Literature DB >> 34415538

Analysis of HiChIP Data.

Martina Dori1, Mattia Forcato2.   

Abstract

HiChIP is a novel method for the analysis of chromatin interactions based on in situ Hi-C that adds an immuno-precipitation (ChIP) step for the investigation of chromatin structures driven by specific proteins. This approach has been shown to be very efficient as it reliably reproduces Hi-C results and displays a higher rate of informative reads with a required lower amount of input cells when compared with other ChIP-based techniques (as ChIA-PET). Although HiChIP data preprocessing can be performed with the same methods developed for other Hi-C techniques, the identification of chromatin interactions needs to take into account specific biases introduced by the ChIP step. In this chapter we describe a computational pipeline for the analysis of HiChIP data obtained with the immuno-precipitation of Rad21 (part of the cohesin complex) in human embryonic stem cells before and after heat-shock treatment. We provide a detailed description of the preprocessing of raw data, the identification of chromatin interactions, the evaluation of the alterations induced by treatment, and, finally, the visualization of differential loops.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Bioinformatics; Chromatin interactions; Differential interactions; HiChIP

Mesh:

Substances:

Year:  2022        PMID: 34415538     DOI: 10.1007/978-1-0716-1390-0_11

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  13 in total

Review 1.  A decade of 3C technologies: insights into nuclear organization.

Authors:  Elzo de Wit; Wouter de Laat
Journal:  Genes Dev       Date:  2012-01-01       Impact factor: 11.361

2.  Mango: a bias-correcting ChIA-PET analysis pipeline.

Authors:  Douglas H Phanstiel; Alan P Boyle; Nastaran Heidari; Michael P Snyder
Journal:  Bioinformatics       Date:  2015-06-01       Impact factor: 6.937

Review 3.  Genome-wide mapping and analysis of chromosome architecture.

Authors:  Anthony D Schmitt; Ming Hu; Bing Ren
Journal:  Nat Rev Mol Cell Biol       Date:  2016-09-01       Impact factor: 94.444

4.  hichipper: a preprocessing pipeline for calling DNA loops from HiChIP data.

Authors:  Caleb A Lareau; Martin J Aryee
Journal:  Nat Methods       Date:  2018-02-28       Impact factor: 28.547

5.  HiChIP: efficient and sensitive analysis of protein-directed genome architecture.

Authors:  Maxwell R Mumbach; Adam J Rubin; Ryan A Flynn; Chao Dai; Paul A Khavari; William J Greenleaf; Howard Y Chang
Journal:  Nat Methods       Date:  2016-09-19       Impact factor: 28.547

6.  The Human Epigenome Browser at Washington University.

Authors:  Xin Zhou; Brett Maricque; Mingchao Xie; Daofeng Li; Vasavi Sundaram; Eric A Martin; Brian C Koebbe; Cydney Nielsen; Martin Hirst; Peggy Farnham; Robert M Kuhn; Jingchun Zhu; Ivan Smirnov; W James Kent; David Haussler; Pamela A F Madden; Joseph F Costello; Ting Wang
Journal:  Nat Methods       Date:  2011-11-29       Impact factor: 28.547

7.  Architectural Proteins and Pluripotency Factors Cooperate to Orchestrate the Transcriptional Response of hESCs to Temperature Stress.

Authors:  Xiaowen Lyu; M Jordan Rowley; Victor G Corces
Journal:  Mol Cell       Date:  2018-08-16       Impact factor: 17.970

8.  Identification of significant chromatin contacts from HiChIP data by FitHiChIP.

Authors:  Sourya Bhattacharyya; Vivek Chandra; Pandurangan Vijayanand; Ferhat Ay
Journal:  Nat Commun       Date:  2019-09-17       Impact factor: 14.919

9.  HiC-Pro: an optimized and flexible pipeline for Hi-C data processing.

Authors:  Nicolas Servant; Nelle Varoquaux; Bryan R Lajoie; Eric Viara; Chong-Jian Chen; Jean-Philippe Vert; Edith Heard; Job Dekker; Emmanuel Barillot
Journal:  Genome Biol       Date:  2015-12-01       Impact factor: 13.583

10.  diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data.

Authors:  Caleb A Lareau; Martin J Aryee
Journal:  Bioinformatics       Date:  2018-02-15       Impact factor: 6.937

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