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.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-04112013-150610 |
| Date | 20 April 2013 |
| Creators | Hird, Sarah Michelle |
| Contributors | Brumfield, Robb, Carstens, Bryan, Ullmer, Brygg, Christner, Brent, Rohli, Robert |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-04112013-150610/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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