Course Introduction

Welcome!

We’re based at Cancer Research Uk Cambridge Institute, about 2 miles south of here

Who’s who

Top Row

• Mark
  • Bioinformatics Training Coordinator, Bioinformatics Core
• Andy
  • Senior Research Associate, Tavare lab (Statistics and Computational Biology )
• Oscar
  • Senior Research Associate, Caldas lab (Breast Cancer Genomics)

Second Row

• Geoff
  • Senior Research Associate, Markowetz (Systems Genetics of Cancer) and Brenton (Functional genomics of ovarian cancer)
• Matt
  • Bioinformatics Core Head
• Cathy
  • Bioinformatics Training Facility, Administrator
• Paul
  • Bioinformatics Training Facility, Sys Admin

Huge thanks too to Helen Anderson (CRUK) for admin support and Maria Secrier for materials

Who are you?

Admin

• Course runs 09:30 ~ 17:00 each day
  • Room open fom 09:00 each day
• Lunch provided 12:30 - 13:30 each day
  • different room on the same campus, we’ll take you there
• No fixed coffee and tea breaks
  • but you’ll probably have noticed the coffee machine at the back
• Please refrain from using the coffee machine when someone is lecturing
  • you’ll discover why when you use the machine!
• Workshop dinner on Thursday night at Christ’s College
  • We assume you’re coming unless you’ve told us otherwise
  • Please let us know a.s.a.p if you won’t be coming!

About the Course - “Analysis of Cancer Genomes”

• You will (probably) not come away being an expert
• We cannot teach you everything about NGS data
  • plus, it is a fast-moving field
  • will cover a selection of tools / methods
• Whole genome and exome DNA-seq only
  • much of the initial processing is the same for other assays
  • see our previous summer school for details of RNA- and ChIP-seq
• However, we hope that you will
  • Understand how your data are processed
  • Be able to explore your data - no programming required
  • Understand the issues and compromises involved in current methods
  • Communicate more effectively with the Bioinformaticians dealing with your data
  • Increase confidence with R and Bioconductor
  • Know where to get help
  • Be able to explore new technologies, methods, tools as they come out
  • Meet your colleagues from other CRUK institutes
• All materials will be online after the course
  • tell your colleagues, friends!
  • you’ll be able to take the data away with you
• The software used will also be available to you
  • more on this soon

Course Outline

Day 1

• Recap of R
• Overview of NGS data
• Hands-on experience with NGS data

Day 2

• Theory of SNV-calling
• How SNV calls are represented
• Copy-number analysis

Day 3

• Theory of Somatic calling
• Hands-on practice assessing and filtering Somatic calls
• Annotating and prioritsing calls

Day 4

• Structural Variants (SVs)
• Methods for visualising SVs
• Interpreting SVs
• Complex rearrangements

Day 5

• Dealing with collections of genomes
• Generating mutational signatures
• Integrating different data types

Disclaimer

To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.”. R.A. Fisher, 1938

If you haven’t designed your experiment properly, then all the Bioinformatics we teach you won’t help: Consult with your local statistician - preferably not the day before your grant is due!!!!

• We have some materials you can look at

About the data

We will use publically-available datasets that you can take away with you at the end of the course

HCC1143

A breast-cancer cell-line on which we have data available and has been used as part of a “benchmarking” dataset to evaluate different variant-callers

• http://www.pawefish.path.cam.ac.uk/BreastCellLineDescriptions/HCC1143.html
• https://cansar.icr.ac.uk/cansar/cell-lines/HCC1143/copy_number_variation/
• https://cghub.ucsc.edu/datasets/benchmark_download.html

Choice of software

• Trade-off between what is easiest to use, and what would be used in practice
  • and what runs in a reasonable time
  • focus on tools that enable you to explore the data
  • mostly using R
• No conflicts of interest
  • we don’t have any particular tools that we wish to plug
• You’ll probably have to decided what “looks best” on your own data

Distribution of software

• When you return home, you’ll probably want to try-out the tools on your own data
• We’ll be using RStudio, which hopefully most of you will already have
• However, installing software can be challenging, even for those with many years of experience
• The Docker platform is becoming an increasingly-popular way of packaging and distributing software
  • The Sanger institute have wrapped their genome analysis pipeline in a docker container; cgpbox
  • We have created one too for other software required for the course; CRUK Docker

• We’ll go through this at the end of the course