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Related Concept Videos

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.
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Related Experiment Video

Updated: May 13, 2026

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling
08:04

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling

Published on: October 8, 2019

Simultaneously learning DNA motif along with its position and sequence rank preferences through expectation

ZhiZhuo Zhang1, Cheng Wei Chang, Willy Hugo

  • 1National University of Singapore, Singapore, Singapore.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|March 7, 2013
PubMed
Summary
This summary is machine-generated.

SEME, a new de novo motif discovery algorithm, accurately identifies transcription factor (TF) binding sites and co-regulated TF (coTF) motifs by modeling binding features without user input. This method enhances understanding of TF interactions.

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

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Published on: July 14, 2015

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Traditional de novo motif discovery methods often miss true motifs or yield false positives.
  • Existing approaches typically require users to provide prior knowledge on binding features like position and rank preferences.

Purpose of the Study:

  • To present SEME (sampling with expectation maximization for motif elicitation), a novel algorithm for de novo motif discovery.
  • To develop an algorithm that learns sequence motifs and their binding preferences (position and rank) automatically, without user-provided priors.
  • To improve the accuracy and efficiency of identifying transcription factor (TF) binding sites and co-regulated TF (coTF) motifs.

Main Methods:

  • Utilizes a probabilistic mixture model to represent motif binding features.
  • Employs expectation-maximization (EM) algorithms for simultaneous learning of motifs and their preferences.
  • Incorporates variable motif length extension and importance sampling for enhanced efficiency and accuracy.

Main Results:

  • SEME demonstrates superior performance in identifying TF binding sites across synthetic, benchmark, and ChIP-Seq datasets compared to existing programs.
  • The algorithm successfully identifies more correct coTF motifs and predicts them with better accuracy in ChIP-Seq libraries.
  • Learned preferences reveal potential interaction mechanisms between TFs and coTFs, with some findings experimentally validated.

Conclusions:

  • SEME offers a significant advancement in de novo motif discovery by automating the learning of binding preferences.
  • The algorithm provides a powerful tool for identifying TF and coTF binding sites, aiding in the understanding of gene regulation.
  • SEME's ability to uncover TF interaction mechanisms opens new avenues for biological research and experimental validation.