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Point-Wise Ribosome Translation Speed Prediction with Recurrent Neural Networks

Author

Listed:
  • Pietro Bongini

    (Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy)

  • Niccolò Pancino

    (Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy)

  • Veronica Lachi

    (Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy)

  • Caterina Graziani

    (Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy)

  • Giorgia Giacomini

    (IRCCS Ospedale San Raffaele, Via Olgettina 60, 20132 Milano, Italy)

  • Paolo Andreini

    (Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy)

  • Monica Bianchini

    (Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy)

Abstract

Escherichia coli is a benchmark organism, which has been deeply studied by the scientific community for decades, obtaining a vast amount of metabolic and genetic data. Among these data, estimates of the translation speed of ribosomes over their genome are available. These estimates are based on Ribo-Seq profiles, where the abundance of a particular fragment of mRNA in a profile indicates that it was sampled many times inside a cell. Various measurements of Ribo-Seq profiles are available for Escherichia coli , yet they do not always show a high degree of correspondence, which means that they can vary significantly in different experimental setups, being characterized by poor reproducibility. Indeed, within Ribo-Seq profiles, the translation speed for some sequences is easier to estimate, while for others, an uneven distribution of consensus among the different estimates is evidenced. Our goal is to develop an artificial intelligence method that can be trained on a small pool of highly reproducible sequences to establish their translation rate, which can then be exploited to calculate a more reliable estimate of the translation speed on the rest of the genome.

Suggested Citation

  • Pietro Bongini & Niccolò Pancino & Veronica Lachi & Caterina Graziani & Giorgia Giacomini & Paolo Andreini & Monica Bianchini, 2024. "Point-Wise Ribosome Translation Speed Prediction with Recurrent Neural Networks," Mathematics, MDPI, vol. 12(3), pages 1-12, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:465-:d:1330766
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    References listed on IDEAS

    as
    1. Gene-Wei Li & Eugene Oh & Jonathan S. Weissman, 2012. "The anti-Shine–Dalgarno sequence drives translational pausing and codon choice in bacteria," Nature, Nature, vol. 484(7395), pages 538-541, April.
    2. Stuart K. Archer & Nikolay E. Shirokikh & Traude H. Beilharz & Thomas Preiss, 2016. "Dynamics of ribosome scanning and recycling revealed by translation complex profiling," Nature, Nature, vol. 535(7613), pages 570-574, July.
    Full references (including those not matched with items on IDEAS)

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