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Open Access Software

TISs-ST: a web server to evaluate polymorphic translation initiation sites and their reflections on the secretory targets

Renato Vicentini12 and Marcelo Menossi12*

Author Affiliations

1 Functional Genomics Laboratory, Center for Molecular Biology and Genetic Engineering, State University of Campinas, P.O. Box 6010, 13083-875, Campinas, SP, Brazil

2 Department of Genetics and Evolution, Institute of Biology, State University of Campinas, Campinas, SP, Brazil

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BMC Bioinformatics 2007, 8:160  doi:10.1186/1471-2105-8-160

Published: 21 May 2007

Abstract

Background

The nucleotide sequence flanking the translation initiation codon (start codon context) affects the translational efficiency of eukaryotic mRNAs, and may indicate the presence of an alternative translation initiation site (TIS) to produce proteins with different properties. Multi-targeting may reflect the translational variability of these other protein forms. In this paper we present a web server that performs computations to investigate the usage of alternative translation initiation sites for the synthesis of new protein variants that might have different functions.

Results

An efficient web-based tool entitled TISs-ST (

    T
ranslation
    I
nitiation
    S
ite
    s
and
    S
ecretory
    T
argets) evaluates putative translation initiation sites and indicates the prediction of a signal peptide of the protein encoded from this site. The TISs-ST web server is freely available to both academic and commercial users and can be accessed at http://ipe.cbmeg.unicamp.br/pub/TISs-ST webcite.

Conclusion

The program can be used to evaluate alternative translation initiation site consensus with user-specified sequences, based on their composition or on many position weight matrix models. TISs-ST provides analytical and visualization tools for evaluating the periodic frequency, the consensus pattern and the total information content of a sequence data set. A search option allows for the identification of signal peptides from predicted proteins using the PrediSi software.