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Description
Understanding the diversity, evolutionary relationships, and geographic distribution of species is foundational knowledge in biology. However, this knowledge is lacking for many diverse lineages of the tree of life. This is the case for the desert stink beetles in the tribe Amphidorini LeConte, 1862 (Coleoptera: Tenebrionidae) – a lineage of arid-adapted flightless beetles found throughout western North America. Four interconnected studies that jointly increase our knowledge of this group are presented. First, the darkling beetle fauna of the Algodones sand dunes in southern California is examined as a case study to explore the scientific practice of checklist creation. An updated list of the species known from this region is presented, with a critical focus on material now made available through digitization and global aggregation. This part concludes with recommendations for future biodiversity checklist authors. Second, the psammophilic genus Trogloderus LeConte, 1879 is revised. Six new species are described, and the first, multi-gene phylogeny for the genus is inferred. In addition, historical biogeographic reconstructions along with novel hypotheses of speciation patterns within the Intermountain Region are given. In particular, the Kaibab Plateau and Kaiparowitz Formation are found to have promoted speciation on the Colorado Plateau. The Owens Valley and prehistoric Bouse Embayment are similarly hypothesized to drive species diversification in southern California. Third, a novel phylogenomic analysis for the tribe Amphidorini is presented, based on 29 de novo partial transcriptomes. Three putative ortholog sets were discovered and analyzed to infer the relationships between species groups and genera. The existing classification of the tribe is found to be highly inadequate, though the earliest-diverging relationships within the tribe are still in question. Finally, the new phylogenetic framework is used to provide a genus-level revision for the Amphidorini, which previously contained six valid genera and 253 valid species. This updated classification includes more than 100 taxonomic changes and results in the revised tribe consisting of 16 genera, with three being described as new to science.
ContributorsJohnston, Murray Andrew (Author) / Franz, Nico M (Thesis advisor) / Cartwright, Reed (Committee member) / Taylor, Jesse (Committee member) / Pigg, Kathleen (Committee member) / Arizona State University (Publisher)
Created2018
Description
Next-generation sequencing is a powerful tool for detecting genetic variation. How-ever, it is also error-prone, with error rates that are much larger than mutation rates.
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.
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.
ContributorsOrr, Adam James (Author) / Cartwright, Reed (Thesis advisor) / Wilson, Melissa (Committee member) / Kusumi, Kenro (Committee member) / Taylor, Jesse (Committee member) / Pfeifer, Susanne (Committee member) / Arizona State University (Publisher)
Created2020