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A novel method for cross-species gene expression analysis

Journal article
Authors Erik Kristiansson
Tobias Österlund
Lina-Maria Gunnarsson
Gabriella Arne
D. G. Joakim Larsson
Olle Nerman
Published in BMC Bioinformatics
Volume 14
Pages artikel nr 70
ISSN 1471-2105
Publication year 2013
Published at Institute of Neuroscience and Physiology, Department of Physiology
Department of Mathematical Sciences, Mathematical Statistics
Institute of Biomedicine, Department of Pathology
Institute of Biomedicine, Department of Infectious Medicine
Pages artikel nr 70
Language en
Links dx.doi.org/10.1186/1471-2105-14-70
https://gup.ub.gu.se/file/106439
Keywords Gene expression, Evolution, Meta-analysis, Orthologs, Paralogs, Microarray, RNA-seq
Subject categories Other Medical Sciences

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.

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