2026-09-18T22:52:28Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1242622026-06-06T00:49:16Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Douglas Lauffenburger and Florian Gnad.
Simpson, Claire M.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2020-03-24T15:36:56Z
2020-03-24T15:36:56Z
2019
https://hdl.handle.net/1721.1/124262
1145169108
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis. "September 2019."
Includes bibliographical references (pages 34-38).
Perturbed 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.
by Claire M. Simpson.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
43 pages
eng
Massachusetts Institute of Technology
MIT 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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Systemic analysis of changes in protein modification and gene coexpression networks in cancer
Thesis
application/pdf
2020-03-24T15:36:55Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Perturbed 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.</Abstract>
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