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 Experiment Videos

Efficient algorithms for counting and reporting segregating sites in genomic sequences.

Manolis Christodoulakis1, G Brian Golding, Costas S Iliopoulos

  • 1Department of Computer Science, King's College London, London, United Kingdom. manolis@dcs.kcl.ac.uk

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|September 7, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Systemic Acquired Resistance signaling molecule N-hydroxypipecolic acid is involved in Age-Related Resistance in Arabidopsis thaliana.

Plant physiology·2026
Same author

Ankh-Score Produces Better Sequence Alignments Than AlphaFold3.

Proteins·2026
Same author

Protein embeddings and local alignments.

Computational and structural biotechnology journal·2026
Same author

Component puzzle protein-protein interaction prediction.

Briefings in bioinformatics·2025
Same author

Explainability of Protein Deep Learning Models.

International journal of molecular sciences·2025
Same author

Reply to: Insufficient evidence for natural selection associated with the Black Death.

Nature·2025
Same journal

Mosquito Species and Gender Identification System Based on Artificial Intelligence and Image Processing Methods.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

This study introduces efficient algorithms and a data structure to identify DNA sequence variation, specifically segregating sites. These methods accelerate the analysis of genetic diversity for biological, pharmaceutical, and medical research.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • DNA sequence variation is crucial for biological, pharmaceutical, and medical research.
  • Identifying segregating sites is key to understanding genetic diversity.

Purpose of the Study:

  • To develop efficient algorithms for finding segregating sites in DNA sequences.
  • To create a dynamic data structure for tracking segregating sites during sequence set updates.

Main Methods:

  • Linear-time and expected-sublinear-time algorithms were designed to locate all segregating sites.
  • A novel data structure was implemented for incremental updates of segregating sites.

Main Results:

  • The proposed algorithms efficiently identify segregating sites.

Related Experiment Videos

  • The data structure allows for rapid updates without re-scanning entire datasets.

    • Conclusions:

    • The developed methods significantly improve the speed and efficiency of analyzing DNA sequence variation.
    • These advancements facilitate a deeper understanding of genetic diversity in various scientific fields.