Prediction of Ribosomal -1 Frameshifts in the Escherichia coli K12 Genome (original) (raw)

Abstract

Ribosomal frameshifting at a particular site can yield two protein products from one coding sequence or one protein product from two overlapping open reading frames. Many organisms are known to utilize ribosomal frameshifting to express a minority of genes. However, finding ribosomal frameshift sites by a computational method is difficult because frameshift signals are diverse and dependent on the organisms and environments. There are few computer programs available for public use to identify frameshift sites from genomic sequences. We have developed a web-based application program called FSFinder2 for predicting frameshift sites of general type. We tested FSFinder2 on the Escherichia coli K12 genome to detect potential -1 frameshifting genes. From the genome sequence, we identified 18,401 frameshift sites following the X XXY YYZ motif. 11,530 frameshift sites out of the 18,401 sites include secondary structures. Comparison with the GenBank annotation produced 11 potential frameshift sites, including 3 known frameshift sites. The program is useful for analyzing frameshifts of various types and for discovering new genes expressed by frameshifts.

Chapter PDF

Similar content being viewed by others

References

  1. Farabaugh, P.J.: Programmed Translational Frameshifting. Ann. Rev. Genetics 30, 507–528 (1996)
    Article Google Scholar
  2. Gesteland, R.F., Atkins, J.F.: Recoding: Dynamic Reprogramming of Translation. Annu. Rev. Biochem. 65, 741–768 (1996)
    Article Google Scholar
  3. Herr, A.J., Gesteland, R.F., Atkins, J.F.: One Protein From Two Open Reading Frames: Mechanism of a 50 ntTranslational Bypass. EMBO J. 19, 2671–2680 (2000)
    Article Google Scholar
  4. Baranov, P.V., Gesteland, R.F., Atkins, J.F.: Recoding: Translational Bifurcations in Gene Expression. Gene 286, 187–201 (2002)
    Article Google Scholar
  5. Moon, S., Byun, Y., Kim, H.-J., Jeong, S., Han, K.: Predicting Genes Expressed via -1 and +1 Frameshifts. Nucleic Acids Research 32, 4884–4892 (2004)
    Article Google Scholar
  6. Bekaert, M., Bidou, L., Denise, A., Duchateau-Nguyen, G., Forest, J., Froidevaux, C., Hatin, R.J., Termier, M.: Towards a Computational Model for -1 Eukaryotic Frameshifting Sites. Bioinformatics 19, 327–335 (2003)
    Article Google Scholar
  7. Hammell, A.B., Taylor, R.C., Peltz, S.W., Dinman, J.D.: Identification of Putative Programmed -1 Ribosomal Frameshift Signals in Large DNA Databases. Genome Research 9, 417–427 (1999)
    Google Scholar
  8. Ramos, F.D., Carrasco, M., Doyle, T., Brierley, I.: Programmed -1 Ribosomal Frameshifting in the SARS Coronavirus. Biochemical Society Transactions 32, 1081–1083 (2004)
    Article Google Scholar
  9. Shah, A.A., Giddings, M.C., Parvaz, J.B., Gesteland, R.F., Atkins, J.F., Ivanov, I.P.: Computational Identification of Putative Programmed Translational Frameshift Sites. Bioinformatics 18, 1046–1053 (2002)
    Article Google Scholar

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Inha University, Inchon, 402-751, Korea
    Sanghoon Moon, Yanga Byun & Kyungsook Han

Authors

  1. Sanghoon Moon
  2. Yanga Byun
  3. Kyungsook Han

Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China
    De-Shuang Huang
  2. Queen’s University, Belfast, UK
    Kang Li & George William Irwin &

Rights and permissions

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Moon, S., Byun, Y., Han, K. (2006). Prediction of Ribosomal -1 Frameshifts in the Escherichia coli K12 Genome. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102\_65

Download citation

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Publish with us