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oai:digital.lib.washington.edu:1773/404332026-02-16T02:26:31Zcom_1773_4888col_1773_4899

Abernethy, Neil Hood, Tressa Renee 2017-10-26T20:45:22Z 2017-10-26T20:45:22Z 2017-10-26 2017-08 Hood_washington_0250O_17920.pdf http://hdl.handle.net/1773/40433 Thesis (Master's)--University of Washington, 2017-08 Gene fusions have long been known to drive cancer. Initial discovery of gene fusions was opportunistic, and functional assessment was done individually and experimentally. There is no comprehensive systems biology approach to understanding the impact of gene fusions on the signaling networks within tumor cells. An integrative computational approach was taken to achieve a better understanding of gene fusions and their complex influence on pathways and interaction networks in the context of lung cancer. Using well-studied fusions and publicly available gene expression data, the effect of fusion events on the expression pattern of gene networks revealed unique differences in tumors with gene fusions, tumors without gene fusions, and normal samples. This approach identifies gene expression signatures associated with specific fusions, and provides a model for integrating experimental and pathway data to better understand the biology of a fusion genes and their roles in oncogenesis. application/pdf en_US creatingRankGSEAFile.R; code/script; Ranking gene list for GSEA. DESeqx26edgeR.R; code/script; Differential gene expression. ensembl_id_converter.R; code/script; Convert ensembl ids to gene names. interactionsBiogrid.R; code/script; Gene interactions accessing bioGRID. tcgafilescrub.R; code/script; File scrubbing for TCGA. TCGAProcessing.R; code/script; File processing for TCGA. Gene Fusions by Cancer type with function.xlsx; data; Gene Fusion Summary. CC BY gene expression gene fusions interaction network pathway analysis Bioinformatics To Be Assigned A Systems Biology Approach to Characterizing Gene Fusion Pathways in Cancer Thesis Open Access open.access