GenomicInteractions: An R/Bioconductor package for manipulating and investigating chromatin interaction data

被引:34
|
作者
Harmston, Nathan [1 ,2 ]
Ing-Simmons, Elizabeth [1 ]
Perry, Malcolm [1 ]
Baresic, Anja [1 ]
Lenhard, Boris [1 ]
机构
[1] Univ London Imperial Coll Sci Technol & Med, Fac Med, MRC Clin Sci Ctr, Computat Regulatory Genom, London W12 0NN, England
[2] Duke NUS Grad Med Sch, Program Cardiovasc & Metab Dis, Singapore 169857, Singapore
来源
BMC GENOMICS | 2015年 / 16卷
基金
英国医学研究理事会;
关键词
CHROMOSOME CONFORMATION CAPTURE; RANGE GENOMIC INTERACTIONS; HI-C; GENE-EXPRESSION; HIGH-RESOLUTION; RECEPTOR NR4A2; ORGANIZATION; TRANSCRIPTION; LEUKEMIA; MAP;
D O I
10.1186/s12864-015-2140-x
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Background: Precise quantitative and spatiotemporal control of gene expression is necessary to ensure proper cellular differentiation and the maintenance of homeostasis. The relationship between gene expression and the spatial organisation of chromatin is highly complex, interdependent and not completely understood. The development of experimental techniques to interrogate both the higher-order structure of chromatin and the interactions between regulatory elements has recently lead to important insights on how gene expression is controlled. The ability to gain these and future insights is critically dependent on computational tools for the analysis and visualisation of data produced by these techniques. Results and conclusion: We have developed GenomicInteractions, a freely available R/Bioconductor package designed for processing, analysis and visualisation of data generated from various types of chromosome conformation capture experiments. The package allows the easy annotation and summarisation of large genome-wide datasets at both the level of individual interactions and sets of genomic features, and provides several different methods for interrogating and visualising this type of data. We demonstrate this package's utility by showing example analyses performed on interaction datasets generated using Hi-C and ChIA-PET.
引用
收藏
页数:9
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