Interrogating Microbial Populations: from Large-scale Data to Algorithms to Field-deployed Software

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dc.contributor.advisorTreangen, Todd J
dc.creatorSapoval, Nick
dc.date.accessioned2024-05-21T21:45:49Z
dc.date.available2024-05-21T21:45:49Z
dc.date.created2024-05
dc.date.issued2024-04-17
dc.date.submittedMay 2024
dc.date.updated2024-05-21T21:45:49Z
dc.description.abstractIn this work we present a set of studies that explore genomic sequencing data and offer computational methods to process these data at scale. Broadly the topics of this theses can be grouped into two categories: those that bridge the gap of efficient large scale data analysis with applications in public health and those that explore algorithmic solutions to analyses of clinically relevant metagenomic data. Across this set of topics we make several contributions that include scientific data analysis, and algorithm and software development. In the realm of public health, we contribute an exploratory study of the genomic variation within SARS-CoV-2 and its impacts on our ability to track the virus and its spread. We also propose an efficient pipeline for characterization of wastewater derived SARS-CoV-2 samples which is employed for routine monitoring in Houston, USA. On the clinical metagenomics side we explore a scalable database-free approach for characterization of longitudinal changes in human gut microbiota. We also propose a laptop-friendly software for taxonomic profiling of long-read metagenomic samples. Together the contributions of this thesis span two major application areas and cover topics of data-driven algorithm and software design.
dc.format.mimetypeapplication/pdf
dc.identifier.citationSapoval, Nick. Interrogating Microbial Populations: from Large-scale Data to Algorithms to Field-deployed Software. (2024). PhD diss., Rice University. https://hdl.handle.net/1911/116125
dc.identifier.urihttps://hdl.handle.net/1911/116125
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectmetagenomics
dc.subjectinfectious disease surveillance
dc.subjectSARS-CoV-2
dc.subjectbioinformatics
dc.subjectcomputational biology
dc.titleInterrogating Microbial Populations: from Large-scale Data to Algorithms to Field-deployed Software
dc.typeThesis
dc.type.materialText
thesis.degree.departmentComputer Science
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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