This virtual course will focus on the analysis of data from genomic studies of cancer. It will also highlight the application of transcriptomic analysis and single-cell technologies in cancer. Talks and interactive sessions will give an insight into the bioinformatic concepts required to analyse such data, whilst practical sessions will enable the participants to apply statistical methods to the analysis of cancer genomics data under the guidance of the trainers.
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 asynchronous communication via Slack.
Pre-recorded material may be provided before the course starts that participants will need to watch, read or work through to gain the most out of the actual training event. 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 08:45 – 17:30 BST each day of the course. Trainers will be available to assist, answer questions, and provide further explanations during these times.
Who is this course for?
We welcome applications from PhD students, post-doc researchers, and those working in industry. This course is well-suited for those who are applying, or planning to apply, high throughput sequencing and single-cell technologies in cancer research and wish to familiarise themselves with bioinformatics tools and data analysis methodologies specific to cancer data.
Familiarity with the technology and biological use cases of high throughput sequencing (HTS) is required, as is some experience with R/Bioconductor (basic understanding of the R syntax and ability to manipulate R objects) and the Unix/Linux operating system. There are many tutorials available online and here are some that may be of help:
To complete the following suggested tutorials you may want to install Ubuntu for Windows Users if you are using a computer with a Windows Operating System.
What will I learn?
After the course you should be able to:
- Evaluate the applications and challenges of HTS in the study of cancer genomics
- Detect, visualise, and annotate copy number variation
- Interpret complex genomic rearrangements such as structural variants
- Explain the principles of tumour purity, heterogeneity, and evolution and how we detect or quantify them using bioinformatic approaches
- Explore the application of CRISPR-Cas9 genome editing in cancer studies
- Perform alignment and quantification of expression of RNA-seq datasets
- Explore the application of single-cell sequencing in cancer studies
During this course you will learn about:
- Application of high throughput sequencing (HTS) in cancer
- Introduction to cancer genomics and epigenetics
- Structural variation, SNV and CNV analysis, and data visualisation
- Application of CRISPR-Cas9 genome editing in studying cancer
- RNA-seq analysis (both short and long reads)
- Single-cell research in cancer