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

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

You might also read

Related Articles

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

Sort by
Same author

Unbiased and error-detecting combinatorial pooling experiments with balanced constant-weight Gray codes for consecutive positives detection.

Bioinformatics (Oxford, England)·2025
Same author

Missing value replacement in strings and applications.

Data mining and knowledge discovery·2025
Same author

Pangenome comparison via ED strings.

Frontiers in bioinformatics·2024
Same author

Universal Identification of Pathogenic Viruses by Liquid Chromatography Coupled with Tandem Mass Spectrometry Proteotyping.

Molecular & cellular proteomics : MCP·2024
Same author

copepodTCR: Identification of Antigen-Specific T Cell Receptors with combinatorial peptide pooling.

bioRxiv : the preprint server for biology·2023
Same author

Establishing a national research software award.

Open research Europe·2023
Same journal

Rapid Evolution of Expression Levels in Hepatocellular Carcinoma.

International journal of computational biology and drug design·2021
Same journal

Identifying the dynamic gene regulatory network during latent HIV-1 reactivation using high-dimensional ordinary differential equations.

International journal of computational biology and drug design·2021
Same journal

PATH: An interactive web platform for analysis of time-course high-dimensional genomic data.

International journal of computational biology and drug design·2021
Same journal

Modelling of hypoxia gene expression for three different cancer cell lines.

International journal of computational biology and drug design·2020
Same journal

Brain-wide structural connectivity alterations under the control of Alzheimer risk genes.

International journal of computational biology and drug design·2020
Same journal

Native State of Complement Protein C3d Analysed via Hydrogen Exchange and Conformational Sampling.

International journal of computational biology and drug design·2019
See all related articles

Related Experiment Video

Updated: May 14, 2026

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp
10:44

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Published on: June 20, 2018

Querying highly similar sequences.

Carl Barton1, Mathieu Giraud, Costas S Iliopoulos

  • 1Department of Informatics, King's College London, London, UK. carl.barton@kcl.ac.uk

International Journal of Computational Biology and Drug Design
|February 23, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces efficient algorithms for the extreme similarity sequencing problem, enabling pattern discovery in highly similar DNA sequences. These methods significantly improve the speed of identifying patterns with minor variations.

More Related Videos

Novel Sequence Discovery by Subtractive Genomics
09:40

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Related Experiment Videos

Last Updated: May 14, 2026

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp
10:44

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Published on: June 20, 2018

Novel Sequence Discovery by Subtractive Genomics
09:40

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The extreme similarity sequencing problem involves finding patterns in datasets where sequences are nearly identical.
  • Existing methods struggle with the computational demands of identifying patterns with minor variations across multiple sequences.

Purpose of the Study:

  • To develop novel algorithms for efficiently solving the extreme similarity sequencing problem.
  • To provide both theoretical and practical solutions for pattern matching in datasets with high sequence similarity.

Main Methods:

  • An asymptotically fast algorithm with O(n + occ logocc) time complexity was developed.
  • A practical algorithm with O(nk/w) time complexity was also designed for real-world applications.

Main Results:

  • The proposed algorithms effectively address the challenge of finding patterns with a constant number of errors (approximately 10) across k sequences.
  • Demonstrated significant improvements in computational efficiency for sequence analysis.

Conclusions:

  • The developed algorithms offer a substantial advancement in handling the extreme similarity sequencing problem.
  • These solutions are expected to accelerate pattern discovery in large-scale genomic and biological sequence datasets.