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

An introduction to hidden Markov models.

Benjamin Schuster-Böckler1, Alex Bateman

  • 1Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom.

Current Protocols in Bioinformatics
|April 23, 2008
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

Folding the unfoldable 2: using AlphaFold and ESMFold to explore spurious proteins.

Bioinformatics advances·2026
Same author

InterProScan 6: a modern large-scale protein function annotation pipeline.

Bioinformatics advances·2026
Same author

Viral non-coding RNA structure annotation and API-based data retrieval with Rfam and R2DT.

bioRxiv : the preprint server for biology·2026
Same author

Amos Bairoch (1957-2025): pioneer of bioinformatics and founder of Swiss-Prot.

Bioinformatics advances·2026
Same author

GOFlowLLM-curating miRNA literature with large language models and flowcharts.

Bioinformatics (Oxford, England)·2026
Same author

Expression Atlas in 2026: enabling FAIR and open expression data through community collaboration and integration.

Nucleic acids research·2025
Same journal

Protein Sequence Analysis Using the MPI Bioinformatics Toolkit.

Current protocols in bioinformatics·2020
Same journal

Exploring Manually Curated Annotations of Intrinsically Disordered Proteins with DisProt.

Current protocols in bioinformatics·2020
Same journal

Network Building with the Cytoscape BioGateway App Explained in Five Use Cases.

Current protocols in bioinformatics·2020
Same journal

Expanding the Perseus Software for Omics Data Analysis With Custom Plugins.

Current protocols in bioinformatics·2020
Same journal

Exploring Non-Coding RNAs in RNAcentral.

Current protocols in bioinformatics·2020
Same journal

How to Illuminate the Dark Proteome Using the Multi-omic OpenProt Resource.

Current protocols in bioinformatics·2020
See all related articles

This unit explains hidden Markov models (HMMs) for computational biology using simple examples. It covers HMM history, applications, and limitations, minimizing mathematical complexity for broader understanding.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Hidden Markov Models (HMMs) are statistical tools used in bioinformatics.
  • Understanding HMMs is crucial for analyzing biological sequences.
  • Prior knowledge of advanced mathematics is often a barrier to learning HMMs.

Purpose of the Study:

  • To introduce the fundamental concepts of Hidden Markov Models (HMMs).
  • To explain HMMs using accessible biological examples.
  • To provide a foundational understanding of HMMs for computational biology applications.

Main Methods:

  • Conceptual explanation of HMMs.
  • Illustrative biological examples.
  • Historical overview and current applications of HMMs.

Related Experiment Videos

Main Results:

  • Demystification of HMMs for a non-mathematical audience.
  • Demonstration of HMM applicability in biological sequence analysis.
  • Identification of HMM limitations.

Conclusions:

  • Hidden Markov Models are powerful yet accessible tools in computational biology.
  • This unit provides a simplified entry point for understanding HMMs.
  • Further exploration of HMMs can enhance biological data analysis capabilities.