Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

A novel method for cross-species gene expression analysis

Erik Kristiansson1*, Tobias Österlund2, Lina Gunnarsson3, Gabriella Arne4, D G Joakim Larsson5 and Olle Nerman1

Author Affiliations

1 Department of Mathematical Statistics, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden

2 Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden

3 Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden

4 Sahlgrenska Cancer Center, Department of Pathology, Sahlgrenska Academy at The University of Gothenburg, Gothenburg, Sweden

5 Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden

For all author emails, please log on.

BMC Bioinformatics 2013, 14:70  doi:10.1186/1471-2105-14-70

Published: 27 February 2013

Abstract

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.

Keywords:
Gene expression; Evolution; Meta-analysis; Orthologs; Paralogs; Microarray; RNA-seq