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Project Description

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.

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.
Release History

Release History

0.2.5-r2

This version

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0.2.5-r1

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0.2.5

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0.2.4

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TADLib-0.2.5-r2.tar.gz (394.4 kB) Copy SHA256 Checksum SHA256 Source Apr 14, 2016

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