Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China, Gordon Life Science Institute, Belmont, Massachusetts, USA, Department of Physics, School of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia.
Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China, Gordon Life Science Institute, Belmont, Massachusetts, USA, Department of Physics, School of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi ArabiaKey Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China, Gordon Life Science Institute, Belmont, Massachusetts, USA, Department of Physics, School of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia.
Bioinformatics. 2014 Jun 1;30(11):1522-9. doi: 10.1093/bioinformatics/btu083. Epub 2014 Feb 6.
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.
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.
A user-friendly web-server, iNuc-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iNuc-PseKNC.
核小体定位参与许多细胞活动，并在调节细胞过程中发挥重要作用。在后基因组时代，基因组序列呈雪崩式增长，因此非常需要开发自动化方法来快速有效地识别核小体定位。尽管已经提出了一些计算方法，但它们大多数是特定于物种的，并且忽略了可能在确定 DNA 序列上核小体定位中起重要作用的内在局部结构特性。
在这里，分别为人类、秀丽隐杆线虫和黑腹果蝇基因组开发了一个名为“iNuc-PseKNC”的预测因子。在新的预测因子中，通过一种新的特征向量“伪 k- 元核苷酸组成”来构建 DNA 序列样本，其中包含了六个 DNA 局部结构特性。通过对三个严格基准数据集的严格交叉验证测试观察到，iNuc-PseKNC 在预测上述三个基因组核小体定位方面的总体成功率分别为 86.27%、86.90%和 79.97%。同时，iNuc-PseKNC 在以前的研究人员为不同基因组使用的各种基准数据集上获得的结果也表明，当前的预测因子明显优于其对应物。
iNuc-PseKNC 是一个用户友好的网络服务器，可在 http://lin.uestc.edu.cn/server/iNuc-PseKNC 上免费访问。
