Abstract
Within bioinformatics, phylogenetics is the study of the evolutionary relationships between different species and organisms. The genetic revolution has caused an explosion in the amount of raw genomic information that is available to scientists for study. While there has been an explosion in available data, analysis methods have lagged behind. A key task in phylogenetics is identifying homology clusters. Current methods rely on using heuristics based on pairwise sequence comparison to identify homology clusters. We propose the Orthology Group Cleaner (the OGCleaner) as a method to evaluate cluster level verification of putative homology clusters in order to create higher quality phylogenetic tree reconstruction.
Degree
MS
College and Department
Physical and Mathematical Sciences; Computer Science
Rights
http://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Fujimoto, Masaki Stanley, "The OGCleaner: Detecting False-Positive Sequence Homology" (2017). Theses and Dissertations. 6410.
https://scholarsarchive.byu.edu/etd/6410
Date Submitted
2017-06-01
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd9342
Keywords
machine learning, orthology clusters, phylogenetics
Language
english