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TRIP Database 2.0: A Manually Curated Information Hub for Accessing TRP Channel Interaction Network

Overview of attention for article published in PLOS ONE, October 2012
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Title
TRIP Database 2.0: A Manually Curated Information Hub for Accessing TRP Channel Interaction Network
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0047165
Pubmed ID
Authors

Young-Cheul Shin, Soo-Yong Shin, Jung Nyeo Chun, Hyeon Sung Cho, Jin Muk Lim, Hong-Gee Kim, Insuk So, Dongseop Kwon, Ju-Hong Jeon

Abstract

Transient receptor potential (TRP) channels are a family of Ca(2+)-permeable cation channels that play a crucial role in biological and disease processes. To advance TRP channel research, we previously created the TRIP (TRansient receptor potential channel-Interacting Protein) Database, a manually curated database that compiles scattered information on TRP channel protein-protein interactions (PPIs). However, the database needs to be improved for information accessibility and data utilization. Here, we present the TRIP Database 2.0 (http://www.trpchannel.org) in which many helpful, user-friendly web interfaces have been developed to facilitate knowledge acquisition and inspire new approaches to studying TRP channel functions: 1) the PPI information found in the supplementary data of referred articles was curated; 2) the PPI summary matrix enables users to intuitively grasp overall PPI information; 3) the search capability has been expanded to retrieve information from 'PubMed' and 'PIE the search' (a specialized search engine for PPI-related articles); and 4) the PPI data are available as sif files for network visualization and analysis using 'Cytoscape'. Therefore, our TRIP Database 2.0 is an information hub that works toward advancing data-driven TRP channel research.

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Geographical breakdown

Country Count As %
Saudi Arabia 1 3%
Unknown 30 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 29%
Student > Ph. D. Student 5 16%
Professor > Associate Professor 3 10%
Student > Bachelor 2 6%
Lecturer 1 3%
Other 4 13%
Unknown 7 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 32%
Medicine and Dentistry 5 16%
Biochemistry, Genetics and Molecular Biology 4 13%
Neuroscience 2 6%
Computer Science 1 3%
Other 2 6%
Unknown 7 23%