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

Mismatch Repair01:20

Mismatch Repair

6.0K
Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
6.0K
Mismatch Repair01:36

Mismatch Repair

43.0K
Overview
43.0K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.8K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.8K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

3.3K
3.3K
Mutations01:35

Mutations

42.2K
Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...
42.2K
Mutations01:39

Mutations

92.8K
Overview
92.8K

You might also read

Related Articles

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

Sort by
Same author

A randomized controlled trial of staged cardiac rehabilitation based on the Health Belief Model on kinesiophobia, self-efficacy and activities of daily living in elderly patients with coronary heart disease.

Frontiers in medicine·2026
Same author

Propagating Cross-View Semantics for Multi-view Clustering: A Unified Anchor Refinement Paradigm.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Spatial transcriptomics in cancer research: insights into tumorigenesis, diagnosis and therapeutics.

Cell death discovery·2026
Same author

The ArMAPK5-ArbHLH3-Ar4CL3 module is associated with salicylic acid-induced phenolic biosynthesis in Agastache rugosa.

Plant physiology and biochemistry : PPB·2026
Same author

A novel two-in-one Eu-MOFs integrates electrochemiluminescence and ratiometric fluorescence dual-mode sensor for tetracycline detection.

Biosensors & bioelectronics·2026
Same author

A heterozygous deletion and inversion at the NHEJ1‑IHH locus associated with shank length in Yunlong short-leg chicken.

BMC genomics·2026

Related Experiment Video

Updated: Dec 3, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K

Allowing mutations in maximal matches boosts genome compression performance.

Yuansheng Liu1, Limsoon Wong2, Jinyan Li1

  • 1Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia.

Bioinformatics (Oxford, England)
|October 29, 2020
PubMed
Summary

This study introduces memRGC, a novel genome compression algorithm that effectively utilizes mutation-containing matches (MCMs) to significantly improve compression performance and reduce resource usage for genomic data.

More Related Videos

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
11:36

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

11.2K
Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

11.0K

Related Experiment Videos

Last Updated: Dec 3, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K
A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
11:36

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

11.2K
Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

11.0K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic data compression is crucial for storage and transmission.
  • Mutations in DNA can disrupt maximal matches, increasing compression costs.
  • Existing reference-based compression methods struggle with mutation-induced breaks in matches.

Purpose of the Study:

  • To develop a novel reference-based genome compression algorithm.
  • To leverage mutation-containing matches (MCMs) for enhanced genome encoding.
  • To improve compression performance and reduce computational resource requirements.

Main Methods:

  • Implemented a coprime double-window k-mer sampling search scheme to detect maximal matches.
  • Extended detected matches to incorporate mismatches (mutations) and adjacent maximal matches, forming MCMs.
  • Developed the memRGC algorithm for reference-based genome compression.

Main Results:

  • Achieved an average 27% boost in compression performance.
  • Outperformed state-of-the-art methods on benchmark datasets, with improvements up to 50%.
  • Demonstrated reduced memory and de-compression resource usage with comparable compression speed.

Conclusions:

  • memRGC offers significant advantages in genome data storage and transmission.
  • The algorithm effectively handles mutations within matches, improving compression efficiency.
  • memRGC represents a substantial advancement in reference-based genome compression technology.