Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Bioinformatics. 2018 Jul 1;34(13):i475-i483. doi: 10.1093/bioinformatics/bty265.
Three-dimensional chromosome structure has been increasingly shown to influence various levels of cellular and genomic functions. Through Hi-C data, which maps contact frequency on chromosomes, it has been found that structural elements termed topologically associating domains (TADs) are involved in many regulatory mechanisms. However, we have little understanding of the level of similarity or variability of chromosome structure across cell types and disease states. In this study, we present a method to quantify resemblance and identify structurally similar regions between any two sets of TADs.
We present an analysis of 23 human Hi-C samples representing various tissue types in normal and cancer cell lines. We quantify global and chromosome-level structural similarity, and compare the relative similarity between cancer and non-cancer cells. We find that cancer cells show higher structural variability around commonly mutated pan-cancer genes than normal cells at these same locations.
Software for the methods and analysis can be found at https://github.com/Kingsford-Group/localtadsim.
三维染色体结构已被越来越多地证明会影响细胞和基因组功能的各个层面。通过 Hi-C 数据，即绘制染色体上接触频率的图谱，人们发现了结构元件，称为拓扑关联域（TAD），它们参与了许多调控机制。然而，我们对跨细胞类型和疾病状态的染色体结构的相似性或可变性水平知之甚少。在这项研究中，我们提出了一种方法，可以量化任意两组 TAD 之间的相似程度并识别结构相似的区域。
我们分析了 23 个人类 Hi-C 样本，代表了正常和癌细胞系中的各种组织类型。我们量化了全局和染色体级别的结构相似性，并比较了癌症细胞和非癌症细胞之间的相对相似性。我们发现，与正常细胞相比，在相同位置，癌症细胞中常见的泛癌基因突变周围的结构可变性更高。
该方法和分析的软件可在 https://github.com/Kingsford-Group/localtadsim 上获得。
