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

A probabilistic measure for alignment-free sequence comparison.

Tuan D Pham1, Johannes Zuegg

  • 1School of Computing and Information Technology, Griffith University, Nathan Campus, QLD 4111, Australia. t.pham@griffith.edu.au <t.pham@griffith.edu.au>

Bioinformatics (Oxford, England)
|July 24, 2004
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

High-throughput screening assays for novel antibacterial identification: current advances and future prospects.

Expert opinion on drug discovery·2026
Same author

Explainable Knowledge-Guided Algorithm for Contrast Extravasation Detection on Computed Tomography.

IEEE journal of translational engineering in health and medicine·2026
Same author

Pyrazolyl-Pyridine Ruthenium Complexes: A New Metallic Line of Defense Against Acinetobacter baumannii.

Chembiochem : a European journal of chemical biology·2026
Same author

Framework for evaluating explainable AI in antimicrobial drug discovery.

Journal of cheminformatics·2026
Same author

Classification of pediatric dental diseases from panoramic radiographs using natural language transformer and deep learning models.

Frontiers in artificial intelligence·2026
Same author

High prevalence and co-existence of SHV and CTX-M genes in extensively drug-resistant (XDR) Gram-negative bacteria isolated from hospital settings in Faisalabad District, Pakistan.

Molecular biology reports·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces a novel alignment-free method for biological sequence comparison using Markov models. The approach offers a probabilistic measure of similarity, advancing sequence analysis techniques.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Alignment-free sequence comparison methods are less developed than alignment-based methods.
  • Existing alignment-free techniques require further advancement for biological sequence analysis.

Purpose of the Study:

  • To introduce a novel probabilistic measure for biological sequence similarity without alignment.
  • To evaluate the efficacy of a new alignment-free sequence comparison method.

Main Methods:

  • The study constructs Markov models for biological sequences.
  • It calculates a probabilistic similarity measure based on comparing these Markov models.
  • The method was implemented using MATLAB.

Main Results:

Related Experiment Videos

  • The novel method was validated on six DNA sequences, including threonine operon genes from E. coli and S. flexneri.
  • Performance was benchmarked against CLUSTAL W (alignment-based) and chaos game representation (alignment-free).
  • Further testing on 40 DNA sequences demonstrated competitive results compared to existing alignment-free measures.

Conclusions:

  • The developed probabilistic Markov model-based method provides a viable alignment-free approach for biological sequence comparison.
  • This method shows potential for advancing sequence analysis, particularly in comparative genomics.
  • The study contributes a new tool to the field of bioinformatics.