Improved nucleosome-positioning algorithm iNPS for accurate nucleosome positioning from sequencing data
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Updated: Apr 23, 2026

Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis
Published on: December 12, 2017
Weizhong Chen1, Yi Liu2, Shanshan Zhu3
11] 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.
Abstract:
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.
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