Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

187
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
187

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Interpretation of Survival Outcomes in Multicancer Early Detection Testing Requires Caution.

JCO precision oncology·2026
Same author

Response to 'Impact of control selection strategies on GWAS results: a study of prostate cancer in the UK Biobank'.

Briefings in bioinformatics·2026
Same author

Colonoscopy surveillance in Lynch syndrome: what it prevents and what it does not.

Journal of medical genetics·2026
Same author

Relationship between the microbiome and obesity-associated cancer risk using Mendelian randomisation.

International journal of obesity (2005)·2026
Same author

Comprehensive repertoire of the chromosomal alteration and mutational signatures across 16 cancer types.

Nature genetics·2026
Same author

Contrasting Features of Papillary and Chromophobe Renal Cell Carcinoma Revealed by Whole-Genome Sequencing.

Molecular cancer research : MCR·2026

Related Experiment Video

Updated: Aug 18, 2025

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C
09:32

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

3.6K

Algorithmic considerations when analysing capture Hi-C data.

Linden Disney-Hogg1,2, Ben Kinnersley1, Richard Houlston1

  • 1Division of Genetics and Epidemiology, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Sutton, Surrey, SM2 5NG, UK.

Wellcome Open Research
|December 8, 2022
PubMed
Summary
This summary is machine-generated.

Capture Hi-C (CHi-C) offers higher resolution for studying 3D genome structure and gene regulation. This study evaluates algorithms for analyzing CHi-C data, addressing biases introduced by targeted enrichment for accurate interaction inference.

Keywords:
CancerCapture Hi-CModel assessment

More Related Videos

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
10:16

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

Published on: June 28, 2018

32.6K
Capturing Chromosome Conformation Across Length Scales
10:15

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

3.6K

Related Experiment Videos

Last Updated: Aug 18, 2025

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C
09:32

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

3.6K
Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
10:16

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

Published on: June 28, 2018

32.6K
Capturing Chromosome Conformation Across Length Scales
10:15

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

3.6K

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Chromosome conformation capture (3C) techniques reveal the 3D genome's role in gene regulation.
  • Capture Hi-C (CHi-C) enhances Hi-C by targeting specific genomic regions, improving resolution.
  • CHi-C introduces analysis complexities due to variable capture efficiencies, necessitating robust algorithms.

Purpose of the Study:

  • To identify key features for evaluating algorithms analyzing CHi-C data.
  • To ensure accurate inference of meaningful chromatin interactions from CHi-C experiments.
  • To assess the performance of the CHICAGO program on promoter capture Hi-C data.

Main Methods:

  • Analysis of promoter capture Hi-C data from 28 diverse cell lines.
  • Evaluation of algorithmic features critical for CHi-C data analysis.
  • Case study utilizing the CHICAGO analysis program.

Main Results:

  • The study outlines essential algorithmic considerations for CHi-C data analysis.
  • The CHICAGO program was applied to a large dataset, providing a practical evaluation.
  • Identified biases and complexities inherent in CHi-C data were addressed.

Conclusions:

  • Accurate analysis of CHi-C data is crucial for understanding 3D genome organization and gene regulation.
  • The evaluation framework presented aids in selecting and developing appropriate CHi-C analysis tools.
  • This work contributes to reliable interpretation of chromatin interaction data.