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In Vivo Monitoring of Transcriptional Activity During Metabolic Transition Using a Bioluminescent Reporter in Yeast
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Mobilomics in Saccharomyces cerevisiae strains.

Giulia Menconi1, Giovanni Battaglia, Roberto Grossi

  • 1Istituto Nazionale di Alta Matematica, Città Universitaria, Roma, Italia.

BMC Bioinformatics
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Summary

This study introduces regender, a fast algorithm and software tool for discovering mobile genetic elements (MGEs) by comparing closely related genomes. It efficiently identifies MGEs like Ty elements in yeast, even in low-coverage genomes.

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Evolution

Background:

  • Mobile Genetic Elements (MGEs) are DNA sequences that can move within a genome.
  • Current MGE detection relies on comparing sequences against known MGEs.
  • Discovering all MGEs and their dynamics requires analyzing large comparative genomics datasets, necessitating computational efficiency.

Purpose of the Study:

  • To develop a computational approach for discovering MGEs within genomes.
  • To alleviate the computational burden associated with comparative genomics for MGE identification.
  • To enable MGE discovery without requiring template sequences.

Main Methods:

  • Utilized a progressive comparative genomics strategy exploiting high similarity between homologous chromosomes of closely related strains.
  • Developed and applied a fast algorithm and software tool named regender to identify conserved regions between chromosomes.
  • Analyzed non-conserved regions to identify MGEs, specifically Ty retrotransposons, and other putative mobile elements (PMEs).

Main Results:

  • Regender efficiently compared 39 S. cerevisiae strains in minutes.
  • Successfully located known Ty elements and mapped putative Tys across all strains.
  • Identified PMEs as markers of inter-specific evolution and demonstrated their utility in inferring evolutionary relationships, similar to SNP-based phylogenies.

Conclusions:

  • The proposed methodology effectively identifies MGEs without needing template sequences.
  • Applicable to MGE inference in low-coverage genomes with unresolved bases, overcoming limitations of traditional methods.
  • Offers a significant advancement for mobilomics and understanding genome dynamics.