School of Infection & Immunity

First week:

Next generation sequencing (NGS) and bioinformatics has become an essential tool in genetic and genomic analysis. It is increasingly important for experimental scientists to gain the bioinformatics skills required to assess and analyse the large volumes of sequencing data produced by next generation sequencers. With the ongoing omics initiatives, we would like to give PhD students and postdocs the opportunity to learn about NGS and bioinformatics.

This week-long course aims to provide experimental biologists working with omics data with (i) a comprehension of “bioinformatics” language and terminology, (ii) an understanding of  the underlying concepts (iii) hands-on experience in genomic-scale data analysis for

-          Linux: The operating system for most bioinformatics software. This part will also explain how to process data on the Glasgow high performance compute cluster (HPCC each participant will need an account, information to follow). This will allow participants to apply the knowledge they have gained in the future.

-          NGS mapping and variant calling: Learn how to map sequencing reads and visualize the data. Find copy number and sequence variants and understand the different types of variation.

-          Transcriptomics:

• Analyse a dataset by comparing the transcriptome of a knock-out versus a wildtype parasite - from the mapping of short reads, visualization, differential expression up to GO enrichment!
• Single cell RNA-Seq - basic introduction using Seurat and 10X Chromium data

-          R is a statistical package based on a Linux like syntax. Many tools for genomics and transcriptomics analysis are written in R.  We will explore using some of these tools and creating publication-ready figures in R

-          Databases: Get an overview of the most common databases and learn how to download published data or perform simple analysis on them.

The data used during the workshop will be derived from bacteria, single celled parasites and human or mouse, but the learning outcomes can be applied to other organisms.

At the end of the course you will have the opportunity to work on a larger task with a group to gain experience of applying the learning outcomes of the course independently.  If you wish to bring your own data for the group task, please contact the course organisers in advance.

Initially, participants will work from a virtual machine, which will be provided on a USB hard disc,  and can be taken away at the end of the course at no additional cost. Some exercises will be taught on the Glasgow high performance compute cluster.

Second Week:

We encourage participants to do both weeks, but they can choose to do just one week. If you want to visit just the second week we expect you to have basic knowledge of R, be able to perform a simple single cell analysis with Seurat and are able to do GO analysis.

The aim of the advanced scRNA-Seq week is to give the participants all the tools to allow a confident and full analysis of single cell data, based on the droplet systems, just as 10X Chromium.

The topics are

• Understanding of the basics of single-cell analysis: Through lectures we will teach how dimension reduction, parameter selection, integration cell-cell communication and pseudo time are implemented and parameters are important for the user
• Integration of dataset: Test and compare different integration methods and discuss the pro- and contra
• Differential expression: Discussion which parameter are important and why different tools give different results - and how to deal with that.
• Pseudo time: This methods is very powerfull to understand the development of cells. We will compare different methods and discuss the different results
• Cell-cell interaction: What changes after pertubation? Does the communication between cells change and how can we find these
• Advanced R: It is important to understand the underllying data structure of the different packages and how one can take some short cuts here and there - and maybe make nicer heatmaps?
• Spatial transcriptomics: This field is developing rapidely, with the promise to merge the spatial information of cells with their transcription... we are going to figure this out.
• Advanced Stuff: Mix and Match! We will have several short modules to work of which you can choose three:
  • SNP splitting - Split you samples based on mutations between indivuduals
  • Ambient RNA-Seq - Why and how to remove this
  • Doublet finding tools - Which tools exist and how to deal with doublets
  • CiteSeq - ADT/HTO are powerful, but how to include them into the analysis?
  • Pseudo bulk RNA-Seq - Rather than doing differential expression at the the single-cell level, be more robust, with pseudo bulk
  • Download existing data - Sounds easy, and it is once you know how to download a matrix of scRNA-Seq in integrate it in your code
  • Other methods - There are different scRNA-Seq methods just as BDRhapsody and SMART-Seq2. Once you understand the underlying methods, they are easy to use

The course finishes with a 1.5 days group task.

Please bring your own data, or papers of datasets you want to re-analysis. We will have several group tasks with the opportunity to discuss the different analysis steps with a wide range of data!

Compared to week one, you won't get a USB disc, but will first work on an R-server. But please bring your computer with you, as in an evening session (with some food) we will help you to install needed software on your computer.

After attending this course, participants should be able to:

• Comfortably work with state-of-the-art bioinformatics software in a Linux-like environment
• Have a good understanding of the application, opportunities, visualization and analysis of next-generation sequencing datasets
• Integrate and query large-scale genomic and functional genomic databases to answer biology-related research questions and develop testable hypotheses
• Experience analysing RNA-Seq and scRNA-Seq data
• Ideas around integrative analysis and how to run basic analysis in R

Please note: The practical sessions will be taught exclusively through Unix/Linux. Knowledge in Linux is helpful, but not required. The course aims to provide a hands-on introduction to bioinformatics for next-generation sequencing and should not be considered a complete education in the theoretical and mathematical foundations of the topics. This course is aimed at applicants solely interested in the analysis of NGS data and bioinformatics.

Thomas Otto is a Senior lecturer for Bioinformatics at the University of Glasgow. He has a vast experience in NGS genomics, from the basics of sequencing technologies, quality control of data, genome and transcriptomic projects and genotype to phenotype projects.

Through his career, he taught many workshops on NGS genomics, including de novo assembly, Introduction to bioinformatics (Wellcome Trust advanced courses (WTAC)), more applied workshops like working with pathogen genomes or working with parasite databases. He also teaches at the University of Glasgow data analysis, R and transcriptomics for undergraduate and masters students.

Kathryn Crouch is a team leader for the bioinformatics team at the Wellcome Centre for  Integrative Parasitology, University of Glasgow. Her background is in comparative immunology and she worked in the pharmaceutical industry for several years before taking up her current position. 

In her current role, she manages a team providing advice and computational analysis to Wellcome Centre members working with NGS and other high throughput data in eukaryotic pathogens. She also develops software for the VEuPathDB suite of databases (https://veupathdb.org/). Teaching experience includes running an applied bioinformatics module on the MSc Bioinformatics at the University of Glasgow for the last three years, and contributing to applied courses including the Wellcome Trust Advanced Courses Working with Parasite Database Resources workshop for the last six years.

The pair have run the annual Bioinformatics summer school together since 2018.

New this year will be three PhD students that will complement the teaching as instructors in the second week

Olympia Hardy

Jack Frey

Ross Laidlaw