The human genome exhibits a rich structure resulting from a long history of genomic changes, including single base-pair mutations and larger scale rearrangements such as inversions, deletions, translocations, and duplications. The number and order of the genomic changes that resulted in the present-day human genome is not known, but can sometimes be inferred by comparison to the genomes of other species. In particular, genome rearrangements are modeled as operations on signed strings of characters representing blocks of conserved sequences. Genome rearrangement distance measures quantify the similarity between two or more genome sequences by counting the minimum, or most likely, number of rearrangement operations needed to transform one sequence into another. The development of efficient algorithms for computing genome rearrangement distances has been instrumental both in computing phylogenies for sets of known genetic sequences (such as gene families or the whole genomes of present-day species) and in constructing ancestral genome sequences.In this thesis, we develop algorithms to study recent genome rearrangements in human and cancer genomes. We introduce a novel measure, called duplication distance, to quantify the similarity between two genomic regions containing segmental duplications. We give an efficient algorithm to compute the duplication distance between a pair of signed strings and provide several generalizations of duplication distance that also measure inversions and deletions. We demonstrate the utility of the duplication distance measure in constructing the evolutionary history of segmental duplications in the human genome using both parsimony and likelihood techniques. Further, motivated by recent cancer genome sequencing studies, we present a new algorithm for the block ordering problem of inferring a whole genome sequence from a partial assembly by maximizing its similarity to another genome.
Kahn, Crystal Louise,
"Algorithms for Analyzing Human Genome Rearrangements"
(2011).
Computer Science Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.7301/Z04J0CBV
