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Predicting transfer RNA gene activity from sequence and genome context.

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  • 1Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA.

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Transfer RNA (tRNA) gene expression is complex. Guanine + cytosine content and CpG density near tRNA genes correlate with their activity, aiding in understanding gene regulation and evolution.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Evolutionary Biology

Background:

  • Transfer RNA (tRNA) genes are crucial for protein synthesis and highly transcribed.
  • Understanding tRNA gene expression regulation and evolution is challenging due to expression variability and sequence similarity.
  • Existing methods for assessing tRNA gene activity are limited.

Purpose of the Study:

  • To establish sequence-based correlates for tRNA gene expression.
  • To develop a robust tRNA gene classification method.
  • To investigate the evolutionary rates of tRNA gene functional changes.

Main Methods:

  • Developed a classification method using sequence-based correlates, independent of comparative genomics.
  • Analyzed guanine + cytosine (G + C) content and CpG density around tRNA loci.
  • Utilized predictions with ortholog sets across 29 placental mammals to estimate evolutionary rates.

Main Results:

  • Identified strong correlations between local genomic context (G+C content, CpG density) and tRNA gene activity.
  • Achieved classification accuracy comparable to molecular assays without direct measurement.
  • Estimated evolutionary rates of functional changes among tRNA gene orthologs.

Conclusions:

  • Local genomic context plays a significant role in regulating tRNA gene expression.
  • The developed method enhances large-scale tRNA functional prediction and prioritization of variants.
  • The approach is adaptable for other gene families and evolutionary studies.