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

GeneMark.hmm: new solutions for gene finding

A V Lukashin1, M Borodovsky

  • 1School of Biology and Schools of Biology and Mathematics, Georgia Institute of Technology, Atlanta, GA 30332-0230, USA.

Nucleic Acids Research
|March 21, 1998
PubMed
Summary

A new algorithm, GeneMark.hmm, significantly improves bacterial gene prediction accuracy by refining gene boundaries and translation initiation sites. This advancement offers more precise gene identification in rapidly growing genomic data.

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

The Spirodela polyrhiza genome reveals insights into its neotenous reduction fast growth and aquatic lifestyle.

Nature communications·2014
Same author

Reannotation of Shewanella oneidensis genome.

Omics : a journal of integrative biology·2003
Same author

A novel genetic island of meningitic Escherichia coli K1 containing the ibeA invasion gene (GimA): functional annotation and carbon-source-regulated invasion of human brain microvascular endothelial cells.

Functional & integrative genomics·2002
Same author

GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.

Nucleic acids research·2001
Same author

Leaderless transcripts of the crenarchaeal hyperthermophile Pyrobaculum aerophilum.

Journal of molecular biology·2001
Same author

Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters.

Bioinformatics (Oxford, England)·2001

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • The rapid growth of sequenced bacterial genomes necessitates more accurate computational gene prediction methods.
  • Existing algorithms like GeneMark have limitations in precisely identifying exact gene boundaries.

Purpose of the Study:

  • To develop and evaluate the GeneMark.hmm algorithm for enhanced accuracy in bacterial gene prediction, specifically focusing on exact gene boundary identification.
  • To refine the prediction of translation initiation codons using a specialized ribosome binding site pattern.

Main Methods:

  • Embedding existing GeneMark models within a hidden Markov model (HMM) framework.
  • Modeling gene boundaries as transitions between hidden states in the HMM.
  • Utilizing a derived ribosome binding site pattern to improve translation initiation codon prediction.

Main Results:

  • The GeneMark.hmm algorithm demonstrated significantly higher accuracy in exact gene prediction compared to the original GeneMark.
  • High gene-finding accuracy was achieved even with lower-order Markov models (zero, one, and two).
  • Analysis of false positive and false negative predictions was conducted, acknowledging limitations of public database annotations.

Conclusions:

  • GeneMark.hmm represents a substantial improvement in bacterial gene prediction accuracy, particularly for precise gene boundary identification.
  • The algorithm's effectiveness across different Markov model orders suggests robustness.
  • Further analysis is needed to fully understand prediction errors in the context of current genomic annotations.

Related Experiment Videos