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

Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

You might also read

Related Articles

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

Sort by
Same author

Proof-of-concept study for the detection of somatic structural variant driver alterations using HiFi long-read sequencing in a pediatric leukemia cohort.

NPJ genomic medicine·2026
Same author

Accurate somatic small variant discovery for multiple sequencing technologies with DeepSomatic.

Nature biotechnology·2025
Same author

Clinical Long-Read Sequencing Test for Genetic Disease Diagnosis.

JAMA pediatrics·2025
Same author

Severus detects somatic structural variation and complex rearrangements in cancer genomes using long-read sequencing.

Nature biotechnology·2025
Same author

Successful classification of clinical pediatric leukemia genetic subtypes via structural variant detection using HiFi long-read sequencing.

medRxiv : the preprint server for health sciences·2025
Same author

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies.

bioRxiv : the preprint server for biology·2024

Related Experiment Video

Updated: Jul 15, 2026

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

SEAM: a Stochastic EM-type Algorithm for Motif-finding in biopolymer sequences.

Chengpeng Bi1

  • 1Children's Mercy Hospitals and Clinics, 2401 Gillham Road, Pediatrics Research Building, Third Floor, Kansas City, Missouri 64108, USA. cbi@cmh.edu

Journal of Bioinformatics and Computational Biology
|May 5, 2007
PubMed
Summary

A new algorithm, the Stochastic EM-type Algorithm for Motif-finding (SEAM), enhances motif discovery in biopolymer sequences. SEAM demonstrates robustness and power in identifying sequence motifs through in silico experiments.

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

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Related Experiment Videos

Last Updated: Jul 15, 2026

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

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

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Identifying functional sites (motifs) in unaligned biopolymer sequences is crucial for understanding biological processes.
  • Existing motif-finding algorithms face challenges in accuracy and efficiency.

Purpose of the Study:

  • To introduce and evaluate a novel algorithm, the Stochastic EM-type Algorithm for Motif-finding (SEAM), for motif discovery.
  • To compare the performance of SEAM against its deterministic counterpart (DEM) and other popular motif-finding tools.

Main Methods:

  • Development and implementation of the Stochastic EM-type Algorithm for Motif-finding (SEAM).
  • Redesign and implementation of the deterministic EM (DEM) algorithm for comparative analysis.
  • Utilizing position weight matrix-based statistical modeling.
  • Validation using simulated data and biological sequences, including cyclic adenosine monophosphate receptor protein (CRP) binding sites.

Main Results:

  • SEAM demonstrates convergence through simulations.
  • In silico experiments show SEAM's power and robustness in de novo motif discovery.
  • Comparative analysis highlights SEAM's performance against DEM and other motif-finding programs.

Conclusions:

  • SEAM is a powerful and robust algorithm for de novo motif discovery in biopolymer sequences.
  • The algorithm shows promise for identifying and characterizing motif sites in biological data.
  • SEAM offers an effective approach for motif identification in computational biology and bioinformatics.