CSCI2950-C: Computational Cancer Genomics
Spring 2013

Professor: Ben Raphael

Time: Tuesday/Thursday 1-2:20pm [CIT 241]


It has been known for decades that cancer is driven largely by mutations that accumulate in an s genome during their lifetime. However, it was only in 2008 that the first cancer genome was sequenced, an advance made possible by new DNA sequencing technologies. Cancer sequencing data is now being generated at an exponentially increasing rate. The challenge has shifted from producing cancer genome sequencing data to interpreting this data in order to advance cancer biology and treatment. This seminar will explore algorithms, statistical methods, and techniques from machine learning that address four important challenges in cancer genome sequencing and interpretation.

  • Identification of different classes of somatic mutations (single-nucleotide variants, copy number aberrations and rearrangements) from high-throughput DNA sequencing data.
  • Reconstruction of tumor evolution from DNA sequencing data using techniques from phylogenetics and population genetics.
  • Comparison of cancer genomes from different individuals to distinguish causal somatic mutations from random mutations.
  • Analysis of combinations of mutations in signaling pathways and cellular interaction networks.
  • News

  • Jan. 24, 2013: First class. Course website undergoing revisions. Please check back for updated information.
  • Course Organization





    Computer Assignment.

    Proposal:Due TBD (Specific Aims and Significance) and TBD (All).

    Paper Reviews

    Course Credits (for Computer Science students)


    Previous offerings of this course are available here: