Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

Karyotyping

Karyotyping

You might also read

Related Articles

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

Sort by

Same author

Peptide immunoarrays for rationale development of vaccines with enhanced cross-reactivity.

PloS one·2025

Same author

Synthetic lethal interactions of DEAD/H-box helicases as targets for cancer therapy.

Frontiers in oncology·2023

Same author

Activation of the Anaphase Promoting Complex Reverses Multiple Drug Resistant Cancer in a Canine Model of Multiple Drug Resistant Lymphoma.

Cancers·2022

Same author

Generation of synthetic antibody fragments with optimal complementarity determining region lengths for Notch-1 recognition.

Frontiers in microbiology·2022

Same author

NTD-DR: Nonnegative tensor decomposition for drug repositioning.

PloS one·2022

Same author

Editorial: Advancement in Gene Set Analysis: Gaining Insight From High-Throughput Data.

Frontiers in genetics·2022

Search research articles

Related Experiment Video

Updated: Dec 22, 2025

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

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

Published on: October 14, 2022

Computational methods for predicting 3D genomic organization from high-resolution chromosome conformation capture

Kimberly MacKay¹, Anthony Kusalik¹

¹Department of Computer Science, University of Saskatchewan.

Briefings in Functional Genomics

|May 1, 2020

Summary

High-resolution chromosome conformation capture assays enable genome structure studies. Computational tools predict 3D genome organization, but algorithmic diversity is lacking for complete genome reconstruction.

Keywords:

3D genome prediction 3D genome reconstruction problem 5C Hi-C genome organization high-resolution chromosome conformation capture data

More Related Videos

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

Related Experiment Videos

Last Updated: Dec 22, 2025

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

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

Published on: October 14, 2022

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

High-resolution chromosome conformation capture (3C) assays (e.g., 5C, Hi-C, Pore-C) provide sequence-level insights into genome structure-function relationships.
Predicting 3D genome organization from 3C data is crucial for understanding these relationships.

Purpose of the Study:

To conduct a comprehensive review and comparison of computational tools for 3D genome reconstruction.
To analyze the algorithmic approaches used in existing tools (November 2006 - September 2019).

Main Methods:

Systematic literature review of computational tools for 3D genome organization prediction.
Categorization of algorithms into dimensionality reduction, graph/network theory, maximum likelihood estimation (MLE), and statistical modeling.

Main Results:

Existing tools primarily utilize a limited set of algorithms across the identified categories.
Algorithmic solutions are not yet mature, with unexplored breadth and depth.
While tools for single regions or chromosomes are diverse, a lack of algorithmic diversity exists for complete 3D genome reconstruction.

Conclusions:

Further exploration of diverse algorithmic approaches is needed for comprehensive 3D genome reconstruction.
The field requires advancement in computational methods to fully leverage 3C assay data for understanding genome organization.