abstract: Understanding intratumor heterogeneity and their driver genes is critical to designing personalized treatments and improving clinical outcomes of cancers. Such investigations require accurate delineation of the subclonal composition of a tumor, which to date can only be reliably inferred from deep-sequencing data (>300x depth). The resulting algorithm from the work presented here, incorporates an adaptive error model into statistical decomposition of mixed populations, which corrects the mean-variance dependency of sequencing data at the subclonal level and enables accurate subclonal discovery in tumors sequenced at standard depths (30-50x). Tested on extensive computer simulations and real-world data, this new method, named model-based adaptive grouping of subclones (MAGOS), consistently outperforms existing methods on minimum sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports subclone analysis using single nucleotide variants and copy number variants from one or more samples of an individual tumor. GUST algorithm, on the other hand is a novel method in detecting the cancer type specific driver genes. Combination of MAGOS and GUST results can provide insights into cancer progression. Applications of MAGOS and GUST to whole-exome sequencing data of 33 different cancer types’ samples discovered a significant association between subclonal diversity and their drivers and patient overall survival. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2019