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

BEESEM: estimation of binding energy models using HT-SELEX data.

Shuxiang Ruan1, S Joshua Swamidass2, Gary D Stormo1

  • 1Department of Genetics.

Bioinformatics (Oxford, England)
|April 6, 2017
PubMed
Summary

We developed BEESEM, an algorithm for transcription factor binding motif discovery from high-throughput SELEX data. BEESEM improves motif estimation accuracy using a biophysical model, outperforming previous methods on in vitro data.

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

Autoregulation of three yeast ribosomal protein genes by splicing inhibition.

G3 (Bethesda, Md.)·2026
Same author

Superior transplant recipient outcome prediction and pathology assessment using rapid deep learning applied to procurement kidney biopsies.

Scientific reports·2025
Same author

Domain Knowledge Inclusive Monotonic Neural Network Guides Patient-Specific Induction of General Anesthesia Dosing.

A&A practice·2025
Same author

Autoregulation of RPL7B by inhibition of a structural splicing enhancer.

Nucleic acids research·2025
Same author

Autoregulation of <i>RPL7B</i> by inhibition of a structural splicing enhancer.

bioRxiv : the preprint server for biology·2025
Same author

Aberrant homeodomain-DNA cooperative dimerization underlies distinct developmental defects in two dominant <i>CRX</i> retinopathy models.

Genome research·2024

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Accurate characterization of transcription factor (TF) binding specificities is essential for understanding gene expression regulation.
  • High-throughput experimental methods like protein binding microarrays (PBM) and high-throughput SELEX (HT-SELEX) generate extensive TF binding data.
  • Existing algorithms for motif discovery from HT-SELEX data are limited, and simple methods risk overestimating motif information content.

Purpose of the Study:

  • To develop an efficient and accurate algorithm for estimating transcription factor binding motifs from high-throughput experimental data, specifically HT-SELEX.
  • To improve upon existing methods for motif discovery by incorporating a comprehensive biophysical model of protein-DNA interactions.
  • To evaluate the performance of the developed algorithm against established methods using both in vitro and in vivo datasets.

Related Experiment Videos

Main Methods:

  • Developed BEESEM, an algorithm utilizing a comprehensive biophysical model of protein-DNA interactions.
  • Trained the model using the expectation maximization method.
  • BEESEM is capable of selecting optimal motif length and calculating confidence intervals for estimated parameters.

Main Results:

  • BEESEM demonstrated significant improvements in motif estimation compared to published methods using the same HT-SELEX data.
  • BEESEM showed superior performance in fitting in vitro data (PBM and HT-SELEX) compared to other motif discovery algorithms.
  • While performance on in vivo data (ChIP-seq) was comparable to other methods using AUROC, quantitative binding data confirmed BEESEM's improved model accuracy.

Conclusions:

  • BEESEM offers a robust and accurate approach for transcription factor binding motif discovery from HT-SELEX data.
  • The algorithm's biophysical modeling provides more reliable motif estimations, particularly for in vitro binding data.
  • The study highlights the limitations of rank-based evaluation criteria like AUROC and emphasizes the value of quantitative binding data for model assessment.