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Finding genes in DNA with a Hidden Markov Model

J Henderson1, S Salzberg, K H Fasman

  • 1Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA. jhndrsn@cs.jhu.edu

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|July 1, 1997
PubMed
Summary

A new Hidden Markov Model (HMM) system, VEIL, accurately segments genomic DNA into exons, introns, and intergenic regions. This gene structure prediction method achieves 92% base accuracy, improving upon previous results.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate gene structure prediction is crucial for understanding genome function.
  • Existing methods for segmenting genomic DNA face challenges with accuracy and efficiency.

Purpose of the Study:

  • To develop a novel Hidden Markov Model (HMM) system for segmenting uncharacterized genomic DNA sequences.
  • To improve the accuracy of identifying exons, introns, and intergenic regions.

Main Methods:

  • Designed and trained separate HMM modules for specific DNA regions (exons, introns, intergenic regions, splice sites).
  • Integrated these modules into a biologically feasible topology to form the Viterbi Exon-Intron Locator (VEIL) system.
  • Trained and tested VEIL on eukaryotic DNA sequences for gene structure prediction.

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Main Results:

  • VEIL achieved 92% overall accuracy in correctly labeling bases.
  • The system demonstrated a correlation coefficient of 0.73 for sequence segmentation.
  • VEIL correctly located both ends of 53% of coding exons and predicted 49% of exons exactly correctly.

Conclusions:

  • The VEIL system, utilizing HMMs, significantly advances gene structure prediction accuracy.
  • HMMs offer a powerful approach for segmenting genomic DNA sequences.
  • VEIL's performance compares favorably with the best existing gene structure prediction methods.