Nucleosomal profiling is an effective method to determine the positioning and occupancy of nucleosomes, which is essential to understand their roles in genomic processes. However, the positional randomness across the genome and its relationship with nucleosome occupancy remains poorly understood. Here we present a computational method that segments the profile into nucleosomal domains and quantifies their randomness and relative occupancy level. Applying this method to published data, we find on average ~ 3-fold differences in the degree of positional randomness between regions typically considered “well-ordered”, as well as an unexpected predominance of only two types of domains of positional randomness in yeast cells. Further, we find that occupancy levels between domains actually differ maximally by ~ 2–3-fold in both cells, which has not been described before. We also developed a procedure by which one can estimate the sequencing depth that is required to identify nucleosomal positions even when regional positional randomness is high. Overall, we have developed a pipeline to quantitatively characterize domain-level features of nucleosome randomness and occupancy genome-wide, enabling the identification of otherwise unknown features in nucleosomal organization.

核糖体谱分析是确定核小体定位和占有率的有效方法，这对于了解其在基因组过程中的作用至关重要。但是，整个基因组的位置随机性及其与核小体占用的关系仍然知之甚少。在这里，我们提出了一种计算方法，可将配置文件细分为核小体结构域并量化其随机性和相对占用水平。将这种方法应用于已发表的数据，我们发现通常被认为是“有序的”区域之间的位置随机性程度平均相差约3倍，并且在酵母细胞中只有两种类型的位置随机性域出乎意料的优势。此外，我们发现，两个单元格中域之间的占用水平实际上最大相差约2-3倍，之前没有描述过。我们还开发了一种程序，即使区域位置随机性很高，也可以通过该程序来估计鉴定核小体位置所需的测序深度。总体而言，我们已经开发出了一种管道，可以定量表征核小体随机性和全基因组占用的域级特征，从而能够鉴定核小体组织中其他未知的特征。