Fast Bayesian inference for gene regulatory networks using ScanBMA

Young, William Chad; Raftery, Adrian E.; Yeung, Ka Yee

doi:10.1186/1752-0509-8-47

Cited by 76 publications

(94 citation statements)

References 47 publications

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“…We performed a multistep validation of our pipeline by testing first, GENIST's DBN inference step, and next, the incorporation of clustering into the algorithm, as the inference step and its integration with clustering had never been tested. Accordingly, we tested GENIST's inference step with in silico time-series datasets (DREAM 4 challenge 2) (22-24) and found that, in terms of precision and area under the precision recall curve (AUPRC), our algorithm outperformed previously published methods [ebdbnet (25), ScanBMA (26), ARACNE (27), CLR (28) . Furthermore, we validated the integration of the clustering and inference steps by testing the performance of both GENIST's inference step alone, as well as together with clustering of coexpressed genes across distinct cell types.…”

Section: Resultsmentioning

confidence: 99%

Predicting gene regulatory networks by combining spatial and temporal gene expression data inArabidopsisroot stem cells

Balaguer

Fisher

Clark

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is essential for understanding the initiation and growth of plant tissues and organs. Although many TFs have been shown to have a role in the Arabidopsis root stem cells, a comprehensive view of the transcriptional signature of the stem cells is lacking. In this work, we used spatial and temporal transcriptomic data to predict interactions among the genes involved in stem cell regulation. To accomplish this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network inference algorithm that combines clustering with dynamic Bayesian network inference. We leveraged the topology of our networks to infer potential major regulators. Specifically, through mathematical modeling and experimental validation, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center function. The results presented in this work show that our combination of molecular biology, computational biology, and mathematical modeling is an efficient approach to identify candidate factors that function in the stem cells.root stem cell | root development | cell-type expression profile | gene regulatory network | modeling I dentifying the transcriptional signature underlying stem cell regulation is fundamental to understanding the initiation and growth of plant tissues and organs. The Arabidopsis thaliana root provides a tractable system to study stem cells since they are spatially confined at the tip of the root, in the stem cell niche (SCN), and are anatomically well characterized. The SCN contains several stem cell populations that include the cortexendodermis initials (CEIs), vascular initials [including phloem and xylem (XYL)], columella initials, and epidermal/lateral root cap initials. These stem cell populations divide asymmetrically to replenish the stem cell and produce a daughter cell that later differentiates into the different tissues of the root. In the center of all of these stem cell populations is the quiescent center (QC), which acts as the organizing center and maintains the surrounding stem cells in an undifferentiated state (1). Major players in stemcell regulation have been previously identified, such as BABY BOOM (BBM) and PLETHORA1-3 (PLT1, PLT2, PLT3/AIL6), which are important for proper root formation and maintenance (2). Additionally in the QC, the homeodomain transcription factor WUSCHEL-RELATED HOMEOBOX 5 acts noncell autonomously to maintain the columella stem cells (3-5). In the endodermal and cortical layers, the GRAS family transcription factors SHORTROOT (SHR) and SCARECROW (SCR) activate the transcription of the cell-cycle gene CYCLIN D6 (CYCD6) to trigger the asymmetric cell division of the CEI (6, 7). Additional TFs, such as REVOLUTA (REV) and PHABULOSA (PHB), have been shown to regulate tissue specification and differentiation in the vascular stem cells (8-11). Despite these findings, a transcriptional signature within and across each of these diff...

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Section: Resultsmentioning

confidence: 99%

Predicting gene regulatory networks by combining spatial and temporal gene expression data inArabidopsisroot stem cells

Balaguer

Fisher

Clark

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Then, model-based methods use the learned parameters (coefficients) of regulators as regulatory interaction scores. Ridge regression, LASSO and Bayesian Model Averaging (BMA) are some of the representative methods of model-based methods [14, 15, 18, 19]. BGRMI, a recently developed GRN inference method, computes regulatory interaction scores using posterior probabilities obtained by BMA [18].…”

Section: Introductionmentioning

confidence: 99%

BTNET : boosted tree based gene regulatory network inference algorithm using time-course measurement data

Park

Kim²,

Shin

et al. 2018

BMC Syst Biol

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BackgroundIdentifying gene regulatory networks is an important task for understanding biological systems. Time-course measurement data became a valuable resource for inferring gene regulatory networks. Various methods have been presented for reconstructing the networks from time-course measurement data. However, existing methods have been validated on only a limited number of benchmark datasets, and rarely verified on real biological systems.ResultsWe first integrated benchmark time-course gene expression datasets from previous studies and reassessed the baseline methods. We observed that GENIE3-time, a tree-based ensemble method, achieved the best performance among the baselines. In this study, we introduce BTNET, a boosted tree based gene regulatory network inference algorithm which improves the state-of-the-art. We quantitatively validated BTNET on the integrated benchmark dataset. The AUROC and AUPR scores of BTNET were higher than those of the baselines. We also qualitatively validated the results of BTNET through an experiment on neuroblastoma cells treated with an antidepressant. The inferred regulatory network from BTNET showed that brachyury, a transcription factor, was regulated by fluoxetine, an antidepressant, which was verified by the expression of its downstream genes.ConclusionsWe present BTENT that infers a GRN from time-course measurement data using boosting algorithms. Our model achieved the highest AUROC and AUPR scores on the integrated benchmark dataset. We further validated BTNET qualitatively through a wet-lab experiment and showed that BTNET can produce biologically meaningful results.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0547-0) contains supplementary material, which is available to authorized users.

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“…Various frameworks have been proposed for identifying gene regulatory networks including Boolean networks [1,2], neural networks [3], differential equations [4], factor graphs [5] and Bayesian networks [6,7,8]. Among these approaches, Bayesian dynamical networks have been particularly popular [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Estimation of gene expression by a bank of particle filters

Bugallo

Tasdemir

Djurić

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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This paper addresses the problem of joint estimation of time series of gene expressions and identification of the coefficients of gene interactions defining the network. The proposed method exploits a state-space structure describing the system so that a bank of particle filters can be used to efficiently track each of the time series separately. Since each gene interacts with some of the other genes, the individual filters need to exchange information about the states (genes) that they track. The analytical derivation of the posterior distribution of the states given the observed data allows for marginalization of the matrix describing the interactions in the network and for efficient implementation of the method. Computer simulations reveal a promising performance of the proposed approach when compared to the conventional particle filter that attempts to track the time series of all the genes and which, as a result, suffers from the curse-of-dimensionality.

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Fast Bayesian inference for gene regulatory networks using ScanBMA

Cited by 76 publications

References 47 publications

Predicting gene regulatory networks by combining spatial and temporal gene expression data inArabidopsisroot stem cells

Predicting gene regulatory networks by combining spatial and temporal gene expression data inArabidopsisroot stem cells

BTNET : boosted tree based gene regulatory network inference algorithm using time-course measurement data

Estimation of gene expression by a bank of particle filters

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