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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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Predicting functional upstream open reading frames in Saccharomyces cerevisiae.

Selpi1, Christopher H Bryant, Graham J L Kemp

  • 1Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden. selpi@chalmers.se

BMC Bioinformatics
|January 1, 2010
PubMed
Summary

This study introduces a computational method to predict functional upstream open reading frames (uORFs) in yeast. The approach identifies 398 novel uORFs, offering candidates for experimental verification to understand gene regulation.

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Area of Science:

  • Molecular Biology
  • Computational Biology
  • Genetics

Background:

  • Upstream open reading frames (uORFs) are functional elements regulating gene expression and organism health.
  • The precise mechanisms of uORF-mediated gene regulation remain incompletely understood.
  • Experimental verification of functional uORFs is costly and time-consuming, necessitating predictive approaches.

Purpose of the Study:

  • To develop and present a novel computational approach for predicting functional uORFs in Saccharomyces cerevisiae.
  • To identify a comprehensive set of candidate functional uORFs for future experimental validation.
  • To contribute to a deeper understanding of uORF roles in gene regulation.

Main Methods:

  • Utilized inductive logic programming for predicting functional uORFs.
  • Integrated biological conservation, Gene Ontology annotations, and gene expression data under various conditions.
  • Developed a predictive model yielding simple and informative hypotheses.

Main Results:

  • The computational method achieved an estimated sensitivity of 76% in predicting functional uORFs.
  • Identified 398 novel functional uORFs across 301 genes in yeast.
  • Highlighted 11 specific uORFs as strong candidates for experimental validation based on comparison with related studies.

Conclusions:

  • Learning-based prediction of functional uORFs is feasible with high sensitivity.
  • The predicted functional uORFs provide a valuable resource for guiding wet-lab verification efforts.
  • This work aids in elucidating the regulatory functions of uORFs in gene expression.