Bioinformatics course timetable

R is a highly-regarded, free, software environment for statistical analysis, with many useful features that promote and facilitate reproducible research.

In this course, we give an introduction to the R environment and explain how it can be used to import, manipulate and analyse tabular data. After the course you should feel confident to start exploring your own dataset using the materials and references provided.

Course materials are available here.

Please note that although we will demonstrate how to perform statistical analysis in R, we will not cover the theory of statistical analysis in this course. Those seeking an in-depth explanation of how to perform and interpret statistical tests are advised to see the list of Related courses. Moreover, those with some programming experience in other languages (e.g. Python, Perl) might wish to attend the follow-on Data Analysis and Visualisation in R course.

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 cover all aspects of the analysis of DNA methylation using sequencing, including primary analysis, mapping and quality control of BS-Seq data, common pitfalls and complications.

It will also include exploratory analysis of methylation, looking at different methods of quantitation, and a variety of ways of looking more widely at the distribution of methylation over the genome. Finally, the course will look at statistical methods to predict differential methylation.

The course will be comprised of a mixture of theoretical lectures and practicals covering a range of different software packages.

Course materials are available here.

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

This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core aspects of Python illustrated by a series of example programs. Upon completion of the course, attentive participants will be able to write simple Python programs from scratch and to customize more complex code to fit their needs.

Course materials are available here.

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

This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core aspects of Python illustrated by a series of example programs. Upon completion of the course, attentive participants will be able to write simple Python programs from scratch and to customize more complex code to fit their needs.

Course materials are available here.

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

This three day course is intended to open doors to applying statistics - whether directly increasing skills and personally undertaking analyses, or by expanding knowledge towards identifying collaborators. The end goal is to drive confident engagement with data analysis and further training - increasing the quality and reliability of interpretation, and putting that interpretation and subsequent presentation into the hands of the researcher. Each day of the course will deliver a mixture of lecture, workshop and hands-on practice – and will focus on the following specific elements.

Day 1 focuses on basic approaches and the computer skills required to do downstream analysis. Covering: Basic skills for data manipulation in R. How to prepare your data effectively. Principles of experimental design and how this influences analysis.

On day 2, participants will explore the core concepts of statistics – so that they can begin to see how they can be applied to their own work, and to also help with better critical evaluation of the work of others. Covering: Basic statistics concepts and practice: power, variability, false discovery, t-test, effect size, simulations to understand what a p-value means.

On day 3 will use some practical statistics examples in R to introduce concepts in data presentation for publication. Covering: Some practical examples of statistics in R. Visualising and publishing your data.

Course materials are available here.

This event is sponsored by CRUK.

This three day course is intended to open doors to applying statistics - whether directly increasing skills and personally undertaking analyses, or by expanding knowledge towards identifying collaborators. The end goal is to drive confident engagement with data analysis and further training - increasing the quality and reliability of interpretation, and putting that interpretation and subsequent presentation into the hands of the researcher. Each day of the course will deliver a mixture of lecture, workshop and hands-on practice – and will focus on the following specific elements.

Day 1 focuses on basic approaches and the computer skills required to do downstream analysis. Covering: Basic skills for data manipulation in R. How to prepare your data effectively. Principles of experimental design and how this influences analysis.

On day 2, participants will explore the core concepts of statistics – so that they can begin to see how they can be applied to their own work, and to also help with better critical evaluation of the work of others. Covering: Basic statistics concepts and practice: power, variability, false discovery, t-test, effect size, simulations to understand what a p-value means.

On day 3 will use some practical statistics examples in R to introduce concepts in data presentation for publication. Covering: Some practical examples of statistics in R. Visualising and publishing your data.

Course materials are available here.

This event is sponsored by CRUK.

This three day course is intended to open doors to applying statistics - whether directly increasing skills and personally undertaking analyses, or by expanding knowledge towards identifying collaborators. The end goal is to drive confident engagement with data analysis and further training - increasing the quality and reliability of interpretation, and putting that interpretation and subsequent presentation into the hands of the researcher. Each day of the course will deliver a mixture of lecture, workshop and hands-on practice – and will focus on the following specific elements.

Day 1 focuses on basic approaches and the computer skills required to do downstream analysis. Covering: Basic skills for data manipulation in R. How to prepare your data effectively. Principles of experimental design and how this influences analysis.

On day 2, participants will explore the core concepts of statistics – so that they can begin to see how they can be applied to their own work, and to also help with better critical evaluation of the work of others. Covering: Basic statistics concepts and practice: power, variability, false discovery, t-test, effect size, simulations to understand what a p-value means.

On day 3 will use some practical statistics examples in R to introduce concepts in data presentation for publication. Covering: Some practical examples of statistics in R. Visualising and publishing your data.

Course materials are available here.

This event is sponsored by CRUK.

This course will focus on computational methods for analysing cellular images and extracting quantitative data from them. The aim of this course is to familiarise the participants with computational image analysis methodologies, and to provide hands-on training in running quantitative analysis pipelines.

On day 1 we will introduce principles of image processing and analysis, giving an overview of commonly used algorithms through a series of talks and practicals based on Fiji, an extensible open source software package.

On day 2, we will describe the open Icy platform developed at the Institut Pasteur. Icy is a next-generation, user-friendly software offering powerful acquisition, visualization, annotation and analysis algorithms for 5D bioimaging data, together with unique automation/scripting capabilities (notably via its graphical programming interface) and tight integration with existing software (e.g. ImageJ, Matlab, Micro-Manager).

On day 3, we will cover time series processing and cell tracking using TrackMate. Additionally, in the afternoon we will run a study design and data clinic (sign up will be required) for participants that wish to discuss their experiments.

A timetable is available here.

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 focus on computational methods for analysing cellular images and extracting quantitative data from them. The aim of this course is to familiarise the participants with computational image analysis methodologies, and to provide hands-on training in running quantitative analysis pipelines.

On day 1 we will introduce principles of image processing and analysis, giving an overview of commonly used algorithms through a series of talks and practicals based on Fiji, an extensible open source software package.

On day 2, we will describe the open Icy platform developed at the Institut Pasteur. Icy is a next-generation, user-friendly software offering powerful acquisition, visualization, annotation and analysis algorithms for 5D bioimaging data, together with unique automation/scripting capabilities (notably via its graphical programming interface) and tight integration with existing software (e.g. ImageJ, Matlab, Micro-Manager).

On day 3, we will cover time series processing and cell tracking using TrackMate. Additionally, in the afternoon we will run a study design and data clinic (sign up will be required) for participants that wish to discuss their experiments.

A timetable is available here.

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 focus on computational methods for analysing cellular images and extracting quantitative data from them. The aim of this course is to familiarise the participants with computational image analysis methodologies, and to provide hands-on training in running quantitative analysis pipelines.

On day 1 we will introduce principles of image processing and analysis, giving an overview of commonly used algorithms through a series of talks and practicals based on Fiji, an extensible open source software package.

On day 2, we will describe the open Icy platform developed at the Institut Pasteur. Icy is a next-generation, user-friendly software offering powerful acquisition, visualization, annotation and analysis algorithms for 5D bioimaging data, together with unique automation/scripting capabilities (notably via its graphical programming interface) and tight integration with existing software (e.g. ImageJ, Matlab, Micro-Manager).

On day 3, we will cover time series processing and cell tracking using TrackMate. Additionally, in the afternoon we will run a study design and data clinic (sign up will be required) for participants that wish to discuss their experiments.

A timetable is available here.

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.

Microscopy experiments have proven to be a powerful means of generating information-rich data for biological applications. From small-scale microscopy experiments to time-lapse movies and high-throughput screens, automatic image analysis is more objective and quantitative and less tedious than visual inspection.

This course will introduce users to the free open-source image analysis program CellProfiler and its companion data exploration program CellProfiler Analyst. We will show how CellProfiler can be used to analyse a variety of types of imaging experiments. We will also briefly discuss the basic principles of supervised machine learning with CellProfiler Analyst in order to score complex and subtle phenotypes.

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.

Open Targets was founded by three global leading institutions in the fields of pharmaceuticals, bioinformatics and genomics: GSK, EMBL-EBI, the Wellcome Trust Sanger Institute, and recently joined by Biogen, the world’s oldest independent biotechnology company.

We develop two major areas of work, which are further subdivided into projects. The major areas are the Core Bioinformatics and Computational Pipelines, which created and developed the Target Validation Platform, and Experimental projects, which combine large-scale genomics with statistical and computational techniques to identify and validate the causal links between targets, pathways and diseases. The Target Validation Platform integrates comprehensive datasets from a myriad of renowned public databases, such as UniProt, ChEMBL, Ensembl, NHGRI-EBI GWAS, EuropePMC, COSMIC, amongst others, and will incorporate the newly generated data by our Experimental projects in Oncology, Immunity, Inflammation and other areas for free and open access.

The purpose of this half-day workshop is to acquaint participants with the Open Targets consortium. We will highlight the experimental projects in the Consortium but focus on the Target Validation Platform to introduce the tools for visualisation and interpretation of gene-disease associations and target validation. By the end of this half-day workshop, users will be able to carry out effective searches of data, and use the web application to visualise genes, variants, ontology, pathways (and more) in the context of human disease and therapeutics.

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.

In many domains of research the rapid generation of large amounts of data is fundamentally changing how research is done. The deluge of data presents great opportunities, but also many challenges in managing, analyzing and sharing data.

Data Carpentry workshops are designed to teach basic concepts, skills and tools for working more effectively with data. The workshop is aimed at researchers in the life sciences at all career stages and is designed for learners with little to no prior knowledge of programming, shell scripting, or command line tools.

Course materials are available here.

This course is organized in collaboration with ElixirUK and the Software Sustainability Institute.

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

In many domains of research the rapid generation of large amounts of data is fundamentally changing how research is done. The deluge of data presents great opportunities, but also many challenges in managing, analyzing and sharing data.

Data Carpentry workshops are designed to teach basic concepts, skills and tools for working more effectively with data. The workshop is aimed at researchers in the life sciences at all career stages and is designed for learners with little to no prior knowledge of programming, shell scripting, or command line tools.

Course materials are available here.

This course is organized in collaboration with ElixirUK and the Software Sustainability Institute.

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

This workshop is an introduction to EMBL-EBI and the life science data resources it provides. Participants will be shown how to navigate the website and search for appropriate database resources and tools, whilst also highlighting resources such as Train online (our e-learning portal) and the literature resources at Europe PMC.

This workshop will not focus on a set of specific resources; for more focused workshops please see the others within this series (see the Related Courses section below)

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

This course introduces some relatively new additions to the R programming language: dplyr and ggplot2. In combination these R packages provide a powerful toolkit to make the process of manipulating and visualising data easy and intuitive.

Materials for this course can be found here.

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

This workshop will give an introduction to the basic concepts of ontologies and how they are useful in biological applications. The workshop will have three sections:

• A brief, practical introduction to ontologies and semantics
• Focus on Gene Ontology (GO), annotations made using GO and tools leveraging those annotations for biomedical discovery.
• A practical exercise in annotating data with ontologies using EBI tools. A standard annotation exercise will be provided, but attendees are encouraged to bring examples of their own data for annotation, preferably in spreadsheet form.

Also note: This event is part of a series of short introductions focusing on EMBL-EBI resources. If you want to learn more about these separate training events, see the Related Courses section below.

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

This workshop will give an introduction to the protein sequence analysis & classification database Interpro.

Interpro is a bioinformatics resource that provides functional analysis of protein sequences by classifying them into families and predicting the presence of domains and important sites. To classify proteins in this way, InterPro uses predictive models, known as signatures, provided by several different databases (referred to as member databases) that make up the InterPro Consortium.

Also note: This event is part of a series of short introductions focusing on EMBL-EBI resources. If you want to learn more about these separate training events, see the Related Courses section below.

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