All-provider course timetable

Falls from ladders and other height access devices continue to cause injuries to people at work in the University. The new Work at Height Regulations (April 2005) have implications for the University because the emphasis has changed to any fall from any height. Anyone who uses ladders or who supervises work with ladders should attend this session.

This workshop will focus on the core steps involved in calling germline short variants, somatic short variants, and copy number alterations with the Broad’s Genome Analysis Toolkit (GATK), using “Best Practices” developed by the GATK methods development team. A team of methods developers and instructors from the Data Sciences Platform at Broad will give talks explaining the rationale, theory, and real-world applications of the GATK Best Practices. You will learn why each step is essential to the variant-calling process, what key operations are performed on the data at each step, and how to use the GATK tools to get the most accurate and reliable results out of your dataset. If you are an experienced GATK user, you will gain a deeper understanding of how the GATK works under-the-hood and how to improve your results further, especially with respect to the latest innovations.

• Day 1: Introductory and Overview. The first day of the workshop gives a high-level overview of various topics in the morning, and in the afternoon we show how these concepts apply to a case study. The case study is tailored based on the audience, as represented by their answers in our pre-workshop survey.

• Day 2: Germline Short Variant Discovery. Today we dive deep into the tools that make up the GATK Best Practices Pipeline. In the morning we discuss variant discovery, and in the afternoon we look at refinement and filtering. You will have the opportunity both in the morning and in the afternoon to get hands-on with these tools and run them yourself.

• Day 3: Somatic Variant Discovery. Today we will cover Somatic Variant Discovery in more depth. In the morning we primarily focus on calling short variants with Mutect2, and in the afternoon we look at copy number alterations. Both sections have a paired hands-on activity.

• Day 4: Pipelining. Over the first three days, you would have learned a lot about different pipelines and tools that you can use in GATK. Today we will be learning all about how those pipelines are written in a language called WDL. In the afternoon we cover other useful topics to working on the cloud, including Docker and BigQuery.

Please note that this workshop is focused on human data analysis. The majority of the materials presented does apply equally to non-human data, and we will address some questions regarding adaptations that are needed for analysis of non-human data, but we will not go into much detail on those points.

The hands-on GATK tutorials in this workshop will be conducted on Terra, a new platform developed at Broad in collaboration with Verily Life Sciences for accessing data, running analysis tools and collaborating securely and seamlessly.

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.

This 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 a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to generalised linear model analysis. 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.

This course covers the practical steps you need to take in order to ensure that work submitted for publication by University of Cambridge researchers is compliant for REF2021.

We will introduce the principles of open access and open research, and guide you through the necessary steps to meet the open access requirements of REF2021. We will demonstrate key processes for uploading work to Symplectic, including choosing the right version of a work to upload. There will be plenty of time in the session to ask questions, and for you to try out uploading papers.

This course will be useful to you if you:

• administer the uploading of research outputs to Symplectic Elements to make them open access
• manage Symplectic profiles