Bioinformatics course timetable
January 2025
| Thu 23 |
The Ensembl Project provides an interface and an infrastructure for accessing genomic information, including genes, variants, comparative genomics and gene regulation data, covering over 300 vertebrate species. This workshop offers a comprehensive practical introduction to the use of the Ensembl Genome Browser as well as essential background information. This course will focus on the vertebrate genomes in Ensembl, however much of what will be covered is also applicable to the non-vertebrates (plants, bacteria, fungi, metazoa and protists) in Ensembl Genomes.
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 24 |
The Ensembl project provides an interface and an infrastructure for accessing genomic information, including genes, variants, comparative genomics and gene regulation data, covering over 300 vertebrate species. This workshop is aimed at researchers and developers interested in exploring Ensembl beyond the website. The workshop covers how to use the Ensembl REST APIs, including understanding the major endpoints and how to write scripts to call them.
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 28 |
This course introduces the basic theory and concepts of network analysis. Attendees will learn how to construct protein-protein interaction networks and subsequently use these to overlay large-scale data such as that obtained through RNA-Seq or mass-spec proteomics. The course will focus on giving attendees hands-on experience in the use of one of the most used open-source Network Visualisation Platforms, Cytoscape. The course will also access and analyse the data through Cytoscape apps, including IntAct app.
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 29 |
This course introduces the basic theory and concepts of network analysis. Attendees will learn how to construct protein-protein interaction networks and subsequently use these to overlay large-scale data such as that obtained through RNA-Seq or mass-spec proteomics. The course will focus on giving attendees hands-on experience in the use of one of the most used open-source Network Visualisation Platforms, Cytoscape. The course will also access and analyse the data through Cytoscape apps, including IntAct app.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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February 2025
| Mon 3 |
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. Additional information
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| Tue 4 |
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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| Fri 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. Additional information
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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. Additional information
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| Mon 10 |
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. Additional information
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| Wed 12 |
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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| Thu 13 |
As the use of AI chatbots continues to rise, it is crucial to understand what they are, how they work, and how to make the most of them. Prompting is the method of interacting with AI, and as AI chatbots become more openly and widely available, a good and effective prompt could make all the difference. In this course, we will provide background on the history of AI chatbots as well as an understanding of how they work. We will provide hands-on use cases of how to prompt like a bioinformatician/software engineer as well as providing strategies and tactics of prompting to unleash the full potential of AI chatbots in biological data analysis. This course is aimed at researchers with no computational background.
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 |
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. Additional information
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| Mon 17 |
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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| 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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