Department of Computer Science and Engineering
University of South Carolina
Author : Ruofan Xia
Advisor : Dr. Jijun Tang
Date : Nov 1st, 2019
Time : 11:00 am
Place : Meeting Room 2265, Innovation Center
Genome rearrangement is known as one of the main evolutionary mechanisms on the genomic level. Phylogenetic analysis based on rearrangement played a crucial role in biological research in the past decades, especially with the increasing availability of fully sequenced genomes. In general, the phylogenetic analysis aims to solve two problems: Small Parsimony Problem (SPP) and Big Parsimony Problem (BPP). Maximum parsimony is a popular approach for SPP and BPP which relies on iteratively solving an NP-hard problem, the median problem. As a result, current median solvers and phylogenetic inference methods based on the median problem all face serious problems on scalability and cannot be applied to datasets with large and distant genomes. In this thesis, we propose a new median solver for gene order data that combines double-cut-join (DCJ) sorting with the Simulated Annealing algorithm (SAMedian). Based on this median solver, we built a new phylogenetic inference method to solve both SPP and BPP problems. Our experimental results show that the new median solver achieves excellent performance on simulated datasets and the phylogenetic inference tool built based on the new median solver has a better performance than other existing methods.
Cancer is known for its heterogeneity and is regarded as an evolutionary process driven by somatic mutations and clonal expansions. This evolutionary process can be modeled by a phylogenetic tree and phylogenetic analysis of multiple subclones of cancer cells can facilitate the study of the tumor variants progression. Copy-number aberration occurs frequently in many types of tumors in terms of segmental amplifications and deletions. In this thesis, we developed a distance-based method for reconstructing phylogenies from copy-number profiles of cancer cells. We demonstrate the importance of distance correction from the edit (minimum) distance to the estimated actual number of events. Experimental results show that our approaches provide accurate and scalable results in estimating the actual number of evolutionary events between copy number profiles and in reconstructing phylogenies.
High-throughput sequencing of tumor samples has reported various degrees of genetic heterogeneity between primary tumors and their distant subpopulations. The clonal theory of cancer evolution shows that tumor cells are descended from a common origin cell. This origin cell includes an advantageous mutation that causes a clonal expansion with a large amount of population of cells descended from the origin cell. To further investigate cancer progression, phylogenetic analysis on the tumor cells is imperative. In this thesis, we developed a novel approach to infer the phylogeny to analyze both Next-Generation Sequencing and Long-Read Sequencing data. Experimental results show that our new proposed method can infer the entire phylogenetic progression very accurately on both Next-Generation Sequencing and Long-Read Sequencing data.
In this thesis, we focused on phylogenetic analysis on both gene order sequence and copy number variations. Our thesis work can be categorized into three parts. First, we developed a new median solver to solve the median problem and phylogeny inference with DCJ model and apply our method to both simulated data and real yeast data. Second, we explored a new approach to infer the phylogeny of copy number profiles for a wide range of parameters (e.g., different number of leaf genomes, different number of positions in the genome, and different tree diameters). Third, we concentrated our work on the phylogeny inference on the high-throughput sequencing data and proposed a novel approach to further investigate and phylogenetic analyze the entire expansion process of cancer cells on both Next-Generation Sequencing and Long-Read Sequencing data.