iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition
| Autor: | Shou-Hui Guo, Hao Lin, En-Ze Deng, Kuo-Chen Chou, Wei Chen, Liqin Xu, Hui Ding |
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| Rok vydání: | 2014 |
| Předmět: |
Statistics and Probability
Sequence analysis Computational biology Biochemistry Genome DNA sequencing chemistry.chemical_compound Animals Humans Nucleosome A-DNA Caenorhabditis elegans Molecular Biology Genetics biology Nucleotides DNA Genomics Sequence Analysis DNA biology.organism_classification Nucleosomes Computer Science Applications Computational Mathematics Drosophila melanogaster Computational Theory and Mathematics chemistry Software |
| Zdroj: | Bioinformatics. 30:1522-1529 |
| ISSN: | 1367-4811 1367-4803 |
| Popis: | 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. Contact: hlin@uestc.edu.cn, wchen@gordonlifescience.org, kcchou@gordonlifescience.org Supplementary information: Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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