Matching Items (7)
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- All Subjects: Bioinformatics
- Creators: Cartwright, Reed
Description
Callithrix penicillata, also known as the Black-tufted marmoset primarily lives in the Brazilian highlands and has had little research conducted on it. For this project I performed a genome curation on the newly assembled genome of this species. The scaffolds obtained by the Dovetail Genomics reads were organized and labeled into chromosomes using the 2014 Callithrix jacchus genome as a reference. Then, using that same genome as a reference, 13 of the chromosomes were reverse complimented to be continuous with the 2014 Callithrix jacchus genome. The N50 statistics of the assembly were calculated and found to be 124 Mb. Quality scores were run for the final genome using referee and visualized with a bar plot, with 99% of sites scoring above 0. Heterozygosity was also calculated and found to be 0.3%. Finally, the final version of the genome was visually compared to the 2017 Callithrix jacchus genome and the GRCh38 human genome. This genome was submitted to the NCBIs database to await further approval.
ContributorsJohnson, Joelle Genevieve (Author) / Cartwright, Reed (Thesis director) / Stone, Anne (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
With the rising data output and falling costs of Next Generation Sequencing technologies, research into data compression is crucial to maintaining storage efficiency and costs. High throughput sequencers such as the HiSeqX Ten can produce up to 1.8 terabases of data per run, and such large storage demands are even more important to consider for institutions that rely on their own servers rather than large data centers (cloud storage)1. Compression algorithms aim to reduce the amount of space taken up by large genomic datasets by encoding the most frequently occurring symbols with the shortest bit codewords and by changing the order of the data to make it easier to encode. Depending on the probability distribution of the symbols in the dataset or the structure of the data, choosing the wrong algorithm could result in a compressed file larger than the original or a poorly compressed file that results in a waste of time and space2. To test efficiency among compression algorithms for each file type, 37 open-source compression algorithms were used to compress six types of genomic datasets (FASTA, VCF, BCF, GFF, GTF, and SAM) and evaluated on compression speed, decompression speed, compression ratio, and file size using the benchmark test lzbench. Compressors that outpreformed the popular bioinformatics compressor Gzip (zlib -6) were evaluated against one another by ratio and speed for each file type and across the geometric means of all file types. Compressors that exhibited fast compression and decompression speeds were also evaluated by transmission time through variable speed internet pipes in scenarios where the file was compressed only once or compressed multiple times.
ContributorsHowell, Abigail (Author) / Cartwright, Reed (Thesis director) / Wilson Sayres, Melissa (Committee member) / Taylor, Jay (Committee member) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Mammary gland development in humans during puberty involves the enlargement of breast tissue, but this is not true in non-human primates. To identify potential causes of this difference, I examined variation in substitution rates across genes related to mammary development. Genes undergoing purifying selection show slower-than-average substitution rates, while genes undergoing positive selection show faster rates. These may be related to the difference between humans and other primates. Three genes were found to be accelerated were FOXF1, IGFBP5, and ATP2B2, but only the latter one was found in humans and it seems unlikely that it would be related to the differences between mammary gland development at puberty between humans and non-human primates.
ContributorsArroyo, Diana (Author) / Cartwright, Reed (Thesis director) / Wilson Sayres, Melissa (Committee member) / Schwartz, Rachel (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Protein-surface interactions, no matter structured or unstructured, are important in both biological and man-made systems. Unstructured interactions are more difficult to study with conventional techniques due to the lack of a specific binding structure. In this dissertation, a novel approach is employed to study the unstructured interactions between proteins and heterogonous surfaces, by looking at a large number of different binding partners at surfaces and using the binding information to understand the chemistry of binding. In this regard, surface-bound peptide arrays are used as a model for the study. Specifically, in Chapter 2, the effects of charge, hydrophobicity and length of surface-bound peptides on binding affinity for specific globular proteins (&beta-galactosidase and &alpha1-antitrypsin) and relative binding of different proteins were examined with LC Sciences peptide array platform. While the general charge and hydrophobicity of the peptides are certainly important, more surprising is that &beta-galactosidase affinity for the surface does not simply increase with the length of the peptide. Another interesting observation that leads to the next part of the study is that even very short surface-bound peptides can have both strong and selective interactions with proteins. Hence, in Chapter 3, selected tetrapeptide sequences with known binding characteristics to &beta-galactosidase are used as building blocks to create longer sequences to see if the binding function can be added together. The conclusion is that while adding two component sequences together can either greatly increase or decrease overall binding and specificity, the contribution to the binding affinity and specificity of the individual binding components is strongly dependent on their position in the peptide. Finally, in Chapter 4, another array platform is utilized to overcome the limitations associated with LC Sciences. It is found that effects of peptide sequence properties on IgG binding with HealthTell array are quiet similar to what was observed with &beta-galactosidase on LC Science array surface. In summary, the approach presented in this dissertation can provide binding information for both structured and unstructured interactions taking place at complex surfaces and has the potential to help develop surfaces covered with specific short peptide sequences with relatively specific protein interaction profiles.
ContributorsWang, Wei (Author) / Woodbury, Neal W (Thesis advisor) / Liu, Yan (Committee member) / Chaput, John (Committee member) / Arizona State University (Publisher)
Created2014
Description
The healthcare system in this country is currently unacceptable. New technologies may contribute to reducing cost and improving outcomes. Early diagnosis and treatment represents the least risky option for addressing this issue. Such a technology needs to be inexpensive, highly sensitive, highly specific, and amenable to adoption in a clinic. This thesis explores an immunodiagnostic technology based on highly scalable, non-natural sequence peptide microarrays designed to profile the humoral immune response and address the healthcare problem. The primary aim of this thesis is to explore the ability of these arrays to map continuous (linear) epitopes. I discovered that using a technique termed subsequence analysis where epitopes could be decisively mapped to an eliciting protein with high success rate. This led to the discovery of novel linear epitopes from Plasmodium falciparum (Malaria) and Treponema palladium (Syphilis), as well as validation of previously discovered epitopes in Dengue and monoclonal antibodies. Next, I developed and tested a classification scheme based on Support Vector Machines for development of a Dengue Fever diagnostic, achieving higher sensitivity and specificity than current FDA approved techniques. The software underlying this method is available for download under the BSD license. Following this, I developed a kinetic model for immunosignatures and tested it against existing data driven by previously unexplained phenomena. This model provides a framework and informs ways to optimize the platform for maximum stability and efficiency. I also explored the role of sequence composition in explaining an immunosignature binding profile, determining a strong role for charged residues that seems to have some predictive ability for disease. Finally, I developed a database, software and indexing strategy based on Apache Lucene for searching motif patterns (regular expressions) in large biological databases. These projects as a whole have advanced knowledge of how to approach high throughput immunodiagnostics and provide an example of how technology can be fused with biology in order to affect scientific and health outcomes.
ContributorsRicher, Joshua Amos (Author) / Johnston, Stephen A. (Thesis advisor) / Woodbury, Neal (Committee member) / Stafford, Phillip (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2014
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
Pathways of Distinction Analysis of Liver Cancer Data: Genetic Differences Between Males and Females
Description
The Pathways of Distinction Analysis (PoDA) program calculates relationships between a given group of genes contained within a pathway, and a disease state. It was used here to investigate liver cancer, and to explore how genetic variability may contribute to the different rates of development of the disease in males and females. The goal of the study was to identify germline variation that differs by sex in hepatocellular carcinoma. Using the program, multiple pathways and genes were identified to have significant differences in their relationship to liver cancer in males and females. In animal studies, the genes which were identified using the PoDA analysis have been shown to impact liver cancer, often with different results for males and females. While these genes are often the focus in animal models, they are absent from current Genome Wide Association Studies (GWAS) catalogs for humans. By working to bridge the results of animal studies and human studies, the results help to identify the causes of liver cancer, and more specifically, the reason the disease affects males at much higher rates. The differences in pathways identified to be significant for the two sexes indicate the germline variance may play sex-specific roles in the development of hepatocellular carcinoma. Additionally, these results reinforce the capacity of the PoDA analysis to identify genes that may be missed by more traditional GWAS methods. This study lays the groundwork for further investigations into the identified genes and pathways, and how they behave differently within males and females.
ContributorsOlson, Erik Jon (Author) / Buetow, Kenneth (Thesis advisor) / Wilson, Melissa (Committee member) / Cartwright, Reed (Committee member) / Arizona State University (Publisher)
Created2021