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dc.contributor.authorWilson, Mark Christopher
dc.date.accessioned2014-03-04T20:34:34Z
dc.date.available2014-03-04T20:34:34Z
dc.date.issued2012-05
dc.identifier.otherwilson_mark_c_201205_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/wilson_mark_c_201205_ms
dc.identifier.urihttp://hdl.handle.net/10724/28163
dc.description.abstractMetabolic profiling is one of the pillars of functional genomics, and along with the development of other omics tools for understanding cellular processes there has come a need for high throughput metabolite matching that displays results in a visually intuitive way. A web-based pipeline called MetaLab was developed to facilitate the storage, processing, analysis and retrieval of metabolite profiling data. Retention index and mass spectral similarity coefficient are used for peak matching. Normalization methods are available for assessing relative metabolite abundance for user-defined experimental groups. Matched metabolite sets can be further explored using a variety of analytical and statistical tools. Export of selected metabolites yields Excel spreadsheets displaying the alignment using a multi-color scheme. MetaLab offers a platform that greatly simplifies manual curation of metabolite profiling data, allowing researchers to focus more on the biological interpretation of their data.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectMetabolic
dc.subjectProfiling
dc.subjectMetabolite
dc.subjectRetention index
dc.subjectSimilarity coefficient
dc.subjectDatabase
dc.titleMetalab
dc.title.alternativea metabolic profiling database and analysis toolkit
dc.typeThesis
dc.description.degreeMS
dc.description.departmentBioinformatics
dc.description.majorBioinformatics
dc.description.advisorChung-Jui Tsai
dc.description.committeeChung-Jui Tsai
dc.description.committeeScott Harding
dc.description.committeeLiming Cai


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