title: Improved nucleosome-positioning algorithm iNPS for accurate nucleosome positioning from sequencing data.
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.
authors.affiliation: 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.
authors.forename: Weizhong
authors.initials: W
authors.lastname: Chen
authors.affiliation: 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.
authors.forename: Yi
authors.initials: Y
authors.lastname: Liu
authors.affiliation: 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.
authors.forename: Shanshan
authors.initials: S
authors.lastname: Zhu
authors.affiliation: 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.
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Green
authors.affiliation: 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.
authors.forename: Gang
authors.initials: G
authors.lastname: Wei
authors.affiliation: 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.
authors.forename: Jing-Dong Jackie
authors.initials: JD
authors.lastname: Han
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D005189:False Positive Reactions; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D008213:Lymphocyte Activation; D008836:Micrococcal Nuclease / Q000378:metabolism; D009707:Nucleosomes / Q000737:chemistry*; D011401:Promoter Regions, Genetic; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D013601:T-Lymphocytes / Q000166:cytology
chemical_list: D009707:Nucleosomes; D008836:Micrococcal Nuclease