Bioinformatics for Cancer Genomics (BiCG) (2013)

 

Course Objectives

Cancer research has rapidly embraced high throughput technologies into its research, using various microarray, tissue array, and next generation sequencing platforms. The result has been a rapid increase in cancer data output and data types. Now more than ever, having the informatic skills and knowledge of available bioinformatic resources specific to cancer is critical.
The CBW has developed a 5-day workshop covering the key bioinformatics concepts and tools required to analyze cancer genomic data sets. Participants will gain experience in genomic data visualization tools which will be applied throughout the development of the skills required to analyze cancer -omic data for gene expression, genome rearrangement, somatic mutations and copy number variation. The workshop will conclude with analyzing and conducting pathway analysis on the resultant cancer gene list and integration of clinical data.

Target Audience

This workshop is developed for clinical researchers, research scientists, post-doctoral fellows, and graduate students with cancer genomics research projects.

Prerequisite: UNIX and R familiarity is required. Familiarity can be gained through online activities. You should be familiar with these UNIX concepts (tutorial 1-3) and these R concepts (chapters 1-5) or review the past Statistics tutorials provided by CBW.

You will also require your own laptop computer. Minimum requirements: 1024x768 screen resolution, 1.5GHz CPU, 1GB RAM, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements). If you do not have access to your own computer, you may loan one from the CBW. Please contact course_info@bioinformatics.ca for more information.

Course Outline

Day 1

Module 1 - Introduction to Cancer Genomics (2013) (Faculty: John McPherson)

• Overview of cancer genomics field
• Common applications of HT technologies in cancer genomics
• Concepts and case studies of cancer genomics from the literature:
• Cancer genetics
• Pharmacogenomics
• Diagnostic vs. prognostic markers and druggable targets
• Data security and privacy

Module 2 - Visualizing Cancer Genomic Data (2013) (Faculty: Francis Ouellette)

Overview of genome browsing and cancer genome browsing
The browser tools:

• IGV
• Savant

Lab Practical: How to use the genome browsers to visualize transcripts, mutations, and other cancer genome features. Subsequent modules and lab practicals will use the same browser tools.
R Review until 8pm

Day 2

Module 3 - Integration of Clinical Data (2013) (Faculty: Anna Lapuk)

• Introduction to correlating clinical outcomes with genomic data
• How do variants discovered in genomic data result in clinical outcomes?
• Challenges with integration of heterogeneous data types (clinical vs. genomics)
• Survival analysis (univariate and multivariate)

Lab Practical: Analysis of clinical cancer data using R

Module 4 - Genome rearrangements (2013)

• Importance of structural variation in the cancer genome
• The Technology Platform: Paired end DNA sequencing
• Overview of experimental technique
• Experimental design considerations
• Limitations of technique and platform
• The Analysis Tools:
• Aligner tools and selection, data pre-processing
• Detection strategies: Searching for discordant mate pairs and split read analysis
• Structural variation detection tools and how they compare

Lab Practical: Variant detection from paired end reads and visualization within the genome using Savant.

Day 3

Module 5 - Copy Number Alterations (2013) (Faculty: Sohrab Shah)

• Importance of copy number alterations in cancer
• Methods for detecting copy number alterations
• Tools for evaluating CNAs in HT-seq data

Module 6 - Somatic Mutations (2013) (Faculty: Sohrab Shah)

• Relevance of detecting somatic mutations in cancer genomics
• The Technology Platform: High-throughput sequencing of genomes or exomes
• Overview of experimental techniques
• Experimental design considerations
• Limitations of data
• The Analysis Tools:
• Aligner tools and selection, data pre-processing
• Strategies for detection of somatic mutations and factors considered by SNP callers
• Binomial mixture models to model allelic counts
• Simultaneous analysis of tumor and normal data
• Sources of artifacts and false positives

Lab Practical: Hands on lab exercises using SNP calling tools for somatic mutation detection

Day 4

Module 7 - Pathway Analysis and Biological Interpretation (2013) (Faculty: Lincoln Stein)

• Introduction to pathway and network analysis in cancer genomics
• Basic network concepts
• Types of pathway and network information
• Pathway Databases: Reactome, KEGG
• Pathway analysis of large-scale cancer genomics data sets to gain biological meaning

Lab Practical: Evaluation of a cancer gene list using Cytoscape and the Reactome plugin
Guest lecture: A public lecture on the topic of cancer genomics and its impact on health care in cancer.

Day 5

Module 8 - Gene Expression Profiling (2013) (Faculty: Paul Boutros)

• Role of gene expression profiles in the cancer continuum
• The Technology Platform: Microarrays
• Variety of platforms and their differences
• Experimental design considerations
• Limitations of microarray experiments
• The Analysis Tools:
• Outline of a microarray analysis pipeline
• R statistical package analysis of microarray data

Lab Practical: Upload microarray data into R. Pre-process data and visualize data on QC plots. Parametric analysis of differential gene expression.

Module 9 - RNA-seq Analysis (2013) (Faculty: Obi Griffith)

• Overview of common research questions that can be addressed by RNA-seq experiments
• Overview of RNA-seq analysis and current tools for conducting analysis

Lab Practical: Hands-on practice with Cufflinks suite of tools for analysis of gene expression from RNA-Seq data.
Closing Remarks

• Workshop Feedback and Survey
• Certificate for Workshop Completion