iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. In eukaryotic genes, exons are generally interrupted by introns. Accurately removing introns and joining exons together are essential processes in eukaryotic gene expression. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapid and effective detection of splice sites that play important roles in gene structure annotation and even in RNA splicing. Although a series of computational methods were proposed for splice site identification, most of them neglected the intrinsic local structural properties. In the present study, a predictor called ”iSS-PseDNC” was developed for identifying splice sites. In the new predictor, the sequences were formulated by a novel feature-vector called ”pseudo dinucleotide composition” (PseDNC) into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on two benchmark datasets that the overall success rates achieved by iSS-PseDNC in identifying splice donor site and splice acceptor site were 85.45% and 87.73%, respectively. It is anticipated that iSS-PseDNC may become a useful tool for identifying splice sites and that the six DNA local structural properties described in this paper may provide novel insights for in-depth investigations into the mechanism of RNA splicing.

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. 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)
  3. Rout, Subhashree; Mahapatra, Rajani Kanta: \textitInsilico analysis of \textitplasmodiumfalciparum CDPK5 protein through molecular modeling, docking and dynamics (2019)
  4. 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)
  5. Kanatchikov, Igor V.: Schrödinger wave functional in quantum Yang-Mills theory from precanonical quantization (2018)
  6. 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)
  7. Zhang, Shengli; Liang, Yunyun: Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC (2018)
  8. Amiri, Saeid; Dinov, Ivo D.: Comparison of genomic data via statistical distribution (2016)
  9. Jia, Jianhua; Liu, Zi; Xiao, Xuan; Liu, Bingxiang; Chou, Kuo-Chen: pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach (2016)
  10. Aram, Reza Zohouri; Charkari, Nasrollah Moghadam: A two-layer classification framework for protein fold recognition (2015)
  11. Bag, Susmita; Ramaiah, Sudha; Anbarasu, Anand: fabp4 is central to eight obesity associated genes: a functional gene network-based polymorphic study (2015)
  12. Ding, Yanrui; Wang, Xueqin; Mou, Zhaolin: Communities in the iron superoxide dismutase amino acid network (2015)
  13. Ganjtabesh, Mohammad; Montaseri, Soheila; Zare-Mirakabad, Fatemeh: Using temperature effects to predict the interactions between two RNAs (2015)
  14. Khan, Zaheer Ullah; Hayat, Maqsood; Khan, Muazzam Ali: Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model (2015)
  15. Liu, Guoqing; Xing, Yongqiang; Cai, Lu: Using weighted features to predict recombination hotspots in \textitSaccharomycescerevisiae (2015)
  16. Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan: R3P-Loc: a compact multi-label predictor using ridge regression and random projection for protein subcellular localization (2014)