scholarly journals ­­Ensemble-based network aggregation improves the accuracy of gene network reconstruction

2013 ◽  
Author(s):  
Jeffrey D. Allen ◽  
Yang Xie ◽  
Guanghua Xiao
Keyword(s):  
Mrna Expression ◽  
Regulatory Networks ◽  
Network Inference ◽  
R Package ◽  
Network Reconstruction ◽  
Specific Gene ◽  
Expression Data ◽  
Mrna Expression Data ◽  
Network Aggregation ◽  
Context Specific

Reverse engineering approaches to construct context-specific gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings. However, the reliability and reproducibility of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improve the accuracy of the network topology constructed from mRNA expression data. To evaluate the performance of different approaches, we created dozens of simulated networks and also tested our methods on three Escherichia coli datasets. We demonstrate three novel applications from this development. First, bootstrapping can be done on the available samples, turning any network reconstruction approach into an ensemble method. Second, this aggregation approach can be used to combine GRNs from different network inference methods, creating a novel network reconstruction approach that consistently outperforms any constituent method. Third, the approach can be used to effectively integrate GRNs constructed from different studies – producing more accurate networks. We are releasing an implementation of these techniques as an R package “ENA” which is able to run network inference in parallel across multiple servers. We made all of the code and data used in our simulations and analysis available online at https://github.com/QBRC/ENA-Research to ensure the reproducibility of our results.

scholarly journals ­­Ensemble-based network aggregation improves the accuracy of gene network reconstruction

2013 ◽  
Author(s):  
Jeffrey D. Allen ◽  
Yang Xie ◽  
Guanghua Xiao
Keyword(s):  
Mrna Expression ◽  
Regulatory Networks ◽  
Network Inference ◽  
R Package ◽  
Network Reconstruction ◽  
Specific Gene ◽  
Expression Data ◽  
Mrna Expression Data ◽  
Network Aggregation ◽  
Context Specific

Reverse engineering approaches to construct context-specific gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings. However, the reliability and reproducibility of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improve the accuracy of the network topology constructed from mRNA expression data. To evaluate the performance of different approaches, we created dozens of simulated networks and also tested our methods on three Escherichia coli datasets. We demonstrate three novel applications from this development. First, bootstrapping can be done on the available samples, turning any network reconstruction approach into an ensemble method. Second, this aggregation approach can be used to combine GRNs from different network inference methods, creating a novel network reconstruction approach that consistently outperforms any constituent method. Third, the approach can be used to effectively integrate GRNs constructed from different studies – producing more accurate networks. We are releasing an implementation of these techniques as an R package “ENA” which is able to run network inference in parallel across multiple servers. We made all of the code and data used in our simulations and analysis available online at https://github.com/QBRC/ENA-Research to ensure the reproducibility of our results.

scholarly journals Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nisar Wani ◽  
Debmalya Barh ◽  
Khalid Raza
Keyword(s):  
Network Analysis ◽  
Mrna Expression ◽  
Mirna Expression ◽  
Regulatory Networks ◽  
Network Inference ◽  
Expression Profiles ◽  
Interaction Network ◽  
Pathway Enrichment Analysis ◽  
Expression Data ◽  
Cancer Data

Abstract Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.

scholarly journals Identification of Subtype Specific miRNA-mRNA Functional Regulatory Modules in Matched miRNA-mRNA Expression Data: Multiple Myeloma as a Case

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Yunpeng Zhang ◽  
Wei Liu ◽  
Yanjun Xu ◽  
Chunquan Li ◽  
Yingying Wang ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Mrna Expression ◽  
Regulatory Networks ◽  
Mirna Target ◽  
Expression Profiles ◽  
Biological Data ◽  
Expression Data ◽  
Target Information ◽  
Mrna Expression Data ◽  
Regulatory Modules

Identification of miRNA-mRNA modules is an important step to elucidate their combinatorial effect on the pathogenesis and mechanisms underlying complex diseases. Current identification methods primarily are based upon miRNA-target information and matched miRNA and mRNA expression profiles. However, for heterogeneous diseases, the miRNA-mRNA regulatory mechanisms may differ between subtypes, leading to differences in clinical behavior. In order to explore the pathogenesis of each subtype, it is important to identify subtype specific miRNA-mRNA modules. In this study, we integrated the Ping-Pong algorithm and multiobjective genetic algorithm to identify subtype specific miRNA-mRNA functional regulatory modules (MFRMs) through integrative analysis of three biological data sets: GO biological processes, miRNA target information, and matched miRNA and mRNA expression data. We applied our method on a heterogeneous disease, multiple myeloma (MM), to identify MM subtype specific MFRMs. The constructed miRNA-mRNA regulatory networks provide modular outlook at subtype specific miRNA-mRNA interactions. Furthermore, clustering analysis demonstrated that heterogeneous MFRMs were able to separate corresponding MM subtypes. These subtype specific MFRMs may aid in the further elucidation of the pathogenesis of each subtype and may serve to guide MM subtype diagnosis and treatment.

scholarly journals ProteoMirExpress: Inferring MicroRNA and Protein-centered Regulatory Networks from High-throughput Proteomic and mRNA Expression Data

2013 ◽  
Vol 12 (11) ◽  
pp. 3379-3387 ◽  
Author(s):  
Jing Qin ◽  
Mulin Jun Li ◽  
Panwen Wang ◽  
Nai Sum Wong ◽  
Maria P. Wong ◽  
...  
Keyword(s):  
Mrna Expression ◽  
High Throughput ◽  
Regulatory Networks ◽  
Expression Data ◽  
Mrna Expression Data

scholarly journals Improving Gene Regulatory Network Inference by Incorporating Rates of Transcriptional Changes

2016 ◽  
Author(s):  
Jigar S. Desai ◽  
Ryan C. Sartor ◽  
Lovely Mae Lawas ◽  
SV Krishna Jagadish ◽  
Colleen J. Doherty
Keyword(s):  
Regulatory Networks ◽  
Network Inference ◽  
R Package ◽  
Rate Of Change ◽  
Model Systems ◽  
Data Sets ◽  
Expression Data ◽  
Model Species ◽  
Gene Regulatory Network Inference ◽  
Transcriptional Regulatory Networks

AbstractOrganisms respond to changes in their environment through transcriptional regulatory networks (TRNs). The regulatory hierarchy of these networks can be inferred from expression data. Computational approaches to identify TRNs can be applied in any species where quality RNA can be acquired, However, ChIP-Seq and similar validation methods are challenging to employ in non-model species. Improving the accuracy of computational inference methods can significantly reduce the cost and time of subsequent validation experiments. We have developed ExRANGES, an approach that improves the ability to computationally infer TRN from time series expression data. ExRANGES utilizes both the rate of change in expression and the absolute expression level to identify TRN connections. We evaluated ExRANGES in five data sets from different model systems. ExRANGES improved the identification of experimentally validated transcription factor targets for all species tested, even in unevenly spaced and sparse data sets. This improved ability to predict known regulator-target relationships enhances the utility of network inference approaches in non-model species where experimental validation is challenging. We integrated ExRANGES with two different network construction approaches and it has been implemented as an R package available here: http://github.com/DohertyLab/ExRANGES. To install the package type: devtools::install_github(“DohertyLab/ExRANGES”)

scholarly journals Normalization strategies for mRNA expression data in cartilage research

2008 ◽  
Vol 16 (8) ◽  
pp. 947-955 ◽  
Author(s):  
K. Fundel ◽  
J. Haag ◽  
P.M. Gebhard ◽  
R. Zimmer ◽  
T. Aigner
Keyword(s):  
Mrna Expression ◽  
Expression Data ◽  
Mrna Expression Data

scholarly journals BICORN: An R package for integrative inference of de novo cis-regulatory modules

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xi Chen ◽  
Jinghua Gu ◽  
Andrew F. Neuwald ◽  
Leena Hilakivi-Clarke ◽  
Robert Clarke ◽  
...  
Keyword(s):  
Gene Transcription ◽  
Target Genes ◽  
De Novo ◽  
R Package ◽  
Model Parameters ◽  
Expression Data ◽  
Regulatory Modules ◽  
Genome Wide ◽  
Context Specific ◽  
Tf Gene

Abstract Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites, based on which cis-regulatory modules (CRMs) can be inferred. CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html.

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