TADLib 0.2.5-r2

A Library to Explore Chromatin Interaction Patterns for Topologically Associating Domains

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 regulation in three-dimensional (3D) space. To systematically investigate the relationship between structure and function, it is important to develop a quantitative parameter to measure the structural characteristics of TAD. TADLib is such a package to explore the chromatin interaction pattern of TAD.

Inspired by the observation that there exist great differences in chromatin interaction pattern and gene expression level among TADs, a chromatin interaction feature called Aggregation Preference (AP) is developed to capture the aggregation degree of long-range chromatin interactions. Application to human and mouse cell lines (including both traditional Hi-C and in situ Hi-C datasets) shows that there exist heterogeneous structures among TADs and the structural rearrangement across cell lines is significantly associated with transcription activity remodelling.

TADLib package is written in Python and provides a three-step pipeline:

• Selecting long-range chromatin interactions in each TAD

• Finding aggregation patterns of selected interactions

• Calculating chromatin interaction feature of TAD

Installation

Please check the file “INSTALL.rst” in the distribution.

Links

• Detailed Documentation

• Repository (At GitHub, Track the package issue)

• PyPI (Download and Installation)

Citation

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

Notes

By default, we suppose that the input Hi-C data are corrected appropriately. Otherwise, systematic biases in source data will negatively impact chromatin interaction selection and then parameter calculation. Several correction schemes are available online:

[1]

Yaffe E, Tanay A. Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat Genet, 2011, 43: 1059-65.

[2]

Imakaev M, Fudenberg G, McCord RP et al. Iterative correction of Hi-C data reveals hallmarks ofchromosome organization. Nat Methods, 2012, 9: 999-1003.

[3]

Hu M, Deng K, Selvaraj S et al. HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics, 2012, 28: 3131-3.