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Biological Network Series

Description

The Biological Network Series consists of three webinars, each presented in the form of a 2-hour lecture as well as a tutorial with step-wise screenshots that enable listeners to emulate the subject at hand. Please note that these are webinars and not a coding exercises.

LECTURE 1: An Introduction to Biological Networks & their Visualization

This webinar is an Introduction to Biological Networks, their types, and applications. It will include two of the most commonly used open source Network Visualisation Platforms (R-igraph and Cytoscape) with step-wise protocols for creating and visualising your own data as a network. It will present some of the major layout algorithms, visual styles and tips for effective visualisation, with examples from biology revealing how these can improve analysis and provide insights.

Pre-requisites

  • This webinar is suitable for students and early career researchers in the Life Sciences.
  • None required.

For additional information on the Introduction to Biological Network lecture, follow this link.

LECTURE 2: Inferring Co-Expressing Genes and Regulatory Networks from RNA-Seq Data

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.

Pre-requisites

  • This webinar is suitable for students and early career researchers with interest in Genomics
  • 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.

For additional information on the Inferring Co-Expressing Genes lecture, follow this link.

LECTURE 3: Identification of Eigen-genes, consensus modules and Network Motifs in co-expression (or other biological) networks

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.

Pre-requisites

For additional information on the Identification of Eigen-genes lecture, follow this link.

 

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