Variability in gene expression is important for creating differences in populations of genetically identical organisms, helps populations survive changes in the environment, and allows infective yeasts to adapt to a host’s immune status more easily. This variability can be due to intrinsic stochastic differences between identical cells grown under the same conditions. Small extrinsic differences in environmental conditions can also result in variation in gene expression between genetically identical cells. We sought to leverage existing S. cerevisiae expression data to identify genes with consistently high variability in their expression levels. Methods used to extract expression data can create technical variation, which may make it difficult to identify actual biologically relevant results. The presence of cell walls in many organisms, including yeast, has limited the amount of single-cell gene expression data. Because of this, our analyses were conducted on Affymetrix microarray and RNA-seq expression datasets, comprised of non-single cell studies done under 240 unique growth conditions. We identified 22 genes with consistently high variation in their expression in S. cerevisiae. Gene Ontology analysis using goseq indicated a significant biological relatedness between the genes, finding that 10 out of the 22 genes are involved in ribosome biogenesis. These results suggest that ribosome biogenesis genes have evolved to have high variability in their expression, and the results are not due to technical variation. Transcription factors that regulate these 22 genes were identified to better understand if they played a role in variation. Analyses found 15 transcription factors that are used significantly more by the genes with high expression variation in comparison to the genome. These transcription factors emphasize the biological relatedness of the genes and may play a role in the variation itself. These findings offer the first step in characterizing genes with high variation in their expression, helping to gain a greater understanding of the importance of variability in gene expression.
