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.