iNuc-PhysChem

iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties. Nucleosome positioning has important roles in key cellular processes. Although intensive efforts have been made in this area, the rules defining nucleosome positioning is still elusive and debated. In this study, we carried out a systematic comparison among the profiles of twelve DNA physicochemical features between the nucleosomal and linker sequences in the Saccharomyces cerevisiae genome. We found that nucleosomal sequences have some position-specific physicochemical features, which can be used for in-depth studying nucleosomes. Meanwhile, a new predictor, called iNuc-PhysChem, was developed for identification of nucleosomal sequences by incorporating these physicochemical properties into a 1788-D (dimensional) feature vector, which was further reduced to a 884-D vector via the IFS (incremental feature selection) procedure to optimize the feature set. It was observed by a cross-validation test on a benchmark dataset that the overall success rate achieved by iNuc-PhysChem was over 96% in identifying nucleosomal or linker sequences. As a web-server, iNuc-PhysChem is freely accessible to the public at http://lin.uestc.edu.cn/server/iNuc-PhysChem. For the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented just for the integrity in developing the predictor. Meanwhile, for those who prefer to run predictions in their own computers, the predictor’s code can be easily downloaded from the web-server. It is anticipated that iNuc-PhysChem may become a useful high throughput tool for both basic research and drug design.


References in zbMATH (referenced in 24 articles )

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  1. Bai, Xiaolu; Chen, Xiaolin: Rational design, conformational analysis and membrane-penetrating dynamics study of Bac2A-derived antimicrobial peptides against gram-positive clinical strains isolated from pyemia (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. 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)
  4. Akbar, Shahid; Hayat, Maqsood: iMethyl-STTNC: identification of N(^6)-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences (2018)
  5. 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)
  6. Jia, Cangzhi; Yang, Qing; Zou, Quan: NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC (2018)
  7. Sabooh, M. Fazli; Iqbal, Nadeem; Khan, Mukhtaj; Khan, Muslim; Maqbool, H. F.: Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC (2018)
  8. Bag, Susmita; Ramaiah, Sudha; Anbarasu, Anand: fabp4 is central to eight obesity associated genes: a functional gene network-based polymorphic study (2015)
  9. Georgiou, D. N.; Karakasidis, T. E.; Megaritis, A. C.; Nieto, Juan J.; Torres, A.: An extension of fuzzy topological approach for comparison of genetic sequences (2015)
  10. 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)
  11. Kou, Gaoshan; Feng, Yonge: Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts (2015)
  12. Liu, Guoqing; Xing, Yongqiang; Cai, Lu: Using weighted features to predict recombination hotspots in \textitSaccharomycescerevisiae (2015)
  13. Nasiri, Jaber; Naghavi, Mohammad Reza; Kayvanjoo, Amir Hossein; Nasiri, Mojtaba; Ebrahimi, Mansour: Precision assessment of some supervised and unsupervised algorithms for genotype discrimination in the genus \textitpisumusing SSR molecular data (2015)
  14. Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil: Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology (2014)
  15. Chen, Xiao; Peng, Qinke; Han, Libin; Zhong, Tao; Xu, Tao: An effective haplotype assembly algorithm based on hypergraph partitioning (2014)
  16. Lin, Thy-Hou; Tsai, Tsung-Lin: Constructing a linear QSAR for some metabolizable drugs by human or pig flavin-containing monooxygenases using some molecular features selected by a genetic algorithm trained SVM (2014)
  17. Lyons, James; Biswas, Neela; Sharma, Alok; Dehzangi, Abdollah; Paliwal, Kuldip K.: Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping (2014)
  18. Pavesi, Angelo: Prediction of the determinants of thermal stability by linear discriminant analysis: the case of the glutamate dehydrogenase protein family (2014)
  19. Fan, Guo-Liang; Li, Qian-Zhong: Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou’s pseudo amino acid composition (2013)
  20. Feng, Peng-Mian; Ding, Hui; Chen, Wei; Lin, Hao: Naïve Bayes classifier with feature selection to identify phage virion proteins (2013)

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