Course at EMBL-EBI
Systems biology: from large datasets to biological insight
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.
In-person course
We plan to deliver this course in an in-person manner onsite at our training suite at EMBL-EBI, Hinxton. Please be aware that we are continually evaluating the ongoing pandemic situation and, as such, may need to change the format of courses at short notice. Your safety is paramount to us; you can read our COVID guidance policy for more information. All information is correct at time of publishing.
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 methods
- 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:
- Data reduction and data integration methods – including comparisons of major approaches through lectures and practical exercises
- Machine and deep learning – practical exercises on supervised machine learning, including classification and regression, graph neural network 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 signaling pathways from transcriptomics datasets
- Network inference and signal propagation – network inference approaches from omics data
- Introduction to executable modelling – including how to fit omics data to executable and predictive logic models
Trainers
Ricard Argelaguet
Altoslab Danila Bredikhin
EMBL Emma Dann
Wellcome Sanger Institute and EMBL-EBI Javier De Las Rivas
University of Salamanca Aurelien Dugourd
Heidelberg University Sara-Jane Dunn
DeepMind Federica Eduati
Eindhoven University of Technology Konrad Förstner
TH Köln – University of Applied Sciences Girolamo Giudice
EMBL-EBI Ioannis Kamzolas
EMBL-EBI Mikhail Papkov
University of Tartu Evangelia Petsalaki
EMBL-EBI Alfonso Valencia
Barcelona Supercomputing Center
Programme
Time (BST)
Topic
Trainer
Day one - Monday 4 July 2022 - Data reduction and batch effects
10:30 - 10:45
Arrival and registration
10:45 - 11:30
Intro to the course and EMBL-EBI
Patricia Carvajal-López
11:30 - 12:00
Icebreaker
Patricia Carvajal-López
12:00 - 13:00
Data reduction and batch effects
Girolamo Giudice, and Ioannis Kamzolas
13:00 - 14:00
Lunch
14:00 - 15.30
Data reduction and batch effects
Girolamo Giudice, and Ioannis Kamzolas
15:30 - 16:00
Break
16:00 - 17:00
Machine Learning
Konrad Förstner
18:00
Dinner in Conference Center
Day two - Tuesday 5 July 2022 - Machine Learning
09:00 - 10:30
Machine Learning
Konrad Förstner
10:30 - 11:00
Break
11:00 - 13:00
Machine Learning
Konrad Förstner
13:00 - 14:00
Lunch Break
14:00 - 15:30
Deep Learning
Mikhail Papkov
15:30 - 16:00
Break
16:00 - 16:45
Deep Learning
Mikhail Papkov
16:45 - 17:30
Flash talks I
17:30 - 18:30
Poster session I
19:00
Dinner at Conference Centre
Day three - Wednesday 6 July 2022 - Data integration
09:00 - 10:30
Integration using MOFA intro + practical
Ricard Argelaguet
10:30 - 11:00
Break
11:00 - 12:30
Integration using MOFA intro + practical
Ricard Argelaguet
12:30 - 13:30
Lunch break
13:30 - 15:00
Single Cell multiomics data integration
Emma Dann
15:00 - 15:30
Break
15:30 - 16:45
Single Cell multiomics data integration
Emma Dann
16:45 - 17:30
Flash talks II
17:30 - 18:30
Poster session II
19:00
Dinner in Conference Center
Day four - Thursday 7 July 2022 - Network inference and signal propagation
09:00 – 10:00
Introduction to Cytoscape
Javier De Las Rivas
10:00 - 10:30
Break
10:30 - 12:00
Network inference
Federica Eduati, and Javier De Las Rivas
12:00 - 13:00
Lunch break
13:00 - 14:00
Network inference practical
Javier De Las Rivas
14:00 - 15:30
Basics of logic modelling + practical
Federica Eduati
15:30 - 16:00
Break
16:00 - 17:00
Keynote Lecture: "
Automated Synthesis and Analysis of Logical
Network Models to Study Pluripotency"
Sara-Jane Dunn (remote)
18:00
Dinner at the Red Lion
Day five - Friday 8 July 2022 - Network inference and signal propagation
09:00 - 11:00
Data analysis to logic modelling + practical
Aurelien Dugourd
11:00 - 11:30
Break
11:30 - 12:30
Keynote Lecture about PerMedCoE project (remote)
Alfonso Valencia (remote)
12:30 - 13:30
Network diffusion for signal propagation (lecture)
Girolamo Guidice
13:30 - 14:00
Final discussion session
All
14:00 - 14:15
Course wrap-up and feedback
Patricia Carvajal-López
14:15
Lunch and end of course
Please note minor programme changes may occur.
Please read our page on application advice 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 providing answers as directed
- 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
- current research
- Upload a letter of support from your supervisor or a senior colleague detailing reasons why you should be selected for the course
Please complete all sections and upload your letter of support by Friday 25 March 2022. We will not consider incomplete applications.
All applicants will be informed of the status of their application (successful, waiting list, unsuccessful) by Friday 08 April 2022. If you have any questions regarding the application process please contact Meredith Willmott (meredith@ebi.ac.uk).
All participants are expected to present a poster that will be displayed during the course outside the training room. Please send your poster in PDF format to Meredith Willmott (meredith@ebi.ac.uk) and we will print it on campus.
All posters should:
- be A2 in size - 420mm x 594 mm
- be in a portrait orientation
- include your photograph and contact information
We cannot display posters of a different size or orientation.
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.
Accommodation will be provided in the Wellcome Genome Campus Conference Centre Monday-Friday inclusive. Please contact the Conference Centre directly if you wish to arrange to stay additional nights. The course fee includes breakfast and evening meals at Hinxton Hall and the Red Lion Pub in nearby Hinxton village, as well as breaks and lunches outside the EMBL-EBI training rooms.
Altoslab
EMBL
Wellcome Sanger Institute and EMBL-EBI
University of Salamanca
Heidelberg University
DeepMind
Eindhoven University of Technology
TH Köln – University of Applied Sciences
EMBL-EBI
EMBL-EBI
University of Tartu
EMBL-EBI
Barcelona Supercomputing Center
Programme
Time (BST) |
Topic |
Trainer |
Day one - Monday 4 July 2022 - Data reduction and batch effects |
||
10:30 - 10:45 |
Arrival and registration |
|
10:45 - 11:30 |
Intro to the course and EMBL-EBI |
Patricia Carvajal-López |
11:30 - 12:00 |
Icebreaker |
Patricia Carvajal-López |
12:00 - 13:00 |
Data reduction and batch effects |
Girolamo Giudice, and Ioannis Kamzolas |
13:00 - 14:00 |
Lunch |
|
14:00 - 15.30 |
Data reduction and batch effects |
Girolamo Giudice, and Ioannis Kamzolas |
15:30 - 16:00 |
Break |
|
16:00 - 17:00 |
Machine Learning |
Konrad Förstner |
18:00 |
Dinner in Conference Center |
|
Day two - Tuesday 5 July 2022 - Machine Learning |
||
09:00 - 10:30 |
Machine Learning |
Konrad Förstner |
10:30 - 11:00 |
Break |
|
11:00 - 13:00 |
Machine Learning |
Konrad Förstner |
13:00 - 14:00 |
Lunch Break |
|
14:00 - 15:30 |
Deep Learning |
Mikhail Papkov |
15:30 - 16:00 |
Break |
|
16:00 - 16:45 |
Deep Learning |
Mikhail Papkov |
16:45 - 17:30 |
Flash talks I |
|
17:30 - 18:30 |
Poster session I |
|
19:00 |
Dinner at Conference Centre |
|
Day three - Wednesday 6 July 2022 - Data integration |
||
09:00 - 10:30 |
Integration using MOFA intro + practical |
Ricard Argelaguet |
10:30 - 11:00 |
Break |
|
11:00 - 12:30 |
Integration using MOFA intro + practical |
Ricard Argelaguet |
12:30 - 13:30 |
Lunch break |
|
13:30 - 15:00 |
Single Cell multiomics data integration |
Emma Dann |
15:00 - 15:30 |
Break |
|
15:30 - 16:45 |
Single Cell multiomics data integration |
Emma Dann |
16:45 - 17:30 |
Flash talks II |
|
17:30 - 18:30 |
Poster session II |
|
19:00 |
Dinner in Conference Center |
|
Day four - Thursday 7 July 2022 - Network inference and signal propagation |
||
09:00 – 10:00 |
Introduction to Cytoscape |
Javier De Las Rivas |
10:00 - 10:30 |
Break |
|
10:30 - 12:00 |
Network inference |
Federica Eduati, and Javier De Las Rivas |
12:00 - 13:00 |
Lunch break |
|
13:00 - 14:00 |
Network inference practical |
Javier De Las Rivas |
14:00 - 15:30 |
Basics of logic modelling + practical |
Federica Eduati |
15:30 - 16:00 |
Break |
|
16:00 - 17:00 |
Keynote Lecture: " Automated Synthesis and Analysis of Logical Network Models to Study Pluripotency" |
Sara-Jane Dunn (remote) |
18:00 |
Dinner at the Red Lion |
|
Day five - Friday 8 July 2022 - Network inference and signal propagation |
||
09:00 - 11:00 |
Data analysis to logic modelling + practical |
Aurelien Dugourd |
11:00 - 11:30 |
Break |
|
11:30 - 12:30 |
Keynote Lecture about PerMedCoE project (remote) |
Alfonso Valencia (remote) |
12:30 - 13:30 |
Network diffusion for signal propagation (lecture) |
Girolamo Guidice |
13:30 - 14:00 |
Final discussion session |
All |
14:00 - 14:15 |
Course wrap-up and feedback |
Patricia Carvajal-López |
14:15 |
Lunch and end of course |
Please note minor programme changes may occur.
Please read our page on application advice 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 providing answers as directed
- 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
- current research
- Upload a letter of support from your supervisor or a senior colleague detailing reasons why you should be selected for the course
Please complete all sections and upload your letter of support by Friday 25 March 2022. We will not consider incomplete applications.
All applicants will be informed of the status of their application (successful, waiting list, unsuccessful) by Friday 08 April 2022. If you have any questions regarding the application process please contact Meredith Willmott (meredith@ebi.ac.uk).
All participants are expected to present a poster that will be displayed during the course outside the training room. Please send your poster in PDF format to Meredith Willmott (meredith@ebi.ac.uk) and we will print it on campus.
All posters should:
- be A2 in size - 420mm x 594 mm
- be in a portrait orientation
- include your photograph and contact information
We cannot display posters of a different size or orientation.
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.
Accommodation will be provided in the Wellcome Genome Campus Conference Centre Monday-Friday inclusive. Please contact the Conference Centre directly if you wish to arrange to stay additional nights. The course fee includes breakfast and evening meals at Hinxton Hall and the Red Lion Pub in nearby Hinxton village, as well as breaks and lunches outside the EMBL-EBI training rooms.