Bioinformatics course timetable
March 2025
| Mon 24 |
Knowing how to use High Performance Computing (HPC) systems is crucial for fields such as bioinformatics, big data analysis, image processing, machine learning, parallel task execution, and other high-throughput applications. In this introductory course, you will learn the fundamentals of HPC, including what it is and how to effectively utilise it. We will cover best practices for working with HPC systems, explain the roles of "login" and "compute" nodes, outline the typical filesystem organization on HPC clusters, and cover job scheduling with the widely-used SLURM scheduler. This hands-on workshop is designed to be accessible to researchers from various backgrounds, providing numerous opportunities to practice and apply the skills you acquire. As an optional session for those interested, we will also introduce the (free) HPC facilities available at Cambridge University (the course is not otherwise Cambridge-specific).
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
|
This award winning course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences. There are three core goals for this course:
R is an open source programming language so all of the software we will use in the course is free. In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory. After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
|
|
| Tue 25 |
Knowing how to use High Performance Computing (HPC) systems is crucial for fields such as bioinformatics, big data analysis, image processing, machine learning, parallel task execution, and other high-throughput applications. In this introductory course, you will learn the fundamentals of HPC, including what it is and how to effectively utilise it. We will cover best practices for working with HPC systems, explain the roles of "login" and "compute" nodes, outline the typical filesystem organization on HPC clusters, and cover job scheduling with the widely-used SLURM scheduler. This hands-on workshop is designed to be accessible to researchers from various backgrounds, providing numerous opportunities to practice and apply the skills you acquire. As an optional session for those interested, we will also introduce the (free) HPC facilities available at Cambridge University (the course is not otherwise Cambridge-specific).
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
| Wed 26 |
Mass spectrometry is an invaluable tool that provides information about a molecule's fundamental feature – its molecular mass. The field of mass spectrometry is vast and available techniques are constantly evolving. Nowadays, mass spectrometry not only provides record-breaking resolving power but also achieves detection limits of only hundreds of molecules or zeptomoles. Its applications include the study of inorganic materials, organic compounds, ancient fossils, artworks and even mummies. It also plays a fundamental role in "omics" applications providing qualitative and quantitative data on proteome, lipidome and metabolome. The aim of this course is to provide a comprehensive overview of mass spectrometry techniques, working principles and applications in STEM. Throughout the course, we will consider different ionization techniques and mass analyzers, hyphenation to chromatography or reaction coils, as well as upstream methodologies suitable for mass spectrometry in general. You will gain an understanding of what kind of data different mass spectrometry techniques provide and how to extract information from this data. This knowledge will enable you to plan and design mass spectrometry experiments for different applications.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
| Thu 27 |
Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing. This course offers an introduction to single-cell RNA sequencing (scRNA-seq) analysis. Participants will gain hands-on experience with key software packages and methodologies for processing, analyzing, and interpreting scRNA-seq data. Key topics include data preprocessing, quality control, normalization, dimensionality reduction, batch correction and data integration, cell clustering and differential expression and abundance analysis. By the end of the course, students will be equipped with the skills to independently conduct and critically analyse data from scRNA-seq experiments.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
| Fri 28 |
Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing. This course offers an introduction to single-cell RNA sequencing (scRNA-seq) analysis. Participants will gain hands-on experience with key software packages and methodologies for processing, analyzing, and interpreting scRNA-seq data. Key topics include data preprocessing, quality control, normalization, dimensionality reduction, batch correction and data integration, cell clustering and differential expression and abundance analysis. By the end of the course, students will be equipped with the skills to independently conduct and critically analyse data from scRNA-seq experiments.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
| Mon 31 |
Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing. This course offers an introduction to single-cell RNA sequencing (scRNA-seq) analysis. Participants will gain hands-on experience with key software packages and methodologies for processing, analyzing, and interpreting scRNA-seq data. Key topics include data preprocessing, quality control, normalization, dimensionality reduction, batch correction and data integration, cell clustering and differential expression and abundance analysis. By the end of the course, students will be equipped with the skills to independently conduct and critically analyse data from scRNA-seq experiments.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
April 2025
| Tue 1 |
This is very much a first course on machine learning. It aims to provide a foundation for future work with machine learning. This course will get you to the point where you can confidently engage with literature referencing machine learning. We will be using the CARET package to apply some basic machine learning methods within R.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
| Wed 2 |
Setting up a computer for running bioinformatic analysis can be a challenging process. Most bioinformatic applications involve the use of many different software packages, which are often part of long data processing pipelines. In this course we will teach you how to overcome these challenges by using package managers and workflow management software. We will have examples of software and pipelines for processing different types of data (RNA-seq, ChIP-seq, variant calling and viral genomes), making this course appealing to researchers working in a wide range of applications. However, please note that we will not cover the details of any specific type of bioinformatic analysis. The idea of this course is to introduce the computational tools to get your work done, not to teach how those tools work. We will also not teach you how to write your own pipelines, or create your own software containers, but rather on how to use existing tools to boost your bioinformatic analysis.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
| Thu 3 |
This course is aimed to provide the tools to conduct Bayesian inference in common situations. This course is aimed to provide the tools to conduct Bayesian inference in common situations. We will be contrasting Bayesian Inference with classical hypothesis testing, covering conjugate distributions and credible intervals. We will also look at modern computational methods such as MCMC approaches using the RSTAN library.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
| Mon 7 |
This one-day course is primarily aimed at life science researchers, but covers many topics that are applicable to other fields. It combines key theoretical knowledge with practical application, which will aid researchers in designing effective experiments. The focus throughout the course is to link experimental design to a clear analysis strategy. This ensures that the collected data will be suitable for statistical analysis. During this course, we cover:
Topics included in the course include: crafting a good research question, operationalising variables effectively, identifying and dealing with confounding variables and pseudoreplication, and practical tips for power analysis and piloting. The course is delivered via a mix of lectures, group discussion and worked examples.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
| Tue 8 |
This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core concepts of Python including Python syntax, data structures and reading/writing files. These are illustrated by a series of example programs. Upon completion of the course, participants will be able to write simple Python programs.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
| Wed 9 |
This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core concepts of Python including Python syntax, data structures and reading/writing files. These are illustrated by a series of example programs. Upon completion of the course, participants will be able to write simple Python programs.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
| Wed 30 |
This course gives an introduction to linear mixed effects models, also called multi-level models or hierarchical models, for the purposes of using them in your own research or studies. We emphasise the practical skills and key concepts needed to work with these models, using applied examples and real datasets. After completing the course, you should have:
Please note that this course builds on knowledge of linear modelling, therefore should not be considered a general introduction to statistical modelling.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
May 2025
| Fri 2 |
This course will teach you how to use molecular data to construct and interpret phylogenies. We will start by introducing basic concepts in phylogenetic analysis, what trees represent and how to interpret them. We will then cover how to produce a multiple sequence alignment from DNA and protein sequences, and the pros and cons of different alignment algorithms. You will then learn about different methods of phylogenetic inference, with a particular focus on maximum likelihood and how to assess confidence in your tree using bootstrap resampling. Finally, we will introduce how Bayesian methods can help to estimate the uncertainty in the inferred tree parameters as well as incorporate information for more advanced/bespoke phylogenetic analysis.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
|
| Tue 6 |
R is one of the leading programming languages in Data Science. It is widely used to perform statistics, machine learning, visualisations and data analyses. It is an open source programming language so all the software we will use in the course is free. This course is an introduction to R designed for participants with no programming experience. We will start from scratch by introducing how to start programming in R and progress our way and learn how to read and write to files, manipulate data and visualise it by creating different plots - all the fundamental tasks you need to get you started analysing your data. During the course we will be working with one of the most popular packages in R; tidyverse that will allow you to manipulate your data effectively and visualise it to a publication level standard.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
|
| Wed 7 |
R is one of the leading programming languages in Data Science. It is widely used to perform statistics, machine learning, visualisations and data analyses. It is an open source programming language so all the software we will use in the course is free. This course is an introduction to R designed for participants with no programming experience. We will start from scratch by introducing how to start programming in R and progress our way and learn how to read and write to files, manipulate data and visualise it by creating different plots - all the fundamental tasks you need to get you started analysing your data. During the course we will be working with one of the most popular packages in R; tidyverse that will allow you to manipulate your data effectively and visualise it to a publication level standard.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
|
|
This course gives an introduction to linear mixed effects models, also called multi-level models or hierarchical models, for the purposes of using them in your own research or studies. We emphasise the practical skills and key concepts needed to work with these models, using applied examples and real datasets. After completing the course, you should have:
Please note that this course builds on knowledge of linear modelling, therefore should not be considered a general introduction to statistical modelling.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
||
| Thu 8 |
The development of Fourier transform mass spectrometry (FTMS) revolutionised our understanding of environmental samples, which have been studied for decades or even centuries. It was discovered that samples of soil, water or air consist of thousands of unknown organic constituents that were overlooked by other techniques. The complexity of such samples has been specifically demonstrated in crude oil, where 250,000 individual signals were detected in a single sample. This “hidden” complexity raises several questions. How can we predict the impact of a petroleum spillage if we don't know what was spilled? How can we describe microbial processes in soil if we miss most of the components of the carbon cycle? Unlike other methods, FTMS provides a unique opportunity to study unknown compounds by directly assigning exact elemental compositions from mass spectra, without the need for references, even in complex mixtures. Determining the elemental composition of a mixture is the starting point for many research questions. Although FTMS is now available worldwide, its potential remains to be fully exploited. The aim of this course is to provide a comprehensive overview of FTMS techniques and their areas of application. Throughout the course we will consider various case studies where the full power of FTMS is demonstrated, including its use in biogeochemistry, ecology, environmental and atmospheric chemistry. Additionally, we will discuss how FTMS is used to study whiskey, beer, meteorites and mummies. You will get an understanding of the types of data provided by FTMS, where you can apply it and how to manage the large data it generates. We will also cover how to analyse and visualise FTMS data in various applications.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
|
| Fri 9 |
This course introduces concepts about reproducibility that can be used when you are programming in R. We will explore how to create notebooks - a way to integrate your R analyses into reports using Rmarkdown. The course also introduces the concept of version control. We will learn how to create a repository on GitHub and how to work together on the same project collaboratively without creating conflicting versions of files.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
|
| Wed 14 |
This course gives an introduction to linear mixed effects models, also called multi-level models or hierarchical models, for the purposes of using them in your own research or studies. We emphasise the practical skills and key concepts needed to work with these models, using applied examples and real datasets. After completing the course, you should have:
Please note that this course builds on knowledge of linear modelling, therefore should not be considered a general introduction to statistical modelling.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
|
| Thu 15 |
This course covers the potential pitfalls of short-read sequencing studies and provides options for visualisation and quality control (QC) for early detection and diagnosis of issues. You will gain an understanding of Illumina sequencing and different QC metrics that can be extracted from sequencing reads, such as base quality scores. The course also covers how QC metrics vary across different library types and thus distinguish between expected and unexpected QC results. You will be introduced to key software tools including FastQC, FastQ Screen, and MultiQC to carry out quality assessment of your sequencing data. Note that the main focus of this course is on how to interpret quality reports produced by these tools, not on how to run them (although we do provide the basic commands you need to do it).
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
|
| Fri 16 |
Core Statistics using R (ONLINE LIVE TRAINING)
Not bookable
This award winning course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences. There are three core goals for this course:
R is an open source programming language so all of the software we will use in the course is free. In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory. After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
|
| Wed 21 |
In this course you will acquire practical skills in RNA-seq data analysis. You will learn about quality control, alignment, and quantification of gene expression against a reference transcriptome. Additionally, you will learn to conduct downstream analysis in R, exploring techniques like PCA and clustering for exploratory analysis. The course also covers differential expression analysis using the DESeq2 R/Bioconductor package. Furthermore, the course covers how to generate visualisations like heatmaps and performing gene set testing to link differential genes with established biological functions or pathways.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|
|
| Thu 22 |
In this course you will acquire practical skills in RNA-seq data analysis. You will learn about quality control, alignment, and quantification of gene expression against a reference transcriptome. Additionally, you will learn to conduct downstream analysis in R, exploring techniques like PCA and clustering for exploratory analysis. The course also covers differential expression analysis using the DESeq2 R/Bioconductor package. Furthermore, the course covers how to generate visualisations like heatmaps and performing gene set testing to link differential genes with established biological functions or pathways.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
|