Expression divergence measured by transcriptome sequencing of four yeast species

BMC Genomics

Research Article

Michael P Stromberg¹  Gabor T Marth¹  Jeffrey H Chuang¹  Derek Barnett¹  Michele A Busby¹  Chip Stewart²  Allen M Costa³  Jesse M Gray⁴  Michael Springer⁵ 

[1] Department of Biology, Boston College, 140 Commonwealth Avenue, 02467-3961, Chestnut Hill, MA, USA;Department of Biology, Boston College, 140 Commonwealth Avenue, 02467-3961, Chestnut Hill, MA, USA;Broad Institute of Harvard and MIT, 7 Cambridge Center, 02142, Cambridge, MA, USA;Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, 02115, Boston, Massachusetts, USA;Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, 02115, Boston, Massachusetts, USA;Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, NRB 0356, 02115, Boston, MA, USA;Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, 02115, Boston, MA, USA;

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

DOI  :  10.1186/1471-2164-12-635

 received in 2011-05-11, accepted in 2011-12-29,  发布年份 2011

来源: Springer

【 摘 要 】

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

【 授权许可】

Unknown   
© Busby et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.