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Whole-genome sequencing of a laboratory-evolved yeast strain

BACKGROUND: Experimental evolution of microbial populations provides a unique opportunity to study evolutionary adaptation in response to controlled selective pressures. However, until recently it has been difficult to identify the precise genetic changes underlying adaptation at a genome-wide scale...

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Detalles Bibliográficos
Autores principales: Araya, Carlos L, Payen, Celia, Dunham, Maitreya J, Fields, Stanley
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2829512/
https://www.ncbi.nlm.nih.gov/pubmed/20128923
http://dx.doi.org/10.1186/1471-2164-11-88
Descripción
Sumario:BACKGROUND: Experimental evolution of microbial populations provides a unique opportunity to study evolutionary adaptation in response to controlled selective pressures. However, until recently it has been difficult to identify the precise genetic changes underlying adaptation at a genome-wide scale. New DNA sequencing technologies now allow the genome of parental and evolved strains of microorganisms to be rapidly determined. RESULTS: We sequenced >93.5% of the genome of a laboratory-evolved strain of the yeast Saccharomyces cerevisiae and its ancestor at >28× depth. Both single nucleotide polymorphisms and copy number amplifications were found, with specific gains over array-based methodologies previously used to analyze these genomes. Applying a segmentation algorithm to quantify structural changes, we determined the approximate genomic boundaries of a 5× gene amplification. These boundaries guided the recovery of breakpoint sequences, which provide insights into the nature of a complex genomic rearrangement. CONCLUSIONS: This study suggests that whole-genome sequencing can provide a rapid approach to uncover the genetic basis of evolutionary adaptations, with further applications in the study of laboratory selections and mutagenesis screens. In addition, we show how single-end, short read sequencing data can provide detailed information about structural rearrangements, and generate predictions about the genomic features and processes that underlie genome plasticity.