Plant Molecular Biology

, Volume 48, Issue 1–2, pp 39–48 | Cite as

Computational gene finding in plants

  • Mihaela Pertea
  • Steven L. Salzberg


Automated methods for identifying protein coding regions in genomic DNA have progressed significantly in recent years, but there is still a strong need for more accurate computational solutions to the gene finding problem. Large-scale genome sequencing projects depend greatly on gene finding to generate accurate and complete gene annotation. Improvements in gene finding software are being driven by the development of better computational algorithms, a better understanding of the cell's mechanisms for transcription and translation, and the enormous increases in genomic sequence data. This paper reviews some of the most widely used algorithms for gene finding in plants, including technical descriptions of how they work and recent measurements of their success on the genomes of Arabidopsis thaliana and rice.

computational gene finding genome sequencing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Adams, M.D., Celniker, S.E., Holt, R.A., Evans, C.A., Gocayne, J.D., Amanatides, P.G., Scherer, S.E., Li, P.W., Hoskins, R.A., Galle, R.F., George, R.A., Lewis, S.E., Richards, S., Ashburner, M., Henderson, S.N., Sutton, G.G., Wortman, J.R., Yandell, M.D., Zhang, Q., Chen, L.X., Brandon, R.C., Rogers, Y.H., Blazej, R.G., Champe, M., Pfeiffer, B.D., Wan, K.H., Doyle, C., Baxter, E.G., Helt, G., Nelson, C.R., Gabor, G.L., Abril, J.F., Agbayani, A., An, H.J., Andrews-Pfannkoch, C., Baldwin, D., Ballew, R.M., Basu, A., Baxendale, J., Bayraktaroglu, L., Beasley, E.M., Beeson, K.Y., Benos, P.V., Berman, B.P., Bhandari, D., Bolshakov, S., Borkova, D., Botchan, M.R., Bouck, J., et al. 2000. The genome sequence of Drosophila melanogaster. Science 287(5461): 2185–2195.Google Scholar
  2. Arabidopsis Genome Initiative. 2000. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408(6814): 796–815.Google Scholar
  3. Brunak, S., Engelbrecht, J. and Knudsen, S. 1991. Prediction of human mRNA donor and acceptor sites from the DNA sequence. J. Mol. Biol. 220: 49–65.Google Scholar
  4. Burge, C. and Karlin, S. 1997. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268: 78–94.Google Scholar
  5. Claverie, J.M. 1997. Computational methods for the identification of genes in vertebrate genomic sequences. Human Mol. Genet. 6: 1735–1744.Google Scholar
  6. Duret L., Mouchiroud D. and Gautier C. 1995. Statistical analysis of vertebrate sequences reveals that long genes are scarce in GCrich isochores. J. Mol. Evol. 40: 308–317.Google Scholar
  7. Ermolaeva, M.D., Khalak, H.G., White, O., Smith, H.O. and Salzberg, S.L. 2000. Prediction of transcription terminators in bacterial genomes. J. Mol. Biol. 301: 27–33.Google Scholar
  8. Farber, R., Lapedes, A. and Sirotkin, K. 1992. Determination of eukaryotic protein coding regions using neural networks and information theory. J. Mol. Biol. 226: 471–479.Google Scholar
  9. Fickett, J.W. 1996. The gene identification problem: an overview for developers. Comp. Chem. 20(1): 103–118.Google Scholar
  10. Franco, G.R., Adams, M.D., Soares, M.B., Simpson, A.J., Venter, J.C. and Pena, S.D. 1995. Identification of new Schistosoma mansoni genes by the EST strategy using a directional cDNA library. Gene 152: 141–147.Google Scholar
  11. Gelfand, M.S. 1995. Prediction of function in DNA sequence analysis. J. Comput. Biol. 2: 87–115.Google Scholar
  12. Guigo, R. 1997. Computational gene identification: an open problem. Comp. Chem. 21: 215–222.Google Scholar
  13. Hebsgaard, S.M., Korning, P.G., Tolstrup, N., Engelbrecht, J., Rouze, P. and Brunak, S. 1996. Splice site prediction in Arabidopsis thaliana DNA by combining local and global sequence information. Nucl. Acids Res. 24: 3439–3452.Google Scholar
  14. Jelinek, F. 1998. Statistical Methods for Speech Recognition. MIT Press.Google Scholar
  15. Krogh, A. 1998. An introduction to hidden Markov models for biological sequences. In: S.L. Salzberg, D.B. Searls and S. Kasif (Eds.) Computational Methods in Molecular Biology, Elsevier, Amsterdam, Chap. 4, pp. 45–65.Google Scholar
  16. Lin, X., Kaul, S., Rounsley, S., Shea, T.P., Benito, M.-I., Town, C.D., Fujii, C.Y., Mason, T., Bowman, C.L., Barnstead, M., Feldblyum, T., Buell, C.R., Ketchum, K.A., Ronning, C.M., Koo, H., Moffat, K., Cronin, L., Shen, M., Pai, G., van Aken, S., Umayam, L., Tallon, L., Gill, J., Adams, M.D., Carrera, A.J., Creasy, T.H., Goodman, H.M., Somerville, C.R., Copenhaver, G., Preuss, D., Nierman, W.C., White, O., Eisen, J.A., Salzberg, S., Fraser, C. and Venter, J.C. 1999. Sequence and analysis of chromosome 2 of the plant Arabidopsis thaliana. Nature 402: 761–768.Google Scholar
  17. Lowe, T.M. and Eddy, S.R. 1997. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucl. Acids Res. 25: 955–964.Google Scholar
  18. Lowe, T.M. and Eddy, S.R. 1999. A computational screen for methylation guide snoRNAs in yeast. Science 283(5405): 1168–1171.Google Scholar
  19. Lukashin, A.V. and Borodovsky, M. 1998. GeneMark.hmm: new solutions for gene finding. Nucl. Acids Res. 26: 1107–1115.Google Scholar
  20. Matis, S., Xu, Y., Shah, M., Guan, X., Einstein, J.R., Mural, R. and Uberbacher, E. 1996. Detection of RNA polymerase II promoters and polyadenylation sites in human DNA sequence. Comp. Chem. 20(1): 135–140.Google Scholar
  21. O'Neill, M.C. 1991. Training back-propagation neural networks to define and detect DNA-binding sites. Nucl. Acids Res. 19: 313–318.Google Scholar
  22. O'Neill, M.C. 1992. Escherichia coli promoters: neural networks develop distinct descriptions in learning to search for promoters of different spacing classes. Nucl. Acids Res. 20: 3471–3477.Google Scholar
  23. Pavy, N., Rombauts, S., Dehais, P., Mathe, C., Ramana, D.V., Leroy, P. and Rouze, P. 1999. Evaluation of gene prediction software using a genomic data set: application to Arabidopsis thaliana sequences. Bioinformatics 15: 887–899.Google Scholar
  24. Quackenbush, J., Cho, J., Lee, D., Liang, F., Holt, I., Karamycheva, S., Parvizi, B., Pertea, G., Sultana, R. and White, J. 2001. The TIGR Gene Indices: analysis of gene transcript sequences in highly sampled eukaryotic species. Nucl. Acids Res. 29: 159–164.Google Scholar
  25. Salzberg, S. 1995. Locating protein coding regions in human DNA using a decision tree algorithm. J. Comput. Biol. 2: 473–485.Google Scholar
  26. Salzberg, S.L. 1997. A method for identifying splice sites and translational start sites in eukaryotic mRNA. Comput. Appl. Biosci. 13: 365–376.Google Scholar
  27. Salzberg, S.L., Searls, D. and Kasif, S. (Eds.). 1998a. Computational Methods in Molecular Biology. Elsevier Science, Amsterdam.Google Scholar
  28. Salzberg, S.L., Delcher, A.L., Kasif, S. and White, O. 1998b. Microbial gene identification using interpolated Markov models. Nucl. Acids Res. 26: 544–548.Google Scholar
  29. Salzberg, S., Delcher, A.L., Fasman, K.H. and Henderson, J. 1998c. A decision tree system for finding genes in DNA. J. Comput. Biol. 5: 667–680.Google Scholar
  30. Salzberg, S.L., Pertea, M., Delcher, A.L., Gardner, M.J. and Tettelin, H. 1999. Interpolated Markov models for eukaryotic gene finding. Genomics 59: 24–31.Google Scholar
  31. Solovyev, V.V., Salamov, A.A. and Lawrence, C.B. 1994. Predicting internal exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames. Nucl. Acids Res. 22: 5156–5163.Google Scholar
  32. Solovyev, V.V., Salamov, A.A. and Lawrence, C.B. 1995. Identification of human gene structure using linear discriminant functions and dynamic programming. In: Proceedings of the International Conference on Intelligent Systems in Molecular Biology 3: 367–375.Google Scholar
  33. Stormo, G.D. 1990. Consensus patterns in DNA. Meth. Enzymol. 183: 211–221.Google Scholar
  34. Stormo, G.D. 2000. Gene-finding approaches for eukaryotes. Genome Res. 10: 394–397.Google Scholar
  35. Tompa, M. 1999. An exact method for finding short motifs in sequences, with application to the ribosome binding site problem. In: Proceedings of the International Conference on Intelligent Systems in Molecular Biology, pp. 262-271.Google Scholar
  36. Yuan, Q., Quackenbush, J., Sultana, R., Pertea, M., Salzberg, S. and Buell, C.R. 2001. Rice bioinformatics. Analysis of rice sequence data and leveraging the data to other plant species. Plant Physiol. 125: 1166–1174.Google Scholar
  37. Zhang, M.Q. and Marr, T.G. 1993. A weight array method for splicing signal analysis. Comput. Appl. Biosci. 9: 499–509.Google Scholar
  38. Zien, A., Ratsch, G., Mika, S., Scholkopf, B., Lengauer, T. and Muller, K.R. 2000. Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics 16: 799–807.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Mihaela Pertea
    • 1
  • Steven L. Salzberg
    • 1
  1. 1.Institute for Genome ResearchRockvilleUSA

Personalised recommendations