Penfold, Christopher A., Millar, Jonathan B. A. and Wild, David L. (2015) Inferring orthologous gene regulatory networks using interspecies data fusion. Bioinformatics, 31 (12). i97-i105. doi:10.1093/bioinformatics/btv267

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Abstract

MOTIVATION:

The ability to jointly learn gene regulatory networks (GRNs) in, or leverage GRNs between related species would allow the vast amount of legacy data obtained in model organisms to inform the GRNs of more complex, or economically or medically relevant counterparts. Examples include transferring information from Arabidopsis thaliana into related crop species for food security purposes, or from mice into humans for medical applications. Here we develop two related Bayesian approaches to network inference that allow GRNs to be jointly inferred in, or leveraged between, several related species: in one framework, network information is directly propagated between species; in the second hierarchical approach, network information is propagated via an unobserved 'hypernetwork'. In both frameworks, information about network similarity is captured via graph kernels, with the networks additionally informed by species-specific time series gene expression data, when available, using Gaussian processes to model the dynamics of gene expression.

RESULTS:

Results on in silico benchmarks demonstrate that joint inference, and leveraging of known networks between species, offers better accuracy than standalone inference. The direct propagation of network information via the non-hierarchical framework is more appropriate when there are relatively few species, while the hierarchical approach is better suited when there are many species. Both methods are robust to small amounts of mislabelling of orthologues. Finally, the use of Saccharomyces cerevisiae data and networks to inform inference of networks in the budding yeast Schizosaccharomyces pombe predicts a novel role in cell cycle regulation for Gas1 (SPAC19B12.02c), a 1,3-beta-glucanosyltransferase.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history
Divisions:	Faculty of Medicine > Warwick Medical School > Biomedical Sciences > Cell & Developmental Biology Faculty of Science > Centre for Systems Biology Faculty of Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH):	Gene regulatory networks -- Mathematical models, Species, Bayesian statistical decision theory, Gaussian processes
Journal or Publication Title:	Bioinformatics
Publisher:	Oxford University Press
ISSN:	1367-4803
Official Date:	2015
Dates:	Date Event 2015 Published
Date of first compliant deposit:	17 January 2017
Volume:	31
Number:	12
Page Range:	i97-i105
DOI:	10.1093/bioinformatics/btv267
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), Medical Research Council (Great Britain) (MRC)
Grant number:	EP/I036575/1, EP/K000128/1, EP/J020281/1 (EPSRC), BB/F005806/1 (BBSRC), MR/K001000/1 (MRC)

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