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Related Experiment Videos

TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders.

W H Majoros1, M Pertea, S L Salzberg

  • 1Bioinformatics Department, The Institute for Genomic Research, Rockville, MD 20850, USA. bmajoros@tigr.org

Bioinformatics (Oxford, England)
|May 18, 2004
PubMed
Summary

Two new Generalized Hidden Markov Model (GHMM) programs were developed for *ab initio* eukaryotic gene prediction. These reusable, open-source tools offer end-user retraining and flexible submodel combinations for accurate genome annotation.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Existing gene-finding programs often lack flexibility and user-trainability.
  • Accurate *ab initio* gene prediction is crucial for genome annotation.

Purpose of the Study:

  • To introduce two novel Generalized Hidden Markov Model (GHMM) implementations for *ab initio* eukaryotic gene prediction.
  • To provide reusable, open-source software with modular and extensible architectures.

Main Methods:

  • Development of two new GHMM implementations.
  • Incorporation of re-trainable and re-configurable probabilistic submodels, including Maximal Dependence Decomposition trees and interpolated Markov models.
  • Modular and extensible software design for high reusability.

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Main Results:

  • Successful application of the programs for the annotation of *Aspergillus fumigatus* and *Toxoplasma gondii* genomes at TIGR.
  • Demonstration of the programs' reusability, re-trainability, and flexibility in combining probabilistic submodels.

Conclusions:

  • The new GHMM implementations offer advanced capabilities for *ab initio* eukaryotic gene prediction.
  • The open-source nature and flexible architecture of these programs enhance their utility for genome annotation research.