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      TICO: a tool for postprocessing the predictions of prokaryotic translation initiation sites

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      Author(s): * , ,
      Publication date (Electronic):
      Journal: Nucleic Acids Research
      Publisher: Oxford University Press

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          Abstract

          Exact localization of the translation initiation sites (TIS) in prokaryotic genomes is difficult to achieve using conventional gene finders. We recently introduced the program TICO for postprocessing TIS predictions based on a completely unsupervised learning algorithm. The program can be utilized through our web interface at http://tico.gobics.de/ and it is also freely available as a commandline version for Linux and Windows. The latest version of our program provides a tool for visualization of the resulting TIS model. Although the underlying method is not based on any specific assumptions about characteristic sequence features of prokaryotic TIS the prediction rates of our tool are competitive on experimentally verified test data.

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          The 3'-terminal sequence of Escherichia coli 16S ribosomal RNA: complementarity to nonsense triplets and ribosome binding sites.

          J. SHINE, L Dalgarno (1974)
          With a stepwise degradation and terminal labeling procedure the 3'-terminal sequence of E. coli 16S ribosomal RNA is shown to be Pyd-A-C-C-U-C-C-U-U-A(OH). It is suggested that this region of the RNA is able to interact with mRNA and that the 3'-terminal U-U-A(OH) is involved in the termination of protein synthesis through base-pairing with terminator codons. The sequence A-C-C-U-C-C could recognize a conserved sequence found in the ribosome binding sites of various coliphage mRNAs; it may thus be involved in the formation of the mRNA.30S subunit complex.
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            A probabilistic method for identifying start codons in bacterial genomes.

            B. E. Suzek, M D Ermolaeva, M. Schreiber … (2001)
            As the pace of genome sequencing has accelerated, the need for highly accurate gene prediction systems has grown. Computational systems for identifying genes in prokaryotic genomes have sensitivities of 98-99% or higher (Delcher et al., Nucleic Acids Res., 27, 4636-4641, 1999). These accuracy figures are calculated by comparing the locations of verified stop codons to the predictions. Determining the accuracy of start codon prediction is more problematic, however, due to the relatively small number of start sites that have been confirmed by independent, non-computational methods. Nonetheless, the accuracy of gene finders at predicting the exact gene boundaries at both the 5' and 3' ends of genes is of critical importance for microbial genome annotation, especially in light of the important signaling information that is sometimes found on the 5' end of a protein coding region. In this paper we propose a probabilistic method to improve the accuracy of gene identification systems at finding precise translation start sites. The new system, RBSfinder, is tested on a validated set of genes from Escherichia coli, for which it improves the accuracy of start site locations predicted by computational gene finding systems from the range 67-77% to 90% correct.
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              ZCURVE: a new system for recognizing protein-coding genes in bacterial and archaeal genomes.

              Sai-Hong Ou, Feng Guo, Ting-Ting Zhang (2003)
              A new system, ZCURVE 1.0, for finding protein- coding genes in bacterial and archaeal genomes has been proposed. The current algorithm, which is based on the Z curve representation of the DNA sequences, lays stress on the global statistical features of protein-coding genes by taking the frequencies of bases at three codon positions into account. In ZCURVE 1.0, since only 33 parameters are used to characterize the coding sequences, it gives better consideration to both typical and atypical cases, whereas in Markov-model-based methods, e.g. Glimmer 2.02, thousands of parameters are trained, which may result in less adaptability. To compare the performance of the new system with that of Glimmer 2.02, both systems were run, respectively, for 18 genomes not annotated by the Glimmer system. Comparisons were also performed for predicting some function-known genes by both systems. Consequently, the average accuracy of both systems is well matched; however, ZCURVE 1.0 has more accurate gene start prediction, lower additional prediction rate and higher accuracy for the prediction of horizontally transferred genes. It is shown that the joint applications of both systems greatly improve gene-finding results. For a typical genome, e.g. Escherichia coli, the system ZCURVE 1.0 takes approximately 2 min on a Pentium III 866 PC without any human intervention. The system ZCURVE 1.0 is freely available at: http://tubic. tju.edu.cn/Zcurve_B/.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (publisher-id): Nucleic Acids Research
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: 01 July 2006
                Publication date (Print): 01 July 2006
                Publication date (Electronic): 14 July 2006
                Volume: 34
                Issue: Web Server issue
                Pages: W588-W590
                Affiliations
                Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany
                Author notes
                *To whom correspondence should be addressed. Tel: +49 551 3914927; Fax: +49 551 3914929; E-mail: maike@ 123456gobics.de
                Article
                DOI: 10.1093/nar/gkl313
                PMC ID: 1538874
                PubMed ID: 16845076
                SO-VID: ed187696-63fc-4332-821b-05e53e4a6bbf
                Copyright © © The Author 2006. Published by Oxford University Press. All rights reserved
                License:

                The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

                History
                Date received : 14 February 2006
                Date revision received : 06 March 2006
                Date accepted : 11 April 2006
                Categories
                Subject: Article

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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