A novel method for cross-species gene expression analysis
Journal article, 2013

Background Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex. Results In this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified. Conclusions The method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/ webcite.




Gene expression





Erik Kristiansson

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Tobias Österlund

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Lina-Maria Gunnarsson

University of Gothenburg

Gabriella Arne

University of Gothenburg

D. G. Joakim Larsson

University of Gothenburg

Olle Nerman

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

BMC Bioinformatics

1471-2105 (ISSN)

Vol. 14 1 artikel nr 70- 70

Subject Categories

Other Medical and Health Sciences



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