Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Modeling the tumor specific network rewiring by integrating alternative splicing events with structural interactome
Download
index.pdf
Date
2019
Author
Demirel, Habibe Cans
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
177
views
62
downloads
Cite This
Alternative splicing is a post-transcriptional regulation which is important for the diversity of the proteome and eventually the interactome. It enables the production of multiple proteins from a single gene with different structures. In a network point of view, these structural changes can introduce new interactions or cause the loss of the existing ones. The variations in this mechanism has been associated with various diseases including cancer. In this study, we reconstructed patient specific networks with tumor specific protein isoforms by integrating the protein structures and the interaction losses they bring with. For this purpose, we collected 400 breast cancer tumors and 112 normal RNA-seq data from the Cancer Genome Atlas (TCGA) and found the transcripts that show increased expression patterns in tumor cells. We mapped these transcripts to their available protein isoforms found in UniProt. Additionally, we compiled a structural human interactome from multiple sources and aligned the missing residues on isoforms with the known/predicted protein interfaces to find potential interaction losses. At the end, we constructed two interactomes for each sample; one filtered based on the lost interfaces as a result of predominant isoforms (called “terminal set”) and one filtered based on the expression. Then, we used the same terminal set with Omics Integrator to model two sets of networks based on the two patient-specific interactomes. Finally, we compared the resulting two networks and all tumor specific networks simultaneously to reveal pathway, protein-protein interaction and protein patterns that can cluster the tumors according to their similarities. The results of our analysis will contribute to the elucidation of tumor mechanisms and will help for target selection and developing therapeutic strategies.
Subject Keywords
Cancer.
,
Alternative Splicing
,
Network Modelling
,
Multi-omics data
URI
http://etd.lib.metu.edu.tr/upload/12623588/index.pdf
https://hdl.handle.net/11511/43855
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Modeling complex nonlinear responses of shallow lakes to fish and hydrology using artificial neural networks
Tan, Can Ozan; Beklioğlu, Meryem (Elsevier BV, 2006-07-10)
Mathematical abstractions may be useful in providing insight that is otherwise very difficult to obtain due to complex interactions in the ecosystems. The difficulty associated with the nonlinearity and complexity of ecological processes and interactions can be avoided with artificial neural networks (ANN) and generalized logistic models (GLMs) which are practically ANNs without hidden layer. An ANN and a GLM were developed to determine the probability of submerged plant occurrence in five shallow Anatolian...
A probabilistic approach to microRNA-target binding
OĞUL, HASAN; Umu, Sinan U.; Tuncel, Y. Yener; Akkaya, Mahinur (Elsevier BV, 2011-09-16)
Elucidation of microRNA activity is a crucial step in understanding gene regulation. One key problem in this effort is how to model the pairwise interactions of microRNAs with their targets. As this interaction is strongly mediated by their sequences, it is desired to set-up a probabilistic model to explain the binding preferences between a microRNA sequence and the sequence of a putative target. To this end, we introduce a new model of microRNA-target binding, which transforms an aligned duplex to a new se...
Employing decomposable partially observable Markov decision processes to control gene regulatory networks
Erdogdu, Utku; Polat, Faruk; Alhajj, Reda (2017-11-01)
Objective: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).
Automated Large-Scale Control of Gene Regulatory Networks
Tan, Mehmet; Alhajj, Reda; Polat, Faruk (Institute of Electrical and Electronics Engineers (IEEE), 2010-04-01)
Controlling gene regulatory networks (GRNs) is an important and hard problem. As it is the case in all control problems, the curse of dimensionality is the main issue in real applications. It is possible that hundreds of genes may regulate one biological activity in an organism; this implies a huge state space, even in the case of Boolean models. This is also evident in the literature that shows that only models of small portions of the genome could be used in control applications. In this paper, we empower...
An evaluation of a novel approach for clustering genes with dissimilar replicates
Cinar, Ozan; İyigün, Cem; İlk Dağ, Özlem (Informa UK Limited, 2020-12-01)
Clustering the genes is a step in microarray studies which demands several considerations. First, the expression levels can be collected as time-series which should be accounted for appropriately. Furthermore, genes may behave differently in different biological replicates due to their genetic backgrounds. Highlighting such genes may deepen the study; however, it introduces further complexities for clustering. The third concern stems from the existence of a large amount of constant genes which demands a hea...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
H. C. Demirel, “Modeling the tumor specific network rewiring by integrating alternative splicing events with structural interactome,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Biotechnology., Middle East Technical University, 2019.