Metadata-Version: 1.1
Name: TADLib
Version: 0.2.4
Summary: A Library to Explore Chromatin Interaction Patterns for Topologically Associating Domains
Home-page: https://github.com/XiaoTaoWang/TADLib/
Author: XiaoTao Wang
Author-email: wangxiaotao868@gmail.com
License: UNKNOWN
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.
        
        Links
        -----
        - `Detailed Documentation <https://pythonhosted.org/TADLib/>`_
        - `Repository <https://github.com/XiaoTaoWang/TADLib>`_ (At GitHub, Track the package issue)
        - `PyPI <https://pypi.python.org/pypi/TADLib>`_ (Download and Installation)
        
        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.
        
Keywords: Hi-C TAD aggregation structure feature annotation polygon
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: POSIX
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Mathematics
