Genome 541 - Introduction to Computational Molecular Biology

Cancer Genomics Module

Genome 541 Course Website

Schedule of lectures and homework

The Cancer Genomics Module is the third module in the course and consists of 4 lectures.
Homework #5 and #6 accompany this module.

Dates: April 28 - May 7

Times: Tuesday & Thursday @ 10:30am - 11:50am

Date Lecture Homework Assigned
April 28 Introduction to Cancer Genome Analysis
April 30 Probabilistic Methods for Mutation Detection HW #5
(Due May 8 @ 11:59pm)
May 5 Probabilistic Methods for Copy Number Alteration Detection
May 7 Additional Topics: Structural Variation, Signature Analysis HW #6
(Due May 15 @ 11:59pm)

Module Outline

Lecture 1: Introduction to cancer genome analysis


  • Cancer biology: background & motivation
  • Types of genomic alterations in cancer
  • Cancer genomics concepts: Tumor heterogeneity, drivers vs passengers
  • Tumor DNA sequencing: Technology, data & resources
  • Cancer genome analysis: Computational strategies & methods for detection genomic alterations
  • Primer on topics:
    • Probability/uncertainty, statistical modeling (mixture modeling), graphical models
    • Bayesian statistics and model inference
  • Overview of tools/methods to be taught in next lectures
  • References:

Lecture 2: Probabilistic methods for mutation detection


  • Introduction to single-nucleotide variants in cancer
    • Drivers vs passengers, clonality
    • Sequencing data: whole exome & genome, targeted panels
  • Overview of SNV prediction strategy
    • Read-level data from tumor and normal sequencing
    • Problem formulation and probabilistic model
    • Parametric vs non-parametric approaches
  • Mutation detection tools: Mutect and SNVMix
  • References:

Homework #5

Download Homework #5

Lecture 3: Probabilistic methods for copy number alteration detection


  • Introduction to copy number alterations (CNA) in cancer
    • Gene dosage, somatic vs germline
    • Data: SNP genotype arrays, whole genome & exome
  • Overview of CNA prediction strategy
    • Data normalization
    • Tumor vs Reference (normal) signals
    • Parametric vs non-parametric approaches
  • Copy number segmentation algorithms (tools):
    • Circular binary segmentation (CBS/DNAcopy)
    • Hidden markov models (ichorCNA/HMMcopy)
  • References:

Lecture 4: Additional topics


Homework #6

Download Homework #6