scholarly journals Integrated analyses of chromatin accessibility and gene expression data for elucidating the transcriptional regulatory mechanisms during early hematopoietic development in mouse

2013 ◽  
Vol 6 (S1) ◽  
Author(s):  
Mahalingam S Viiavabaskar ◽  
Nadine Obier ◽  
Stella Pearson ◽  
Maarten Hoogenkamp ◽  
Monika Lichtinger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Chromatin Accessibility ◽  
Regulatory Mechanisms ◽  
Expression Data ◽  
Hematopoietic Development ◽  
Transcriptional Regulatory ◽  
Transcriptional Regulatory Mechanisms

scholarly journals EXPath 2.0: An Updated Database for Integrating High-Throughput Gene Expression Data with Biological Pathways

2020 ◽  
Vol 61 (10) ◽  
pp. 1818-1827
Author(s):  
Kuan-Chieh Tseng ◽  
Guan-Zhen Li ◽  
Yu-Cheng Hung ◽  
Chi-Nga Chow ◽  
Nai-Yun Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Metabolic Pathways ◽  
Expression Profiles ◽  
Regulatory Mechanisms ◽  
Biological Processes ◽  
Expression Data ◽  
Rna Seq ◽  
Correlation Networks ◽  
Transcriptional Regulatory Mechanisms

Abstract Co-expressed genes tend to have regulatory relationships and participate in similar biological processes. Construction of gene correlation networks from microarray or RNA-seq expression data has been widely applied to study transcriptional regulatory mechanisms and metabolic pathways under specific conditions. Furthermore, since transcription factors (TFs) are critical regulators of gene expression, it is worth investigating TFs on the promoters of co-expressed genes. Although co-expressed genes and their related metabolic pathways can be easily identified from previous resources, such as EXPath and EXPath Tool, this information is not simultaneously available to identify their regulatory TFs. EXPath 2.0 is an updated database for the investigation of regulatory mechanisms in various plant metabolic pathways with 1,881 microarray and 978 RNA-seq samples. There are six significant improvements in EXPath 2.0: (i) the number of species has been extended from three to six to include Arabidopsis, rice, maize, Medicago, soybean and tomato; (ii) gene expression at various developmental stages have been added; (iii) construction of correlation networks according to a group of genes is available; (iv) hierarchical figures of the enriched Gene Ontology (GO) terms are accessible; (v) promoter analysis of genes in a metabolic pathway or correlation network is provided; and (vi) user’s gene expression data can be uploaded and analyzed. Thus, EXPath 2.0 is an updated platform for investigating gene expression profiles and metabolic pathways under specific conditions. It facilitates users to access the regulatory mechanisms of plant biological processes. The new version is available at http://EXPath.itps.ncku.edu.tw.

scholarly journals Causal Inference Engine: a platform for directional gene set enrichment analysis and inference of active transcriptional regulators

2019 ◽  
Author(s):  
Saman Farahmand ◽  
Corey O’Connor ◽  
Jill A Macoska ◽  
Kourosh Zarringhalam
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Gene Expression Data ◽  
Transcriptional Regulators ◽  
Regulatory Mechanisms ◽  
Gene Interactions ◽  
Expression Data ◽  
Inference Algorithms ◽  
Tf Gene ◽  
Mode Of Regulation

Abstract Inference of active regulatory mechanisms underlying specific molecular and environmental perturbations is essential for understanding cellular response. The success of inference algorithms relies on the quality and coverage of the underlying network of regulator–gene interactions. Several commercial platforms provide large and manually curated regulatory networks and functionality to perform inference on these networks. Adaptation of such platforms for open-source academic applications has been hindered by the lack of availability of accurate, high-coverage networks of regulatory interactions and integration of efficient causal inference algorithms. In this work, we present CIE, an integrated platform for causal inference of active regulatory mechanisms form differential gene expression data. Using a regularized Gaussian Graphical Model, we construct a transcriptional regulatory network by integrating publicly available ChIP-seq experiments with gene-expression data from tissue-specific RNA-seq experiments. Our GGM approach identifies high confidence transcription factor (TF)–gene interactions and annotates the interactions with information on mode of regulation (activation vs. repression). Benchmarks against manually curated databases of TF–gene interactions show that our method can accurately detect mode of regulation. We demonstrate the ability of our platform to identify active transcriptional regulators by using controlled in vitro overexpression and stem-cell differentiation studies and utilize our method to investigate transcriptional mechanisms of fibroblast phenotypic plasticity.

scholarly journals Human Hepatocyte Nuclear Factor 4-α encodes isoforms with distinct transcriptional functions

2019 ◽  
Author(s):  
Élie Lambert ◽  
Jean-Philippe Babeu ◽  
Joël Simoneau ◽  
Dominique Lévesque ◽  
Émilie Jolibois ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Mechanisms ◽  
Nuclear Factor ◽  
Omics Data ◽  
Hepatocyte Nuclear Factor ◽  
Transcriptional Regulatory ◽  
Hepatocyte Nuclear Factor 4 ◽  
Transcriptional Regulatory Mechanisms ◽  
First Time ◽  
Reduced Activity

SUMMARYHNF4α is a nuclear receptor produced as 12 isoforms from two promoters by alternative splicing. In order to characterize the transcriptional capacities of all 12 HNF4α isoforms, stable lines expressing each isoform were generated. The entire transcriptome associated with each isoform was analyzed as well as their respective interacting proteome. Major differences were noted in the transcriptional function of these isoforms. The α1 and α2 isoforms were the most potent regulators of gene expression while the α3 isoform exhibited significantly reduced activity. The α4, α5 and α6 isoforms, which use an alternative first exon, were characterized for the first time, and showed a greatly reduced transcriptional potential with an inability to recognize the consensus response element of HNF4α. Several transcription factors and coregulators were identified as potential specific partners for certain HNF4α isoforms. An analysis integrating the vast amount of omics data enabled the identification of transcriptional regulatory mechanisms specific to certain HNF4α isoforms, hence demonstrating the importance of considering all isoforms given their seemingly diverse functions.

scholarly journals Identification of transcriptional regulatory network associated with response of host epithelial cells to SARS-CoV-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chen Su ◽  
Simon Rousseau ◽  
Amin Emad
Keyword(s):  
Gene Expression ◽  
Epithelial Cells ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Organ Damage ◽  
Host Cells ◽  
Regulatory Mechanisms ◽  
Transcriptional Regulatory ◽  
Transcriptional Regulatory Mechanisms ◽  
The Impact

AbstractIdentification of transcriptional regulatory mechanisms and signaling networks involved in the response of host cells to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable the identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, our goal was to identify a transcriptional regulatory network that is associated with gene expression changes between samples infected by SARS-CoV-2 and those that are infected by other respiratory viruses to narrow the results on those enriched or specific to SARS-CoV-2. We combined a series of recently developed computational tools to identify transcriptional regulatory mechanisms involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus when compared to other viruses. In addition, using network-guided analyses, we identified kinases associated with this network. The results identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory network identified herein reflects a combination of known hits and novel candidate pathways supporting the novel computational pipeline presented herein to quickly narrow down promising avenues of investigation when facing an emerging and novel disease such as COVID-19.

scholarly journals Causal Inference Engine: A platform for directional gene set enrichment analysis and inference of active transcriptional regulators

2019 ◽  
Author(s):  
Saman Farahmand ◽  
Corey O’Connor ◽  
Jill A. Macoska ◽  
Kourosh Zarringhalam
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Gene Expression Data ◽  
Transcriptional Regulators ◽  
Regulatory Mechanisms ◽  
Gene Interactions ◽  
Expression Data ◽  
Inference Algorithms ◽  
Tf Gene ◽  
Mode Of Regulation

ABSTRACTInference of active regulatory mechanisms underlying specific molecular and environmental perturbations is essential for understanding cellular response. The success of inference algorithms relies on the quality and coverage of the underlying network of regulator-gene interactions. Several commercial platforms provide large and manually-curated regulatory networks and functionality to perform inference on these networks. Adaptation of such platforms for open-source academic applications has been hindered by the lack of availability of accurate, high-coverage networks of regulatory interactions and integration of efficient causal inference algorithms. In this work, we present CIE, an integrated platform for causal inference of active regulatory mechanisms form differential gene expression data. Using a regularized Gaussian Graphical Model, we construct a transcriptional regulatory network by integrating publicly available ChIP-Seq experiments with gene-expression data from tissue-specific RNA-Seq experiments. Our GGM approach identifies high confidence TF-gene interactions and annotates the interactions with information on mode of regulation (activation vs. repression). Benchmarks against manually-curated databases of TF-gene interactions show that our method can accurately detect mode of regulation. We demonstrate the ability of our platform to identify active transcriptional regulators by using controlled in vitro overexpression and stem-cell differentiation studies and utilize our method to investigate transcriptional mechanisms of fibroblast phenotypic plasticity.

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