Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Highly Accessed Open Badges Methodology article

Whole-genome sequencing of a laboratory-evolved yeast strain

Carlos L Araya1, Celia Payen1, Maitreya J Dunham1 and Stanley Fields123*

Author affiliations

1 Department of Genome Sciences, University of Washington, Box 355065, Seattle, Washington, 98195 USA

2 Department of Medicine, University of Washington, Box 355065, Seattle, Washington, 98195 USA

3 Howard Hughes Medical Institute, University of Washington, Box 355065, Seattle, Washington, 98195 USA

For all author emails, please log on.

Citation and License

BMC Genomics 2010, 11:88  doi:10.1186/1471-2164-11-88

Published: 3 February 2010



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.


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.


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.