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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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

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:

1. Use R confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

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:

• Practices in experimental design that lead to high quality research
• Common design pitfalls, and how to avoid or mitigate them
• A brief introduction to more advanced analysis techniques for experiments with unusual or complex designs

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.

• ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

This workshop focuses on expression proteomics, which aims to characterise the protein diversity and abundance in a particular system. You will learn about the bioinformatic analysis steps involved when working with these kind of data, in particular several dedicated proteomics Bioconductor packages, part of the R programming language. We will use real-world datasets obtained from label free quantitation (LFQ) as well as tandem mass tag (TMT) mass spectrometry. We cover the basic data structures used to store and manipulate protein abundance data, how to do quality control and filtering of the data, as well as several visualisations. Finally, we include statistical analysis of differential abundance across sample groups (e.g. control vs. treated) and further evaluation and biological interpretation of the results via gene ontology analysis. By the end of this workshop you should have the skills to make sense of expression proteomics data, from start to finish.

If you do not have a University of Cambridge Raven account please book or register your interest here.

• ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

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 using R 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.

• ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

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:

• A conceptual understanding of what mixed effects models are, and when they should be used
• Familiarity with fitting and interpreting mixed effects models using the lme4 package in R

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.

• ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

The goal of metabolomics is to identify and quantify the complete biochemical composition of a biological sample. With the increase in genomic, transcriptomic and proteomic information there is a growing need to understand the metabolic phenotype that these genes and proteins ultimately control.

The aim of this course is to provide an introductory overview of metabolomics and its applications in life sciences and environmental settings. We will introduce different techniques used to extract metabolites and analyse samples to collect metabolomic data (such as HPLC or GC-based MS and NMR), present how to analyse such data, how to identify metabolites using online databases and how to map the metabolomic data to metabolic pathways.

If you do not have a University of Cambridge Raven account please book or register your interest here.

• ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

This workshop will focus on the theory and applications of metagenomics for the analysis of complex microbiomes (microbial communities). We will cover a range of methods from the fastest, simplest and cheapest amplicon-based methods up to Hi-C metagenomics techniques that give highly detailed results on complex microbial communities. In addition to the theory, we will introduce several bioinformatic software packages suited for the analysis of metagenomic data, quality control and downstream analysis and interpretation of the results.

If you do not have a University of Cambridge Raven account please book or register your interest here.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

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.

• ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

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.

• ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

Congratulations! You have just created a new programme/script that is likely to be of use to other researchers. Naturally, you want to publish this software, others to use it and you to be cited. How hard can that step be? (SPOILER Alert – it’s harder than you think).

• This course explores the myriad challenges in producing code that works on other researchers’ computers and not just yours.
• We tour various possible software solutions and evaluate their suitability
• We examine the potential solution that is Software Containers (focusing on Docker).

If you do not have a University of Cambridge Raven account please book or register your interest here.

• ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance, including for University of Cambridge students. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

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.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.