Improved nucleosome-positioning algorithm iNPS for accurate nucleosome positioning from sequencing data

Weizhong Chen, Yi Liu, Shanshan Zhu, Christopher D. Green, Gang Wei and Jing-Dong Jackie Han ()
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Weizhong Chen: 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
Yi Liu: 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
Shanshan Zhu: 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
Christopher D. Green: 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
Gang Wei: 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
Jing-Dong Jackie Han: 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

Nature Communications, 2014, vol. 5, issue 1, 1-14

Abstract: 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.

Date: 2014
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DOI: 10.1038/ncomms5909

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