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A novel method for cross-species gene expression analysis

Erik Kristiansson (Institutionen för matematiska vetenskaper, matematisk statistik) ; Tobias Österlund (Institutionen för kemi- och bioteknik, Systembiologi) ; Lina-Maria Gunnarsson ; Gabriella Arne ; D. G. Joakim Larsson ; Olle Nerman (Institutionen för matematiska vetenskaper, matematisk statistik)
BMC Bioinformatics (1471-2105). Vol. 14 (2013), p. artikel nr 70.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Gene expression, Evolution, Meta-analysis, Orthologs, Paralogs, Microarray, RNA-seq

Denna post skapades 2013-06-17. Senast ändrad 2017-09-14.
CPL Pubid: 178615


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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)
Institutionen för kemi- och bioteknik, Systembiologi (2008-2014)
Institutionen för neurovetenskap och fysiologi, sektionen för fysiologi (GU)
Institutionen för biomedicin, avdelningen för patologi (GU)
Institutionen för biomedicin, avdelningen för infektionssjukdomar (GU)


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