Browsing Master's Theses by Authors
iMicrobe: Tools and Data-Driven Discovery Platform for the Microbiome SciencesHurwitz, Bonnie; Youens-Clark, Charles Kenneth; U'Ren, Jana; Hartman, John (The University of Arizona., 2019)Background: Scientists have amassed a wealth of microbiome datasets making it possible to study microbes in biotic and abiotic systems on a population- or planetary-scale; however, this potential hasn’t been fully realized given that the tools, data sets, and computation are available in diverse repositories and locations. To address this challenge, we developed iMicrobe.us, a community-driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with those from the broader community. Findings: The iMicrobe platform brings together analysis tools and microbiome data sets by leveraging National Science Foundation-supported cyberinfrastructure and computing resources from CyVerse, Agave, and XSEDE. The primary purpose of iMicrobe is to provide users with a freely available, web-based platform to (1) maintain and share project data, metadata, and analysis products, (2) search for related public datasets, and (3) use and publish bioinformatics tools that run on highly-scalable computing resources. Analysis tools are implemented in containers that encapsulate complex software dependencies and run on freely available XSEDE resources via the Agave API which can retrieve datasets from the CyVerse Data Store or any web-accessible location (e.g., FTP, HTTP). Conclusions: iMicrobe promotes data integration, sharing, and community-driven tool development by making open source data and tools accessible to the research community in a web-based platform.
Optimizing Detection of Clinically Relevant Microbes from Whole Blood of Febrile Neutropenia PatientsHurwitz, Bonnie L.; Thornton, James Eric, Jr.; Hurwitz, Bonnie L.; Watts, George; U'Ren, Jana (The University of Arizona., 2017)Febrile Neutropenia is a potentially fatal side effect of cancer chemotherapy. Weakened immune systems make FN patients vulnerable to infection by a wide range of opportunistic pathogens. Culture-positive rates in these patients is notoriously low. Treatment consists of broad-spectrum antibiotics that are often ineffective in treating the patient and may be harmful in some cases. The advancement of sequencing technology and the decrease in associated costs suggests an imminent shift from culture to molecular based diagnostics. Described here is a streamlined bioinformatics workflow for analyzing whole genome shotgun reads from blood in 6 Febrile Neutropenia and 5 bone marrow transplant patients. To ensure that taxonomic assignments are accurate, binary mixtures of bacteria (with varying taxonomic distance across a log-scale of abundance) and a mock bacterial community in known staggered abundance were sequenced and subjected to the bioinformatics workflow. Reported are the effects of using various bioinformatics steps, strategies, and parameters to establish standard protocols for the expanded use of NGS for analyzing clinical samples. Quality control using modest parameters resulted in significant reductions in total number of reads ranging from 38% to 57% and showed bias in removing viral reads. Host screening against the human genome removed 42% to 96% of reads from NF samples, including Human Parvovirus B19 an endogenous microbe in the human genome representing a potentially causative organism. Time series analysis of bone marrow transplant samples revealed the dynamic nature of microbial communities and identified growing systemic infection of Escherichia coli. Consistent presence of contaminating organisms including Human Endogenous Virus and Cutibacterium acnes was observed in samples with few total microbial sequences, compared to samples with potentially causative organisms with a higher abundance of microbial reads. Taken together, this work lays the foundation for bioinformatics pipelines that use open-source software, require minimal computational resource, and provide rapid and accurate identification of known microorganisms.