Bioinformatics course timetable
February 2025
| Wed 19 |
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. Additional information
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| Fri 21 |
This comprehensive course equips you with essential skills and knowledge in bacterial genomics analysis, primarily using Illumina-sequenced samples. You'll gain an understanding of how to select the most appropriate analysis workflow, tailored to the genome diversity of a given bacterial species. Through hands-on training, you'll apply both de novo assembly and reference-based mapping approaches to obtain bacterial genomes for your isolates. You will apply standardised workflows for genome assembly and annotation, including quality assessment criteria to ensure the reliability of your results. Along with typing bacteria using methods such as MLST, you'll learn how to construct phylogenetic trees using whole genome and core genome alignments, enabling you to explore the evolutionary relationships among bacterial isolates. You’ll extend this to estimate a time-scaled phylogeny using a starting phylogenetic tree. Lastly, you'll apply methods to detect antimicrobial resistance genes. As examples we will use Mycobacterium tuberculosis, Staphylococcus aureus and Streptococcus pneumoniae, allowing you to become well-equipped to conduct bacterial genomics analyses on a range of species.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Mon 24 |
This comprehensive course equips you with essential skills and knowledge in bacterial genomics analysis, primarily using Illumina-sequenced samples. You'll gain an understanding of how to select the most appropriate analysis workflow, tailored to the genome diversity of a given bacterial species. Through hands-on training, you'll apply both de novo assembly and reference-based mapping approaches to obtain bacterial genomes for your isolates. You will apply standardised workflows for genome assembly and annotation, including quality assessment criteria to ensure the reliability of your results. Along with typing bacteria using methods such as MLST, you'll learn how to construct phylogenetic trees using whole genome and core genome alignments, enabling you to explore the evolutionary relationships among bacterial isolates. You’ll extend this to estimate a time-scaled phylogeny using a starting phylogenetic tree. Lastly, you'll apply methods to detect antimicrobial resistance genes. As examples we will use Mycobacterium tuberculosis, Staphylococcus aureus and Streptococcus pneumoniae, allowing you to become well-equipped to conduct bacterial genomics analyses on a range of species.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Tue 25 |
Generalised linear models are the kind of models we would use if we had to deal with non-continuous response variables. For example, this happens if you have count data or a binary outcome. This course aims to introduce generalised linear models, using the R software environment. Similar to Core statistics using R this course addresses the practical aspects of using these models, so you can explore real-life issues in the biological sciences. The Generalised linear models course builds heavily on the knowledge gained in the core statistics sessions, which means that the Core statistics using R course is a firm prerequisite for joining. There are several aims to this course: 1. Be able to distinguish between linear models and generalised linear models 2. Analyse binary outcome and count data using R 3. Critically assess model fit R is an open-source programming language so all of the software we will use in the course is free. We will be using the R Studio interface throughout the course. Most of the code will be focussed around the tidyverse and tidymodels packages, so a basic understanding of the tidyverse syntax is essential. If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Wed 26 |
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. Additional information
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| Fri 28 |
This comprehensive course equips you with essential skills and knowledge in bacterial genomics analysis, primarily using Illumina-sequenced samples. You'll gain an understanding of how to select the most appropriate analysis workflow, tailored to the genome diversity of a given bacterial species. Through hands-on training, you'll apply both de novo assembly and reference-based mapping approaches to obtain bacterial genomes for your isolates. You will apply standardised workflows for genome assembly and annotation, including quality assessment criteria to ensure the reliability of your results. Along with typing bacteria using methods such as MLST, you'll learn how to construct phylogenetic trees using whole genome and core genome alignments, enabling you to explore the evolutionary relationships among bacterial isolates. You’ll extend this to estimate a time-scaled phylogeny using a starting phylogenetic tree. Lastly, you'll apply methods to detect antimicrobial resistance genes. As examples we will use Mycobacterium tuberculosis, Staphylococcus aureus and Streptococcus pneumoniae, allowing you to become well-equipped to conduct bacterial genomics analyses on a range of species.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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March 2025
| Mon 3 |
Determining the 3 dimensional (3D) structure of a protein from its amino sequence is vital for understanding its core biological functions. This can be done using experimental approaches, which are the standard for validating high-resolution and accurate structures. However, these methods can be costly, time-consuming and technically difficult to achieve for certain proteins. To complement these approaches, computational methods can be used, which increase the speed of prediction, can be scaled to higher throughput and are much cheaper to run. This course covers how to computationally predict the 3D structure of proteins from their amino acid sequences. We will focus on AlphaFold, a software that has revolutionised this process due to its outstanding (near-experimental) prediction accuracy. Other key aspects will be covered such as retrieving structural information from public databases, evaluating the quality of the predicted models, model visualisation with PyMOL, multimer predictions, prediction of ligand binding sites and docking. After this course you should be able to produce 3D predictions of your proteins, while critically evaluating the output of the methods covered in the course.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Tue 4 |
Determining the 3 dimensional (3D) structure of a protein from its amino sequence is vital for understanding its core biological functions. This can be done using experimental approaches, which are the standard for validating high-resolution and accurate structures. However, these methods can be costly, time-consuming and technically difficult to achieve for certain proteins. To complement these approaches, computational methods can be used, which increase the speed of prediction, can be scaled to higher throughput and are much cheaper to run. This course covers how to computationally predict the 3D structure of proteins from their amino acid sequences. We will focus on AlphaFold, a software that has revolutionised this process due to its outstanding (near-experimental) prediction accuracy. Other key aspects will be covered such as retrieving structural information from public databases, evaluating the quality of the predicted models, model visualisation with PyMOL, multimer predictions, prediction of ligand binding sites and docking. After this course you should be able to produce 3D predictions of your proteins, while critically evaluating the output of the methods covered in the course.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Wed 5 |
Determining the 3 dimensional (3D) structure of a protein from its amino sequence is vital for understanding its core biological functions. This can be done using experimental approaches, which are the standard for validating high-resolution and accurate structures. However, these methods can be costly, time-consuming and technically difficult to achieve for certain proteins. To complement these approaches, computational methods can be used, which increase the speed of prediction, can be scaled to higher throughput and are much cheaper to run. This course covers how to computationally predict the 3D structure of proteins from their amino acid sequences. We will focus on AlphaFold, a software that has revolutionised this process due to its outstanding (near-experimental) prediction accuracy. Other key aspects will be covered such as retrieving structural information from public databases, evaluating the quality of the predicted models, model visualisation with PyMOL, multimer predictions, prediction of ligand binding sites and docking. After this course you should be able to produce 3D predictions of your proteins, while critically evaluating the output of the methods covered in the course.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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Many experimental designs end up producing lists of hits, usually based around genes or transcripts. Sometimes these lists are small enough that they can be examined individually, but often it is useful to do a more structured functional analysis to try to automatically determine any interesting biological themes which turn up in the lists. This course looks at the various software packages, databases and statistical methods which may be of use in performing such an analysis. As well as being a practical guide to performing these types of analysis the course will also look at the types of artefacts and bias which can lead to false conclusions about functionality and will look at the appropriate ways to both run the analysis and present the results for publication. Course materials are available here.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Thu 6 |
Determining the 3 dimensional (3D) structure of a protein from its amino sequence is vital for understanding its core biological functions. This can be done using experimental approaches, which are the standard for validating high-resolution and accurate structures. However, these methods can be costly, time-consuming and technically difficult to achieve for certain proteins. To complement these approaches, computational methods can be used, which increase the speed of prediction, can be scaled to higher throughput and are much cheaper to run. This course covers how to computationally predict the 3D structure of proteins from their amino acid sequences. We will focus on AlphaFold, a software that has revolutionised this process due to its outstanding (near-experimental) prediction accuracy. Other key aspects will be covered such as retrieving structural information from public databases, evaluating the quality of the predicted models, model visualisation with PyMOL, multimer predictions, prediction of ligand binding sites and docking. After this course you should be able to produce 3D predictions of your proteins, while critically evaluating the output of the methods covered in the course.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Fri 7 |
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. Additional information
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| Mon 10 |
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
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| Fri 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. Additional information
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| Mon 17 |
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
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| Tue 18 |
The Unix shell (command line) is a powerful and essential tool for modern researchers, in particular those working in computational disciplines such as bioinformatics and large-scale data analysis. In this course we will explore the basic structure of the Unix operating system and how we can interact with it using a basic set of commands. You will learn how to navigate the filesystem, manipulate text-based data and combine multiple commands to quickly extract information from large data files. You will also learn how to write scripts and use programmatic techniques to automate task repetition.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Wed 19 |
The Unix shell (command line) is a powerful and essential tool for modern researchers, in particular those working in computational disciplines such as bioinformatics and large-scale data analysis. In this course we will explore the basic structure of the Unix operating system and how we can interact with it using a basic set of commands. You will learn how to navigate the filesystem, manipulate text-based data and combine multiple commands to quickly extract information from large data files. You will also learn how to write scripts and use programmatic techniques to automate task repetition.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| 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. Additional information
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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
|
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| 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. Additional information
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| 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. Additional information
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| Thu 27 |
Single-cell RNA-seq analysis (IN-PERSON)
Not bookable
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. Additional information
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| Fri 28 |
Single-cell RNA-seq analysis (IN-PERSON)
Not bookable
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. Additional information
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| Mon 31 |
Single-cell RNA-seq analysis (IN-PERSON)
Not bookable
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. Additional information
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April 2025
| 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. Additional information
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