CAP-RNAseq: an online platform for RNA-seq data clustering, annotation and prioritization based on gene essentiality and congruence between mRNA and protein levels

buir.advisorKarakayalı, Özlen Konu
dc.contributor.authorÖzdeniz, Merve Vural
dc.date.accessioned2024-05-06T11:00:39Z
dc.date.available2024-05-06T11:00:39Z
dc.date.issued2024-04
dc.date.submitted2024-05-02
dc.departmentDepartment of International Relations
dc.descriptionCataloged from PDF version of article.
dc.description Thesis (Ph.D.): Bilkent University, Graduate Program in Neuroscience, İhsan Doğramacı Bilkent University, 2024.
dc.descriptionIncludes bibliographical references (leaves 325-389).
dc.description.abstractIn recent years, there has been a remarkable growth in the application of RNA-seq in both clinical and molecular biology research contexts. The analysis and interpretation of these RNA-seq data demands a good knowledge of bioinformatics. Many different applications are available to perform the analysis, but more comprehensive applications are needed, especially for researchers without coding experience. Therefore, I developed an all-in-one novel RNA-seq analysis tool, CAP-RNAseq (http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/), which provide valuable analysis for co-expression cluster prioritization and annotation. CAP-RNAseq in particular performs clustering of the genes based on their expression patterns, annotates mirror clusters that display inverse patterns with a network-based visualizations before prioritization of clusters and/or genes based on "gene essentiality", protein levels and the degree of congruence between mRNA and protein levels of genes. Furthermore, for illustration of the use of CAP-RNAseq in this thesis, I reanalyzed a number of published RNA-seq datasets and identified novel pathways modulated by NTRK2 overexpression (GSE136868) in neural stem cells and also showed significance of the essential genes/pathways in senescent cell clearance focusing on NTRK2 (fibroblast; GSE190998) and THBD (Huh7, GSE228941) siRNA models. In addition, I analyzed our lab’s novel RNA-seq data obtained from breast cancer cell lines in CAP-RNAseq; and the findings revealed a) the complex associations between steroid hormones; Drospirenone, Aldosterone, and Estrogen in hormone positive T47D and mineralocorticoid receptor-overexpressing MCF-7 cells; and b) significant differences in essential and non-essential gene expression of the isogenic MCF7 cells overexpressing wildtype or mutant TP53. I also studied a public breast cancer dataset (GSE201085) demonstrating CAP-RNAseq’s ability to identify novel breast cancer markers exhibiting high mRNA-protein level correlations. In conclusion, this thesis not only demonstrates the use and power of CAP-RNAseq as a tool to identify essential genes and pathways by analyzing RNA-seq data, but also provides new insights into the roles of essential genes in glioma, senescence and breast cancer.
dc.description.degreePh.D.
dc.description.statementofresponsibilityby Merve Vural Özdeniz
dc.embargo.release2024-11-02
dc.format.extentxxii, 393 leaves : illustrations (some color) ; 30 cm.
dc.identifier.itemidB120909
dc.identifier.urihttps://hdl.handle.net/11693/115162
dc.language.isoEnglish
dc.publisherBilkent University
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectClustering
dc.subjectAnnotation
dc.subjectPrioritization
dc.subjectTranscriptome
dc.subjectRNA-seq
dc.subjectCancer
dc.titleCAP-RNAseq: an online platform for RNA-seq data clustering, annotation and prioritization based on gene essentiality and congruence between mRNA and protein levels
dc.title.alternativeCAP-RNAseq: gen esansiyelliğine ve mRNA ve protein düzeyleri arasındaki uyuma dayalı RNA-Seq veri kümeleme, annotasyon ve önceliklendirme için çevrimiçi bir platform
dc.typeThesis
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