Resolving the complexity of the cancer transcriptome through 3’ UTRs

Date

2017-06-30

Author

Erson Bensan, Ayşe Elif

Metadata

Show full item record

Item Usage Stats

115
views

0
downloads

Advancements in sequencing and transcriptome analysis methods contributed to a better understanding of the complexity of cancer. These findings are paving the way toward the development of improved diagnostics, prognostic predictions, and targeted treatment options. In an effort to have a more comprehensive understanding of cancer, we focus on the 3’ UTRs (untranslated regions) of genes as we came to appreciate the value of the noncoding regions of our genomes, partly due to microRNAs. The 3’UTRs have long been known to have important roles in maintaining the stability, localization, and half-life of mRNAs but a detailed mechanistic explanation as to how these properties are regulated or the consequences of deregulation are just beginning to be appreciated. Our group is interested in the regulation and consequences of 3’UTR length changes in mRNA isoforms in breast cancers. 3’UTR length is controlled mainly by the position of the polyadenylation (poly(A)) signal. Interestingly, majority of human genes harbor multiple poly(A) signals on their 3’ UTRs that can be differentially selected on the basis of the physiologic state of cells, resulting in alternative mRNA isoforms. Hence, deregulation of alternative polyadenylation (APA) has increasing interest in cancer research, because APA generates mRNA isoforms with potentially different protein functions. In this talk, our groups efforts to identify and characterize novel cancer related genes will be discussed based on our combinatorial approach to reveal APA events. Overall, detection of deregulated APA-generated isoforms in cancer may implicate some proto-oncogene activation cases of unknown causes and may help the discovery of novel cases; thus, contributing to a better understanding of molecular mechanisms of cancer.

URI

https://hdl.handle.net/11511/86518

Conference Name

HIBIT 2017

Collections

Department of Biology, Conference / Seminar

Suggestions

OpenMETU
Core

Alternative Polyadenylation: Another Foe in Cancer Erson Bensan, Ayşe Elif; Can, Tolga (2016-06-01) Advancements in sequencing and transcriptome analysis methods have led to seminal discoveries that have begun to unravel the complexity of cancer. These studies are paving the way toward the development of improved diagnostics, prognostic predictions, and targeted treatment options. However, it is clear that pieces of the cancer puzzle are still missing. In an effort to have a more comprehensive understanding of the development and progression of cancer, we have come to appreciate the value of the noncoding...
Prediction of protein subcellular localization using global protein sequence feature Bozkurt, Burçin; Atalay, Mehmet Volkan; Department of Computer Engineering (2003) The problem of identifying genes in eukaryotic genomic sequences by computational methods has attracted considerable research attention in recent years. Many early approaches to the problem focused on prediction of individual functional elements and compositional properties of coding and non coding deoxyribonucleic acid (DNA) in entire eukaryotic gene structures. More recently, a number of approaches has been developed which integrate multiple types of information including structure, function and genetic p...
DETECTION OF CANCER STEM CELLS IN MICROSCOPIC IMAGES BY USING REGION COVARIANCE AND CODIFFERENCE METHOD Oguz, Oguzhan; Muenzenmayer, Christian; Wittenberg, Thomas; ÜNER, AYŞEGÜL; ÇETİN, AHMET ENİS; Atalay, Rengül (2015-10-30) This paper presents a cancer stem cell detection method using region covariance and codifference method. It focuses on detection of Cancer Stem Cell (CSC) in microscopic images which are stained with CD13 marker. Features of CSC images are extracted by using both covariance method and its multiplication free version codifference method and these features are fed into a Support Vector Machine (SVM) for classification. Experimental results are presented.
Analyzing the Information Distribution in the fMRI Measurements by Estimating the Degree of Locality Onal, Itir; Ozay, Mete; Firat, Orhan; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-07-07) In this study, we propose a new method for analyzing and representing the distribution of discriminative information for data acquired via functional Magnetic Resonance Imaging (fMRI). For this purpose, we form a spatially local mesh with varying size, around each voxel, called the seed voxel. The relationship among each seed voxel and its neighbors is estimated using a linear regression model by minimizing the square error. Then, we estimate the optimal mesh size that represents the connections among each ...
Capturing Wheat Phenotypes at the Genome Level Hussaın, Babar; et. al. (2022-07-04) Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 ...

Citation Formats

A. E. Erson Bensan, “Resolving the complexity of the cancer transcriptome through 3’ UTRs,” presented at the HIBIT 2017, 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/86518.