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

Author

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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
    Graduate University of 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
    Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University)

  • 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)

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.

Suggested Citation

  • Weizhong Chen & Yi Liu & Shanshan Zhu & Christopher D. Green & Gang Wei & Jing-Dong Jackie Han, 2014. "Improved nucleosome-positioning algorithm iNPS for accurate nucleosome positioning from sequencing data," Nature Communications, Nature, vol. 5(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5909
    DOI: 10.1038/ncomms5909
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    Cited by:

    1. Yawen Lei & Yaoguang Yu & Wei Fu & Tao Zhu & Caihong Wu & Zhihao Zhang & Zewang Yu & Xin Song & Jianqu Xu & Zhenwei Liang & Peitao Lü & Chenlong Li, 2024. "BCL7A and BCL7B potentiate SWI/SNF-complex-mediated chromatin accessibility to regulate gene expression and vegetative phase transition in plants," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Tianbao Li & Qi Liu & Zhong Chen & Kun Fang & Furong Huang & Xueqi Fu & Qianben Wang & Victor X. Jin, 2022. "Dynamic nucleosome landscape elicits a noncanonical GATA2 pioneer model," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

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