36天“速成生信工程師”課程

今天瀏覽網(wǎng)頁的時候偶遇一個生信培訓(xùn)課程丧慈，比較像生信技能樹的滾動網(wǎng)課耸采，但是這個課程的費用價格真的不是一般的高兴泥，看廣告

Costs

If you would like to attend any of these courses then the costs are:

Half day courses, ￡105 + VAT
Short day courses (10 til 4), ￡165 + VAT
Full day courses, ￡196 + VAT

當(dāng)然了，課程還是不錯的虾宇，分門別類的搓彻，應(yīng)有盡有。這些英文也不難就不翻譯了文留，

Training Courses

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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. 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.

Babraham Software

Core Bioinformatics Skills

Statistics

Application focussed courses

Comprehensive longer Bootcamp courses

Analysing Mapped Sequence Data with SeqMonk (One 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
  • Starting and configuring the program
  • Creating a project and importing data
  • Using the chromosome viewer
  • Quantitating and Filtering Data
  • Creating Reports
  • Using Quantitation Pipelines
  • Correcting and Normalising Quantitations
  • Scaling analysis to larger studies
  • Running statistical tests
  • Interacting with external programs
  • 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:

Statistical Analysis using SPSS (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 friendly SPSS package.

Course Content:

  • Introduction to SPSS
  • Importing data from other software packages
  • Preparing your data for analysis
  • Getting to know your data
  • Graphical representations
  • Choosing an appropriate analysis
  • Interpreting analysis output

Course Material:

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:

Sample Size Estimation and Experimental Design (Short course)

Sample size estimation and experimental design (Short day course) Power analysis is used to estimate the appropriate sample size needed to detect biologically meaningful differences. It should be central to any experimental approach as samples provide the evidence on which scientists build the confidence they have in the results of their research. This course covers the basic principles of power analysis and experimental design. Basic statistical knowledge is useful but not compulsory.

Course Content:

  • Experimental Design

  • Choice of statistical approach

  • Type of design

  • Technical versus biological replicates

  • Definition of Power

  • Variables in Power Analysis

  • Power analysis for

  • Comparing 2 proportions

  • Comparing 2 means

  • Comparing more than 2 means

  • Correlation

Course Material:

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:

  • Experimental Design
  • Sample size estimation: power analysis
  • Descriptive statistics and data exploration
  • Analysis of quantitative data
  • Linear modelling
  • Analysis of qualitative data

Course Material:

Statistics bootcamp using GraphPad Prism (2.5 days)

A more in depth look at statistical analyses using GraphPad Prism

Course Content:

  • Experimental Design
  • Sample size estimation: power analysis
  • Descriptive statistics and data exploration
  • Analysis of quantitative data
  • Linear modelling
  • Analysis of qualitative data

Course Material:

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:

Introduction to Python (3 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 introduces the basic features of Python3. 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:

  • coming soon

Course Material:

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:

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:

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:

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:

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:

Additional 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:

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
  • Permissions
  • Pipes

Course Material:

An Introduction to Machine Learning (One day)

This course explores machine learning as a tool for the analysis of biological questions. It covers the basic concepts of machine learning and discusses practical considerations of its use in data mining. The course is a mix of lectures and practicals using the graphical software package Weka (no coding skills required).

Course Content:

  • Introduction to the concepts of machine learning
  • Introduction to Weka
  • Preparing input data
  • Evaluating classifiers
  • Interpreting and visualising results

Course Material (coming soon):

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:

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:

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:

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:

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:

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:

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:

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:

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 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:

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

Course Material:

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