Parallelization of functional flow to predict protein functions

Download
index.pdf
2011
Akkoyun, Emrah
Show full item record
214
views
88
downloads
Protein-protein interaction networks provide important information about what the biological function of proteins whose roles are unknown might be in a cell. These interaction networks were analyzed by a variety of approaches by running them on a single computer and the roles of the proteins identified were used to predict the function of the proteins unidentified. The functional flow is an approach that takes the network connectivity, distance effect, topology of the network with local and global views into account. With these advantages, that the functional flow produces more accurate results on the prediction of protein functions was presented by the previos conducted researches. However, the application implemented for this approach could not be practically applied on the large and complex network produced for the complex species because of memory limitation. The purpose of this thesis is to provide a new application be implemented on the high computing performance where the application can be scaled on the large data sets. Therefore, Hadoop, one of the open source map/reduce environments, was installed on 18 hosts each of which has eight cores. Method; the first map/reduce job distributes the protein interaction network as a format which allows parallel distributed computing to all the worker nodes, the other map/reduce job generates flows for each known protein function and the role of the proteins unidentified are predicted by accumulating all of these generated flows. It has been observed in the experiments we performed that the application requiring high performance computing can be decomposed into worker nodes efficiently and the application can provide better performance as the resources increase.
Collections of monographs., Protein-protein interactions., Medical Informatics.
http://etd.lib.metu.edu.tr/upload/12612932/index.pdf
https://hdl.handle.net/11511/20476
Graduate School of Informatics, Thesis

Suggestions

Visualizing the protein-protein interactions network in virtual reality and mixed reality environments
Şenderin, Büşra; Sürer, Elif; Tunçbağ, Nurcan; Department of Modeling and Simulation (2021-8)
Protein-protein interactions (PPI) define the physical contact of two or more protein structures. When these interactions are combined, the protein-protein interaction network (PPIN) is formed. The interactions between protein structures are distinct interactions—they happen in specific binding locations on proteins, and they have a specific biological function that they take on. With these networks, the processes within a cell or a living organism when healthy or diseased can be studied. In this thesis, a ...
Integration of topological measures for eliminating non-specific interactions in protein interaction networks
BAYIR, Murat Ali; GUNEY, Tacettin Dogacan; Can, Tolga (Elsevier BV, 2009-05-28)
High-throughput protein interaction assays aim to provide a comprehensive list of interactions that govern the biological processes in a cell. These large-scale sets of interactions, represented as protein-protein interaction networks, are often analyzed by computational methods for detailed biological interpretation. However, as a result of the tradeoff between speed and accuracy, the interactions reported by high-throughput techniques occasionally include non-specific (i.e., false-positive) interactions. ...
Fast Screening of Protein-Protein Interactions Using Forster Resonance Energy Transfer (FRET-) Based Fluorescence Plate Reader Assay in Live Cells
Durhan, Seyda Tugce; Sezer, Enise Nalli; Son, Çağdaş Devrim; Küçük Baloğlu, Fatma (2022-11-01)
Protein-protein interactions (PPIs) have great importance for intracellular signal transduction and sustaining the homeostasis of an organism. Thus, the identification of PPIs is necessary to better understand the downstream signaling functions of the proteins in healthy and pathological conditions. Forster resonance energy transfer (FRET) between fluorescent proteins (FPs) is a powerful tool for detecting PPIs in living cells. In literature, FRET analysis methods such as donor photobleaching (FLIM), accept...
Enzyme prediction with word embedding approach
Akın, Erkan; Atalay, M. Volkan.; Department of Computer Engineering (2019)
Information such as molecular function, biological process, and cellular localization can be inferred from the protein sequence. However, protein sequences vary in length. Therefore, the sequence itself cannot be used directly as a feature vector for pattern recognition and machine learning algorithms since these algorithms require fixed length feature vectors. We describe an approach based on the use of the Word2vec model, more specifically continuous skip-gram model to generate the vector representation o...
Architectures and functional coverage of protein-protein interfaces
Tunçbağ, Nurcan; Guney, Emre; NUSSINOV, Ruth; Keskin, Ozlem (2008-09-05)
The diverse range of cellular functions is performed by a limited number of protein folds existing in nature. One may similarly expect that cellular functional diversity would be covered by a limited number of protein-protein interface architectures. Here, we present 8205 interface clusters, each representing a unique interface architecture. This data set of protein-protein interfaces is analyzed and compared with older data sets. We observe that the number of both biological and crystal interfaces increase...
Citation Formats
E. Akkoyun, “Parallelization of functional flow to predict protein functions,” M.S. - Master of Science, Middle East Technical University, 2011.
METU IIS - Integrated Information Service open@metu.edu.tr