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Ribosome Profiling02:24

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
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Full-length ribosome density prediction by a multi-input and multi-output model.

Tingzhong Tian1, Shuya Li1, Peng Lang1

  • 1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.

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|March 26, 2021
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We developed RiboMIMO, a deep learning model that accurately predicts translation elongation dynamics across entire messenger RNA (mRNA) coding sequences (CDS). This approach reveals long-range codon impacts on protein synthesis, advancing our understanding of translation regulation.

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Area of Science:

  • Molecular Biology
  • Computational Biology
  • Genomics

Background:

  • Translation elongation is a complex biological process regulated by intricate mechanisms in prokaryotes and eukaryotes.
  • Ribosome profiling offers codon-resolution insights into translation but current computational models often overlook long-range codon interactions and the continuity of elongation.
  • Modeling translation elongation at the full-length coding sequence (CDS) level remains an underexplored area.

Purpose of the Study:

  • To develop a novel deep learning framework, RiboMIMO, for modeling ribosome density distributions across entire mRNA CDS regions.
  • To capture correlations between neighboring and distant codons influencing translation efficiency.
  • To identify key biological factors affecting translation elongation dynamics through interpretable analysis.

Main Methods:

  • Developed a multi-input, multi-output deep learning model (RiboMIMO) to analyze full-length CDS ribosome profiling data.
  • Incorporated analysis of correlations between codon translation efficiencies, considering both local and remote codon effects.
  • Utilized an interpretable metric, the codon impact score, to assess individual codon contributions to elongation rates.

Main Results:

  • RiboMIMO significantly outperforms existing methods in predicting ribosome density distributions along full-length mRNA CDS.
  • The model successfully identifies known patterns of translation efficiency and reveals novel long-range codon associations.
  • Demonstrated that codons distant from the ribosomal A site can influence translation elongation rate, a previously unreported finding.

Conclusions:

  • RiboMIMO provides a powerful and accurate tool for studying translation elongation regulation at the whole CDS level.
  • The discovery of long-range codon impacts offers new insights into the regulatory mechanisms governing protein synthesis.
  • This work advances computational approaches for analyzing ribosome profiling data and understanding gene expression regulation.