Inferring gene coexpression networks with Biclustering based on Scatter Search

Author(s):  
Juan A. Nepomuceno ◽  
Alicia Troncoso ◽  
Jesus S. Aguilar-Ruiz
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pooja Singh ◽  
Ehsan Pashay Ahi ◽  
Christian Sturmbauer

Abstract Background The oral and pharyngeal jaw of cichlid fishes are a classic example of evolutionary modularity as their functional decoupling boosted trophic diversification and contributed to the success of cichlid adaptive radiations. Most studies until now have focused on the functional, morphological, or genetic aspects of cichlid jaw modularity. Here we extend this concept to include transcriptional modularity by sequencing whole transcriptomes of the two jaws and comparing their gene coexpression networks. Results We show that transcriptional decoupling of gene expression underlies the functional decoupling of cichlid oral and pharyngeal jaw apparatus and the two units are evolving independently in recently diverged cichlid species from Lake Tanganyika. Oral and pharyngeal jaw coexpression networks reflect the common origin of the jaw regulatory program as there is high preservation of gene coexpression modules between the two sets of jaws. However, there is substantial rewiring of genetic architecture within those modules. We define a global jaw coexpression network and highlight jaw-specific and species-specific modules within it. Furthermore, we annotate a comprehensive in silico gene regulatory network linking the Wnt and AHR signalling pathways to jaw morphogenesis and response to environmental cues, respectively. Components of these pathways are significantly differentially expressed between the oral and pharyngeal jaw apparatus. Conclusion This study describes the concerted expression of many genes in cichlid oral and pharyngeal jaw apparatus at the onset of the independent life of cichlid fishes. Our findings suggest that – on the basis of an ancestral gill arch network—transcriptional rewiring may have driven the modular evolution of the oral and pharyngeal jaws, highlighting the evolutionary significance of gene network reuse. The gene coexpression and in silico regulatory networks presented here are intended as resource for future studies on the genetics of vertebrate jaw morphogenesis and trophic adaptation.


Author(s):  
Fabricio Almeida-Silva ◽  
Kanhu C Moharana ◽  
Thiago M Venancio

Abstract In the past decade, over 3000 samples of soybean transcriptomic data have accumulated in public repositories. Here, we review the state of the art in soybean transcriptomics, highlighting the major microarray and RNA-seq studies that investigated soybean transcriptional programs in different tissues and conditions. Further, we propose approaches for integrating such big data using gene coexpression network and outline important web resources that may facilitate soybean data acquisition and analysis, contributing to the acceleration of soybean breeding and functional genomics research.


2019 ◽  
Vol 120 (9) ◽  
pp. 15182-15189
Author(s):  
Fang‐Fang Liu ◽  
Juan Wang ◽  
Fan Hu ◽  
Qing Wei ◽  
Ke Li

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Alfredo Benso ◽  
Paolo Cornale ◽  
Stefano Di Carlo ◽  
Gianfranco Politano ◽  
Alessandro Savino

Undirected gene coexpression networks obtained from experimental expression data coupled with efficient computational procedures are increasingly used to identify potentially relevant biological information (e.g., biomarkers) for a particular disease. However, coexpression networks built from experimental expression data are in general large highly connected networks with an elevated number of false-positive interactions (nodes and edges). In order to infer relevant information, the network must be properly filtered and its complexity reduced. Given the complexity and the multivariate nature of the information contained in the network, this requires the development and application of efficient feature selection algorithms to be able to exploit the topological characteristics of the network to identify relevant nodes and edges. This paper proposes an efficient multivariate filtering designed to analyze the topological properties of a coexpression network in order to identify potential relevant genes for a given disease. The algorithm has been tested on three datasets for three well known and studied diseases: acute myeloid leukemia, breast cancer, and diffuse large B-cell lymphoma. Results have been validated resorting to bibliographic data automatically mined using the ProteinQuest literature mining tool.


2017 ◽  
Vol 52 (1) ◽  
pp. 317-326 ◽  
Author(s):  
J. Asselman ◽  
M. E. Pfrender ◽  
J. A. Lopez ◽  
J. R. Shaw ◽  
K. A. C. De Schamphelaere

2010 ◽  
Vol 11 (S4) ◽  
Author(s):  
Sudhir Naswa ◽  
Gary L Rogers ◽  
Rachel M Lynch ◽  
Stephen A Kania ◽  
Suchita Das ◽  
...  

Endocrinology ◽  
2009 ◽  
Vol 150 (3) ◽  
pp. 1235-1249 ◽  
Author(s):  
Atila van Nas ◽  
Debraj GuhaThakurta ◽  
Susanna S. Wang ◽  
Nadir Yehya ◽  
Steve Horvath ◽  
...  

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