Pawel Przytycki will present his FPO "Algorithms for deciphering cancer genomes: from differential mutation to differential allele specific expression" on Friday, 8/31/2018 at 11:30 in CS 402. The members of his committee are as follows: Mona Singh (adviser); Joshua Akey (LSI) and Olga Troyanskaya (readers); Mona Singh, Ben Raphael, and Yibin Kang (MOL) (examiners). All are welcome to attend. A copy of his thesis is available in CS 310. The thesis abstract follows below. Large-scale cancer genome sequencing consortia have provided a huge influx of somatic mutation data across large cohorts of patients. Understanding how these observed genetic alterations give rise to specific cancer phenotypes represents a major aim of cancer genomics. In this dissertation, I present two methods for utilizing natural variation as a background for interpreting cancer genomes. In Chapter 2, I introduce di↵erential mutation analysis, a framework for uncovering cancer genes that compares the mutational profiles of genes across cancer genomes with natural germline variation across healthy individuals. I hypothesize that if a gene is less constrained with respect to variation across the healthy population, it may also be able to tolerate a greater amount of somatic mutation without experiencing a drastic detrimental functional change. I develop a fast and simple approach that uncovers genes that are di↵erentially mutated between cancer genomes and the genomes of healthy individuals. I demonstrate that my di↵erential mutation approach outperforms considerably more sophisticated approaches for discovering cancer genes. In Chapter 3, I propose the concept of di↵erential allele-specific expression to identify genes within an individual’s cancer whose allele-specific expression (ASE) differs from what is found in matched normal tissue, with the overall goal of uncovering genes whose regulation is altered via functional noncoding somatic mutations. I reason that since specific noncoding mutations usually occur on only one chromosome, they are expected to a↵ect only the expression of the allele derived from that chromosome. Thus, ASE is a potential avenue towards detecting cis mutations that lead to regulatory changes. I present three methods to identify di↵erential ASE in paired tumor-normal samples, and apply them to breast cancer tumor samples. I demonstrate that di↵erential ASE can detect dysregulation caused by nonsense mediated decay and copy number variation, that known cancer-related genes are enriched for di↵erential ASE, and that genes with cis noncoding mutations are enriched for difiii ferential ASE. Finally, I show that noncoding mutations in cis with genes exhibiting di↵erential ASE often disrupt known regulatory mechanisms. I thus conclude that di↵erential ASE is a powerful means for characterizing gene dysregulation due to cis noncoding mutations.