## Training Courses

As part of its work with the Babraham Institute, the Bioinformatics group runs a regular series of training courses on many aspects of bioinformatics.

These courses are run regularly on the Babraham site but we are also able to come out and present them on other sites and also deliver them remotely. You can see the list of current Babraham dates which are available, and you can contact us to discuss options for running courses on your site.

You can also sign up to our mailing list to get the latest training news delivered direct to your inbox every couple of months.

Where possible we also aim to make the material from our courses publicly available so that anyone who wants to can download them for their own use.

Below is a list of the courses we currently run. Where they are available there is a link to the training manual and course exercises.

#### Core Bioinformatics Skills

- R (tidyverse) Courses
- R (just core) Courses
- Other R courses
- Python Courses
- Perl Courses
- Unix Courses
- Machine learning

#### Statistics

- Modular Statistics courses using R
- Statistical Analysis using R One Day
- Statistical Analysis using R Bootcamp
- Modules
- Power analysis: Sample size estimation
- Descriptive statistics and Data exploration
- Analysis of quantitative data - Introduction
- Analysis of quantitative data - Student's t-test
- Analysis of quantitative data - One-way and Two-way ANOVA
- Analysis of quantitative data - Linear Regression
- Introduction to Linear modelling
- Analysis of qualitative data - Non parametric statistics
- Analysis of qualitative data
- Modular statistics courses using GraphPad Prism
- Statistical Analysis using GraphPad Prism One day
- Statistical Analysis using GraphPad Prism Bootcamp
- Modules
- Power analysis: Sample size estimation
- Descriptive statistics and Data exploration
- Analysis of quantitative data - Introduction
- Analysis of quantitative data - Student's t-test
- Analysis of quantitative data - One-way and Two-way ANOVA
- Analysis of quantitative data - Linear Regression
- Introduction to Linear modelling
- Analysis of qualitative data - Non parametric statistics
- Analysis of qualitative data

#### Application focussed courses

- Data Resources
- Next Generation Sequencing
- Quality control in Sequencing Experiments
- Analysing Mapped Sequence Data with SeqMonk
- RNA-Seq Analysis
- 10X Single Cell RNA-Seq Analysis
- ChIP-Seq Analysis
- Analysing bisulfite methylation sequence data
- Proteomics
- Modelling
- Interpretation and Presentation

#### Comprehensive longer Bootcamp courses

- Introduction to NGS Analysis for Biologists bootcamp
- Introduction to Linux bootcamp
- Introduction to R for Biologists bootcamp
- Statistics bootcamp using R
- Statsitics bootcamp using GraphPad Prism

### Analysing Mapped Sequence Data with SeqMonk (Half day)

SeqMonk is a program which can analyse large data sets of mapped genomic positions. It is most commonly used to work with data coming from high-throughput sequencing pipelines.

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 analyse 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.

#### Course content

- What is SeqMonk
- Installing and configuring the program
- Creating a project and importing data
- Using the chromosome viewer
- Quantitating and Filtering Data
- Creating Reports
- Exporting text and graphics

#### Course Material:

### Statistical Analysis using R (One day)

Statistics are an important part of most modern studies and being able to effectively use a statistics package can help you to understand your results. This course provides an introduction to statistics illustrated though the use of the R language.

#### Course Content:

- Introduction to Power Analysis
- Qualitative and Quantitative Data Exploration
- Graphical representations
- Chi-square, Fisher's exact test, T-Test, ANOVA and correlation
- Choosing an appropriate analysis
- Interpreting analysis output

#### Course Material:

- Course Manual (pdf)
- Course Manual (docx)
- Course Slides (pdf)
- Course Slides (pptx)
- Course Data (zip 13kb)

### Statistical Analysis using GraphPad Prism (One day)

GraphPad Prism is a powerful and friendly package which allows you to plot and analyse your data. This course acts not only as an introduction to Prism, but also goes through the basic statistical knowledge which should allow you to make the most of your data.

#### Course Content:

- Introduction to GraphPad Prism
- Getting to know your data
- Graphical representations
- Choosing an appropriate analysis
- Interpreting analysis output

#### Course Material:

- Course Manual (pdf)
- Course Manual (docx)
- Course Slides (pdf)
- Course Slides (pptx)
- Exercises (pptx)
- Exercises (pdf)
- Course Data Files (zip)

### Statistics bootcamp using R (3 days)

A more in depth look at statistical analyses using R.

Prerequisite: Introduction to R with Tidyverse (1 day)

#### Course Content Modules:

- Power analysis: Sample size estimation
- Descriptive statistics and Data exploration
- Analysis of quantitative data - Introduction
- Analysis of quantitative data - Student's t-test
- Analysis of quantitative data - One-way and Two-way ANOVA
- Analysis of quantitative data - Linear Regression
- Introduction to Linear modelling
- Analysis of quantitative data - Non parametric statistics
- Analysis of qualitative data

#### Course Material modules:

- Descriptive statistics and Data exploration (pptx)
- Descriptive statistics and Data exploration (pdf)

- Quantitative Data analysis - Student's t-test(pptx)
- Quantitative Data analysis - Student's t-test(pdf)

- Quantitative Data analysis - One-Way and Two-Way ANOVA(pptx)
- Quantitative Data analysis - One-Way and Two-Way ANOVA(pdf)

- Quantitative Data analysis -Linear Regression (pptx)
- Quantitative Data analysis - Linear Regression (pdf)

- Analysis of quantitative data - Non parametric analysis (pptx)
- Analysis of quantitative - Non parametric analysis (pdf)

### Statistics bootcamp using GraphPad Prism (2.5 days)

A more in depth look at statistical analyses using GraphPad Prism

#### Course Content modules:

- Power analysis: Sample size estimation
- Descriptive statistics and Data exploration
- Analysis of quantitative data - Introduction
- Analysis of quantitative data - Student's t-test
- Analysis of quantitative data - One-way and Two-way ANOVA
- Analysis of quantitative data - Linear and Non Linear relationship
- Introduction to Linear modelling
- Analysis of quantitative data - Non parametric statistics
- Analysis of qualitative data
- Survival Analysis

#### Course Material:

- Descriptive statistics and Data exploration (pptx)
- Descriptive statistics and Data exploration (pdf)

- Quantitative Data analysis - Student's t-test(pptx)
- Quantitative Data analysis - Student's t-test(pdf)

- Quantitative Data analysis - One-Way and Two-Way ANOVA(pptx)
- Quantitative Data analysis - One-Way and Two-Way ANOVA(pdf)

- Quantitative Data analysis - Linear and Non Linear relationship (pptx)
- Quantitative Data analysis - Linear and Non Linear relationship (pdf)

- Analysis of quantitative data - Non parametric analysis (pptx)
- Analysis of quantitative data - Non parametric analysis (pdf)

### Learning to Program with Perl (6 x 1.5 hour sessions)

For a long time, Perl has been a popular language among those starting out with programming. Although it is a powerful language, many of its features make it especially suited to first time programmers as it reduces the complexity found in many other languages. Perl is also one of the world's most popular languages which means there are a huge number of resources available to anyone setting out to learn it.

This course aims to introduce the basic features of the Perl language. At the end you should have everything you need to write moderately complicated programs, and enough pointers to other resources to get you started on bigger projects. The course tries to provide a grounding in the basic theory you'll need to write programs in any language, as well as an appreciation for the right way to do things in Perl.

#### Course Content:

- Getting Started with Perl
- Conditions, Arrays, Hashes and Loops
- File Handling
- Regular Expressions
- Subroutines, References and Complex Data Structures
- Perl Modules
- Interacting with External Programs
- Cross Platform Issues and Compiling

#### Course Material:

- Course Manual (pdf)
- Course Manual (doc)
- Course Exercises (pdf)
- Course Exercises (doc)
- Code used in the course (zip)

### Introduction to Python (2 day, or 4 half-day bootcamp)

Python has established itself as one of the most commonly used programming languages. It is a very powerful language, which makes it relatively easy to write programs from simple automation scripts to more fully featured applications. In bioinformatics python has become widely used both as a language to write scripts and applications, but also, via packages like pandas, numpy and seaborn as an environment for data analysis, competing with more focussed languages such as R. In this course we focus on the use of python to develop simple scripts and larger applications. These can be used for simple data processing and aggreagation, for automating repeated tasks or to write larger user-facing command line programs. We start from the ground up, and make no assumption of any previous programming experience.

#### Course Content:

- Setting up your python environment
- Variables and Data Types
- Functions and Methods
- Python data structures
- Iterators, Loops and Conditional Statements
- Text Processing
- Reading and Writing Files
- Writing Functions and Larger Scripts
- Using external resources

#### Course Material:

- Course Slides (pptx)
- Course Slides (pdf)
- Course Exercises (docx)
- Course Exercises (pdf)
- Course Data (zip)[2.2MB]

### Advanced Python (2 day bootcamp)

In recent years, the programming language Python has become ever more popular in the bioinformatics and computational biology communities and indeed, learning this language marks many people's first introduction to writing code. This success of Python is due to a number of factors. Perhaps most importantly for a beginner, Python is relatively easy to use, being what we term a "high-level" programming language. Don't let this terminology confuse you however: "high-level" simply means that much of the computational tasks are managed for you, enabling you to write shorter and simpler code to get your jobs done.

This course builds on the basic features of Python3 introduced in the Introdcution to Python course. At the end of this course you should be able to write moderately complicated programs, and be aware of additional resources and wider capabilities of the language to undertake more substantial projects. The course tries to provide a grounding in the basic theory you'll need to write programs in any language as well as an appreciation of the right way to do things in Python.

#### Course Content:

- More code structuring with Iterators
- Write more elegant code with Python Comprehensions
- Python Generators create data
- Python scoping and exception handling
- Using modules
- Analysing text with Regular Expressions
- Introduction to Object Oriented Programming

#### Course Material:

- Course Manual (pdf)
- Course Manual (docx)
- Course Slides (pdf)
- Course Slides (pptx)
- Course Exercises (pdf)
- Course Exercises (docx)
- Course Data (zip)

### Understanding Object Oriented Python (One day)

A strength of Python and a feature that makes this language attractive to so many, is that Python is what is known as an object-oriented programming language (OOP).

This is a short course that introduces the basic concepts of OOP. It then goes into more detail explaining how to build and manipulate objects. While this course does not provide an exhaustive discussion of OOP in Python, by the end of the course attendees should be able to build sophisticated objects to aid analysis and research.

#### Course Content:

- Introducing Object Oriented Programming
- Creating objects and classes
- Structuring objects
- Using Inheritance to write succinct code

#### Course Material:

- Course Manual (docx)
- Course Manual (pdf)
- Course Slides (pdf)
- Course Slides (pptx)
- Course Exercises (pdf)
- Course Exercises (docx)
- Course Data (zip)

### Introduction to R with Tidyverse (One day)

R is a popular language and environment that allows powerful and fast manipulation of data, offering many statistical and graphical options. This course aims to introduce R as a tool for statistics and graphics, with the main aim being to become comfortable with the R environment. As well as introducing core R language concepts this course also provides the basics of using the Tidyverse for data maniupulation, and ggplot for plotting. It will focus on entering and manipulating data in R and producing simple graphs. A few functions for basic statistics will be briefly introduced, but statistical functions will not be covered in detail.

#### Course Content:

- What is R
- Getting familiar with the R console
- Entering Data
- Manipulating data
- Importing data files
- Creating Graphs (scatterplots, line graphs, line graphs, histograms and density plots)

#### Course Material:

- Intro to R with Tidyverse Slides (pdf)
- Intro to R with Tidyverse Slides (pptx)
- Course Exercises (pdf)
- Course Exercises (doc)
- Answers to exercise questions (html)
- Course data (zip)

#### Post-Course Material:

### Introduction to Core R (Half a day)

R is a popular language and environment that allows powerful and fast manipulation of data, offering many statistical and graphical options. This course aims to introduce R as a tool for statistics and graphics, with the main aim being to become comfortable with the R environment. It will focus on entering and manipulating data in R and producing simple graphs. A few functions for basic statistics will be briefly introduced, but statistical functions will not be covered in detail.

#### Course Content:

- What is R
- Getting familiar with the R console
- Entering Data
- Manipulating data
- Importing data files
- Creating Graphs (boxplots, barplots, scatterplots, line graphs)

#### Course Material:

- Course Manual (pdf)
- Course Manual (docx)
- Course Exercises (pdf)
- Course Exercises (doc)
- Intro to R Slides (pdf)
- Intro to R Slides (pptx)
- R command cheatsheet (pdf)
- Answers to exercise questions (html)
- Course data (zip)

### Advanced Core R (Half a day)

This course follows on from the introductory course. It goes into more detail on practical guides to filtering and combining complex data sets. It also looks at other core R concepts such as looping with apply statements and using packages. Finally, it looks at how to document your R analyses and generate complete analysis reports.

#### Course Content:

- Filtering and selection review
- Text manipulation
- Merging large datasets
- Looping
- Using and writing functions
- R packages
- Documenting your analysis

#### Course Material:

- Course Manual (pdf)
- Course Manual (docx)
- Course Exercises (pdf)
- Course Exercises (docx)
- Course data (zip)

### Plotting complex figures with Core R (Half a day)

This course is a comprehensive guide to the use of the built-in R plotting functionality to construct everything from customised simple plots to complex multi-layered figures. It follows on from the material in our introductory R course and participants are expected to have a basic understanding of R - enough to load and do basic manipulation of datasets.

#### Course Content:

- The R painters model
- Core graph types and options
- Plot area customisation
- Using colour in plots
- Adding plot overlays
- Useful extension packages
- Writing plots to files

#### Course Material:

- Course Manual (pdf)
- Course Manual (docx)
- Course Exercises (pdf)
- Course Exercises (docx)
- Presentation Slides (pdf)
- Presentation Slides (pptx)
- Course data (zip)
- Exercise Answers (html)

### Advanced R with Tidyverse (One day)

The 'Tidyverse' is a set of add-in R packages for data loading, modelling, manipulation and plotting. It is an attempt to make data analysis and plotting cleaner, simpler and more consistent by addressing some poor design decisions in the original language.

This course follows on from our Introduction to R with tidyverse and focusses on the manipulation and restructuring of data using the tidyverse packages. The course shows how to do complex transformations on large data structures and how to deal efficiently with data which is both large and sometimes not well behaved.

#### Course Content:

- Reading in data and dealing with problems
- Advanced filtering and selections
- Restructuring data into 'tidy' format
- Mutating, grouping and summarising data
- Merging datasets together
- Using custom functions

#### Course Material:

- Course Slides (pptx)
- Course Slides (pdf)
- Course Exercises (docx)
- Course Exercises (pdf)
- Course Data (zip)
- Exercise Answers (html)

### Using R Notebooks (Half day)

This course is designed for people who are already familiar with R and are ready for a more integrated way to perform and report their analyses. It will show the use of R Notebooks for interactive analysis and then demonstrate how to apply this to the production of complete reports.

#### Course Content:

- The structure of R Notebooks
- Using Markdown to format text
- Controlling and customising R code blocks
- Customising the appearance of your document
- Automated notebook compilation

#### Course Material:

### Plotting figures with ggplot (One day)

This course is normally taught as part of the R with Tidyverse bootcamp. Ggplot is the most popular plotting extension to R and replicates many of the graph types found in the core plotting libraries. This course provides an introduction to the ggplot2 libraries and gives a practical guide for how to use these to create different types of graphs.

#### Course Content:

- How ggplot2 works
- Plotting different graph types
- Changing annotation, scaling and colours
- Adding statistical summaries and other overlays
- Faceting and highlighting
- Saving plots

#### Course Material:

- Course Slides (pdf)
- Course Slides (pptx)
- Course Exercises (docx)
- Course Exercises (pdf)
- Course data (zip)
- Exercise Answers (html)

### Writing R Packages (One day)

R packages are the best way to create robust re-usable code, either for internal use or for sharing with the wider community. In this course we will look at how to write functions which are robust for use by others. We will then go through the process of authoring function based R packages with the help of the recommended development tools.

#### Course Content:

- Developing robust functions
- Setting up git based package sources
- Adapting function code for a package
- Writing help files and vignettes
- Writing a test suite
- Installing the finished package

#### Course Material:

### Introduction to Shiny (One day)

Shiny is an R package that enables interactive web applications to be built using R. They are a great way of allowing users to explore a dataset and make use of the graphical and statistical functionality of R without having to write any code.

#### Course Content:

This course is a combination of talks and practical exercises. It covers the concepts required to create a functioning Shiny application including:

- Layouts
- Inputs
- Outputs
- Reactivity

To write Shiny applications you should be comfortable with using R. It is recommended that students should have completed Introductory and Advanced R courses (core or tidyverse) before attending this course.

#### Course Material:

- Course Slides Part 1 (html)
- Course Slides Part 1 v2(html)
- Part 1 Exercises (zip)
- Part 1 solutions (zip)
- Course Slides Part 2 (html)
- Part 2 Exercises (zip)
- Part 2 solutions (zip)

### Using git and GitHub with RStudio (2 hours)

RStudio has embedded tools to facilitate the use of git with RProjects. This short course explores this functionality.

#### Course Content:

This course is a combination of talks and practical exercises and covers the following:

- Version control theory
- Using git with RProjects
- Using GitHub as a remote repository

#### Course Material:

- Initial setup instructions (html)
- Exercises (html)
- Course Slides (pdf)
- Course Slides (pptx)
- Course Data (zip)

### An Introduction to Unix (Half a day)

Increasing amounts of bioinformatics work is done in a command line unix environment. Most large scale processing applications are written for unix and most large scale compute environments are also based on this.

This course provides an introduction to the concepts of unix and provides a practical introduction to working in this environment. Internally we link this course to a more specific course illustrating the use of our internal cluster environment and this part of the course could be adapted for other sites with different compute infrastructure

#### Course Content:

- Unix commands
- Files and Directories
- Viewing, Creating, Copying, Moving and Deleting Files
- Pipes and Loops

#### Course Material:

- Course Slides (pdf)
- Course Slides (pptx)
- Course Exercises (pdf)
- Course Exercises (docx)
- Unix cheat sheet (pdf) (External content from Fosswire)
- Course Data (tar.gz) [5MB]

### An Introduction to Machine Learning (One day)

This course provides a theoretical and practical introduction to the use of machine learning on biological datasets. For the final section of the course we will introduce the tidymodels framework for machine learning in R, so it will be helpful to have attended our introductory and advanced R courses, or to have had equivalent experience, although this is not a prerequisite to attend the course.

#### Course Content:

- What is machine learning?
- Different types of machine learning model
- Evaluating models
- Preparing input data
- Running simple models in tidymodels
- Automation with recipes and worklows

#### Course Material

- Course Slides (pdf)
- Course Slides (pptx)
- Course Exercises (pdf)
- Course Exercises (docx)
- Course Data (zip)

### Analysing bisulfite methylation sequencing data (One day)

This course builds on the core skills introduced in the Introduction to R, Introduction to Unix and Introduction to SeqMonk courses to provide a more in depth look at the analysis of bisulfite sequencing data. The course is a mix of theoretical lectures and hands-on practicals which go through the whole analysis pipeline, starting from raw sequence data and covering QC, visualisation, quantitation and differential methylation analysis.

#### Course Content:

- The theoretical basis for BS-Seq
- Processing raw sequencing data with Bismark
- Visualisation and exploration of methylation calls with SeqMonk
- The theory of differential methylation calling
- Differential methylation analysis practical

#### Course Material:

- BS-Seq data processing lecture (pptx)
- BS-Seq data processing lecture (pdf)
- BS-Seq data processing exercises (docx)
- BS-Seq data processing exercises (pdf)
- Visualisation and exploration lecture (pptx)
- Visualisation and exploration lecture (pdf)
- SeqMonk tools for methylation analysis (pptx)
- SeqMonk tools for methylation analysis (pdf)
- Visualisation and exploration practical (docx)
- Visualisation and exploration practical (pdf)
- Differential methylation lecture (pptx)
- Differential methylation lecture (pdf)
- Differential methylation practical (docx)
- Differential methylation practical (pdf)
- Data for all practicals (tar) [WARNING 9GB]
- Course VirtualBox Machine Image (ova) [WARNING 9GB]

### Extracting biological information from gene lists (One day)

Many experimental designs end up producing lists of hits, usually based around genes or transcripts. Sometimes these lists are small enough that they can be examined individually, but often it is useful to do a more structured functional analysis to try to automatically determine any interesting biological themes which turn up in the lists.

This course looks at the various software packages, databases and statistical methods which may be of use in performing such an analysis. As well as being a practical guide to performing these types of analysis the course will also look at the types of artefacts and bias which can lead to false conclusions about functionality and will look at the appropriate ways to both run the analysis and present the results for publication.

#### Course Content:

- Functional databases
- Statistical test for testing functional enrichment
- Common artefacts in functional analysis
- Presenting functional analysis in publications
- Motif detection tools

#### Course Material:

- Introduction to Gene Set analysis lecture (pptx)
- Introduction to Gene Set analysis lecture (pdf)
- Gene Set analysis practical (docx)
- Gene Set analysis practical (pdf)
- Artefacts and Biases Lecture (pptx)
- Artefacts and Biases lecture (pdf)
- Exploring and Presenting Results Lecture (pptx)
- Exploring and Presenting Results Lecture (pdf)
- Quantitative Gene List practical (docx)
- Quantitative Gene List practical (pdf)
- Motif Searching lecture (pptx)
- Motif Searching lecture (pdf)
- Motif Searching practical (docx)
- Motif Searching practical (pdf)
- Gene List and Motif Practical Data (zip) [1 MB]
- Quantitative Gene Set Practical Data (zip) [339 MB]

### ChIP-Seq Analysis (One day)

This course provides a complete introduction to the theory and practice of the analysis of ChIP-Seq data. It is designed for biologists who may have limited practical bioinformatics skills, but who would like to use ChIP-Seq as part of their work. By the end of the course students should be able to process and analyse their own data.

Students on this course would benefit from having attended the SeqMonk or Unix introduction courses, but these are not required in order to attend.

#### Course Content:

- The theory of ChIP-Seq analysis
- Processing ChIP-Seq data
- Exploring and Visualising ChIP-Seq data
- Analysing for peak calling and differential enrichment

#### Course Material:

- ChIP-Seq Course Slides (pptx)
- ChIP-Seq Course Slides (pdf)
- Processing ChIP data exercise (docx)
- Processing ChIP data exercise (pdf)
- Exploring ChIP data exercise (docx)
- Exploring ChIP data exercise (pdf)
- Analysing ChIP data exercise (docx)
- Analysing ChIP data exercise (pdf)
- Course Linux Mapping Data (tar.gz) (660MB)
- Course Desktop Analysis Data (zip) (1.6GB)

### RNA-Seq Analysis (One day)

This course provides an introduction to the QC, processing and analysis of RNA-Seq data. It focuses on a workflow where RNA-Seq is performed on a large eukaryotic genome for which there is a reference genome available. The course starts with a comprehensive lecture covering the theory of RNA-Seq data generation and analysis and is then followed by hands-on practical sessions which run though the entire RNA-Seq analysis pipeline from raw fastq files to a list of differentially expressed candidate genes.

#### Course Content:

- The theory of RNA-Seq analysis
- Raw data QC
- Mapping RNA-Seq data with hisat2
- Viewing RNA-Seq data with SeqMonk
- Differential expression analysis with DESeq
- Reviewing and visualising differential expression hits
- Analysing more complex multi-condition studies

#### Course Material:

- Course Presentation (pptx)
- Course Presentation (pdf)
- Practical instructions (docx)
- Practical instructions (pdf)
- Multi-Condition Exercise (docx)
- Multi-Condition Exercise (pdf)
- Yeast data for mapping (tar.gz) (470MB)
- Mapped mouse data for seqmonk (zip) (2.4GB)

### 10X Single Cell RNA-Seq Analysis (One day)

This course gives a practical introduction to the processing, qc and analysis of a simple single cell RNA-Seq experiment performed on the 10X platform. It explains the technology used to create the data and goes through some common analysis tools. The course also goes through the theory and practice of the dimension reduction techniques which are very often used to present this kind of data.

#### Course Content:

- How 10X scRNA libraries are made
- Processing raw data with CellRanger and assessing quality
- Dimension reduction theory - PCA and tSNE
- Reviewing processed data with the Loupe Browser
- R package systems for scRNA analysis
- Using Seurat to analyse 10X data

#### Course Material:

- Introduction to 10X preparation and processing (pptx)
- Introduction to 10X preparation and processing (pdf)

### An Introduction to Biological Big Data (3 days)

This couse provides both a biological and technical introduction to Biological Big Data. It is divided into three, day-long sessions where participants learn about the available big data resources, what they mean, and how to use them. There are extensive practicals to give time for people to familiarise themselves with the sites they are shown.

#### Course Content:

**Day1:**Central Dogma Data Resources - a refresher on the main biological concepts surround the central dogma, and an introduction to the data resources which allow you to access the current state of knowledge about your genes of interest**Day2:**Experimental Techniques, Datatypes and Resources. An introduction to the technologies and equipment which allows us to expermentally measure relevant data at scale. We cover both the generation of new data, but also the repositories of existing public data which can be re-used.**Day3:**Practical Computation for Bioinformatics. Finally we look at the practicalities of processing and analysing the data coming from high throughput experiments. We look at both hardware and software platforms and introduce the main techniques, languages and frameworks which are commonly used for large scale data analysis.

#### Course Material:

- Course Slides (pptx)
- Course Slides (pdf)
- Course Excercises (docx)
- Course Excercises (pdf)
- Course Data (tar.gz) [405MB]

### Quality Control in Sequencing Experiments (Half a day)

This course looks at the different ways in which sequencing based studies can fail and the options for visualisation and QC which allow you to identify and diagnose these failures at an early stage. It is designed to be of use to anyone who is using sequencing as part of their research, not just those who are running sequencing facilities.

#### Course Content:

- Why QC is important
- How sequencing experiments fail
- Implementing sequencing QC
- Existing QC software

#### Course Material:

### An Introduction to Mathematical Modelling (Half a day)

This course was developed in collaboration with the Le Novère lab at The Babraham Institute. The course is not currently running and is not supported, but we are leaving course materials here for reference.

It provides an introduction to the concepts of modelling biological systems. It is intended for biologists who have no experience in modelling but would like to know how it might apply to their area of research. The course provides a complete background to the history of modelling and the different approaches through which a biological system can be approximated by mathematical methods. The course also provides a practical introduction to the COPASI modelling environment.

#### Course Content:

- An introduction to modelling
- An overview of chemical kinetics
- Mathematical modelling with COPASI

#### Course Material:

- Introduction to Modelling (pdf)
- Introduction to Chemical Kinetics (pdf)
- COPASI Modelling Tutorial (pdf)
- Course Data (zip) [9.3MB]

### An Introduction to Proteomics (One day)

This course provides an introduction to the methods, data and analysis of quantitative proteomics data. It goes through the background of how the data is acquired and quantitated and the process of searching the spectra against reference databases to identify them at the spectrum, peptide and protein level. We look at quality control of search results to identify problems.

Data analysis is run using the MSstats package, both via the friendly Shiny interface, and then in more detail using R. Whilst there are no strict pre-requisites for this course, a familiatity with R and ggplot would be very helpful.

#### Course Content:

- The theory of proteomics mass spectrometry
- Acquiring RAW data files
- How database searches work
- Analysing data in MSstats

#### Course Material:

- Introduction to Proteomics Slides (pdf)
- Introduction to Proteomics Slides (pptx)
- Proteomics Exercises (pdf)
- Proteomics Exercises (docx)
- MSstats exercise answer
- Proteomics Course Data (zip)

### Scientific Figure Design (Whole day)

This course provides a practical guide to producing figures for use in reports and publications. It is a wide ranging course which looks at how to design figures to clearly and fairly represent your data, the practical aspects of graph creation, the allowable manipulation of bitmap images and compositing and editing of final figures.

The course will use a number of different open source software packages and is illustrated with a number of example figures adapted from common analysis tools.

#### Course Content:

- Data Visualisation Theory Lecture
- Data Representation Practical
- Ethics of Data Representation Lecture
- Design Theory Lecture
- Inkscape Tutorial
- Inkscape Practical

#### Course Material:

### Research Integrity: How To Be A Good Scientist (Half day)

This course provides a practical guide to doing the right thing when it comes to Research Integrity

The course will provide opportunities for discussion, hands-on illustrations and explorations

#### Course Content:

- Research Integrity: Meanings and definitions
- Research Integrity: The importance of formulating questions
- Research Integrity: In Practice
- Research Integrity: Data Storage and Management
- Research Integrity: Responsibilities
- Research Integrity: In the Lab
- Research Integrity: The bottom line

#### Course Material:

### An Introduction to Using OneNote as a Laboratory Notebook (half day)

This course provides a practical guide to using Microsoft OneNote as a Laboratory Notebook, with special consideration to practices, policies, expectations and responsibilities at The Babraham Institute

The course will use OneNote online as a cross-platform application.

#### Course Content:

- Expectations and responsibilities
- What is OneNote
- Storing notebooks on the ELN
- Getting started
- Functions and tools in OneNote
- Other useful tools
- Sharing a OneNote notebook
- Using OneNote as a Laboratory Notebook

#### Course Material:

- OneNote Slides (pptx)
- OneNote Slides (pdf)
- OneNote manual (docx)
- OneNote manual (pdf)
- OneNote exercises (docx)
- OneNote exercises (pdf)
- OneNote Course Data files (zip)

### Introduction to R for Biologists Bootcamp (3.5 days)

This Bootcamp for Biologists requires no previous experience. Over 3 1/2 days you will gain the practical experience to do your own analysis in R.

#### Course Content:

- Introduction to R using Tidyverse
- Advanced R using Tidyverse
- Introduction to plotting and drawing graphs with ggplot2
- Introduction to basic statistical concepts and how to execute them in R
- Final Practical

#### Course Material:

- Introduction to R with Tidyverse
- Advanced R with Tidyverse
- An introduction to ggplot
- Statistical Analysis using R
- Bootcamp Final Exercises (pdf)
- Bootcamp Final Exercises (doc)

### Introduction to NGS Analysis for Biologists Bootcamp (3.5 days)

This Bootcamp for Biologists requires no previous experience. Over 3 1/2 days you will gain an introduction to sequencing analysis from the ground up. Understand, explore and analyse your data and interpret the results.

#### Course Content:

- Basic Sequencing QC
- RNA Seq Analysis
- ChIP Seq Analysis
- Extracting Biological Information from Gene Lists

#### Course Material:

- Quality control in Sequencing Experiments
- RNA-Seq Analysis
- ChIP-Seq Analysis
- Extracting biological information from gene lists

### Introduction to Linux Bootcamp (2.5 days)

This Bootcamp for Biologists requires no previous experience and will provide an understanding of the Linux environment. This 2 1/2 day course shows how to set up a working Linux environment; how you can install, configure and manage software and packages within it; how to run software and create basic, simple automation to enable execution in a more structured and scalable way.

#### Course Content:

- Install a Linux operating system on your machine, either directly or through a virtual machine
- Run and customise installed applications using the BASH shell
- Perform simple automation, linking programs together and iterating the processing of large numbers of files
- Install and configure new software packages
- Understand how to use Linux in a variety of environments from personal computers to cloud infrastructure