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 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 Elucidating the Response of Crop Plants towards Individual, Combined and Sequentially Occurring Abiotic Stresses

2021 ◽  
Vol 22 (11) ◽  
pp. 6119
Author(s):  
Khalid Anwar ◽  
Rohit Joshi ◽  
Om Parkash Dhankher ◽  
Sneh L. Singla-Pareek ◽  
Ashwani Pareek
Keyword(s):  
Abiotic Stresses ◽  
Regulatory Networks ◽  
Extreme Event ◽  
Experimental Studies ◽  
Complex Stress ◽  
Crop Plants ◽  
Interactive Effects ◽  
Plant Responses ◽  
Physiological Mechanisms ◽  
Recent Developments

In nature, plants are exposed to an ever-changing environment with increasing frequencies of multiple abiotic stresses. These abiotic stresses act either in combination or sequentially, thereby driving vegetation dynamics and limiting plant growth and productivity worldwide. Plants’ responses against these combined and sequential stresses clearly differ from that triggered by an individual stress. Until now, experimental studies were mainly focused on plant responses to individual stress, but have overlooked the complex stress response generated in plants against combined or sequential abiotic stresses, as well as their interaction with each other. However, recent studies have demonstrated that the combined and sequential abiotic stresses overlap with respect to the central nodes of their interacting signaling pathways, and their impact cannot be modelled by swimming in an individual extreme event. Taken together, deciphering the regulatory networks operative between various abiotic stresses in agronomically important crops will contribute towards designing strategies for the development of plants with tolerance to multiple stress combinations. This review provides a brief overview of the recent developments in the interactive effects of combined and sequentially occurring stresses on crop plants. We believe that this study may improve our understanding of the molecular and physiological mechanisms in untangling the combined stress tolerance in plants, and may also provide a promising venue for agronomists, physiologists, as well as molecular biologists.

scholarly journals Finding Groups in Gene Expression Data

2005 ◽  
Vol 2005 (2) ◽  
pp. 215-225 ◽  
Author(s):  
David J. Hand ◽  
Nicholas A. Heard
Keyword(s):  
Cluster Analysis ◽  
Regulatory Networks ◽  
Large Scale ◽  
Disease Diagnosis ◽  
Expression Data ◽  
Data Set ◽  
Microarray Experiments ◽  
Data Analyst ◽  
Bump Hunting ◽  
Function Discovery

The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus provided the data analyst with a distinctive, high-dimensional field of study. Many questions in this field relate to finding subgroups of data profiles which are very similar. A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for microarray data. Cluster analysis, however, implies a partitioning of the entire data set, and this does not always match the objective. Sometimes pattern discovery or bump hunting tools are more appropriate. This paper reviews these various tools for finding interesting subgroups.

scholarly journals graphsim: An R package for simulating gene expression data from graph structures of biological pathways

2020 ◽  
Author(s):  
S. Thomas Kelly ◽  
Michael A. Black
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Simulated Data ◽  
R Package ◽  
Biological Pathways ◽  
Graph Structure ◽  
Expression Data

SummaryTranscriptomic analysis is used to capture the molecular state of a cell or sample in many biological and medical applications. In addition to identifying alterations in activity at the level of individual genes, understanding changes in the gene networks that regulate fundamental biological mechanisms is also an important objective of molecular analysis. As a result, databases that describe biological pathways are increasingly uesad to assist with the interpretation of results from large-scale genomics studies. Incorporating information from biological pathways and gene regulatory networks into a genomic data analysis is a popular strategy, and there are many methods that provide this functionality for gene expression data. When developing or comparing such methods, it is important to gain an accurate assessment of their performance. Simulation-based validation studies are frequently used for this. This necessitates the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This procedure has been released as the graphsim R package on CRAN and GitHub (https://github.com/TomKellyGenetics/graphsim) and is compatible with any graph structure that can be described using the igraph package. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression.

scholarly journals Scanpy for analysis of large-scale single-cell gene expression data

2017 ◽  
Author(s):  
F. Alexander Wolf ◽  
Philipp Angerer ◽  
Fabian J. Theis
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Expression Data ◽  
Cell Gene Expression ◽  
Gene Regulatory ◽  
Cell Gene

We present Scanpy, a scalable toolkit for analyzing single-cell gene expression data. It includes preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing and simulation of gene regulatory networks. The Python-based implementation efficiently deals with datasets of more than one million cells and enables easy interfacing of advanced machine learning packages. Code is available fromhttps://github.com/theislab/scanpy.

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

2019 ◽  
Author(s):  
Xi Chen
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Target Genes ◽  
De Novo ◽  
R Package ◽  
Expression Data ◽  
Regulatory Modules ◽  
Regulatory Module

AbstractBICORN is an R package developed to integrate prior transcription factor binding information and gene expression data for cis-regulatory module (CRM) inference. BICORN searches for a list of candidate CRMs from binary bindings on potential target genes. Applying Gibbs sampling, BICORN samples CRMs for each gene using the fitting performance of transcription factor activities and regulation strengths of TFs in each CRM on gene expression. Consequently, sparse regulatory networks are inferred as functional CRMs regulating target genes. The BICORN package is implemented in R and is available at https://cran.r-project.org/web/packages/BICORN/index.html.

scholarly journals ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks

2017 ◽  
Author(s):  
Sebastian Vlaic ◽  
Christian Tokarski-Schnelle ◽  
Mika Gustafsson ◽  
Uta Dahmen ◽  
Reinhard Guthke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Whole Genome ◽  
Expression Data ◽  
Regulatory Modules ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

AbstractThe identification of disease associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. A major problem hampering their identification is the detection of protein communities within large-scale, whole-genome PPINs. Current strategies solve the maximal clique enumeration (MCE) problem, i.e., the enumeration of all non-extendable groups of proteins, where each pair of proteins is connected by an edge. The MCE problem however is non-deterministic polynomial time hard and can thus be computationally overwhelming for large-scale, whole-genome PPINs.We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs in conjunction with gene-expression data. ModuleDiscoverer is a heuristic that approximates the community structure underlying PPINs. Based on a high-confidence PPIN of Rattus norvegicus and publicly available gene expression data we apply our algorithm to identify the regulatory module of a rat-model of diet induced non-alcoholic steatohepatitis (NASH). We validate the module using single-nucleotide polymorphism data from independent genome-wide association studies. Structural analysis of the module reveals 10 sub-modules. These sub-modules are associated with distinct biological functions and pathways that are relevant to the pathological and clinical situation in NASH.ModuleDiscoverer is freely available upon request from the corresponding author.

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