Cancer and Blood Disorders Center, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02115, USA.
Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
BMC Genomics. 2023 Oct 13;24(1):614. doi: 10.1186/s12864-023-09675-w.
Chromosomal compartmentalization plays a critical role in maintaining proper transcriptional programs in cell differentiation and oncogenesis. However, currently the prevalent method for comparative analysis of compartmentalization landscapes between different cell types is limited to the qualitative switched compartments.
To identify genomic regions with quantitatively differential compartmentalization changes from genome-wide chromatin conformation data like Hi-C, we developed a computational framework named DARIC. DARIC includes three modules: compartmentalization quantification, normalization, and differential analysis. Comparing DARIC with the conventional compartment switching analysis reveals substantial regions characterized by quantitatively significant compartmentalization changes without switching. These changes are accompanied by changes in gene expression, chromatin accessibility, H3K27ac intensity, as well as the interactions with nuclear lamina proteins and nuclear positioning, highlighting the functional importance of such quantitative changes in gene regulation. We applied DARIC to dissect the quantitative compartmentalization changes during human cardiomyocyte differentiation and identified two distinct mechanisms for gene activation based on the association with compartmentalization changes. Using the quantitative compartmentalization measurement module from DARIC, we further dissected the compartment variability landscape in the human genome by analyzing a compendium of 32 Hi-C datasets from 4DN. We discovered an interesting correlation between compartmentalization variability and sub-compartments.
DARIC is a useful tool for analyzing quantitative compartmentalization changes and mining novel biological insights from increasing Hi-C data. Our results demonstrate the functional significance of quantitative compartmentalization changes in gene regulation, and provide new insights into the relationship between compartmentalization variability and sub-compartments in the human genome.
染色体区室化在维持细胞分化和肿瘤发生过程中的适当转录程序中起着关键作用。然而，目前用于比较不同细胞类型区室化景观的流行方法仅限于定性切换区室。
为了从全基因组染色质构象数据（如 Hi-C）中识别具有定量差异区室化变化的基因组区域，我们开发了一种名为 DARIC 的计算框架。DARIC 包括三个模块：区室化量化、归一化和差异分析。将 DARIC 与传统的区室切换分析进行比较，揭示了大量具有定量显著区室化变化而没有切换的区域。这些变化伴随着基因表达、染色质可及性、H3K27ac 强度以及与核纤层蛋白和核定位的相互作用的变化，突出了这种基因调控中定量变化的功能重要性。我们应用 DARIC 来剖析人类心肌细胞分化过程中的定量区室化变化，并根据与区室化变化的关联，确定了两种不同的基因激活机制。使用 DARIC 的定量区室化测量模块，我们通过分析来自 4DN 的 32 个 Hi-C 数据集的汇编，进一步剖析了人类基因组的区室变异性景观。我们发现区室化变异性与子区室之间存在有趣的相关性。
DARIC 是分析定量区室化变化和从不断增加的 Hi-C 数据中挖掘新的生物学见解的有用工具。我们的结果证明了定量区室化变化在基因调控中的功能意义，并为区室化变异性与人类基因组中子区室之间的关系提供了新的见解。
