Expression divergence measured by transcriptome sequencing of four yeast species

The evolution of gene expression is a challenging problem in evolutionary biology, for which accurate, well-calibrated measurements and methods are crucial. We quantified gene expression with whole-transcriptome sequencing in four diploid, prototrophic strains of Saccharomyces species grown under th...

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Bibliographic Details
Published in:BMC genomics 2011-12, Vol.12 (1), p.635-635, Article 635
Main Authors: Busby, Michele A, Gray, Jesse M, Costa, Allen M, Stewart, Chip, Stromberg, Michael P, Barnett, Derek, Chuang, Jeffrey H, Springer, Michael, Marth, Gabor T
Format: Article
Language:English
Subjects:
Comparative transcriptomics
Confidence intervals
Data processing
Diploids
DNA sequencing
Evolution
Gene expression
Genetic aspects
Genetic transcription
Genetics
Genomes
Genomics
Methods
Neurosciences
Nucleotide sequencing
Polymerase Chain Reaction
RNA
RNA-Seq
S. bayanus
S. cerevisiae
S. mikatae
S. paradoxus
Saccharomyces
Saccharomyces - classification
Saccharomyces - genetics
Sampling
Species Specificity
Statistics
Transcriptome
Yeast fungi
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Summary:The evolution of gene expression is a challenging problem in evolutionary biology, for which accurate, well-calibrated measurements and methods are crucial. We quantified gene expression with whole-transcriptome sequencing in four diploid, prototrophic strains of Saccharomyces species grown under the same condition to investigate the evolution of gene expression. We found that variation in expression is gene-dependent with large variations in each gene's expression between replicates of the same species. This confounds the identification of genes differentially expressed across species. To address this, we developed a statistical approach to establish significance bounds for inter-species differential expression in RNA-Seq data based on the variance measured across biological replicates. This metric estimates the combined effects of technical and environmental variance, as well as Poisson sampling noise by isolating each component. Despite a paucity of large expression changes, we found a strong correlation between the variance of gene expression change and species divergence (R² = 0.90). We provide an improved methodology for measuring gene expression changes in evolutionary diverged species using RNA Seq, where experimental artifacts can mimic evolutionary effects.GEO Accession Number: GSE32679.
ISSN:1471-2164
1471-2164
DOI:10.1186/1471-2164-12-635