Temporal Graphical Models for Cross-Species Gene Regulatory Network Discovery

Liu, Yan; Niculescu-Mizil, Alexandru; Lozano, Aurélie; Lu, Yong

doi:10.1142/s0219720011005525

Cited by 10 publications

(9 citation statements)

References 39 publications

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“…Time series data generated in the two species could therefore be informative of one another. In these cases, it might be of interest to identify the similarities and differences in the GRNs of the two species (Baumbach et al ., 2009; Liu et al ., 2011). Such an approach could prove immensely useful for leveraging information from simpler, but better understood model systems, into more complex organisms, allowing for useful biological insights, such as addressing why some plants are capable of nodulation while others are not (see e.g.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

et al. 2012

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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.Contact: d.l.wild@warwick.ac.ukSupplementary Information: Supplementary data is available for this article.

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Section: Introductionmentioning

confidence: 99%

Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

et al. 2012

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“…In addition to performing well between these extremes, our method allows pooling of data even when there is incomplete orthology. By introducing constraints on the similarity of individual interactions, rather than on the networks as a whole [38], we can pool some information across species even when a small fraction of genes have orthologs.…”

Section: Fused Gene Regulatory Networkmentioning

confidence: 99%

Fused regression for multi-source gene regulatory network inference

Lam¹,

Westrick²,

Müller

et al. 2016

Preprint

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Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

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“…More generally, for different species containing similar network structures and regulators [77], one can integrate data on similar organisms to generalise certain fundemental biological process.…”

Section: Data Integrationmentioning

confidence: 99%