Browsing by Author "Hawkins, Troy"

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    Development and Evaluation of Quality Metrics for Bioinformatics Analysis of Viral Insertion Site Data Generated Using High Throughput Sequencing
    (MDPI, 2014-05-06) Gao, Hongyu; Hawkins, Troy; Jasti, Aparna; Chen, Yu-Hsiang; Mockaitis, Keithanne; Dinauer, Mary; Cornetta, Kenneth; Medical and Molecular Genetics, School of Medicine
    Integration of viral vectors into a host genome is associated with insertional mutagenesis and subjects in clinical gene therapy trials must be monitored for this adverse event. Several PCR based methods such as ligase-mediated (LM) PCR, linear-amplification-mediated (LAM) PCR and non-restrictive (nr) LAM PCR were developed to identify sites of vector integration. Coupling the power of next-generation sequencing technologies with various PCR approaches will provide a comprehensive and genome-wide profiling of insertion sites and increase throughput. In this bioinformatics study, we aimed to develop and apply quality metrics to viral insertion data obtained using next-generation sequencing. We developed five simple metrics for assessing next-generation sequencing data from different PCR products and showed how the metrics can be used to objectively compare runs performed with the same methodology as well as data generated using different PCR techniques. The results will help researchers troubleshoot complex methodologies, understand the quality of sequencing data, and provide a starting point for developing standardization of vector insertion site data analysis.
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    Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP
    (BMC, 2010-05-19) Hawkins, Troy; Chitale, Meghana; Kihara, Daisuke; Medical and Molecular Genetics, School of Medicine
    Background A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. Results Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria). The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO) category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted. Conclusion The analyses demonstrate that applying high confidence predictions from PFP can have a significant impact on a researchers' ability to interpret the immense biological data that are being generated today. The newly introduced functional similarity networks of the three organisms show different network properties as compared with the protein-protein interaction networks.
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    Mutation in erythroid specific transcription factor KLF1 causes Hereditary Spherocytosis in the Nan hemolytic anemia mouse model
    (Elsevier, 2010-11) Heruth, Daniel P.; Hawkins, Troy; Logsdon, Derek P.; Gibson, Margaret I.; Sokolovsky, Inna V.; Nsumu, Ndona N.; Major, Stephanie L.; Fegley, Barbara; Woods, Gerald M.; Lewing, Karen B.; Neville, Kathleen A.; Cornetta, Kenneth; Peterson, Kenneth R.; White, Robert A.; Medical and Molecular Genetics, School of Medicine
    KLF1 regulates definitive erythropoiesis of red blood cells by facilitating transcription through high affinity binding to CACCC elements within its erythroid specific target genes including those encoding erythrocyte membrane skeleton (EMS) proteins. Deficiencies of EMS proteins in humans lead to the hemolytic anemia Hereditary Spherocytosis (HS) which includes a subpopulation with no known genetic defect. Here we report that a mutation, E339D, in the second zinc finger domain of KLF1 is responsible for HS in the mouse model Nan. The causative nature of this mutation was verified with an allelic test cross between Nan/+ and heterozygous Klf1(+/-) knockout mice. Homology modeling predicted Nan KLF1 binds CACCC elements more tightly, suggesting that Nan KLF1 is a competitive inhibitor of wild-type KLF1. This is the first association of a KLF1 mutation with a disease state in adult mammals and also presents the possibility of being another causative gene for HS in humans.