A Robust Computational Framework to Characterize the Genetic Diversity Across 3,570 Strains in Saccharomyces cerevisiae
Résumé
Saccharomyces cerevisiae exhibits remarkable genetic diversity, with many strains sampled from diverse ecological and geographical origins. Over the years, thousands of genome sequences have been released across numerous studies, and this dataset continues to grow. This evergrowing genomic dataset calls for a scalable, and robust computational framework to systematically explore the species' genetic diversity, providing an objective and homogeneous view at the population level. To investigate these mechanisms, we developed an integrated computational framework to systematically analyze the genetic diversity of Saccharomyces cerevisiae. This framework consists of: (1) a scalable and HPC-optimized Snakemake pipeline for variant calling, using the GATK toolkit and designed to run efficiently on high-performance computing clusters (via SLURM), (2) an objective and reproducible classification scheme allowing to define populations based on the inference of their genetic ancestries using the sNMF method, (3) a general ploidy and aneuploidy prediction method leveraging allele balance ratios, offering a more straightforward and faster alternative to existing techniques, and (4) a robust framework to quantify heterozygosity without relying on arbitrary thresholds. This framework is designed to be both accessible and scalable, enabling researchers to generate VCF files from ENA IDs and conduct population structure, ploidy, and heterozygosity analyses with minimal bioinformatics expertise. This framework was applied to a dataset of 3,570 genome sequences, representing an unprecedented effort to compile and standardize Saccharomyces cerevisiae genomic diversity. A key component of this effort is a standardized metadata table compiling key ecological, geographical, and sequencing information from 3,570 strains. While our analyses on population structure, admixture, ploidy, and heterozygosity are still ongoing, preliminary results suggest that we are approaching a near-complete representation of the species' genetic diversity, as indicated by rarefaction curve analyses. This dataset, combined with our integrated computational approach, provides a robust foundation for future investigations into the evolutionary forces shaping yeast population structure, adaptation, and genome dynamics.
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