Assessing 16S rRNA Marker-Gene Survey Measurement Process Using Mixtures of Environmental Samples

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dc.contributor.advisorCorrada Bravo, Héctoren_US
dc.contributor.authorOlson, Nathan Den_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2018-09-12T05:53:02Z
dc.date.available2018-09-12T05:53:02Z
dc.date.issued2018en_US
dc.description.abstractMicrobial communities play a fundamental role in environmental and human health. Targeted sequencing of the 16S rRNA gene, 16S rRNA marker-gene surveys, is used to measure and thus characterize these communities. The 16S rRNA marker- gene survey measurement process includes a number of molecular laboratory and computational steps. A rigorous measurement assessment framework can evaluate measurement method performance, in turn improving the validity of marker-gene survey study conclusions. In this dissertation, I present a novel framework and mixture dataset for assessing 16S rRNA marker-gene survey bioinformatic methods. Additionally, I developed software to facilitate working with 16S rRNA reference sequence databases and 16S rRNA marker-gene survey feature data. Computational steps, collectively referred to as bioinformatic pipelines, combine multiple algorithms to convert raw sequence data into a count table, which is subsequently used to test biological hypotheses. Algorithm choice and parameters can significantly impact pipeline results. The assessment framework and software developed for this dissertation improve upon existing assessment methods and can be used to evaluate new computational methods and optimize existing pipelines. Furthermore, the assessment framework presented here can be applied to other microbial community measurement methods such as shotgun metagenomics.en_US
dc.identifierhttps://doi.org/10.13016/M24T6F66D
dc.identifier.urihttp://hdl.handle.net/1903/21268
dc.language.isoenen_US
dc.subject.pqcontrolledBioinformaticsen_US
dc.subject.pqcontrolledMicrobiologyen_US
dc.subject.pquncontrolled16S rRNA sequencingen_US
dc.subject.pquncontrolledbeta-diversityen_US
dc.subject.pquncontrolleddifferential abundanceen_US
dc.subject.pquncontrolledmetrologyen_US
dc.subject.pquncontrolledmicrobiomeen_US
dc.titleAssessing 16S rRNA Marker-Gene Survey Measurement Process Using Mixtures of Environmental Samplesen_US
dc.typeDissertationen_US

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