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

Finding motifs from all sequences with and without binding sites.

Henry C M Leung1, Francis Y L Chin

  • 1Department of Computer Science, The University of Hong Kong Pokfulam, Hong Kong. cmleung2@cs.hku.hk

Bioinformatics (Oxford, England)
|July 28, 2006
PubMed
Summary

This study introduces ALSE, a novel motif-finding algorithm that improves accuracy by incorporating sequences not bound by transcription factors. ALSE demonstrates superior performance compared to existing methods, especially for complex datasets.

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

Characterization of Large Genomic Rearrangements in BRCA1 and BRCA2 Genes in a Chinese High-Risk Cohort.

The Journal of molecular diagnostics : JMD·2025
Same author

Early measurable residual disease detection post-haematopoietic stem cell transplantation in acute myeloid leukaemia.

Therapeutic advances in hematology·2025
Same author

A multi-modal molecular characterization of the Philadelphia translocation featuring long read sequencing.

Gene·2025
Same author

Mutation Spectrum Comparison between Benign Breast Lesion Cohort, Unselected Cancer Cohort and High-Risk Breast Cancer Cohort.

Cancers·2024
Same author

Detecting structural variations with precise breakpoints using low-depth WGS data from a single oxford nanopore MinION flowcell.

Scientific reports·2022
Same author

CONNET: Accurate Genome Consensus in Assembling Nanopore Sequencing Data via Deep Learning.

iScience·2020

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Identifying common patterns (motifs) in coregulated gene promoter regions is crucial in molecular biology.
  • Existing motif-finding algorithms often overlook the significance of sequences not bound by transcription factors.
  • Integrating negative regulatory sequences can enhance motif discovery accuracy.

Purpose of the Study:

  • To develop an improved motif-finding algorithm that leverages both bound and unbound sequences.
  • To enhance the statistical rigor of motif detection through precise probabilistic analysis.
  • To provide a more effective tool for identifying regulatory elements in gene promoter regions.

Main Methods:

  • Proposed a probabilistic analysis for motif detection, considering motif length, sequence length, and binding site count.

Related Experiment Videos

  • Developed a heuristic algorithm, ALSE (Algorithm for Local Sequence Element discovery), based on this probabilistic model.
  • Utilized both simulated and real biological datasets for algorithm evaluation.

    • Main Results:

    • The ALSE algorithm significantly outperforms established methods like SeedSearch and MEME.
    • ALSE shows particular strength when input sequences contain multiple binding sites.
    • Probabilistic analysis provides a more accurate likelihood calculation than simple hyper-geometric analysis.

    Conclusions:

    • The ALSE algorithm offers a more accurate and robust approach to motif discovery in molecular biology.
    • Incorporating unbound sequences and employing precise probabilistic analysis are key to improving motif-finding efficacy.
    • ALSE is a valuable tool for researchers studying gene regulation and promoter analysis.