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Finding Genes in DNA with a Hidden Markov Model
Abstract
This study describes a new Hidden Markov Model (HMM) system for segmenting uncharacterized genomic DNA sequences into exons, introns, and intergenic regions. Separate HMM modules were designed and trained for specific regions of DNA: exons, introns, intergenic regions, and splice sites. The models were then tied together to form a biologically feasible topology. The integrated HMM was trained further on a set of eukaryotic DNA sequences and tested by using it to segment a separate set of sequences. The resulting HMM system which is called VEIL (Viterbi Exon-Intron Locator), obtains an overall accuracy on test data of 92% of total bases correctly labelled, with a correlation coefficient of 0.73. Using the more stringent test of exact exon prediction, VEIL correctly located both ends of 53% of the coding exons, and 49% of the exons it predicts are exactly correct. These results compare favorably to the best previous results for gene structure prediction and demonstrate the benefits of using HMMs for this problem.
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Cited by
GeneMark.hmm: new solutions for gene finding
Lukashin, A. (1998) · Nucleic Acids Research · doi:10.1093/nar/26.4.1107 · Cited by 1,538
Meher, P. K., Sahu, T. K., Saini, V., & Rao, A. R. (2017) · Scientific Reports · doi:10.1038/srep42362 · Cited by 516
Current methods of gene prediction, their strengths and weaknesses
Mathe, C. (2002) · Nucleic Acids Research · doi:10.1093/nar/gkf543 · Cited by 476
Molina, J., Hazzouri, K. M., Nickrent, D., Geisler, M., Meyer, R. S., Pentony, M. M., Flowers, J. M., Pelser, P., Barcelona, J., Inovejas, S. A., et al. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu051 · Cited by 269
Computational methods for the identification of genes in vertebrate genomic sequences
Claverie, J. (1997) · Human Molecular Genetics · doi:10.1093/hmg/6.10.1735 · Cited by 264
Metadata
- Authors
- HENDERSON, J., SALZBERG, S., & FASMAN, K. H.
- Venue
- Journal of Computational Biology
- Publisher
- SAGE Publications
- Published
- 1997-01
- Volume
- 4
- Issue
- 2
- Pages
- 127-141
- ISSN
- 1066-5277 (ISSN-L)
- 1557-8666
- OpenAlex
- W1992396120
- Cited by
- 229
- References
- 0
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- closed
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- HENDERSON, J., SALZBERG, S., & FASMAN, K. H. (1997). Finding Genes in DNA with a Hidden Markov Model. Journal of Computational Biology. https://doi.org/10.1089/cmb.1997.4.127
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
