Liu Lingjie, Xie Jianming, Sun Xiao, Luo Kun, Qin Zhaohui Steve, Liu Hongde
State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China.
Department of Neurosurgery, Xinjiang Evidence-Based Medicine Research Institute, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China.
BMC Genomics. 2017 Feb 7;18(1):135. doi: 10.1186/s12864-017-3541-9.
Nucleosome plays a role in transcriptional regulation through occluding the binding of proteins to DNA sites. Nucleosome occupancy varies among different cell types. Identification of such variation will help to understand regulation mechanism. The previous researches focused on the methods for two-sample comparison. However, a multiple-sample comparison (n ≥ 3) is necessary, especially in studying development and cancer. METHODS: Here, we proposed a Chi-squared test-based approach, named as Dimnp, to identify differential nucleosome regions (DNRs) in multiple samples. Dimnp is designed for sequenced reads data and includes the modules of both calling nucleosome occupancy and identifying DNRs.
We validated Dimnp on dataset of the mutant strains in which the modifiable histone residues are mutated into alanine in Saccharomyces cerevisiae. Dimnp shows a good capacity (area under the curve > 0.87) compared with the manually identified DNRs. Just by one time, Dimnp is able to identify all the DNRs identified by two-sample method Danpos. Under a deviation of 40 bp, the matched DNRs are above 60% between Dimnp and Danpos. With Dimnp, we found that promoters and telomeres are highly dynamic upon mutating the modifiable histone residues.
We developed a tool of identifying the DNRs in multiple samples and cell types. The tool can be applied in studying nucleosome variation in gradual change in development and cancer.
核小体通过阻碍蛋白质与DNA位点的结合在转录调控中发挥作用。核小体占有率在不同细胞类型中有所不同。识别这种差异将有助于理解调控机制。以往的研究主要集中在双样本比较方法上。然而,多样本比较(n≥3)是必要的,尤其是在研究发育和癌症时。
在此,我们提出了一种基于卡方检验的方法,名为Dimnp,用于识别多样本中的差异核小体区域(DNR)。Dimnp是针对测序读段数据设计的,包括调用核小体占有率和识别DNR的模块。
我们在酿酒酵母中可修饰组蛋白残基突变为丙氨酸的突变菌株数据集上验证了Dimnp。与手动识别的DNR相比,Dimnp表现出良好的性能(曲线下面积>0.87)。Dimnp只需一次就能识别出双样本方法Danpos识别出的所有DNR。在40bp的偏差范围内,Dimnp和Danpos之间匹配的DNR超过60%。通过Dimnp,我们发现当可修饰组蛋白残基发生突变时,启动子和端粒具有高度的动态性。
我们开发了一种在多样本和细胞类型中识别DNR的工具。该工具可应用于研究发育和癌症渐进变化中的核小体变异。