Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- All Subjects: Bioinformatics
- Creators: Pfeifer, Susanne
Previous recombination rate estimation studies in rhesus macaques have been mostly restricted to a singular approach (e.g., using microsatellite loci). Here, we employ a bilateral method in estimating recombination rates—pedigree-based and linkage-disequilibrium-based—from whole-genome data of rhesus macaques to estimate CO and NCO recombination events and to compare contemporary and historical rates of recombination.
Recombination is the process of exchange between maternal and paternal chromosomal segments which gives rise to new allele combinations in the offspring. The rate of recombination refers to the chances that such crossing over events will take place. Recombination is an especially important process for driving genetic diversity and therefore evolution. However, the specifics of the factors affecting the recombination frequency and how the rate itself can evolve have not been the focus of many studies in the past. The purpose of this study was to utilize sequenced genomes of parent-offspring trios of Rhesus Macaque to create a high quality dataset from which a direct, sex-specific, estimate of recombination rate can be calculated for the species.
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.