doi:10.1007/s40484-016-0080-3

2016, Vol. 4

Issue (3): 217-225 https://doi.org/10.1007/s40484-016-0080-3

本期目录

Advances in computational ChIA-PET data analysis

Chao He¹,Guipeng Li¹,Diekidel M. Nadhir¹,Yang Chen¹(

),Xiaowo Wang¹(

),Michael Q. Zhang^2,¹(

)

¹. MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Center for Synthetic and Systems Biology/Department of Automation, Tsinghua University, Beijing 100084, China
². Department of Biological Sciences, Center for Systems Biology, the University of Texas at Dallas, Richardson, TX 75080-3021, USA

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Abstract：

Genome-wide chromatin interaction analysis has become important for understanding 3D topological structure of a genome as well as for linking distal cis-regulatory elements to their target genes. Compared to the Hi-C method, chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is unique, in that one can interrogate thousands of chromatin interactions (in a genome) mediated by a specific protein of interest at high resolution and reasonable cost. However, because of the noisy nature of the data, efficient analytical tools have become necessary. Here, we review some new computational methods recently developed by us and compare them with other existing methods. Our intention is to help readers to better understand ChIA-PET results and to guide the users on selection of the most appropriate tools for their own projects.

Author Summary

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) technology was designed for detecting genome-wide chromatin loops mediated by a specific protein of interest, which has become one of the most important biological methods for understanding 3D genome organization. Here we review five bioinformatics tools which are related to ChIA-PET data analysis and data mining. Meanwhile, we also introduce one interesting computational method which is to predict chromatin loops with ChIP-Seq data. Our intention is to help reader to better understand ChIA-PET experiments and to select the most appropriate bioinformatics tools for their 3D genome research.

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收稿日期: 2016-05-14 出版日期: 2016-09-07

PACS:

基金资助:

Corresponding Author(s): Yang Chen,Xiaowo Wang,Michael Q. Zhang

引用本文:

. [J]. Quantitative Biology, 2016, 4(3): 217-225.
Chao He, Guipeng Li, Diekidel M. Nadhir, Yang Chen, Xiaowo Wang, Michael Q. Zhang. Advances in computational ChIA-PET data analysis. Quant. Biol., 2016, 4(3): 217-225.

链接本文:

https://academic.hep.com.cn/qb/CN/10.1007/s40484-016-0080-3
https://academic.hep.com.cn/qb/CN/Y2016/V4/I3/217

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