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Deep learning models reveal degenerate polyadenylation signals in yeast mRNA maturation. These models uncover distinct regulatory elements in Saccharomyces cerevisiae and Schizosaccharomyces pombe, improving understanding of cleavage and polyadenylation.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • 3'-end cleavage and polyadenylation is crucial for eukaryotic mRNA maturation.
  • Yeast polyadenylation signals are degenerate and poorly understood compared to mammalian systems.

Purpose of the Study:

  • To develop deep learning models for identifying degenerate cis-regulatory elements in yeast polyadenylation.
  • To quantify the positional importance of these elements in poly(A) site formation, cleavage heterogeneity, and strength.

Main Methods:

  • Application of deep learning models to analyze yeast polyadenylation signals.
  • Deconvolution of degenerate cis-regulatory elements.
  • Quantification of positional importance for poly(A) site characteristics.

Main Results:

  • In S. cerevisiae, U-rich element depletion and upstream UA-rich elements promote cleavage heterogeneity, correlating with lower poly(A) site strength.
  • Site strength and tandem site distances influence alternative polyadenylation under diauxic stress.
  • Distinct motif configurations in S. pombe poly(A) sites result in more precise cleavage than in S. cerevisiae.

Conclusions:

  • Deep learning models offer novel insights into yeast poly(A) site formation.
  • Significant divergence in poly(A) signals exists between distantly related yeast species.