Computational tools for Hi‐C data analysis

Han, Zhijun; Wei, Gang

doi:10.1007/s40484-017-0113-6

Cited by 10 publications

(13 citation statements)

References 51 publications

(115 reference statements)

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“…The development of chromosome conformation capture (3C) and its high‐throughput relatives, such as 4C‐seq, ChIA‐PET, Hi‐C, HiChIP, and PLAC‐seq, has enabled researchers to uncover a hierarchy of subterritory features involved in chromosome folding, including compartments (Lieberman‐Aiden et al, ), topologically associating domains (TADs; Dixon et al, ; Dowen et al, ; Nora et al, ; Phillips‐Cremins et al, ), and chromatin loops (Rao et al, ). The advent of these high‐throughput technologies has resulted in the generation of a large amount of data, requiring computational approaches for its processing and analysis, many aspects of which have been thoroughly reviewed before (Ay & Noble, ; Davies, Oudelaar, Higgs, & Hughes, ; Forcato et al, ; Han & Wei, ; Schmitt, Hu, & Ren, ).…”

Section: Introductionmentioning

confidence: 99%

“…chromosome folding, including compartments (Lieberman-Aiden et al, 2009), topologically associating domains (TADs; Dixon et al, 2012;Dowen et al, 2014;Nora et al, 2012;Phillips-Cremins et al, 2013), and chromatin loops (Rao et al, 2014). The advent of these high-throughput technologies has resulted in the generation of a large amount of data, requiring computational approaches for its processing and analysis, many aspects of which have been thoroughly reviewed before (Ay & Noble, 2015;Davies, Oudelaar, Higgs, & Hughes, 2017;Forcato et al, 2017;Han & Wei, 2017;.…”

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confidence: 99%

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Computational methods for analyzing and modeling genome structure and organization

Lin

Bonora

Yardımcı

2018

WIREs Mechanisms of Disease

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Recent advances in chromosome conformation capture technologies have led to the discovery of previously unappreciated structural features of chromatin. Computational analysis has been critical in detecting these features and thereby helping to uncover the building blocks of genome architecture. Algorithms are being developed to integrate these architectural features to construct better three-dimensional (3D) models of the genome. These computational methods have revealed the importance of 3D genome organization to essential biological processes. In this article, we review the state of the art in analytic and modeling techniques with a focus on their application to answering various biological questions related to chromatin structure. We summarize the limitations of these computational techniques and suggest future directions, including the importance of incorporating multiple sources of experimental data in building a more comprehensive model of the genome. This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Genetic/Genomic Methods Models of Systems Properties and Processes > Mechanistic Models.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Computational methods for analyzing and modeling genome structure and organization

Lin

Bonora

Yardımcı

2018

WIREs Mechanisms of Disease

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“…As mentioned in the Introduction , there are relatively few tools that allow performing a comprehensive Hi-C data analysis [see, Calandrelli et al (2018) and Han and Wei (2017)] for a short list of the most popular tools). Most of them are implemented either in Python, R, Perl, C++, or as a combination of different programming languages.…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, despite the great effort in the development of tools specifically designed for the analysis of Hi-C, they rarely include all the required functionalities for complete analysis in a single platform. Han and Wei (2017) and Calandrelli et al (2018) provided a recent list of existing general-purpose tools. In general, most of the available tools are designed for expert users with great confidence about command-line applications.…”

Section: Introductionmentioning

confidence: 99%

HiCeekR: A Novel Shiny App for Hi-C Data Analysis

et al. 2019

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The High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers. Moreover, most of the available tools are designed for expert users, who have great confidence with command-line applications. In this paper, we present HiCeekR (), a novel R Graphical User Interface (GUI) that allows researchers to easily perform a complete Hi-C data analysis. With the aid of the Shiny libraries, it integrates several R/Bioconductor packages for Hi-C data analysis and visualization, guiding the user during the entire process. Here, we describe its architecture and functionalities, then illustrate its capabilities using a publicly available dataset.

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“…A multitude of downstream analysis methods exist and have been reviewed elsewhere [10,26,27,28,29,30]. Prominent use cases include the detection of genomic compartments [4] or topologically associating domains (TADs) [31,32], characterizing significant chromatin interactions in Hi-C or CHC data [23,33,34], identifying copy number variations and translocations in cancer data [35] and characterizing chromatin interactions that are associated with genome-wide association study risk loci [36].…”

Section: Computational Tools For Processing Hi-c and Capture Hi-c mentioning

confidence: 99%

Computational Processing and Quality Control of Hi-C, Capture Hi-C and Capture-C Data

Hansen

Gargano

Hecht

et al. 2019

Genes

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Hi-C, capture Hi-C (CHC) and Capture-C have contributed greatly to our present understanding of the three-dimensional organization of genomes in the context of transcriptional regulation by characterizing the roles of topological associated domains, enhancer promoter loops and other three-dimensional genomic interactions. The analysis is based on counts of chimeric read pairs that map to interacting regions of the genome. However, the processing and quality control presents a number of unique challenges. We review here the experimental and computational foundations and explain how the characteristics of restriction digests, sonication fragments and read pairs can be exploited to distinguish technical artefacts from valid read pairs originating from true chromatin interactions.

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Computational tools for Hi‐C data analysis

Cited by 10 publications

References 51 publications

Computational methods for analyzing and modeling genome structure and organization

Computational methods for analyzing and modeling genome structure and organization

HiCeekR: A Novel Shiny App for Hi-C Data Analysis

Computational Processing and Quality Control of Hi-C, Capture Hi-C and Capture-C Data

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