Disease Pathway Finder Using Web-based SWOP (SideWays Observation via Paralogs) System by the Concept of Paralogs

• Main Authors: Li-Wei Lai, 賴立偉
• Other Authors: 李元綺
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/20433933534489221439

碩士 === 臺北醫學大學 === 醫學資訊研究所 === 97 === From the viewpoint of evolution, gene duplication produces two functionally redundant, paralogous genes and thereby frees one of them from selective constraints. Divergent evolution has made tumor counterpart cells survived easier. Recently, microarray emerges as a nice approach to find distinct individual genes that differentially expressed between physiological and pathological cells, where, however, no clues to show the relationship among these gene cluster. Here in the research, we developed a brand new approach that combines the concept of paralogs, microarray gene expression data, gene network, gene ontology to obtain intersectional genes where the point that disease has gone astray from normal pathways. Firstly, we exerted the biological reactions, such as protein-protein interactions, protein-DNA regulations, phosphorylations, methylations, etc. to the genomes to build up network connectivity. Secondly, we partitioned the network into hundreds of thousands of pathways according to the GO cellular component properties of each gene, where helps to calculate the differentially expressed pathways rather than just differentially expressed genes. Finally, We searched for paralogous genes appeared in the contrary differentially pathways of normal and disease status, respectively. It is very likely to find an intersectional gene between different pathways, because this intersectional gene has higher probability to associate with both these paralogous genes in different situation. Users are allowed to upload their microarray data of human, rat or mouse. The system would identify differentially expressed pathways between normal and disease tissues by calculating with mutual information methods. The matched counterpart pathways that associated paired paralogs would be identified and visualized in a graph. The intersectional genes would be therefore spotted and highlighted in the graph. This SWOP system facilitates to transform complex microarray data into simplified intersectional genes between disease and normal pathways by using the duplicate paralogs as which indicate the shortest evolutionary time where disease go wrong from normal pathways. In addition, it helps to design drug targets to inhibit the disease pathways.