W547
iPTMnet: Integrative Bioinformatics for Knowledge Discovery of PTM Networks

Date: Saturday, January 12, 2013
Time: 9:40 AM
Room: Towne
Cathy H. Wu , Center Bioinformatics & Comp Bio – University of Delaware, Newark, DE
Cecilia Arighi , CBCB – University of Delaware, Newark, DE
Jung-Youn Lee , Plant and Soil Sciences – University of Delaware, Newark, DE
Manabu Torii , Center Bioinformatics & Comp Bio – University of Delaware, Newark, DE
Catalina Oana Tudor Tudor , Center Bioinformatics & Comp Bio – University of Delaware, Newark, DE
Gang Li , Center Bioinformatics & Comp Bio – University of Delaware, Newark, DE
Qinghua Wang , Center Bioinformatics & Comp Bio – University of Delaware, Newark, DE
Hongzhan Huang , Center Bioinformatics & Comp Bio – University of Delaware, Newark, DE
K. Vijay-Shanker , Computer & Information Sciences - University of Delaware, Newark, DE
PDF file
While PTMs play a pivotal role in numerous biological processes by modulating regulation of protein function, critical gaps remain in the current research framework for studying PTMs. To support the studies of important PTMs—phosphorylation, glycosylation, acetylation, myristoylation and ubiquitination—we are integrating text mining, data mining, data analysis and visualization tools, and databases and ontologies into a cross-cutting research resource needed to address the knowledge gaps in exploring and discovering PTM networks. The iPTMnet (http://proteininformationresource.org/iPTMnet) is a new bioinformatics resource being developed for  integrative understanding of protein post-translational  modifications (PTMs) in systems biology context, with the  initial focus on phosphorylation. The iPTMnet bioinformatics framework consists of: (i) the PIR iProClass database for  molecular and omics data integration, including many  phosphorylation, pathway, and interaction databases, (ii) the  RLIMS-P/eFIP text mining system for knowledge extraction  from scientific literature, (iii) the Protein Ontology (PRO) for  knowledge representation of specific protein PTM forms, as  well as a web portal linking data and analysis and  visualization tools for scientific queries. The text mining  system allows researchers to provide a list of PubMed IDs or  proteins of interest as input, and returns a ranked list of  abstracts with evidence tagging for phosphsorylation  information (kinase, substrate, phosphorylation site) and its  functional impact, particularly interaction partners of  phosphorylated proteins. A PTM database is under  development to combine text mining results and data  extracted from related databases to capture relevant kinasesubstrate  information and their functional impact and  biological context. Scientific use cases have been developed  to demonstrate the integrative bioinformatics approach for  exploring and discovering PTM networks.
Back to: PIR Workshop: Knowledge Mining Tools for Proteins, Complexes and PTMs
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