Mitra Sneha, Hartemink Alexander J
Department of Computer Science, Duke University, Durham, NC 27708-0129, United States.
Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27710, United States.
Bioinform Adv. 2025 Apr 10;5(1):vbaf080. doi: 10.1093/bioadv/vbaf080. eCollection 2025.
Due to internal and external factors, the epigenomic landscape is constantly changing in ways that are linked to changes in gene expression. Chromatin accessibility data, such as MNase-seq, provide valuable insights into this landscape and have been used to compute chromatin occupancy profiles. Multiple datasets generated over time or under different conditions can thus be used to study dynamic changes in chromatin occupancy across the genome.
Our existing model, RoboCOP, computes a genome-wide chromatin occupancy profile for nucleosomes and hundreds of transcription factors. Here, we present a new method called DynaCOP that takes multiple chromatin occupancy profiles and uses them to generate a series of nucleosome-guided difference profiles. These profiles identify differentially binding transcription factors and reveal changes in nucleosome occupancy and positioning. We apply DynaCOP to chromatin occupancy profiles derived from deeply sequenced time-series MNase-seq data to study differential chromatin occupancy in the yeast genome under cadmium stress. We find strong correlations between the observed chromatin changes and changes in transcription.
由于内部和外部因素，表观基因组格局不断变化，且这些变化与基因表达的改变相关。染色质可及性数据，如MNase-seq，为这一格局提供了有价值的见解，并已用于计算染色质占据图谱。因此，随着时间推移或在不同条件下生成的多个数据集可用于研究全基因组染色质占据的动态变化。
我们现有的模型RoboCOP可计算核小体和数百种转录因子的全基因组染色质占据图谱。在此，我们提出一种名为DynaCOP的新方法，该方法获取多个染色质占据图谱，并利用它们生成一系列核小体引导的差异图谱。这些图谱可识别差异结合的转录因子，并揭示核小体占据和定位的变化。我们将DynaCOP应用于源自深度测序的时间序列MNase-seq数据的染色质占据图谱，以研究镉胁迫下酵母基因组中的差异染色质占据情况。我们发现观察到的染色质变化与转录变化之间存在很强的相关性。
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