Penfold, Christopher A., Buchanan-Wollaston, Vicky, Denby, Katherine J. and Wild, David L.. (2012) Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks. Bioinformatics, Vol.28 (No.12). i233-i241. ISSN 1367-4803

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Abstract

Motivation: The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions; that is, where switching events could potentially arise under the different treatments and (ii) for inference in evolutionary related species in which orthologous GRNs exist. More generally, the method can be used to identify context-specific regulation by leveraging time series gene expression data alongside methods that can identify putative lists of transcription factors or transcription factor targets. Results: The hierarchical inference outperforms related (but non-hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent. The method was subsequently used alongside yeast one hybrid and microarray time series data to infer potential transcriptional switches in Arabidopsis thaliana response to stress. The results confirm previous biological studies and allow for additional insights into gene regulation under various abiotic stresses. Availability: The methods outlined in this article have been implemented in Matlab and are available on request.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Science > Life Sciences (2010- ) Faculty of Science > Centre for Systems Biology
Library of Congress Subject Headings (LCSH):	Genetic regulation -- Statistical methods, Time-series analysis, Bayesian statistical decision theory
Journal or Publication Title:	Bioinformatics
Publisher:	Oxford University Press
ISSN:	1367-4803
Date:	2012
Volume:	Vol.28
Number:	No.12
Page Range:	i233-i241
Identification Number:	10.1093/bioinformatics/bts222
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), Engineering and Physical Sciences Research Council (EPSRC)
Grant number:	BB/F005806/1 (BBSRC), EP/I036575/1 (EPSRC)
References:	Äijö,T. and Lähdesmäki,H. (2009) Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics, 25, 2937–2944. Baumbach,J. et al. (2009) Reliable transfer of transcriptional gene regulatory networks between taxanomically related organisms. BMC Bioinformatics, 3. Breeze,E. et al. (2011) High resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation. Plant Cell, 23, 873–894. Bulyk,M.L. (2005) Discovering DNA regulatory elements with bacteria. Nat. Biotechnol., 23, 942–944. Cooke,E. et al. (2009) Computational approaches to the integration of gene expression, chip-chip and sequence data in the inference of gene regulatory networks. Semin. Cell Dev. Biol., 20, 863–868. Goda,H. et al. (2011) The atgenexpress hormone and chemical treatment data set: experimental design, data evaluation, model data analysis and data access. Plant J., 55, 526–542. Kilian,J. et al. (2007) The atgenexpress global stress expression data set: protocols, evaluation and model data analysis of uv-b light, drought and cold stress responses. Plant J., 50, 347–363. Kimbrough,J.M. et al. (2004) The fast and transient transcriptional network of gravity and mechanical stimulation in the arabidopsis root apex. Plant Physiol., 136, 2790– 2805. Klemm,S.L. (2008) ‘Causal Structure Identification in Nonlinear Dynamical Systems’. Master’s thesis, Department of Engineering, University of Cambridge, UK. Liu,Y. et al. (2011) Temporal graphical models for cross-species gene regulatory network discovery. J. Bioinform. Comput. Biol., 9, 231–250. Lopato,S. et al. (2006) Isolation of plant transcription factors using a modified yeast one-hybrid system. Plant Methods, 2. Marbach,D. et al. (2009) Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J. Comput. Biol, 16, 229–239. Marbach,D. et al. (2010) Revealing strengths and weaknesses of methods for gene network inference. Proc. Natl. Acad. Sci. USA, 107, 6286–6291. Matys,V. et al. (2003) TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acid Res., 31, 374–378. Mitsuda,N. et al. (2010) Efficient yeast one-/two-hybrid screening using a library composed only of transcription factors in Arabidopsis thaliana. Plant Cell Physiol, 51, 2145–2151. Ou,B. et al. (2011) A high-throughput screening system for arabidopsis transcription factors and its application to med25-dependent transcriptional regulation. Mol. Plant, 4, 546–555. Park,P.J. (2009) Chip-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet., 10, 669–680. Penfold,C.A. and Wild,D.L. (2011) How to infer gene networks from expression profiles, revisited. J. R. Soc. Interface Focus, 6, 857–870. Prill,R.J. et al. (2010) Towards a rigorous assessment of systems biology models: The DREAM3 challenges. PLoS One, 5, e9202. Rasmussen,C. and Williams,C.K.I. (2006). Gaussian processes for Machine Learning, 2nd edn. MIT Press, Cambridge, USA. Roth,F.P. et al. (1998) Finding dna regulatory motifs within unaligned noncoding sequences clustered by whole-genome mrna quantitation. Nat. Biotechnol., 16, 939–945. Stegle,O. et al. (2010) A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series. J. Comput. Biol., 17, 355–367. Tsutsui,T. et al. (2009) DEAR1, a transcriptional repressor of DREB protein that mediates plant defense and freezing stress responses in Arabidopsis. J. Plant Res., 122, 633–643. Werhli,A.V. and Husmeier,D. (2007) Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Stat. Appl. Genet. Molec. Biol., 6, Article 15. Werhli,A.V. and Husmeier,D. (2008) Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions. J. Bioinform. Comput. Biol., 6, 543–572. Yamaguchi-Shinozaki,K. and Shinozaki,K. (1994) A novel cis-acting element in an arabidopsis gene is involved in responsiveness to drought, low-temperature, or highsalt stress. Plant Cell, 6, 251–264.
URI:	http://wrap.warwick.ac.uk/id/eprint/48097

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