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Related Experiment Videos

Sensitive protein comparisons with profiles and hidden Markov models.

K Hofmann1

  • 1Bioinformatics & Gene Discovery Group, MEMOREC Stoffel GmbH, Köln, Germany. Kay.hoffmann@memorec.com

Briefings in Bioinformatics
|July 24, 2001
PubMed
Summary

Sequence database searches are vital for gene discovery. Using profiles and hidden Markov models (HMMs) instead of single sequences significantly improves search sensitivity and reliability for functional assignments.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sequence database searches are essential tools in life sciences and biotechnology for gene discovery and functional assignment.
  • Current methods rely on single sequences, which can limit sensitivity and reliability.

Purpose of the Study:

  • To review the concepts of profiles, generalized profiles, and profile-hidden Markov models (HMMs).
  • To discuss methods for constructing these models.
  • To explore their applications in gene discovery and functional assignment.

Main Methods:

  • Utilizing profiles and profile-hidden Markov models (HMMs) as query objects in sequence database searches.
  • Comparing the performance enhancement offered by these models over single-sequence searches.

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Main Results:

  • Profiles and profile-HMMs derived from protein families considerably enhance the performance of sequence alignments and database searches.
  • These models improve both sensitivity and reliability in identifying functional candidates and assigning functions to novel proteins.

Conclusions:

  • Profiles and profile-HMMs represent a significant advancement for sequence analysis in bioinformatics.
  • Their application is crucial for efficient gene discovery and accurate functional annotation in genomic research.