All Bioinformatics courses

This week-long course is aimed at people with little or no experience using statistical analyses in research. It introduces participants to core concepts in statistics and experimental design, aimed at ensuring that the resulting data is able to address the research question using appropriate statistical methods.

The interactive course gives participants a hands-on, applied foundation in statistical data analysis and experimental design. Group exercises and discussions are combined with short lectures that introduce key theoretical concepts. Computational methods are used throughout the course, using the R programming language. Formative assessment exercises allow participants to test their understanding throughout the course and encourage questions and critical thinking.

By the end of the course participants will be able to critically evaluate and design effective research questions, linking experimental design concepts to subsequent statistical analyses. It will allow participants to make informed decisions on which statistical tests are most appropriate to their research questions. The course will provide a solid grounding for further development of applied statistical competencies.

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

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.

• ♿ 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.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a method used to identify binding sites for transcription factors, histone modifications and other DNA-binding proteins across the genome. In this course, we will cover the fundamentals of ChIP-seq data analysis, from raw data to downstream applications.

We will start with an introduction to ChIP-seq methods, including important considerations when designing your experiments. We will cover the bioinformatic steps in a standard ChIP-seq analysis workflow, covering raw data quality control, trimming/filtering, mapping, duplicate removal, post-mapping quality control, peak calling and peak annotation. We will discuss metrics used for quality assessment of the called peaks when multiple replicates are available, as well as the analysis of differential binding across sample groups. Throughout the course we will also cover tools and packages that can be used for visualising and exploring your 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, 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.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a method used to identify binding sites for transcription factors, histone modifications and other DNA-binding proteins across the genome. In this course, we will cover the fundamentals of ChIP-seq data analysis, from raw data to downstream applications.

We will start with an introduction to ChIP-seq methods and cover the bioinformatic steps in processing ChIP-seq data. We will then introduce the use of the graphical program SeqMonk to explore and visualise your data. Finally, you will perform peak calling and perform differential enrichment 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.
• Guidance on visiting Cambridge and finding accommodation is available 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 comprises of a mixture of theoretical lectures and practicals covering a range of different software packages.

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.

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, 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.

This advanced course will cover high-throughput sequencing data processing, ChIP-seq data analysis (including alignment, peak calling), differences in analyses methods for transcription factors (TF) binding and epigenomic datasets, a range of downstream analysis methods for extracting meaningful biology from ChIP-seq data and will provide an introduction to the analysis of open chromatin with ATAC-seq and long-distance interactions with chromosomal conformation capture based Hi-C datasets.

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 or register your interest by linking here.

This course provides an introduction to the tools available through the Bioconductor project for manipulating and analysing high-throughput sequencing (HTS) data. We will present workflows for the analysis of ChIP-Seq and RNA-seq data starting from aligned reads in bam format. We will also describe the various resources available through Bioconductor to annotate and visualize HTS data, which can be applied to any type of sequencing experiment.

The course timetable is 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.

PLEASE NOTE that until further notice, due to the evolving situation with Coronavirus no courses will be offered as classroom based at the Training Facility. The Bioinformatics Team will be teaching the course live online in conjunction with the presenters.

SeqMonk is a graphical program for the visualisation and analysis of large mapped sequencing datasets such as ChIP-Seq, RNA-Seq, and BS-Seq.

The program allows you to view your reads against an annotated genome and to quantitate and filter your data to let you identify regions of interest. It is a friendly way to explore and analysis very large datasets.

This course provides an introduction to the main features of SeqMonk and will run through the analysis of a couple of different datasets to show what sort of analysis options it provides.

Further information 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 provides an introduction to the tools available through the Bioconductor project for manipulating and analysing bulk RNA-seq data. We will present a workflow for the analysis RNA-seq data starting from aligned reads in bam format and producing a list of differentially-expressed genes. We will also describe the various resources available through Bioconductor to annotate, visualise and gain biological insight from the differential expression results.

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

Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). Even though scRNA-seq makes it possible to address problems that are intractable with bulk RNA-seq data, analysing scRNA-seq is also more challenging.

In this course we will be surveying the existing problems as well as the available computational and statistical frameworks available for the analysis of scRNA-seq.

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.

This course focuses on methods for the analysis of small non-coding RNA data obtained from high-throughput sequencing (HTS) applications (small RNA-seq). During the course, approaches to the investigation of all classes of small non-coding RNAs will be presented, in all organisms.

Day 1 will focus on the analysis of microRNAs and day 2 will cover the analysis of other types of small RNAs, including Piwi-interacting (piRNA), small interfering (siRNA), small nucleolar (snoRNA) and tRNA-derived (tsRNA).

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

This webinar is an Introduction to Biological Networks, their types, and applications. It will include two of the most commonly used open source Network Visualisation Platforms (R-igraph and Cytoscape) with step-wise protocols for creating and visualising your own data as a network. It will present some of the major layout algorithms, visual styles and tips for effective visualisation, with examples from biology revealing how these can improve analysis and provide insights.

The webinar will be presented in the form of a lecture as well as a tutorial with step-wise screenshots that enable listeners to emulate simple Network creation and analysis. Please note that this is a webinar and not a coding exercise. Links to publicly available resources and hands-on tutorials will be shared with you for further reading and practice.

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.

Through the use of real world examples and the JMP, JMP Pro, and JMP Genomics software, we will cover best practices used in both industry and academia today to visually explore data, plan biological experiments, detect differential expression patterns, find signals in next-generation sequencing data and easily discover statistically appropriate biomarker profiles and patterns.

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.

Analysis of whole genome data unearths a multitude of variants of different classes, which need to be filtered, annotated and validated to arrive at a causative variant for a disease. The current short length sequences, whilst being excellent at identifying single nucleotide variants and short insertions/deletions, struggle to correctly map structural variants (SVs). Long-read sequencing technologies offer improvements in the characterisation of genetic variation and regions that are difficult to assess with short-read sequences.

The aim of this course is to familiarise participants with long read sequencing technologies, their applications and the bioinformatics tools used to assemble this kind of data. Lectures will introduce this technology and provide insight into methods for the analysis of genomic data, while the hands-on sessions will allow participants to run analysis pipelines, focusing on data generated by the Oxford Nanopore Technologies (ONT) platform.

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 IS NOT RETURNING IN ITS CURRENT FORM. PLEASE CHECK OUR WEBSITE FOR MORE INFORMATION.

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 NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This course aims to give you an introduction to the basics of Matlab. During the two day course we will use a practical based approach to give you the confidence to start using Matlab in your own work. In particular we will show you how to write your own scripts and functions and how to use pre-written functions. We will also explore the many ways in which help is available to Matlab users. In addition we will cover basic computer programming in Matlab to enable you to write more efficient scripts.

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.

The aim of this course is to introduce participants to the basics of statistical analysis and the open source statistical software R, a free software environment for statistical computing and graphics.

Participants will actively use R throughout the course, during which they will be introduced to principles of statistical thinking and interpretation by example, exercises and discussion about a range of problems. The examples will be used to present a variety of statistical concepts and techniques, with no focus on any specific discipline.

Important information: We have 12 configured laptops for use at the workshop. After these laptops have been allocated, participants will either need to share, or bring their own. These laptops will be allocated to the first individuals to express an interest in using them. When booking, please indicate under "Special requirements" if you wish to use one of the 12 laptops or bring your own. Participants bringing their own laptop will be given instructions on what software to install.

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 aimed at those new to programming and provides an introduction to programming using Perl.

During this course you will learn the basics of the Perl programming language, including how to store data in Perl’s standard data structures such as arrays and hashes, and how to process data using loops, functions, and many of Perl’s built in operators. You will learn how to write and run your own Perl scripts and how to pass options and files to them. The course also covers sorting, regular expressions, references and multi-dimensional data structures.

The course will be taught using the online Learning Perl materials created by Sofia Robb of the University of California Riverside.

The course website providing links to the course materials is here.

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

Please note that this course has been discontinued and has been replaced by the Introduction to R for biologists.

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.

The course website providing links to the course materials is 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.

This event is supported by the BBSRC Strategic Training Awards for Research Skills (STARS) grant (BB/P022766/1).

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.

The workshop will cover the basics of de novo genome assembly using a small genome example. This includes project planning steps, selecting fragment sizes, initial assembly of reads into fully covered contigs, and then assembling those contigs into larger scaffolds that may include gaps. The end result will be a set of contigs and scaffolds with sufficient average length to perform further analysis on, including genome annotation (link to that nomination). This workshop will use tools and methods targeted at small genomes. The basics of assembly and scaffolding presented here will be useful for building larger genomes, but the specific tools and much of the project planning will be different.

This workshop will also introduce genome annotation in the context of small genomes. We’ll begin with genome annotation concepts, and then introduce resources and tools for automatically annotating small genomes. The workshop will finish with a review of options for further automatic and manual tuning of the annotation, and for maintaining it as new assemblies or information becomes available.

This session will include an introduction to the Galaxy platform.

This event is co-organized with EMBL-ABR and the Genomics Virtual Lab. Course materials 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 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 lectures, workshops and hands-on practicals – 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 we will continue to explore core concepts of statistics, focusing on linear regression and multiple testing correction.

Course materials are available here.

This event is supported by the BBSRC Strategic Training Awards for Research Skills (STARS) grant (BB/P022766/1).

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

Recent advances in genomics, proteomics, imaging and other technologies, have resulted in data being generated at a faster rate than they can be meaningfully analysed. In this course we will show you how cloud computing can be used to meet the challenges of storage, management and analysis of big data. The first half of the course will introduce cloud infrastructure technologies. The second half will cover tools for collaborative working, resource management, and creation of workflows. The instructors will demonstrate how they are using cloud computing in their own research.

N.B. If you sign up for this course, you will be automatically registered for an AWS educate account, which will provide you with sufficient AWS credits to complete the course exercises. If you decide to continue using cloud computing after the course, you will need to either purchase more credits or apply for a grant from programs like: AWS Cloud Credits for Research, Microsoft Azure for Research or Google Cloud Platform Education Grants.

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

In this course we will introduce web-based, open source tools to analyse and interpret high-throughput biological data.

The main focus will be g:Profiler - a toolset for finding most significant functional groups for a given gene or protein list; MEM - a query engine allowing to mine hundreds of public gene expression datasets to find most co-expressed genes based on a query gene; and ClustVis - a web tool for visualizing clustering of multivariate data using Principal Component Analysis (PCA) plot and heatmap.

MEM and g:Profiler are ELIXIR-Estonia node services.

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

The aim of this workshop is to provide principal investigators with an introduction to the challenges of working with biological data and to the best practices, and tools, needed to perform bioinformatics research effectively and reproducibly.

On day 1, we will cover the importance of experimental design, discuss the challenges associated with (i) the analysis of high-throughput sequencing data (utilising RNA-seq as a working example) and (ii) the application of machine learning algorithms, as well as issues relating to reusability and reproducibility.

On day 2, we will put into practice concepts from day 1, running a RNA-seq data analysis pipeline, going from raw reads through differential expression analysis and the interpretation of downstream analysis results. Challenges encountered at each step of the analytical pipeline will be discussed. Please note that day 2 is optional.

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.