ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Filtering by
- All Subjects: Biology
I collected soil samples from the riparian and terrestrial zones of 21 sites, placing them in growth chambers at one of two temperature regimes, and monitoring emergence of seedlings for 12 weeks. Results showed an approximately equal number of warm and cool specialists in both riparian and terrestrials zones; generalists also were abundant, particularly in the riparian zone. The number of temperature specialists and generalists in the riparian zones did not differ significantly between perennial headwater and ephemeral stream types. In montane streams, alpha diversity of the soil seed bank was highest for ephemeral reaches; in basin streams the intermittent and perennial reaches had higher diversity. Spatial turnover was primarily responsible for within stream beta-diversity—reaches had different species assemblages. The large portion of temperature specialists found in riparian seed banks indicates that even with available moisture riparian zone plant community composition will likely be impacted by changing temperatures. However, the presence of so many temperature generalists in the riparian zones suggests that some component of the seed bank is adapted to variable conditions and might offer resilience in a changing climate. Study results confirm the importance of conserving multiple hydrogeomorphic reach types because they support unique species assemblages.
This can make mutation detection difficult; and while increasing sequencing depth
can often help, sequence-specific errors and other non-random biases cannot be de-
tected by increased depth. The problem of accurate genotyping is exacerbated when
there is not a reference genome or other auxiliary information available.
I explore several methods for sensitively detecting mutations in non-model or-
ganisms using an example Eucalyptus melliodora individual. I use the structure of
the tree to find bounds on its somatic mutation rate and evaluate several algorithms
for variant calling. I find that conventional methods are suitable if the genome of a
close relative can be adapted to the study organism. However, with structured data,
a likelihood framework that is aware of this structure is more accurate. I use the
techniques developed here to evaluate a reference-free variant calling algorithm.
I also use this data to evaluate a k-mer based base quality score recalibrator
(KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing
data. Base quality scores can help detect errors in sequencing reads, but are often
inaccurate. The most popular method for correcting this issue requires a known
set of variant sites, which is unavailable in most cases. I simulate data and show
that errors in this set of variant sites can cause calibration errors. I then show that
KBBQ accurately recalibrates base quality scores while requiring no reference or other
information and performs as well as other methods.
Finally, I use the Eucalyptus data to investigate the impact of quality score calibra-
tion on the quality of output variant calls and show that improved base quality score
calibration increases the sensitivity and reduces the false positive rate of a variant
calling algorithm.