Inferring Co-Expressing Genes and Regulatory Networks from RNA-Seq Data (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.

This webinar will introduce the importance and applications of Gene Expression Datasets (Microarrays and RNA-Seq), followed by methods of extraction and analysis of Co-Expression Networks and Transcriptional Regulatory Networks from these datasets. The webinar will focus on the pros and cons of Weighted and Unweighted Networks, citing examples to aid decisions about which networks to use and when.

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.

Co-expression Networks represent a kind of Biological Network. Listeners should have a basic knowledge of Networks. Some familiarity with Gene Expression Datasets and R will be useful to understand methods and interpretations.

Number of sessions: 1

Transcriptome Datasets, Gene Co-expression Networks, Transcriptional Network Inference, R, Cytoscape

After this course you should be able to:

• Conceptualise your RNA-Seq for Network Analyses
• Differentiate between Hard and Soft Thresholding
• Differentiate between Weighted and Unweighted Gene Networks
• Test significance of gene correlations in your Network using R
• Construct Gene Regulatory and Co-Expression Networks in R

During this course you will learn about:

• How Gene Expression data can be used for Systems Biology
• Two major kinds of Co-Expression Networks
• Tips for measuring gene-level correlations in RNA-Seq data

Presentations and demonstrations

2 Hours

• EMBL-EBI: Network Analysis with Cytoscape (ONLINE LIVE TRAINING)
• An Introduction to Biological Networks & their Visualization (Webinar)
• Identification of Eigen-genes, consensus modules and Network Motifs in co-expression (or other biological) networks. (Webinar)