Text-mining and visualization approach help interpret experimental data and make hypothesis

Teng, Pan
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University of Delaware
Protein phosphorylation plays a central roll in cellular signaling. Kinases are enzyme that participates in protein phosphorylation events, by catalyzing the transfer of phosphate group to a specfic substrate. This phosphorylation typically affects substrate function, typically by either activating or inhibiting the substrates activity. Consequently, identifying kinase and substrate pairs in large-scale gene expression data will help the researcher in understanding the underlying biology of their experiments. With the continuous growth of scientic literature, it becomes more and more difficult for biologists to search for all of the information regarding kinases and substrates manually. To assist in this effort, we developed a web-based tool iGep (Integrating Gene Expression and Phosphorylation) to identify potential kinase and substrate pairs in gene expression data. Other functions including highlighting up and down regulated genes, linking users to PubMed literature describing particular phosphorylation events. In addition, users can visualize corresponding RLIMS-P, (a rule-based text- mining program for extracting protein phosphorylation information from literature) text evidence, sentences in the literature containing co-occurring kinase and substrates pairs and download all results.