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 deterministic motif finding algorithm with application to the human genome.

Lawrence S Hon1, Ajay N Jain

  • 1UCSF Cancer Research Institute and Comprehensive Cancer Center, University of California San Francisco, CA, USA.

Bioinformatics (Oxford, England)
|February 4, 2006
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

CSCAN: Conformational Analysis of Macrocyclic Peptides through NMR Chemical Shifts.

Journal of chemical information and modeling·2026
Same author

Resistance Gene-Guided Genome Mining Reveals RPL10 as the Target of Ligustrone A.

Journal of the American Chemical Society·2025
Same author

Structure-based pose prediction: Non-cognate docking extended to macrocyclic ligands.

Journal of computer-aided molecular design·2024
Same author

From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product.

Journal of computer-aided molecular design·2024
Same author

Correction: Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design.

Journal of computer-aided molecular design·2024
Same author

Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design.

Journal of computer-aided molecular design·2023
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·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
See all related articles

We developed MaMF, a fast algorithm for finding transcription factor (TF) binding sites. MaMF accurately identifies known TF motifs and performs competitively, even on complex human gene sets.

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Identifying transcription factor (TF) binding site motifs is crucial for understanding gene regulation.
  • Existing motif-finding algorithms face challenges, particularly with complex human gene sets.

Purpose of the Study:

  • To introduce MaMF, a novel, deterministic algorithm for efficient and accurate TF binding site motif identification.
  • To evaluate MaMF's performance against existing methods on yeast and human gene datasets.

Main Methods:

  • MaMF utilizes a deterministic approach with an indexing technique for optimized search.
  • The algorithm was tested on standard yeast datasets and challenging human gene sets.

Main Results:

Related Experiment Videos

  • MaMF demonstrated competitive performance on yeast datasets.
  • On human gene sets, MaMF consistently identified annotated motifs among top predictions.
  • MaMF outperformed other motif finders on a larger human gene set comparison.

    • Conclusions:

    • MaMF is a fast and effective algorithm for TF binding site motif discovery.
    • The algorithm's success highlights its suitability for large-scale genomic analyses.
    • Co-occurrence of TF binding sites is a significant factor in human motif finding complexity.