iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Meiotic recombination is an important biological process. As a main driving force of evolution, recombination provides natural new combinations of genetic variations. Rather than randomly occurring across a genome, meiotic recombination takes place in some genomic regions (the so-called ’hotspots’) with higher frequencies, and in the other regions (the so-called ’coldspots’) with lower frequencies. Therefore, the information of the hotspots and coldspots would provide useful insights for in-depth studying of the mechanism of recombination and the genome evolution process as well. So far, the recombination regions have been mainly determined by experiments, which are both expensive and time-consuming. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the recombination regions. In this study, a predictor, called ’iRSpot-PseDNC’, was developed for identifying the recombination hotspots and coldspots. In the new predictor, the samples of DNA sequences are formulated by a novel feature vector, the so-called ’pseudo dinucleotide composition’ (PseDNC), into which six local DNA structural properties, i.e. three angular parameters (twist, tilt and roll) and three translational parameters (shift, slide and rise), are incorporated. It was observed by the rigorous jackknife test that the overall success rate achieved by iRSpot-PseDNC was >82% in identifying recombination spots in Saccharomyces cerevisiae, indicating the new predictor is promising or at least may become a complementary tool to the existing methods in this area. Although the benchmark data set used to train and test the current method was from S. cerevisiae, the basic approaches can also be extended to deal with all the other genomes. Particularly, it has not escaped our notice that the PseDNC approach can be also used to study many other DNA-related problems. As a user-friendly web-server, iRSpot-PseDNC is freely accessible at

References in zbMATH (referenced in 65 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. Chen, Guodong; Cao, Man; Yu, Jialin; Guo, Xinyun; Shi, Shaoping: Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou’s general PseAAC (2019)
  4. 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)
  5. Khan, Yaser Daanial; Jamil, Mehreen; Hussain, Waqar; Rasool, Nouman; Khan, Sher Afzal; Chou, Kuo-Chen: pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments (2019)
  6. Ning, Qiao; Ma, Zhiqiang; Zhao, Xiaowei: Dforml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou’s 5-step rule and pseudo components (2019)
  7. 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)
  8. 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)
  9. 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)
  10. Wang, Lidong; Zhang, Ruijun; Mu, Yashuang: Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou’s general PseAAC (2019)
  11. Zhao, Wei; Li, Guang-Ping; Wang, Jun; Zhou, Yuan-Ke; Gao, Yang; Du, Pu-Feng: Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions (2019)
  12. 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)
  13. 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)
  14. Contreras-Torres, Ernesto: Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC (2018)
  15. Jia, Cangzhi; Yang, Qing; Zou, Quan: NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC (2018)
  16. Liang, Yunyun; Zhang, Shengli: Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence (2018)
  17. 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)
  18. Qiu, Wenying; Li, Shan; Cui, Xiaowen; Yu, Zhaomin; Wang, Minghui; Du, Junwei; Peng, Yanjun; Yu, Bin: Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou’s pseudo-amino acid composition (2018)
  19. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  20. Tarafder, Sumit; Toukir Ahmed, Md.; Iqbal, Sumaiya; Tamjidul Hoque, Md; Sohel Rahman, M.: RBSURFpred: modeling protein accessible surface area in real and binary space using regularized and optimized regression (2018)

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