SYLLABUS

The Cancer Genomics Cloud Online Course

Comprehensive bioinformatic analysis
of cancer genomes


Module I

Basics of Next Generation Sequencing (NGS) and the Cancer Genomics Cloud (CGC)

Lessons & lectures

Lesson I.1: Next Generation Sequencing and Application

Lecture I.1.1: Introduction to NGS
Lecture I.1.2: Approaches and applications of NGS assays and applications

Lesson I.2: The Cancer Genomics Atlas and the Cancer Genomics Cloud

Lesson I.2.1: The Cancer Genome Atlas
Lesson I.2.2: The Cancer Genomics Cloud

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MODULE II

Analysis of Gene Expression by
RNA-Seq

Lessons and lectures

Lesson II.1: Introduction to RNA sequencing and gene expression analysis

Lecture II.1.1: Experimental design of gene expression experiments

Lecture II.1.2: Mapping strategies for obtaining read counts from RNA
sequencing data

Lesson II.2: From read counts to gene expression

Lecture II.2.1: Counting and normalizing counts in RNA sequencing experiments

Lecture II.2.2: Background of statistical analysis of differential gene expression

Lesson II.3: Gene fusion detection and de novo assembly tools

Lecture II.3.1: Detection of gene fusions

Lecture II.3.2: Prioritization of gene fusions

Lecture II.3.3: De novo assembly tools


MODULE III

Genomic Analysis Using Whole-Genome and Whole-Exome Datasets

Lessons and lectures

Lesson III.1: Introduction to DNA sequencing and variant calling.

Lesson III.1.1: Summary of best practices in sequencing analysis pipelines

Lesson III.1.2: Introduction to variant calling

Lesson III.1.3: Variant calling data pre-processing

Lesson III.1.4:  Benchmarking datasets

Lesson III.2: Variant calling: Methods and applications.

Lecture III.2.1: SNV calling

Lecture III.2.2: CNV detection

Lecture III.2.3: Variant calling using a nonlinear reference

Lecture III.2.4: Variation within human populations

Lesson III.3: Post variant calling and variant interpretation.

Lecture III.3.1: Post-calling filtration and prioritization

Lecture III.3.2: Variant annotation

Lecture III.3.3: Variant interpretation tools

Lecture III.3.4: Identifying cancer drivers


Advanced Analytical Topics and Multi-omics

Lessons and lectures

Lesson IV.1: Understanding phenotypes by combining the genomic, transcriptomic and epigenomic information.

Lecture IV.1.1: Combined analysis of genome, epigenome and transcriptome together

Lecture IV.1.2: Additional  analytical methods

MODULE IV


Advanced Features of the CGC Platform

Lessons and lectures

Lesson V.1: Docker and tool wrapping.

Lecture V.1.1: Tool wrapping

Lecture V.1.2: Best practices for making a Docker Container

Lesson V.2: Workflow optimization and APIs

Lesson V.2.1: Workflow optimization

Lesson V.2.2: Automation with the API

 

MODULE V