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An Always Correlated gene expression landscape for ovine skeletal muscle, lessons learnt from comparison with an “equivalent” bovine landscape

Wei Sun12, Nicholas J Hudson23, Antonio Reverter23, Ashley J Waardenberg2, Ross L Tellam3, Tony Vuocolo3, Keren Byrne3 and Brian P Dalrymple23*

Author affiliations

1 Animal Science and Technology College, Yangzhou University, Yangzhou, 225009, China

2 Food Futures Flagship, 306 Carmody Rd., St. Lucia, Brisbane, Queensland, 4067, Australia

3 Livestock Industries, Commonwealth Scientific and Industrial Research Organisation, Queensland Bioscience Precinct, 306 Carmody Rd., St. Lucia, Brisbane, Queensland, 4067, Australia

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Citation and License

BMC Research Notes 2012, 5:632  doi:10.1186/1756-0500-5-632

Published: 13 November 2012

Abstract

Background

We have recently described a method for the construction of an informative gene expression correlation landscape for a single tissue, longissimus muscle (LM) of cattle, using a small number (less than a hundred) of diverse samples. Does this approach facilitate interspecies comparison of networks?

Findings

Using gene expression datasets from LM samples from a single postnatal time point for high and low muscling sheep, and from a developmental time course (prenatal to postnatal) for normal sheep and sheep exhibiting the Callipyge muscling phenotype gene expression correlations were calculated across subsets of the data comparable to the bovine analysis. An “Always Correlated” gene expression landscape was constructed by integrating the correlations from the subsets of data and was compared to the equivalent landscape for bovine LM muscle. Whilst at the high level apparently equivalent modules were identified in the two species, at the detailed level overlap between genes in the equivalent modules was limited and generally not significant. Indeed, only 395 genes and 18 edges were in common between the two landscapes.

Conclusions

Since it is unlikely that the equivalent muscles of two closely related species are as different as this analysis suggests, within tissue gene expression correlations appear to be very sensitive to the samples chosen for their construction, compounded by the different platforms used. Thus users need to be very cautious in interpretation of the differences. In future experiments, attention will be required to ensure equivalent experimental designs and use cross-species gene expression platform to enable the identification of true differences between different species.