README.rst

Note

Since version 0.4.0, the default data format has changed to cool, to comply with 4DN standards.

Introduction

Chromosome conformation capture (3C) derived techniques, especially Hi-C, have revealed that topologically associating domain (TAD) is a structural basis for both chromatin organization and biological functions in three-dimensional (3D) space. TAD is also hierarchically organized by smaller structural units, which are relevant to biological functions. To systematically investigate the relationship between structure and function, it is important to develop quantitative methods to identify and measure the organization of TAD. TADLib is such a library to explore the chromatin interaction patterns inside TAD from Hi-C chromatin interactions.

Currently, TADLib consists of two methods:

• Aggregation Preference (AP)

  AP is a quantitative parameter to measure the overall density of significant chromatin interactions inside TAD. Inspired by the observation that there exist great differences in chromatin interaction pattern among TADs, an empirical parameter called Aggregation Preference (AP) can be used to capture these aggregation degree of significant chromatin interactions. Application to human and mouse cell types (including both traditional Hi-C and in situ Hi-C data sets) shows that there exist heterogeneous structures among TADs and the structural rearrangement across cell types is significantly associated with transcriptional remodelling. [1]

• Hierarchical TAD (HiTAD)

  HiTAD is a method to detect hierarchical TADs, including TADs, sub-TADs and smaller domains. Except local insulations, HiTAD further constrains TADs as the optimal domains to globally separate intra-chromosomal interactions. Under objective functions derived from chromatin interactions, HiTAD adopts an iterative optimization procedure to detect hierarchical TADs. HiTAD performs well in domain sensitivity, replicate reproducibility and inter cell-type conservation. Application to human and mouse cell types (including both traditional Hi-C and in situ Hi-C data sets) reveals that there exist common change types for hierarchical TADs, which are involved in shaping higher-order compartment, replication timing and transcriptional regulation. [2]

Links

• Detailed Documentation

  • Installation
  • Aggregation Preference
  • Hierarchical TAD
  • Detect Single-level TAD
  • Visualization

• Code Repository (At GitHub, Track the package issue)
• PyPI (Download and Installation)

Citation

[1]	Wang XT, Dong PF, Zhang HY, Peng C. Structural heterogeneity and functional diversity of topologically associating domains in mammalian genomes. Nucleic Acids Research, 2015, doi: 10.1093/nar/gkv684

[2]	Wang XT, Cui W, Peng C. HiTAD: detecting the structural and functional hierarchies of topologically associating domains from chromatin interactions. Nucleic Acids Research, 2017, doi: 10.1093/nar/gkx735