This course covers the use of computational tools to extract biological insight from omics datasets. The content will explore a range of approaches – ranging from network inference and data integration to machine learning and logic modelling – 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 groundbreaking 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.

Additional information

Please note that we will operate this course face-to-face at EMBL-EBI in Hinxton. Hybrid options are not currently available. We reserve the right to change the format of this course or cancel it, due to the ongoing coronavirus pandemic.

Who is this course for?

This course is aimed at advanced PhD students, post-doctoral researchers, and non-academic scientists 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 one to two 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 the 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.

We recommend these free tutorials:

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 tutorial: https://www.w3schools.com/python/ 

R tutorial: https://www.datacamp.com/courses/free-introduction-to-r 

Regardless of your current knowledge we encourage successful participants to use these to prepare for attending the course and future work in this area. Selected participants will also be sent materials prior to the course. These might include pre-recorded talks and required reading that will be essential to fully understand the course

What will I learn?

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 method
• Identify strengths and weaknesses of different inference approaches
• Compare signal propagation through logic modelling vs diffusion-based approaches

Course content

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

• Machine and deep learning – practical exercises on supervised machine learning, including classification and regression, graph neural network, and deep learning
• Bulk and single-cell multiomics data integration – introduction and practical on using methods for integrative analysis of multiomics data
• 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, including cell cell communication networks from scRNAseq data 
• Introduction to executable modelling – including how to fit omics data to executable and predictive logic models

Trainers

Please read our page on application support before starting your application. In order to be considered for a place on this course, you must do the following:

• Complete the online application form.
• Ensure you add relevant information to the ‘submission details’ section where you are asked to provide information on your:
  • pre-requisite skills and knowledge
  • current work and course expectations
  • data availability
• Upload one letter of support from your supervisor or a senior colleague detailing reasons why you should be selected for the course.

Please submit all documents during the application process by midnight on 9 July 2023. Items marked * in the application are mandatory. Incomplete registrations will not be processed. 

All applicants will be informed of the status of their application (successful, waiting list, unsuccessful) by 24 July 2023. If you have any questions regarding the application process please contact Jane Reynolds.

The registration fee of £825.00 includes:

• Catering as detailed on the course programme
• Accommodation for four nights (23, 24, 25, and 26 October) at Hinxton Hall Conference Centre.
• Bespoke course handbook with links to all course materials
• Use of a computer in the EMBL-EBI Training suite throughout the course
• Shuttle bus on the final course day to Cambridge train station 

Accommodation 

Hotel rooms will be provided onsite at Hinxton Hall Conference Centre. Please contact them directly if you wish to arrange to stay additional nights around the course dates.

Catering

The course includes catering as detailed on the programme tab. Successful applicants will be asked for any dietary requirements and allergies upon registration. 

Course materials

The course materials from the 2022 edition of the course are now live and available for you to use. They provide a mixture of pre-recorded lectures, presentations, and practicals from the course, and will give you a snapshot of what to expect in the 2023 edition.

Flash talks

All participants will be asked to give a short presentation about their research work as part of the course. These provide an opportunity to share their research with the other participants and provide a forum for discussion. Successful applicants will be required to submit their talks upon registration.

Posters

All participants are expected to present a poster that will be displayed during the course outside the training room. Successful applicants will be asked to submit their poster upon registration. We will print these for you and have them available when you arrive on site.

All posters should:

•         be A2 in size – 420mm x 594 mm
•         be in a portrait orientation
•         include your photograph and contact information

We expect the posters to act as a talking point between you, other participants, and the trainers on the course. The posters will be displayed throughout the week so people can view them during breaks and lunch. They should give the reader an idea of the work you are engaged in, what you are planning to do next, and anything of interest that might be useful for sharing with the gathered participants.