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 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 Large-scale miRNA-Target Data Analysis to Discover miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans

2021 ◽  
Author(s):  
Haowu Chang ◽  
Tianyue Zhang ◽  
Hao Zhang ◽  
Lingtao Su ◽  
Qing-Ming Qin ◽  
...  
Keyword(s):  
Abiotic Stresses ◽  
Regulatory Networks ◽  
Large Scale ◽  
Mirna Target ◽  
Growth Regulation ◽  
Global Analysis ◽  
Experimental Studies ◽  
Expression Data ◽  
Good Opportunity ◽  
Regulatory Modules

AbstractAlthough growing evidence shows that microRNA (miRNA) regulates plant growth and development, miRNA regulatory networks in plants are not well understood. Current experimental studies cannot characterize miRNA regulatory networks on a large scale. This information gap provides a good opportunity to employ computational methods for global analysis and to generate useful models and hypotheses. To address this opportunity, we collected miRNA-target interactions (MTIs) and used MTIs from Arabidopsis thaliana and Medicago truncatula to predict homologous MTIs in soybeans, resulting in 80,235 soybean MTIs in total. A multi-level iterative bi-clustering method was developed to identify 483 soybean miRNA-target regulatory modules (MTRMs). Furthermore, we collected soybean miRNA expression data and corresponding gene expression data in response to abiotic stresses. By clustering these data, 37 MTRMs related to abiotic stresses were identified including stress-specific MTRMs and shared MTRMs. These MTRMs have gene ontology (GO) enrichment in resistance response, iron transport, positive growth regulation, etc. Our study predicts soybean miRNA-target regulatory modules with high confidence under different stresses, constructs miRNA-GO regulatory networks for MTRMs under different stresses and provides miRNA targeting hypotheses for experimental study. The method can be applied to other biological processes and other plants to elucidate miRNA co-regulation mechanisms.

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 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 Discovery of miR-mRNA interactions via simultaneous Bayesian inference of gene networks and clusters using sequence-based predictions and expression data

2013 ◽  
Vol 10 (1) ◽  
pp. 33-45
Author(s):  
Brian Godsey
Keyword(s):  
Multiple Myeloma ◽  
Mrna Expression ◽  
Gene Networks ◽  
Clustering Algorithm ◽  
Simulated Data ◽  
Expression Data ◽  
Data Set ◽  
Mrna Expression Data ◽  
R Language ◽  
Pathway Enrichment

Summary MicroRNAs (miRs) are known to interfere with mRNA expression, and much work has been put into predicting and inferring miR-mRNA interactions. Both sequence-based interaction predictions as well as interaction inference based on expression data have been proven somewhat successful; furthermore, models that combine the two methods have had even more success. In this paper, I further refine and enrich the methods of miR-mRNA interaction discovery by integrating a Bayesian clustering algorithm into a model of prediction-enhanced miR-mRNA target inference, creating an algorithm called PEACOAT, which is written in the R language. I show that PEACOAT improves the inference of miR-mRNA target interactions using both simulated data and a data set of microarrays from samples of multiple myeloma patients. In simulated networks of 25 miRs and mRNAs, our methods using clustering can improve inference in roughly two-thirds of cases, and in the multiple myeloma data set, KEGG pathway enrichment was found to be more significant with clustering than without. Our findings are consistent with previous work in clustering of non-miR genetic networks and indicate that there could be a significant advantage to clustering of miR and mRNA expression data as a part of interaction inference.

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
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