Open Access Highly Accessed Research article

Expression divergence measured by transcriptome sequencing of four yeast species

Michele A Busby1, Jesse M Gray24, Allen M Costa2, Chip Stewart15, Michael P Stromberg1, Derek Barnett1, Jeffrey H Chuang1, Michael Springer3 and Gabor T Marth1*

Author Affiliations

1 Department of Biology, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467-3961, USA

2 Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, Massachusetts 02115, USA

3 Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA

4 Department of Genetics, Harvard Medical School 77 Avenue Louis Pasteur, NRB 0356, Boston, MA 02115, USA

5 Broad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge, MA 02142

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BMC Genomics 2011, 12:635  doi:10.1186/1471-2164-12-635

Published: 29 December 2011

Abstract

Background

The evolution of gene expression is a challenging problem in evolutionary biology, for which accurate, well-calibrated measurements and methods are crucial.

Results

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).

Conclusion

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

Keywords:
RNA-Seq; Comparative transcriptomics; S. cerevisiae; S. paradoxus; S. mikatae; S. bayanus