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dc.contributor.authorKolychev, Dmitri Sergeevich
dc.date.accessioned2014-03-03T21:01:57Z
dc.date.available2014-03-03T21:01:57Z
dc.date.issued2003-08
dc.identifier.otherkolychev_dmitri_s_200308_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/kolychev_dmitri_s_200308_ms
dc.identifier.urihttp://hdl.handle.net/10724/21091
dc.description.abstractMicrosatellites, or simple sequence repeats, are genetic loci where several nucleotide bases are repeated in tandem. Since they can be easily found by Polymerase Chain Reaction (PCR) using unique flanking primers, they are considered excellent genetic markers in making genetic linkage maps among other things. In this thesis we present Microsatellite Polymorphism Finder (MSPF), a program that detects and then verifies microsatellites by modeling PCR digitally from an Expressed Sequence Tag (EST) or a shotgun-sequencing database without the overhead required to perform PCR chemically with human intervention. Moreover, a machine learning enhanced version of MSPF improves the accuracy of microsatellite verification.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectmicrosatellite detection
dc.subjectneural networks
dc.subjectpolymerase chain reaction
dc.subjectconsensus sequence verification
dc.titleMicrosatellite detection and consensus sequence verification by virtual PCR and machine learning
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorKhaled Rasheed
dc.description.committeeKhaled Rasheed
dc.description.committeeDon Potter
dc.description.committeeMarie-Michele Cordonnier-Pratt


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