Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

Prediction of Core Promoter Region with Dependency - Reflecting Decomposition Model

의존성 반영 분해모델에 의한 유전자의 핵심 프로모터 영역 예측

  • Published : 2003.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

A lot of microbial genome projects have been completed to pour the enormous amount of genomic sequence data. In this context. the problem of identifying promoters in genomic DNA sequences by computational methods has attracted considerable research attention in recent years. In this paper, we propose a new model of prokaryotic core promoter region including the -10 region and transcription initiation site, that is Dependency-Reflecting Decomposition Model (DRDM), which captures the most significant biological dependencies between positions (allowing for non-adjacent as well as adjacent dependencies). DRDM showed a good result of performance test and it will be employed effectively in predicting promoters in long microbial genomic Contigs.

다수의 미생물 유전체 프로젝트들이 완료되면서 엄청난 양의 유전체 핵산 염기서열 데이터들이 양산되고 있다. 이러한 상황에서 전산 기법을 이용하여 유전체 DNA 염기서열 상에서 유전자의 프로모터 영역을 규명하는 문제는 최근에 상당한 연구의 관심대상으로 떠오르고 있다. 본 논문에서는 전사조절의 핵심 역할을 하는 -10 영역과 전사개시 부위를 포함한 원핵생물의 핵심 프로모터 영역에 대한 의존성 반영 분해모델 (Dependency-Reflecting Decomposition Model)을 제안한다. 이 모델은 인접한 위치에 존재하는 핵산 염기들 사이의 의존성뿐만 아니라 인접하지 않은 위치의 핵산 염기들간의 의존성까지 고려함으로써 핵산 염기서열 상에 내포되어있는 중요한 생물학적 의존성들을 함축하고 있다. DRDM 모델은 우수한 성능평가 결과를 보였으며. 미생물 유전체 Contig들 상에서 임의의 유전자 프로모터를 예측하는데 효과적으로 이용될 수 있다.

Keywords

References

  1. W. S. Reznikoff, D. A. Siegele, D. W. Cowing, and C. A. Gross, The regulation of transcription initiation in bacteria, Annu. Rev Genet., Vol. 19, pp. 355-387, 1985 https://doi.org/10.1146/annurev.ge.19.120185.002035
  2. Harmen Bussemaker, Hao Li, and Eric Siggia, Building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis, PNAS, Vol. 97, No. 18, pp. 10096-10100, 2000 https://doi.org/10.1073/pnas.180265397
  3. Florea, L., Li, M., Riemer, C., Giardine, B., Miller, W., and Hardison, R., Validating computer programs for functional genomics in gene regulatory regions, Current Genomics, Vol. 1, No. 1, pp. 11-27
  4. K. Frech, K. Quandt, and T. Werner, Software for the analysis of DNA sequence elements of transcription. Comput. Appl. Biosci., Vol 13, pp. 89-97, 1997
  5. H. Salgado, et. al., RegulonDB (version 3.2): transcriptional regulation and operon organization in Escherichia coli K-12. Nucle, Acids Res., Vol. 29, pp. 72-74. 2001 https://doi.org/10.1093/nar/29.1.72
  6. J. Collado-Vides, Grammatical model of the regulation of gene expression. Proc. Natl Acad. USA, Vol. 89. pp. 9405-9409. 1992 https://doi.org/10.1073/pnas.89.20.9405
  7. D. Thieffry, H. Salgado, A. M. Huerta and J. Collado-Vides, Prediction of transcriptional regulatory sites in the complete genome sequence of Escherichiacoli K-12, Bioinformatics, Vol. 14. pp. 391-400, 1998 https://doi.org/10.1093/bioinformatics/14.5.391
  8. G. Z. Hertz, G. W. Hartzell III and G. D. Stormo, Identification of consensus patterns in unaligned DNA sequences known to be functionally related, Comput. Applic. Biosci., Vol. 6, pp. 81-92, 1990
  9. C. A. Gross and M. Lonetto, Bacterial sigma factors, Cold Spring Harbor Laboratory Press, 1992
  10. T. D. Schneider and R. M. Stephens, Sequence Logos: A New Way to Display Consensus Sequences, Nucleic Acids Res., Vol. 18, pp. 6097-6100, 1990 https://doi.org/10.1093/nar/18.20.6097
  11. C. Burge and S. Karlin, Prediction of Complete Gene Structure in Human Genomic DNA. Journal of Molecular Biology, Vol. 268, pp. 78-94, 1997 https://doi.org/10.1006/jmbi.1997.0951
  12. S. L. Salzberg, D. B. Searls and S. Kasif, Computational Methods in Molecular Biology, Elsevier Science B. V., 1998
  13. K. B. Kim, K. J. Park and E. B. Kong, Prokaryotic Promoter Recognition with Dependence Decomposition Weight Matrix Method, 2nd International Symposium on Advanced Intelligent Systems Conference Proceedings, Vol. II No. 2 p277-281, 2001