CS 598MEB: Computational Cancer Genomics

Jump to: description | schedule | papers | project

Semester Spring 2019
Instructor Prof. Mohammed El-Kebir
Time TR 2:00-3:15 PM
Location 1103 Siebel Center
Office hours Tuesdays 3:15-4:15 PM in 3216 Siebel Center

Course description

This course introduces fundamental problems and algorithmic approaches in cancer genomics. Covered topics include:

  1. tumor phylogeny inference,
  2. cancer immunotherapy,
  3. driver mutation identification,
  4. somatic variant calling.
This course will not teach you how to run popular bioinformatics tools. Rather, we will focus on the underlying algorithmic ideas and the issues that arise when translating a biological problem into a computational problem and ultimately an accurate tool for biologists to use. In addition, this course will teach you how to read scientific papers and how to propose and conduct independent research.


This course is appropriate for graduate students in computer science, bioengineering, mathematics and statistics. Familiarity with basic statistics, probability and algorithms is expected.


Course schedule

Date Presenter Slides Reading
01/15/2019 Mohammed El-Kebir Introduction [slides]
  • Biology for Computer Scientists -- Lawrence Hunter [link]
01/17/2019 Mohammed El-Kebir Cancer Phylogenetics I [slides]
01/22/2019 Mohammed El-Kebir Cancer Phylogenetics II [slides]
01/24/2019 Mohammed El-Kebir Cancer Phylogenetics III [slides]
01/29/2019 Mohammed El-Kebir Consensus Trees
01/31/2019 Mohammed El-Kebir Single-cell Phylogeny Inference [slides]
02/05/2019 Mohammed El-Kebir Phylogeny Inference with CNAs [slides]
  • M. El-Kebir*, G. Satas* , L. Oesper and B.J. Raphael. Inferring the Mutational History of a Tumor using Multi-State Perfect Phylogeny Mixtures. Cell Systems, 3(1):43-53, 2016.
02/07/2019 Mohammed El-Kebir Mutation Clustering I [slides]
  • DCF preprint [Google drive, see piazza]
02/12/2019 Mohammed El-Kebir Mutation Clustering II [slides]
  • DCF preprint [Google drive, see piazza]
02/14/2019 Mohammed El-Kebir Mutation Clustering III [slides]
  • DCF preprint [Google drive, see piazza]
02/19/2019 Mohammed El-Kebir ILP [slides]
02/21/2019 Mohammed El-Kebir CNA Phylogenies [slides]
02/26/2019 Mohammed El-Kebir Metastasis I [slides]
02/28/2019 Mohammed El-Kebir Metastasis II [slides]
03/05/2019 Mohammed El-Kebir Paper: CNA calling [slides]
  • Zaccaria, S. & Raphael, B. J. (2018). Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data. bioRxiv.
03/07/2019 Chuanyi Zhang Paper: SNV calling [slides]
  • Cooke, D. P., Wedge, D. C., & Lunter, G. (2018). A unified haplotype-based method for accurate and comprehensive variant calling. bioRxiv.
  • Bryce Kille
  • Yuanyuan Qi
  • Letu, Q., et al. (2018). On the Minimum Copy Number Generation Problem in Cancer Genomics. ACM BCB, 260–269.
  • Deshpande, V., et al. (2019). Exploring the landscape of focal amplifications in cancer using AmpliconArchitect. Nature Communications, 10(1).
  • Ashwin Ramesh
  • Ram Gupta
  • Nuraini Aguse
  • Wei Qian
  • Łuksza, M., et al. (2017). A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature, 551(7681), 517.
  • Bulik-Sullivan, B., et al. (2019). Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nature Biotechnology, 37(1), 55–63.
  • Lan Qin
  • Sarah Christensen
  • Vaishnavi Subramanian
  • Bo Chen
  • Abbosh, C., et al. (2017). Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature, 545(7655), 446–451.
  • Myers, M. A., et al. (2019). Inferring tumor evolution from longitudinal samples. bioRxiv, 526814.
  • Shayan Tabe Bordbar
  • Meng-Yao Huang
  • Bian, S. et al., et al. (2018). Single-cell multiomics sequencing and analyses of human colorectal cancer. Science, 362(6418), 1060–1063.
  • Malikic, S., et al. (2017). Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data. bioRxiv, 234914.
  • Juho Kim
  • Vladimir Smirnov
  • Caravagna, G., et al. (2018). Detecting repeated cancer evolution from multiregion tumor sequencing data. Nature Methods, 15(9), 707–..
  • Zafar, H., et al. (2018). SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data. bioRxiv, 394262.


See schedule.


Example projects: