Identifier

etd-04112013-150610

Degree

Doctor of Philosophy (PhD)

Department

Biological Sciences

Document Type

Dissertation

Abstract

Genetic data are frequently responsible for biological insight and recent advances in sequencing technology (high-throughput sequencing; HTS) have created massive DNA-sequence based datasets. While these technologies are invaluable, there are many analytical and application issues that need to be addressed. With these data we can ask and answer novel biological questions that were previously inaccessible. One major challenge in applying HTS to biological questions is data management: the file formats and sizes are foreign to many primary researchers. In the second and third chapters of this dissertation, I introduce two pieces of software that allow researchers to utilize HTS with minimal time investment. PRGMATIC (Chapter 2) is a pipeline that collates raw HTS data into a more traditional and useable format: two diploid alleles for a given locus. LOCINGS (Chapter 3) uses these loci, the alignments from which the loci were called and demographic data to display and output important summary statistics. This program also reformats appropriate loci into three widely used biological file formats. Chapters 4 and 5 focus on a novel application of HTS to phylogeographic inference. The collective set of microbial organisms on and inside vertebrates (the microbiota) is a vast genetic resource that is poorly understood. What factors shape these communities? Chapter 4 uses an avian brood parasite (Brown-Headed Cowbird) to naturally decouple parental genetics and early environment. Cowbird gut microbiota do not cluster with each other in multivariate space. They also do not strongly affiliate with host species. Age and sampling locality are most strongly associated with the gut microbiota. Chapter 5 looks for host taxonomic and spatial signals in a more broadly sampled dataset of 60 species sampled across Costa Rica. Here, host taxonomy is most significantly associated with gut microbiota and ecological variables like host diet and foraging strata are secondarily important. Together, these chapters present novel tools and uses of HTS for evolutionary inference. The two programs, PRGMATIC and LOCINGS, allow primary researchers to utilize HTS easily. The two bird datasets, cowbirds and Costa Rican birds, demonstrate how analyzing the microbiota with HTS can provide and address novel evolutionary questions.

Date

2013

Document Availability at the Time of Submission

Release the entire work immediately for access worldwide.

Committee Chair

Brumfield, Robb

DOI

10.31390/gradschool_dissertations.3458

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