1] Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China [2] Graduate University of Chinese Academy of Sciences, Beijing 100049, China.
1] Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China [2] Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
Nat Commun. 2014 Sep 18;5:4909. doi: 10.1038/ncomms5909.
Accurate determination of genome-wide nucleosome positioning can provide important insights into global gene regulation. Here, we describe the development of an improved nucleosome-positioning algorithm-iNPS-which achieves significantly better performance than the widely used NPS package. By determining nucleosome boundaries more precisely and merging or separating shoulder peaks based on local MNase-seq signals, iNPS can unambiguously detect 60% more nucleosomes. The detected nucleosomes display better nucleosome 'widths' and neighbouring centre-centre distance distributions, giving rise to sharper patterns and better phasing of average nucleosome profiles and higher consistency between independent data subsets. In addition to its unique advantage in classifying nucleosomes by shape to reveal their different biological properties, iNPS also achieves higher significance and lower false positive rates than previously published methods. The application of iNPS to T-cell activation data demonstrates a greater ability to facilitate detection of nucleosome repositioning, uncovering additional biological features underlying the activation process.
准确确定全基因组核小体定位可以为全局基因调控提供重要的见解。在这里，我们描述了一种改进的核小体定位算法-iNPS-的开发，该算法的性能明显优于广泛使用的 NPS 包。通过更精确地确定核小体边界，并根据局部 MNase-seq 信号合并或分离肩峰，iNPS 可以明确检测到 60%更多的核小体。检测到的核小体显示出更好的核小体“宽度”和相邻中心-中心距离分布，从而产生更清晰的模式和更好的平均核小体轮廓相位以及更高的独立数据集子集之间的一致性。除了通过形状对核小体进行分类以揭示其不同生物学特性的独特优势外，iNPS 还比以前发表的方法具有更高的显著性和更低的假阳性率。iNPS 在 T 细胞激活数据中的应用表明，它能够更有效地促进核小体重定位的检测，揭示激活过程背后的其他生物学特征。
