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This 1-week course provides an introduction to data exploration of biological data. It provides a learning journey starting with learning about how we can automate processes that can be reproduced to analyse our biological data.

The course will begin with discussing what opportunities and challenges are associated with aspects of bioinformatics analyses. We will address a subset of them in greater detail in the central part of the course and provide time for participants to practise using some of the associated bioinformatics tools.

Focusing on solutions around handling biological data, we will cover programming in R, version control, statistical analyses, and data exploration. The R component of the course will cover from the foundations of programming in R to how to use some of the most popular R packages (dplyr and ggplot2) for data manipulation and visualisation. No prior R experience or previous knowledge of programming is required. At the end of the course we will address issues relating to reusability and reproducibility.

More information about the 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.

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.

This workshop will introduce students to EMBL-EBI, the databases and services it offers, and basic concepts in bioinformatics that will be of use to their disease related research work.

It will explain the role of the EMBL-EBI in curating and sharing biological data with scientists around the world, and introduce concepts for locating relevant data and information of interest.

Sessions with trainers from Ensembl, ArrayExpress and the GWAS catalog will introduce practical skills in browsing genes and variation in a genomic context, in exploring SNP-trait associations and will show how further understanding can be gained on the location and level of gene expression across the body.

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 provide participants with an introduction to EMBL-EBI and its data tools and resources, which cover the whole spectrum of biological / life sciences.

Sessions with trainers from ArrayExpress, Expression Atlas and the GWAS catalog will explore SNP-trait associations and show how further understanding can be gained on the location and level of gene expression across the body.

This event is part of a series of training courses organized in collaboration with Dr. Mark Dunning at CRUK Cambridge Institute.

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.

Are you aware of the wide range of protein data resources that can easily be accessed and explored to enhance your research? Do you want to know more about the sequence of your protein and its functions? Wondered whether a structure of your protein exists and how to explore it? Want to know more about the potential complexes and reaction pathways your protein of interest is involved in, giving you a better overview of its biological context?

This three day workshop will introduce you to data resources and tools developed by EMBL-EBI that can help you in your protein studies. Each day will focus on a particular protein topic, with the aim of helping you get more from your data and also to explore publically-available data that can further support your research.

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 clicking here.

This event introduces participants to the KNIME Analytics Platform, an open source data science platform with a visual workflow editor, that can be used by users without prior programming experience or integrated with existing scripts written in R or Python.

These sessions are aimed towards anyone who has an interest in building data science workflows with different kinds of life science data. The sessions will cover how to aggregate data from different sources (e.g., files, databases, web services), how to calculate simple statistics (e.g., for data exploration), network mining (e.g., protein-protein interactions) and big data analytics (e.g., next-generation sequencing data).

The webinar will combine practical and taught content to demonstrate how users can use KNIME to design and utilise reproducible data science workflows, such as analytics tasks, and better explore and understand their data.

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 module introduces a deeper exploration of bioinformatics analysis of genomic data, providing a greater understanding of the different approaches to mapping and alignment of genome sequence data, programming and scripting, along with approaches for the detection and analysis of genomic changes, gene expression and network analysis.

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 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 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.

PLEASE NOTE The Bioinformatics Team are presently teaching many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We continue to monitor advice from the UK government and the University of Cambridge on resuming in-person teaching back in the training room.

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.

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.

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, 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 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.

THIS EVENT IS NOW FULLY BOOKED!

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 School aims to familiarise biomedical students and researchers with principles of Data Science. Focusing on utilising machine learning algorithms to handle biomedical data, it will cover: effects of experimental design, data readiness, pipeline implementations, machine learning in Python, and related statistics, as well as Gaussian Process models.

Providing practical experience in the implementation of machine learning methods relevant to biomedical applications, including Gaussian processes, we will illustrate best practices that should be adopted in order to enable reproducibility in any data science application.

This event is sponsored by Cambridge Centre for Data-Driven Discovery (C2D3).

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.