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dc.contributor.advisorKen Duffy and Muriel Medard.en_US
dc.contributor.authorGurram, Neil (Neil K.)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2016-12-22T15:18:31Z
dc.date.available2016-12-22T15:18:31Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/106012
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 48).en_US
dc.description.abstractThis is a thesis on understanding stutter present in capillary electropherogram readouts as this methodology forms the basis of current DNA fingerprinting. The readouts come from taking samples of various initial template masses of DNA from different individuals, applying polymerase chain reaction (PCR) to the samples, and then running the amplified copies through capillary electrophoresis to produce a readout of peak heights corresponding to alleles on various loci. The alleles correspond to the number of repeats of microsatellites that are usually two to six base pairs in length called short tandem repeats (STRs); the number of repeats of various STRs defines a person's DNA fingerprint. This process introduces artifacts in measurement. Of particular interest in this thesis is stutter, the phenomenon where amplicons with fewer or greater number of STR repeats than the true allele count are generated as an artifact of the PCR. It is of interest to understand the source and nature for this stutter distribution for small starting masses, as it has ramifications on the ability to accurately determine a match between a DNA sample and a crime scene sample. Understanding the stutter distribution in this thesis is achieved through data analysis, probabilistic modeling, and statistics. We find that a mathematical model that combines stochastic effects from PCR with fluorescent noise explains the most significant features of the observed phenomena.en_US
dc.description.statementofresponsibilityby Neil Gurram.en_US
dc.format.extent81 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleA mathematical model of polymerase chain reaction induced stutteren_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc965828460en_US


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