This course covers the use of computational tools to extract biological insight from omics datasets. The content will explore a range of approaches – 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.

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 – https://swcarpentry.github.io/shell-novice/
• Introduction and exercises for Linux – https://training.linuxfoundation.org/free-linux-training 
• Introduction to R – https://swcarpentry.github.io/r-novice-inflammation/ 
• Introduction to Python – http://swcarpentry.github.io/python-novice-gapminder/ 

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 deep learning and apply them on omics datasets to extract biological knowledge
• Infer biological models using statistical methods
• Identify strengths and weaknesses of different inference 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 transcriptomics data 
• Introduction to executable modelling – including how to fit omics data to executable and predictive logic models

Trainers

Please read our support page 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 23:59 on 16 March 2025. Items marked * in the application are mandatory. Incomplete applications will not be processed. 

All applicants will be informed of the status of their application (successful, waiting list, unsuccessful) by 31 March 2025. If you have any questions regarding the application process please contact Juanita Riveros. 

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 2024 edition of the course are now live and available for you to use. They provide a mixture of presentations, and practicals from the course, and will give you a snapshot of what to expect in the 2025 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 and successful applicants will be asked to submit their poster upon registration. We expect the posters to act as a talking point between you, other participants, and the trainers on the course. 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.

All posters should:

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

Registration fees

The registration fee includes: 

• Catering as detailed on the course programme
• Accommodation for 4 nights (7, 8, 9, and 10 July 2025) 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
• Secure virtual machines for practical sessions listed in the programme
• Shuttle bus on the final course day to Cambridge train station 

Financial assistance

Financial assistance is available for a limited number of participants on this course.

Registration fee waivers

We are able to offer a limited number of registration fee waivers for this course. If you receive a waiver, your registration fee will be reimbursed after you have completed the course.

You will need to apply for the fee waiver at the same time as submitting your application for the course, explaining why you require the waiver and how attending this course will benefit your career.

You will be informed about whether you have received the waiver at the same time as you hear about the application outcome for the course. If your course application is successful, you will need to pay the registration fee at the time you register. You will receive the waiver within a month of submitting your form. 

Travel grants

We are able to offer travel grants of up to a maximum of £500 for participants to travel to the course. This will cover the cost of travel to the site where the course is being held and can be used for airfare, train, bus, taxi, or visa costs.

Travel grants are applied for at the same time that you apply for the course. You will be informed about the travel grant, including the amount that you have been awarded, at the same time as the outcome of your course application. 

You will need to pay for the upfront costs of your travel. We will then send you a reimbursement form on your completion of the course. The form must be signed and submitted along with supporting receipts within one month of completion of travel. 

The organisers may reduce the amount offered for the travel grant to accommodate more participants. 

Terms and conditions of fee waivers/ travel grants 

The scientific organisers will select the recipients of financial assistance during the course application selection process. Selection for financial support is based on scientific merit, your current work study or location, the reasons for needing financial support and the impact this event will have on your career. Priority will be given to applications from low and middle income countries.

Visas

Electronic Travel Authorisations (ETAs) are being introduced worldwide for visitors to the UK who do not currently need a visa for short stays, or who do not already have a UK immigration status.

Eligible non-Europeans can now apply for an ETA and will need an ETA to travel from 8 January 2025.

Eligible Europeans can apply from 5 March 2025 and will need an ETA to travel from 2 April 2025.

An ETA is a digital permission to travel – it is not a visa and does not permit entry into the UK – it authorises an individual to travel to the UK.

Gulf Cooperation Council visitors already need an ETA to travel to the UK.

Applying for an ETA is quick and simple. Visitors should apply in advance and find out when to do so on GOV.UK