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dc.contributor.advisorDouglas Lauffenburger and Florian Gnad.en_US
dc.contributor.authorSimpson, Claire M.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2020-03-24T15:36:56Z
dc.date.available2020-03-24T15:36:56Z
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/124262
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.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019en_US
dc.descriptionCataloged from student-submitted PDF version of thesis. "September 2019."en_US
dc.descriptionIncludes bibliographical references (pages 34-38).en_US
dc.description.abstractPerturbed posttranslational modification (PTM) landscapes and gene expression networks commonly cause pathological phenotypes. To elucidate altered PTM landscapes on a large scale, disease-associated mutations from TCGA, Uniprot, and dbSNP were integrated with PTM sites from PhosphoSitePlus. We characterized each dataset individually, compared somatic with germline mutations, and analyzed PTM sites intersecting directly with disease variants. To assess the impact of mutations in the flanking regions of phosphosites, we developed DeltaScansite, a pipeline that compares Scansite predictions on wild type versus mutated sequences. Disease mutations are also visualized in PhosphoSitePlus. TCGA also curates gene expression data from tumor and normal tissues, enabling us to calculate gene coexpression networks and apply graph algorithms in Neo4j.en_US
dc.description.statementofresponsibilityby Claire M. Simpson.en_US
dc.format.extent43 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleSystemic analysis of changes in protein modification and gene coexpression networks in canceren_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1145169108en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2020-03-24T15:36:55Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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