Literature DB >> 9783203

The ribosome scanning model for translation initiation: implications for gene prediction and full-length cDNA detection.

P Agarwal1, V Bafna.   

Abstract

Biological signals, such as the start of protein translation in eukaryotic mRNA, are stretches of nucleotides recognized by cellular machinery. There are a variety of techniques for modeling and identifying them. Most of these techniques either assume that the base pairs at each position of the signal are independently distributed, or they allow for limited dependencies among different positions. In previous work, we provided a statistical model that generalizes earlier methods and captures all significant high-order dependencies among different base positions. In this paper, we use a set of experimentally verified translation initiation (TI) sites (provided by Amos Bairoch) from eukaryotic sequences to train a range of methods, and then compare these methods. None of the methods is effective in predicting TI sites. We take advantage of the ribosome scanning model (Cigan et al., 1988) to significantly improve the prediction accuracy for full-length mRNAs. The ribosome scanning model suggests scanning from the 5' end of the capped mRNA and initiating translation at the first AUG in good context. This reduces the search space dramatically and accounts for its effectiveness. The success of this approach illustrates how biological ideas can illuminate and help solve challenging problems in computational biology.

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Year:  1998        PMID: 9783203

Source DB:  PubMed          Journal:  Proc Int Conf Intell Syst Mol Biol        ISSN: 1553-0833


  2 in total

1.  Computational analysis and mapping of novel open reading frames in influenza A viruses.

Authors:  Yu-Nong Gong; Guang-Wu Chen; Chi-Jene Chen; Rei-Lin Kuo; Shin-Ru Shih
Journal:  PLoS One       Date:  2014-12-15       Impact factor: 3.240

2.  A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts.

Authors:  Hugo W Schneider; Taina Raiol; Marcelo M Brigido; Maria Emilia M T Walter; Peter F Stadler
Journal:  BMC Genomics       Date:  2017-10-18       Impact factor: 3.969

  2 in total

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