Computational

Systems Biology: From Large Datasets to Biological Insight (Virtual)

June 21st - 25th 2021

Virtual Course

Virtual hands-on training in using large-scale multi-omics data and machine learning to infer biological models

Summary

Please note: Due to the ongoing Covid-19 pandemic, the 2021 Systems Biology: From Large Datasets to Biological Insight course will be delivered in a virtual format.

This popular course, run jointly with EMBL-EBI, covers the use of multi-omics data and methodologies in systems biology. The content will cover a range of approaches – ranging from network inference to machine learning – that can be used to extract biological insights from varied data types. Together these techniques will provide participants with a useful toolkit for designing new strategies to extract relevant information and understanding from large-scale biological data.

The motivation for running this course is a result of advances in computer science and high-performance computing that have led to ground-breaking developments in systems biology model inference. With the comparable increase of publicly-available, large-scale biological data, the challenge now lies in interpreting them in a biologically valuable manner. Likewise, machine learning approaches are making a significant impact in our analysis of large omics datasets and the extraction of useful biological knowledge.

Target Audience

This course is aimed at advanced PhD students and post-doctoral researchers who are currently working with large-scale -omics datasets with the aim of discerning biological function and processes. Ideal applicants should already have some experience (ideally 1-2 years) working with systems biology or related large-scale multi-omics data analyses.

Applicants are expected to have a working knowledge of the Linux operating system and ability to use the command line. Experience of using a programming language (i.e. Python) is highly desirable, and while the course will make use of simple coding or streamlined approaches such as Python notebooks, higher levels of competency will allow participants to focus on the scientific methodologies rather than the practical aspects of coding and how they can be applied in their own research.

Numerous free online tutorial are available for the resources used in the course, including:

Basic introduction to the Unix environment:
www.ee.surrey.ac.uk/Teaching/Unix
Introduction and exercises for Linux:
https://training.linuxfoundation.org/free-linux-training
Python turorial:
https://www.w3schools.com/python/
R tutorial:
https://www.datacamp.com/courses/free-introduction-to-r

Programme

Virtual course
This is a joint virtual course in collaboration with EMBL-EBI. Participants will learn via a mix of pre-recorded lectures, live presentations, and trainer Q&A sessions. Practical experience will be developed through group activities and trainer-led computational exercises. Live sessions will be delivered using Zoom with additional support and communication via Slack.

Pre-recorded material will be made available to registered participants prior to the start of the course and in the week before the course there will be a brief induction session. Computational practicals will run on EMBL-EBI’s virtual training infrastructure, meaning participants will not require access to a powerful computer or install complex software on their own machines.

Participants will need to be available between the hours of 09:30-17:30 (BST) each day of the course. Trainers will be available to assist, answer questions and further explain the analysis during these times.

The programme will include, lectures, discussions and practical computational exercises covering the following topics:

  • Data reduction and data integration methods – including comparison of major approaches through lectures and practical exercises
  • Machine and deep learning – practical exercises on supervised machine learning, including classification and regression, and deep learning
  • Functional inference from omics data – approaches to extract signatures of cell state from -omics data including transcription factor activation and kinase activity states. Extraction of upstream signalling pathways from transcriptomics datasets
  • Network inference and signal propagation – network inference approaches from -omics data

 

Learning Outcomes
After the course you should be able to:

  • Discuss and apply a range of data integration and reduction approaches for large scale omics data
  • Apply different approaches to explore omics data at the network level
  • Describe principles behind different machine learning methods and apply them on omics datasets to extract biological knowledge
  • Infer biological models using statistical methods
  • Identify strengths and weaknesses of different inference approaches

Instructors and speakers

Lead instructors/organisers

Evangelia Petsalaki
EMBL-EBI, UK

Konrad Förstner
TH Köln University of Applied Sciences & ZB MED Information Centre for Life Sciences, Germany

Federica Eduati
Eindhoven University, Netherlands

Tom Hancocks
EMBL-EBI, UK

How to apply

Prerequisites
Applicants should be advanced PhD students and post-doctoral researchers who are currently working with large-scale -omics datasets with the aim of discerning biological function and processes. Ideal applicants should already have some experience (ideally 1-2 years) working with systems biology or related large-scale multi-omics data analyses.

Applicants are expected to have a working knowledge of the Linux operating system and ability to use the command line. Experience of using a programming language (i.e. Python) is highly desirable, and while the course will make use of simple coding or streamlined approaches such as Python notebooks, higher levels of competency will allow participants to focus on the scientific methodologies rather than the practical aspects of coding and how they can be applied in their own research.

Numerous free online tutorial are available for the resources used in the course, including:

Basic introduction to the Unix environment:
www.ee.surrey.ac.uk/Teaching/Unix
Introduction and exercises for Linux:
https://training.linuxfoundation.org/free-linux-training
Python turorial:
https://www.w3schools.com/python/
R tutorial:
https://www.datacamp.com/courses/free-introduction-to-r

How to Apply
Please click the Apply button above to begin the online application process. Places are limited and will be awarded on merit. If you have any problems with the online application process, please contact us.

Please note: Applications must be supported by a recommendation from a scientific or clinical sponsor (e.g. supervisor, line manager or head of department). A request for a supporting statement will be sent to your nominated sponsor automatically during the application process. Applicants must ensure that their sponsor provides this supporting statement by the application deadline. Applications without a supporting statement cannot be considered.

Cost

Cost
*Course fee £200 Due to the ongoing Covid-19 pandemic this course will be delivered in a virtual format.

*The course fee is subsidised by Wellcome Genome Campus Advanced Courses and Scientific Conferences and applies to non-commercial applicants. Please contact us for the commercial fee.

Bursaries
Limited bursaries are available (up to 50% reduction on the course fee) and are awarded on merit. If you would like to apply for a bursary, please complete the bursary section of the online application form.

Where there are many bursary applications, the selection committee may issue smaller amounts.

Bursaries can be applied for as part of the course application form. Applicants will be notified of a bursary award along with their place on the course, usually within one month of the application deadline. The decision of the selection committee is final.

Please note that both the applicant and sponsor are required to provide a justification for the bursary as part of the application.

Additional funding opportunities
Visit our support page for additional financial support currently available.


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