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PLEASE BE AWARE: This event is run in-person, if you wish to book for the online version, please click here.

The aim of this course is to familiarize the participants with the primary analysis of RNA-seq data.

This course starts with a brief introduction to RNA-seq and discusses quality control issues. Next, we will present the alignment step, quantification of expression and differential expression analysis. For downstream analysis we will focus on tools available through the Bioconductor project for manipulating and analysing bulk RNA-seq.

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences. Lectures will introduce commonly used algorithms and provide insight into their theoretical underpinnings. In the practicals students will apply these algorithms to real biological data-sets using the R language and environment.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

PLEASE BE AWARE: This event is run in-person, if you wish to book for the online version, please click 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.

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking 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.

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Have you heard about High Performance Computing, but are not sure what it is or whether it is relevant for your work? Would you like to use a HPC, but are not sure where to start? Are you using your personal computer to run computationally demanding tasks, which take long and slow down your work? Do you need to use software that runs on Linux, but don't have access to a Linux computer? If any of these questions apply to you, then this course might be for you!

Knowing how to work on a High Performance Computing system is an essential skill for applications such as bioinformatics, big-data analysis, image processing, machine learning, parallelising tasks, and other high-throughput applications.

In this course we will cover the basics of High Performance Computing, what it is and how you can use it in practice. This is a hands-on workshop, which should be accessible to researchers from a range of backgrounds and offering several opportunities to practice the skills we learn along the way.

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).

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking 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.

• ♿ 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.

PLEASE BE AWARE: This event is run online, if you wish to book for the in-person version, please click 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.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking 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, use programmatic techniques to automate task repetition, and communicate with remote servers (such as High Performance Computing servers).

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to Book or register Interest by linking here.