Identification of Eigen-genes, consensus modules and Network Motifs in co-expression (or other biological) networks. (Webinar)

One of the most important tasks of systems biology is to create explanatory and predictive models of complex biological systems. Availability of gene expression data in different conditions has paved the way for reconstructing direct or indirect regulatory connections between various genes and gene products. Most often, we are not interested in single interactions between gene products; instead, we try to reconstruct networks that provide insights into the investigated biological processes or the entire system as a whole.

This webinar will expand upon the concept of Gene Co-expression Networks to elucidate Weighted Gene Co-expression Network Analysis (WGCNA), and introduce the importance of visualising clustered gene expression profiles as single ‘Eigengenes’. It will describe the complete protocol for WGCNA analysis starting from normalised Gene Expression Datasets (Microarrays or RNA-Seq). This will be followed by a discussion on methods of extraction and analysis of consensus modules and Network motifs from Gene Co-Expression Networks and Transcriptional Regulatory Networks.

The webinar will be presented in the form of a lecture and tutorial with screenshots that enable listeners to emulate the protocols in R. Note that this is a webinar and not a coding exercise. Links to further reading and practice will be shared.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

• This webinar is suitable for students and early career researchers with interest in Genomics
• Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals.
• There is no fee charged for this event.

• Listeners should have a basic knowledge of R and Gene Expression Datasets.
• If possible, please attend the previous webinar in this series: Inferring Co-Expressing Genes and Regulatory Networks from RNA-Seq Data

Number of sessions: 1

WGCNA, EigenGenes, Consensus Modules, Network Motifs

After this course you should be able to:

• Conceptualise your RNA-Seq for Network Analyses
• Perform Soft Thresholding for Weighted Gene Networks
• Apply WGCNA to your own dataset
• Identify clusters, Eigengenes, modules and motifs in Networks

During this course you will learn about:

• How WGCNA can be used in Systems Biology
• Importance of Eigenegenes & consensus modules for RNA-Seq datasets
• Identification of Network motifs in Biological Networks.

Presentations and demonstrations

2 Hours

• EMBL-EBI: Network Analysis with Cytoscape (ONLINE LIVE TRAINING)
• An Introduction to Biological Networks & their Visualization (Webinar)
• Inferring Co-Expressing Genes and Regulatory Networks from RNA-Seq Data (Webinar)