Dragon TIS Spotter: an Arabidopsis-derived predictor of translation initiation sites in plants

Summary: In higher eukaryotes, the identification of translation initiation sites (TISs) has been focused on finding these signals in cDNA or mRNA sequences. Using Arabidopsis thaliana (A.t.) information, we developed a prediction tool for signals within genomic sequences of plants that correspond to TISs. Our tool requires only genome sequence, not expressed sequences. Its sensitivity/specificity is for A.t. (90.75%/92.2%), for Vitis vinifera (66.8%/94.4%) and for Populus trichocarpa (81.6%/94.4%), which suggests that our tool can be used in annotation of different plant genomes. We provide a list of features used in our model. Further study of these features may improve our understanding of mechanisms of the translation initiation. Availability and implementation: Our tool is implemented as an artificial neural network. It is available as a web-based tool and, together with the source code, the list of features, and data used for model development, is accessible at http://cbrc.kaust.edu.sa/dts. Contact: vladimir.bajic@kaust.edu.sa Supplementary information: Supplementary data are available at Bioinformatics online.

• Publication year

  2012

• Venue

  Bioinform.

• Publication date

  2012-10-30

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1093/bioinformatics/bts638PMID 23110968PMCID 3530916
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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• “Artificial Intelligence – A Modern Approach”, Second Edition, Pearson Education, 2003.

  2015cited by this paper
• Highly scalable ab initio genomic motif identification

  2011cited by this paper
• The WEKA data mining software: an update

  2009cited by this paper
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  2008cited by this paper
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  2005cited by this paper
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  2005cited by this paper
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  2005cited by this paper
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  2004cited by this paper
• Data Mining Tools for Biological Sequences

  2003cited by this paper
• Starting the protein synthesis machine: eukaryotic translation initiation.

  2003cited by this paper
• A Method to Improve the Performance of Translation Start Site Detection and Its Application for Gene Finding

  2002influential reference
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  2002cited by this paper
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  2000cited by this paper
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  1997cited by this paper
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  1996cited by this paper
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  1994cited by this paper
• Context effects and inefficient initiation at non-AUG codons in eucaryotic cell-free translation systems

  1989cited by this paper
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  1971cited by this paper

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  2024influential citation
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