methyl): identify RNA 2’-O-methylation sites by convolution neural network and Chou’s pseudo components (2019)
  7. Tian, Baoguang; Wu, Xue; Chen, Cheng; Qiu, Wenying; Ma, Qin; Yu, Bin: Predicting protein-protein interactions by fusing various Chou’s pseudo components and using wavelet denoising approach (2019)
  8. Wang, Lidong; Zhang, Ruijun; Mu, Yashuang: Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou’s general PseAAC (2019)
  9. Zhao, Xiaowei; Zhang, Ye; Ning, Qiao; Zhang, Hongrui; Ji, Jinchao; Yin, Minghao: Identifying N(^6)-methyladenosine sites using extreme gradient boosting system optimized by particle swarm optimizer (2019)
  10. Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen: pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (2018)
  11. Chiu, Jimmy Ka Ho; Dillon, Tharam S.; Chen, Yi-Ping Phoebe: Large-scale frequent stem pattern mining in RNA families (2018)
  12. Ju, Zhe; Wang, Shi-Yun: Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm (2018)
  13. Mei, Juan; Fu, Yi; Zhao, Ji: Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition (2018)
  14. Sankari, E. Siva; Manimegalai, D.: Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC (2018)
  15. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  16. Zhang, Shengli; Liang, Yunyun: Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC (2018)