Advancements in Cancer Genomics: Graph-Based Motif Discovery and Single-Cell Analysis

Studying biology of cancer cells enables us to understand how disease is growing and leads to new methods of diagnosing and treatment of many types of cancers. Over the past decades, researchers have surveyed multiple features of cancer cells such as genetic alteration in tumors, mutational patterns, copy number changes, and transcription factor binding sites. For this reason, scientists have employed Next Generation Sequencing (NGS) as it enables sequencing of thousands of DNA molecules. In this thesis, we aim to design and apply effective algorithms for interpreting and analyzing cancer genomics data using NGS technique. In particular, we take advantage of microbiome RNA sequencing to investigate transcription factor binding sites of the genome, known as motifs. A new method for motif discovery is introduced and tested on synthetic and real data. Along with motif discovery method, a new approach for inferring haplotype-specific copy numbers among single cell sequences is presented. The inferred haplotype-specific copy numbers lead to inferring tumor clones and corresponding phylogenetic tree of these clones