ArticleBioinformatics (Oxford, England)2012
Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Expression of the Arabidopsis redox-related LEA protein, SAG21 is regulated by ERF, NAC and WRKY transcription factors.Scientific reports · 2024Article
- Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods.Computational and structural biotechnology journal · 2023Review
- Exact Bayesian inference for the detection of graft-mobile transcripts from sequencing data.Journal of the Royal Society, Interface · 2022Article
- Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling.Frontiers in genetics · 2020Review
- GPseudoRank: a permutation sampler for single cell orderings.Bioinformatics (Oxford, England) · 2019Article
- Branch-recombinant Gaussian processes for analysis of perturbations in biological time series.Bioinformatics (Oxford, England) · 2018Article
- Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Coordination of meristem and boundary functions by transcription factors in the SHOOT MERISTEMLESS regulatory network.Development (Cambridge, England) · 2018Article
- Approximate inference of gene regulatory network models from RNA-Seq time series data.BMC bioinformatics · 2018Article
- Bringing numerous methods for expression and promoter analysis to a public cloud computing service.Bioinformatics (Oxford, England) · 2018Article
- Improving Gene Regulatory Network Inference by Incorporating Rates of Transcriptional Changes.Scientific reports · 2017Article
- Inferring transcriptional logic from multiple dynamic experiments.Bioinformatics (Oxford, England) · 2017Article
- Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.Oncotarget · 2017Article
- The Local Edge Machine: inference of dynamic models of gene regulation.Genome biology · 2016Article
- Inferring orthologous gene regulatory networks using interspecies data fusion.Bioinformatics (Oxford, England) · 2015Article
- A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data.PloS one · 2015Article
- Topological sensitivity analysis for systems biology.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines.BMC systems biology · 2014Article
- Improving crop disease resistance: lessons from research on Arabidopsis and tomato.Frontiers in plant science · 2014Review
- A local regulatory network around three NAC transcription factors in stress responses and senescence in Arabidopsis leaves.The Plant journal : for cell and molecular biology · 2013Article
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Authors and funding
4 authors.
Funding
Abstract
motivationThe 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.
resultsThe 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.
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