Bioinformatics for Cancer Genomics (BiCG)
Workshop Details
Date: May 28 - June 1, 2012
Location: Downtown Toronto, ON
Lead Faculty (2012): John McPherson, Francis Ouellette, Paul Boutros, Malachi Griffith, Sohrab Shah, Gary Bader and Anna Lapuk
Registration Fee for Applications received before April 27, 2012: $950 + HST
Registration Fee for Applications received after April 27, 2012: $1150 + HST
Awards available for 2012.
Apply now as space is limited to 30 participants!
Target Audience
This workshop is geared towards 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 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 will host 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.
Course Outline
Day 1
Welcome and introductions
Ice breaking session for participants (to promote networking)
(9am-11am)
Module 1: Introduction to cancer genomics (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
(11:30am-4pm)
Module 2: Visualizing Cancer Genomic Data (Faculty: Francis Ouellette)
Overview of genome browsing and cancer genome browsing
The browser tools:
− UCSC Genome Browser
− IGV
− More browsers: 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.
(4pm-5pm)
Networking Event: A hosted social for participants and faculty to foster networking and collaboration.
Day 2
(9am-4pm)
Module 3: Gene Expression Profiling (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.
(4pm-5pm)
Guest Lecture: "Personalizing Cancer Medicine with Genomics" Special guest speaker: TBA
This guest lecture is open to the public research community at large as well as workshop participants.
(5pm-7pm)
Hosted Social
Day 3
(8am-2pm)
Module 4: Genome rearrangements (Faculty: TBD)
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.
(2pm-8pm)
Module 5: Copy Number Alterations (Faculty: Sohrab Shah)
The Technology Platform: High density genotyping SNP arrays, whole genome shotgun sequencing
− Overview of platform for SNP and CNV detection
− Experimental design considerations
The Analysis Tools:
− Pre-processing and normalization
− Segmentation
− Using Hidden Markov Models to call CNVs from genotyping arrays
− Extraction of allele-specific signal intensities and integrating SNP spacing and SNP allele frequencies
− Allele specific copy number alterations and loss of heterozygosity analysis
− Estimating copy number alterations from next generation sequence data
Lab Practical: Hands on lab exercises using CNV caller tool for CNV detection in SNP arrays
Day 4
(8am-2pm)
Module 6: Somatic Mutations (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
(2pm-5pm)
Module 7: Pathway Analysis & Biological Interpretation (Faculty: Gary Bader)
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
Day 5
(8am-10am)
Module 7 - Continued: Pathway Analysis & Biological Interpretation
Lab Practical: Evaluation of a cancer gene list using Cytoscape and the Reactome plugin
(10am-5pm)
Module 8: Integration of Clinical Data (Faculty: Anna Lapuk)
Introduction to correlating clinical outcomes with genomic data
Challenges with integration of heterogeneous data types (clinical vs. genomics)
Survival analysis (univariate and multivariate)
Kaplan-Meier estimate of a survival function
Log rank test
Cox proportional hazards model
Lab Practical: Analysis of clinical cancer data using R
Closing Remarks
− Workshop Feedback and Survey
− Certificate for Workshop Completion
