Quantifying evolution and natural selection in vertebrate noncoding sequence
University of Cambridge
European Bioinformatics Institute
Doctor of Philosophy (PhD)
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Hoffman, M. M. (2008). Quantifying evolution and natural selection in vertebrate noncoding sequence (Doctoral thesis). https://doi.org/10.17863/CAM.13940
When studying genomic evolution, biologists find it important to identify varying patterns of natural selection. Many traditional methods of classifying directional selection have relied on models that categorize mutations as function-altering or neutral, and then comparing the rates of the two categories of mutations. The most well-known methods specifically compare nonsynonymous and synonymous substitutions in protein-coding sequence. The recent availability of whole genome sequences, especially those of various mammals and other vertebrates, enables us to develop alternative methods for analyzing molecular evolution and selection that rely on noncoding sequence. Furthermore, our greater understanding of the importance of noncoding DNA demands such methods. This thesis contains the results of the first in-depth genomic-scale analysis using intron substitutions to estimate the neutral rate of evolution. Performing this analysis across several genomes requires the development of a new model of gene evolution and related methods. I find strong correlation between estimates of the neutral rate made with intron methods and estimates made with synonymous coding nucleotides for both human–dog and mouse–rat comparisons. However, the two estimates cannot be considered directly equivalent. This thesis also describes a novel method that estimates a rate of function affecting evolution in promoter regions by inspecting the effect of simulated mutations on transcription factor binding. This involves the development and use of a probabilistic method that uses a hidden Markov model to predict the binding of transcription factors. I report the results of applying these new methods to the human genome for the identification of transcription factor binding sites, and for the identification of natural selection.
This record's DOI: https://doi.org/10.17863/CAM.13940