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• Overview of cancer genomics field
• Common applications of HT technologies in cancer genomics
• {"title"=>"Concepts and case studies of cancer genomics from the literature:", "content"=>["Cancer genetics", "Pharmacogenomics", "Diagnostic vs. prognostic markers and druggable targets"]}

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• Introduction to Cloud computing and virtual machines (VMs)
• Ethical conduct when using genomic data
• Security of big data sets in the Cloud
• VM security and usage best practices: ssh keys, ports, snapshot of VM without privileged data, and shut down of VM when not in use.

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• Overview of cancer specific databases, as well genome browsing and cancer genome browsing.
• {"title"=>"The databases:", "content"=>["Collaboratory, ICGC portal, TCGA, etc.", "COSMIC, dbSNP, etc."]}
• {"title"=>"The browser tools:", "content"=>["IGV", "UCSC"]}

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• 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.

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• Setting up AWS

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• What is involved in mapping reads to a reference genome
• What are the FASTQ and SAM/BAM file formats
• Some common terminology used to describe alignments

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• Run a read alignment.

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• Fundamentals of de novo assembly
• Data structures used by assemblers (de Bruijn graphs and overlap graphs)
• Common steps that assemblers perform
• Overview of commonly used software

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• Run a read assembly.

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

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• Hands-on lab exercises using CNA caller tool for CNA detection in SNP arrays.

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Instructor(s):

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• Relevance of detecting somatic mutations in cancer genomics
• 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

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• Hands on lab exercises using SNP calling tools for somatic mutation detection and visualization in IGV.

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Instructor(s):

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• {"title"=>"The Technology Platform: high-throughput sequencing", "content"=>["Variety of platforms and their differences", "Experimental design considerations", "Limitations of experiments"]}
• {"title"=>"The Analysis Tools:", "content"=>["Outline of a RNA-Seq analysis pipeline", "Tools for analysis of RNA-Seq data"]}

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• Analysis of RNA-Seq data.

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Instructor(s):

Content:

• Genomic rearrangement and its effect on the transcriptome
• Biological relevance of gene fusions in cancer biology
• The technology platform: RNA-Seq
• Overview of the RNA-Seq protocol
• Experimental design considerations
• {"title"=>"The analysis tools:", "content"=>[{"title"=>"Alignment based fusion discovery", "content"=>["Paired end RNA-Seq alignments and gene fusion evidence", "Discerning true fusions from artifacts"]}, {"title"=>"Assembly based fusion discovery", "content"=>["Benefits/drawbacks of assembly methods", "Identifying gene fusions from RNA-Seq assemblies"]}]}
• Clinical applications

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PDF YouTube

Instructors(s):

Content:

• Hands on exercise using deFuse to identify fusions. Visualize RNA-Seq fusion reads in IGV, plot exon expression of fused genes, and use the UCSC genome browser to view fusions in tandem with other genomic features.

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• Considerations when snapshotting a VM
• How to snapshot a VM
• How to share a snapshot with other cloud tenants
• Launching a new VM from a snapshot
• Scaling out your VMs fleet to meet your analysis needs

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Title: Within the Collaboratory environment:

• Setup and launch a VM
• Install Docker engine
• Snapshot your VM and share it with other cloud tenants
• Launch a new VM from a shared image
• From a given data set analysis task (e.g. VCF of 100 normal-tumour samples), determine the size of your job and your compute needs.
• Scale out your VMs to meet the needs of this task

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• Introduction to Docker and Dockstore
• Overview of packaging your tools in Docker
• Overview of Dockstore and its current state
• Introduction to the Common Workflow Language

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• Configure a VM with Docker packages
• Package a tool in Docker
• Practice CWL

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• Large-scale biological activities that generate datasets used in the Cloud

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• How to access data in the Cloud and accessing non-protected data
• Setup and configuration for worked example
• Initiate the sequence alignment task on cell line data

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• Introduction to gene lists
• Gene annotations: Gene identifiers and pathway databases
• What is pathway enrichment analysis?

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• During this hands-on session, students will have the opportunity to play with gene identifiers, and to create a network from gene-set enrichment results using Cytoscape.

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• Overview of pathway and network analysis
• Basic network concepts
• {"title"=>"Types of pathway and network information", "content"=>["Focus on transcription factor regulatory networks. Pathway Databases: KEGG, Reactome"]}

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• Workflow of tools and steps
• Reactome FI

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• Introduction to correlating clinical outcomes with cancer 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)

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PDF YouTube

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Content:

• Analysis of clinical cancer data using R

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