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

Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

You might also read

Related Articles

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

Sort by
Same author

A robust machine learning approach for breast cancer subtype classification using relative gene expression order representations.

Computer methods and programs in biomedicine·2026
Same author

PEPE: scalable extraction of multi-modal protein language model representations.

Bioinformatics (Oxford, England)·2026
Same author

Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood.

Science advances·2026
Same author

Correction: B cell tolerance and antibody production to the celiac disease autoantigen transglutaminase 2.

The Journal of experimental medicine·2026
Same author

inMOTIFin: a lightweight end-to-end simulation software for regulatory sequences.

Bioinformatics (Oxford, England)·2026
Same author

Meta simulation approach for evaluating machine learning method selection in data limited settings.

Scientific reports·2025

Related Experiment Video

Updated: Jul 14, 2026

Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

Improved benchmarks for computational motif discovery.

Geir Kjetil Sandve1, Osman Abul, Vegard Walseng

  • 1Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. sandve@ntnu.no

BMC Bioinformatics
|June 15, 2007
PubMed
Summary

Robust assessment of computational methods for identifying transcription factor binding sites is crucial. New benchmark datasets are introduced to better evaluate motif discovery algorithms and models, distinguishing tool performance from problem complexity.

More Related Videos

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Related Experiment Videos

Last Updated: Jul 14, 2026

Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate identification of transcription factor binding sites is essential for genome annotation.
  • Numerous computational methods exist, making tool selection challenging.
  • Robust assessment is vital for validating existing tools and guiding future research.

Purpose of the Study:

  • To develop improved benchmark datasets for evaluating motif discovery algorithms.
  • To differentiate the performance of motif discovery algorithms from the limitations of motif models.
  • To provide a framework for assessing computational methods in transcription factor binding site identification.

Main Methods:

  • Utilized a machine learning perspective to analyze transcription factor binding sites.
  • Developed algorithms for discovering position weight matrices (PWMs), IUPAC-type motifs, and mismatch motifs.
  • Proposed a novel approach for constructing benchmark datasets based on ranked binding site fragments.

Main Results:

  • Identified limitations in common motif models for discriminating binding sites in existing benchmark datasets.
  • Demonstrated potential bias in synthetic datasets towards presupposed motif models.
  • Created benchmark suites enabling discrimination between algorithm performance and motif model power.

Conclusions:

  • New benchmark suites effectively distinguish motif discovery algorithm performance from motif model capabilities.
  • The developed benchmarks aid in evaluating the true potential of motif discovery tools.
  • A web server is available for accessing benchmark datasets and submitting predictions.