Browsing by Subject "RNA-seq"

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Open Access
CAP-RNAseq: an online platform for RNA-seq data clustering, annotation and prioritization based on gene essentiality and congruence between mRNA and protein levels
(2024-04) Özdeniz, Merve Vural
In 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.
Open Access
Histone H3.3 regulates mitotic progression in mouse embryonic fibroblasts
(Canadian Science Publishing, 2017) Ors, A.; Papin, C.; Favier, B.; Roulland, Y.; Dalkara, D.; Ozturk, M.; Hamiche, A.; Dimitrov, S.; Padmanabhan, K.
H3.3 is a histone variant that marks transcription start sites as well as telomeres and heterochromatic sites on the genome. The presence of H3.3 is thought to positively correlate with the transcriptional status of its target genes. Using a conditional genetic strategy against H3.3B, combined with short hairpin RNAs against H3.3A, we essentially depleted all H3.3 gene expression in mouse embryonic fibroblasts. Following nearly complete loss of H3.3 in the cells, our transcriptomic analyses show very little impact on global gene expression or on the localization of histone variant H2A.Z. Instead, fibroblasts displayed slower cell growth and an increase in cell death, coincident with large-scale chromosome misalignment in mitosis and large polylobed or micronuclei in interphase cells. Thus, we conclude that H3.3 may have an important under-explored additional role in chromosome segregation, nuclear structure, and the maintenance of genome integrity. © 2017 Published by NRC Research Press.
Open Access
A web tool to explore, annotate and classify the Acıbadem Breast Cancer Cohort RNA-seq data with gene signatures and clinical/mutation data, according to molecular subtypes
(2022-06) Çalışır, Kübra
Transcriptomics-based approaches have revealed the molecular heterogeneity and distinct gene expression patterns across breast cancer subtypes since the early 2000s. This led to the usage of molecular subtypes in clinics and translational research in prognostic assessment, therapeutic efficacy prediction, and retrospec- tive analysis of cohort studies. In this thesis, breast cancer subtypes of Acıbadem Breast Cancer Cohort (ABCC) RNA-seq data were classified with immunohisto- chemistry (IHC), PAM50, and SCMOD1 as molecular subtype predictors. The results revealed the moderate concordance of the methods across ABCC and se- lected five other public datasets. In addition, it was shown that the classification of ABCC and TCGA-BRCA RNAseq data strongly depends on the gene sig- nature selection. Further, a machine learning model trained with TCGA-BRCA RNA-seq data and PAM50 genes as predictors showed moderate results for ABCC and MATADOR due to the imbalanced nature of datasets where feature impor- tance revealed a subset of PAM50 genes as predictors. Additionally, the R-Shiny- based classABCC app was developed to facilitate clustering of ABCC with six gene signatures, molecular subtyping of ABCC, and prediction of subtypes with TCGA-BRCA RNA-seq trained machine learning model.