Study of rapidly evolving synthetic yeast genomes and strategies to assemble their genomes

  • Marco Monti

Student thesis: Phd

Abstract

Nearing its completion, the synthetic yeast genome (Sc2.0) project is currently the largest synthetic genomics project worldwide. The Sc2.0 genome was designed with extensive alterations, including incorporating symmetrical loxP sites (loxPsym) downstream of most non-essential genes. Upon induction of Cre-recombinase, loxPsym sites can recombine leading to stochastic large-scale genomic alterations, including deletions, inversions, duplications, and translocations. This process, termed SCRaMbLE, provides on-demand genome diversification and accelerates the evolution of synthetic yeast chromosomes towards the desired phenotype. However, it is necessary to resolve the heavily altered SCRaMbLEd genomes to gain mechanistic insights from linking phenotype to genotype. Indeed, the main limiting factors in a usual SCRaMbLE experiment are the screening capability of strains with desired phenotypes and the ability to solve the resulting complex SCRaMbLEd genomes. Therefore, this project aims to enhance SCRaMbLE applications by increasing our understanding of this synthetic evolutionary process and facilitating the screening and analysis of resulting SCRaMbLEd strains and genomes. This was achieved this by modelling the SCRaMbLE evolution process and generating a software to simulate this evolution, which can help predict SCRaMbLE experiment genotypes outcomes. By developing NanoSeqPipe, a whole-genome nanopore sequencing pipeline to characterise the genome of 24 yeast strains simultaneously. By creating SAiLoR, a software to assemble highly repetitive and complex genomes such as SCRaMbLEd ones. SAiLoR could solve > 98 % of simulated SCRaMbLEd chromosomes, outperforming established assemblers regarding correct solutions and computational time. Finally, it was created an automation high-throughput pipeline to generate and screen many SCRaMbLEd strains in different conditions. Altogether these results should facilitate future SCRaMbLE experiments by making the screening and analysis of SCRaMbLEd strains easier, quicker and more accurate.
Date of Award1 Aug 2023
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorMark Ashe (Supervisor) & Patrick Cai (Supervisor)

Keywords

  • Bioinformatics
  • Synthetic Yeast Genome
  • Sc2.0
  • Yeast
  • DNA sequencing
  • Genome Assembly
  • Genome Evolution
  • Synthetic Biology

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