Genome- and tissue-wide analysis of alternative polyadenylation events using clustering and feature learning methods

Date

2019

Author

Yılmazer, Pınar

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Alternative polyadenylation (APA) is a biological process that takes places during gene transcription and recent studies relate APA events with gene expression regulation and with diseases such as cancer. Studying gene expression across tissues for various conditions is crucial guiding scientists to work on biomarker discoveries and treatment options of the transcriptomic diversity related diseases. In this thesis, several novel genes and tissues that are more prone to 3’UTR shortening, which is an APA event, in diseased state are presented by analyzing significant proximal APA events across human tissues. Most of the identified genes are also validated by existing studies. Furthermore, we demonstrate that hierarchically closer tissues share similar gene set or interactions according to APA events, and tissue hierarchy can be built by just considering top affected genes. Overall, this work covers creation of biological tissue hierarchy, comparison of tissue networks in normal/diseased states and feature learning analysis of protein-protein networks using APA events. To the best of our knowledge, no such human genome- and tissue-wide analysis, based on APA events, has been conducted before. Therefore, our multidisciplinary work may guide researchers to the next step of genomics studies on diseases.

Subject Keywords

Hierarchical clustering (Cluster analysis)., Keywords: Feature Learning, Hierarchical Clustering, Alternative Polyadenylation, 3'UTR Shortening, Cancer, Gene, Disease.

URI

http://etd.lib.metu.edu.tr/upload/12623705/index.pdf
https://hdl.handle.net/11511/44127

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Graduate School of Natural and Applied Sciences, Thesis