iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition

By Shou-Hui Guo, En-Ze Deng, Li-Qin Xu, Hui Ding, Hao Lin, Kuo-Chen Chou. Bioinform., 2014

372 citations recorded by the source corpus.

MOTIVATION Nucleosome positioning participates in many cellular activities and plays significant roles in regulating cellular processes. With the avalanche of genome sequences generated in the post-genomic age, it is highly desired to develop automated methods for rapidly and effectively identifying nucleosome positioning. Although some computational methods were proposed, most of them were species specific and neglected the intrinsic local structural properties that might play important roles in determining the nucleosome positioning on a DNA sequence. RESULTS Here a predictor called 'iNuc-PseKNC' was developed for predicting nucleosome positioning in Homo sapiens, Caenorhabditis elegans and Drosophila melanogaster genomes, respectively. In the new predictor, the samples of DNA sequences were formulated by a novel feature-vector called 'pseudo k-tuple nucleotide composition', into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on the three stringent benchmark datasets that the overall success rates achieved by iNuc-PseKNC in predicting the nucleosome positioning of the aforementioned three genomes were 86.27%, 86.90% and 79.97%, respectively. Meanwhile, the results obtained by iNuc-PseKNC on various benchmark datasets used by the previous investigators for different genomes also indicated that the current predictor remarkably outperformed its counterparts. AVAILABILITY A user-friendly web-server, iNuc-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iNuc-PseKNC.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a3cd3c201fabee39873216daa9f708a0be541099

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In Molecular Biology

1. Kuo-Chen Chou
2. Hao Lin
3. Dongsheng Cao
4. Hua Tang
5. Zhi-Yong Liang
6. Wei Chen
7. Aiping Lu
8. Ning-Ning Wang

Citations in the corpus

• Identifying Sigma70 Promoters with Novel Pseudo Nucleotide Composition (2019, methodology): “In addition, the global-range or the long-range correlation information was also used as the features in the nucleosome positioning prediction [13] and human origin of replication prediction [14].”
• BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, methodology): “Thirdly, the adjacency list file and the molecular features in the above two steps are provided to calculate the final interaction features tasks such as SVM-Prot [61], Cell-Ploc [36], iGPCR-Drug [64], iRSpot-PseDNC [39], IDrug-Target [65] and iNuc-PseKNC [66].”
• Impacts of bioinformatics to medicinal chemistry. (2015, methodology): “Reproduced from [124] with permission.”