Complex Network Analysis for Biologists (ONLINE LIVE TRAINING)

BeginnersNew

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors to assist you with instant and personalised feedback and to help you to run/execute the scripts which we will be using during the course. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

Complex natural systems permeate many aspects of everyday life—including human intelligence, social media, biomedicine, agriculture, economics, even our personal and professional relationships. The past decade has seen intensification of research into structural and dynamical properties of complex networks. This course will introduce the basic principles of network theory, and hands-on DIY Network analysis using Cytoscape, one of the most widely used global platforms for construction and analysis of biomolecular networks such as gene regulatory interactions, protein complexes, hydrogen-bonding meshwork in active sites and neuronal networks. The aim is to conceptualize your own textual, tabular or genomic datasets as networks, and to understand how simple topological features can help to decipher complex properties of systems and processes.

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 tutorial is basic and requires no prior knowledge of any coding language or software.
• Cytoscape will have to be downloaded and installed on your device for the practical sessions.
• Suitable for students and early career researchers, postdocs.
• Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
• Please be aware that these courses are only free for registered University of Cambridge students. All other participants will be charged a registration fee in some form. Registration fees and further details regarding the charging policy are available here.
• Further details regarding eligibility criteria are available here

This tutorial is basic and requires no prior knowledge of any coding language or software.

Number of sessions: 1

Bioinformatics, Network Theory, Graph partitioning, Cytoscape

During this course you will learn about:

• Biological Networks; types, and applications.
• Cytoscape: An Open Source Network Visualisation Platform
• Protocols: Network Creation, Import, Visualisation, Partitioning & Export.
• Major layout algorithms, visual styles and tips for effective visualisation.

After this course you should be able to:

• Conceptualise your own data as a network,
• Create/Import/Analyse Networks using Cytoscape
• Add Node/Edge attributes, Styles and Layouts to your Network
• Partition a Network to Identify hubs or functionally important regions
• Download Topological properties and Sub-networks
• Export High resolution Images

Presentations, demonstrations and practicals

• Free for registered University of Cambridge students
• £ 50/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level
• It remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.
• £ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
• £ 100/day for all Industry participants. These charges must be paid at registration
• Further details regarding the charging policy are available here

1

once a year

• EMBL-EBI: Network Analysis with Cytoscape (ONLINE LIVE TRAINING)