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?
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
Ricard Argelaguet, Altos Labs
Charles Barker, EMBL-EBI
Alex Bateman, EMBL-EBI
Javier De Las Rivas, University of Salamanca
Aurelien Dugourd, Heidelberg University
Federica Eduati, Eindhoven University of Technology
Konrad Förstner, ZB MED - Information for Life Sciences & TH Köln
Theodoros Koutsandreas, EMBL-EBI
Valentina Lorenzi, EMBL-EBI
Evangelia Petsalaki, EMBL-EBI
Till Sauerwein, ZB MED Cologne
Applications close: 9 July 2023
Course dates: 23 - 27 October 2023
Course fee: £825.00 (including accommodation and catering)