Second Faculty Mentor
David Liberles (Temple University)
Understanding the evolutionary history of organisms allows us to better comprehend selective pressures and their effects on larger populations. In our study, we focused on analyzing the DNA of ciliate groups, which are single celled protozoans characterized by the presence of cilia on their outer membrane. We utilized the DNA of the organisms to analyze the changes in population genotype over time. We tested existing evolutionary models (designed to represent natural genetic variation over time in populations) against our data to identify the model with the best fit and likelihood. From the DNA and the evolutionary model with the highest likelihood, we can generate phylogenies for the organisms, which allow us to visualize the evolutionary history of the organisms, and additionally can be used to test for selection. To test for selection, we used the dN/dS ratio (dN representing non-synonymous substitutions and changes in the resulting amino-acid; and dS representing synonymous substitutions or silent substitutions). This ratio can then be used to measure positive, negative, or neutral selective pressure on the population, as values greater than one indicate high alterations in proteins which is indicative of selection. These results allow us to better understand the presence of selective pressures on these populations, and allows us to comprehend how the proteins produced by the DNA of these organisms changed over time. We also hope to be able to use this information to make inferences regarding the future of these organisms and their genotypes based off of modern selective pressures.
Altemose, Quentin D., "Ciliate Codon Translator Program Manual" (2016). Mathematics Summer Fellows. 3.
The Working Code for the translator (written in C++ in CodeBlocks IDE)
Available to all.
Bioinformatics Commons, Biology Commons, Mathematics Commons, Organisms Commons, Software Engineering Commons
Presented during the 18th Annual Summer Fellows Symposium, July 22, 2016 at Ursinus College.
We would like to thank the Temple University Center for Computational Genetics and Genomics team for their support throughout this project.