Hi-Cpipe: a pipeline for high-throughput chromosome capture

The study of genomic interactions has been greatly facilitated by techniques such as chromatin conformation capture with high-throughput sequencing (Hi-C). These genome-wide experiments generate large amounts of data that require careful analysis to obtain useful biological conclusions. However, development of the appropriate software tools is hindered by the lack of basic infrastructure to represent and manipulate genomic interaction data. Here, we present the InteractionSet package that provides classes to represent genomic interactions and store their associated experimental data, along with the methods required for low-level manipulation and processing of those classes. The InteractionSet package exploits existing infrastructure in the open-source Bioconductor project, while in turn being used by Bioconductor packages designed for higher-level analyses. For new packages, use of the functionality in InteractionSet will simplify development, allow access to more features and improve interoperability between packages.

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Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments

F1000Research ◽

10.12688/f1000research.8759.1 ◽

2016 ◽

Vol 5 ◽

pp. 950 ◽

Cited By ~ 18

Author(s):

Aaron T. L. Lun ◽

Malcolm Perry ◽

Elizabeth Ing-Simmons

Keyword(s):

Experimental Data ◽

Open Source ◽

High Throughput ◽

High Throughput Sequencing ◽

Careful Analysis ◽

Chromatin Conformation ◽

Interaction Data ◽

Genome Wide ◽

Bioconductor Project ◽

Basic Infrastructure

The study of genomic interactions has been greatly facilitated by techniques such as chromatin conformation capture with high-throughput sequencing (Hi-C). These genome-wide experiments generate large amounts of data that require careful analysis to obtain useful biological conclusions. However, development of the appropriate software tools is hindered by the lack of basic infrastructure to represent and manipulate genomic interaction data. Here, we present the InteractionSet package that provides classes to represent genomic interactions and store their associated experimental data, along with the methods required for low-level manipulation and processing of those classes. The InteractionSet package exploits existing infrastructure in the open-source Bioconductor project, while in turn being used by Bioconductor packages designed for higher-level analyses. For new packages, use of the functionality in InteractionSet will simplify development, allow access to more features and improve interoperability between packages.

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A scalable method for automatically measuring pharyngeal pumping in C. elegans

10.1101/051243 ◽

2016 ◽

Author(s):

Monika Scholz ◽

Dylan J. Lynch ◽

Kyung Suk Lee ◽

Erel Levine ◽

David Biron

Keyword(s):

Caenorhabditis Elegans ◽

Food Availability ◽

High Throughput ◽

Visual Inspection ◽

Automated Analysis ◽

C Elegans ◽

Automated Method ◽

Controlled Environments ◽

Pharyngeal Pumping ◽

Feeding Environments

We describe a scalable automated method for measuring the pharyngeal pumping of Caenorhabditis elegans in controlled environments. Our approach enables unbiased measurements for prolonged periods, a high throughput, and measurements in controlled yet dynamically changing feeding environments. The automated analysis compares well with scoring pumping by visual inspection, a common practice in the field. In addition, we observed overall low rates of pharyngeal pumping and long correlation times when food availability was oscillated.

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HUGIn: Hi-C Unifying Genomic Interrogator

10.1101/117531 ◽

2017 ◽

Cited By ~ 1

Author(s):

Joshua S. Martin ◽

Zheng Xu ◽

Alex P. Reiner ◽

Karen L. Mohlke ◽

Patrick Sullivan ◽

...

Keyword(s):

High Throughput ◽

Regulatory Elements ◽

Supplementary Information ◽

Chromatin Conformation ◽

Organization Studies ◽

Web Browser ◽

Functional Roles ◽

Gwas Snps ◽

Genome Wide ◽

Coding Variants

AbstractMotivationHigh throughput chromatin conformation capture (3C) technologies, such as Hi-C and ChlA-PET, have the potential to elucidate the functional roles of non-coding variants. However, most of published genome-wide unbiased chromatin organization studies have used cultured cell lines, limiting their generalizability.ResultsWe developed a web browser, HUGIn, to visualize Hi-C data generated from 21 human primary tissues and cell liens. HUGIn enables assessment of chromatin contacts both constitutive across and specific to tissue(s) and/or cell line(s) at any genomic loci, including GWAS SNPs, eQTLs and cis-regulatory elements, facilitating the understanding of both GWAS and eQTLs results and functional genomics data.AvailabilityHUGIn is available at http://yunliweb.its.unc.edu/[email protected] and [email protected] information:

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Chromatin interaction data visualization in the WashU Epigenome Browser

10.1101/239368 ◽

2017 ◽

Author(s):

Daofeng Li ◽

Silas Hsu ◽

Deepak Purushotham ◽

Ting Wang

Keyword(s):

Data Visualization ◽

Chromatin Interaction ◽

Chromatin Conformation ◽

Interaction Data ◽

Computational Resource ◽

Genome Maintenance ◽

Data Format ◽

Chromatin Interactions ◽

Data Formats ◽

Gene Regulations

AbstractMotivationLong-range chromatin interactions are critical for gene regulations and genome maintenance. HiC and Cool are the two most common data formats used by the community, including the 4D Nucleome Consortium (4DN), to represent chromatin interaction data from a variety of chromatin conformation capture experiments, and specialized tools were developed for their analysis, visualization, and conversion. However, there does not exist a tool that can support visualization of both data formats simultaneously.ResultsThe WashU Epigenome Browser has integrated both HiC and Cool data formats into its visualization platform. Investigators can seamlessly explore chromatin interaction data regardless of their underlying data format. For developers it is straightforward to benchmark the differences in rendering speed and computational resource usage between the two data formats.Availabilityhttp://epigenomegateway.wustl.edu/browser/.

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High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer

Nature Communications ◽

10.1038/s41467-021-23213-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Sarah E. Pierce ◽

Jeffrey M. Granja ◽

William J. Greenleaf

Keyword(s):

Transcription Factor ◽

Single Cell ◽

Cancer Cells ◽

High Throughput ◽

Regulatory Networks ◽

Temporal Dynamics ◽

Chromatin Accessibility ◽

Regulatory Regions ◽

Factor Binding ◽

Genome Wide

AbstractChromatin accessibility profiling can identify putative regulatory regions genome wide; however, pooled single-cell methods for assessing the effects of regulatory perturbations on accessibility are limited. Here, we report a modified droplet-based single-cell ATAC-seq protocol for perturbing and evaluating dynamic single-cell epigenetic states. This method (Spear-ATAC) enables simultaneous read-out of chromatin accessibility profiles and integrated sgRNA spacer sequences from thousands of individual cells at once. Spear-ATAC profiling of 104,592 cells representing 414 sgRNA knock-down populations reveals the temporal dynamics of epigenetic responses to regulatory perturbations in cancer cells and the associations between transcription factor binding profiles.

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