In this study, an algorithm named ClustOrth is proposed for determining and matching functionally orthologous protein clusters in different species. The algorithm requires protein interaction networks of the organisms to be compared and GO terms of the proteins in these interaction networks as prior information. After determining the functionally related protein groups using the Repeated Random Walks algorithm, the method maps the identified protein groups according to the similarity metric defined. In order to evaluate the similarities of protein groups, graph theoretical information is used together with the context information about the proteins. The clusters are aligned using GO-Term-based protein similarity measures defined in previous studies. These alignments are used to evaluate cluster similarities by defining a cluster similarity metric from protein similarities. The top scoring cluster alignments are considered as orthologous. Several data sources providing orthology information have shown that the defined cluster similarity metric can be used to make inferences about the orthological relevance of protein groups. Comparison with a protein orthology prediction algorithm named ISORANK also showed that the ClustOrth algorithm is successful in determining orthologies between proteins. However, the cluster similarity metric is too strict and many cluster matches are not able to produce high scores for this metric. For this reason, the number of predictions performed is low. This problem can be overcomed with the introduction of different sources of information related to proteins in the clusters for the evaluation of the clusters. The ClustOrth algorithm also outperformed the NetworkBLAST algorithm which aims to find orthologous protein clusters using protein sequence information directly for determining orthologies. It can be concluded that this study is one of the leading studies addressing the protein cluster matching problem for identifying orthologous functional modules of protein interaction networks computationally.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12612395/index.pdf |
| Date | 01 September 2010 |
| Creators | Yaveroglu, Omer Nebil |
| Contributors | Can, Tolga |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
Page generated in 0.0016 seconds