Recent studies have shown that mutations at non-coding elements can act as cancer drivers. In this thesis, first, we describe CNCDriver (Cornell Non-coding Cancer Driver) algorithm that we have developed to analyze single nucleotide variants from large-scale cancer whole-genome sequencing data. Using this method, we identified drivers at protein-coding genes, promoters, enhancers, 5’UTRS, 3’UTRs, and ncRNAs. In particular, we propose CTCF insulators as a class of non-coding cancer drivers. We used insulator annotations from CTCF and cohesin ChIA-PET and analyzed somatic mutations in 1,962 whole-genomes from 21 cancer types. We find that mutations in an insulator in multiple cancer types, including 16% of melanoma samples, are associated with TGFB1 up-regulation. Using CRISPR-Cas9 in human melanoma A375 cells, we find that alterations at two of the most frequently mutated regions in this insulator increase cell growth by 40–50%, supporting the role of this boundary element as a cancer driver. Thus, our study reveals CTCF insulators as a novel class of putative non-coding cancer drivers. Overall, application of our method on non-coding elements reveals several novel putative drivers. Second, we describe CNCDatabase (Cornell Non-coding Cancer driver Database) in which we curated non-coding cancer drivers from published literature. CNCDatabase includes an interactive web interface from which the catalogue of drivers can be downloaded and the status of non-coding cancer drivers can be queried by gene name, or element type, or cancer type. Furthermore, the database is highly scalable to accommodate more non-coding cancer driver predictions. Overall, CNCDatabase provides a helpful resource for researchers to explore the pathological role of non-coding alterations and their associations with gene expression in human cancers.