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Joint International Semantic Technology Conference

JIST 2013: Semantic Technology pp 103–109Cite as

Federating Heterogeneous Biological Resources on the Web: A Case Study on TRP Channel Ontology Construction

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8388))

Abstract

TRP (Transient receptor potential) channel is a biological component which could be of factors in severe diseases such as heart attack and cancer. In order for researchers to easily search for protein-protein interactions for mammalian TRP channel, TRIP Database was created. However, TRIP Database does not contain information about proteins in details, making researchers in turn visit other database services such as UniProt and PDB. In this paper, we propose a semantic TRP Ontology made from TRIP Database, allowing users to be given the collected contents from other relevant databases as well using a single request. As a practical scenario, we generate RDF triples from TRIP Database by referring to TRP Ontology designed to have links with UniProt. A federated way of collecting contents from two different services is proposed.

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Notes

  1. 1.

    http://www.uniprot.org/

  2. 2.

    http://www.rcsb.org/

References

  1. Shin, Y.-C., Shin, S.-Y., So, I., Kwon, D., Jeon, J.-H.: TRIP Database: a manually curated database of protein–protein interactions for mammalian TRP channels. Nucleic Acids Re. 39(Suppl. 1), D356–D361 (2011)

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  2. Shin, Y.-C., Shin, S.-Y., Chun, J.N., Cho, H.S., Lim, J.M., Kim, H.-G., So, I., Kwon, D., Jeon, J.-H.: TRIP Database 2.0: a manually curated information hub for accessing TRP channel interaction network. PloS ONE 7(10), e47165 (2012)

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  3. SPARQL 1.1, http://www.w3.org/TR/sparql11-query/

  4. Jena ARQ, http://jena.apache.org/documentation/query/

  5. UniProt, http://www.UniProt.org/

  6. TRIP Database, http://trpchannel.org/

  7. OBO Relation Ontology, http://obofoundry.org/ro/

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Acknowledgement(s)

This work was supported by the Industrial Strategic Technology Development Program (10044494, WiseKB: Big data based self-evolving knowledge base and reasoning platform) funded by the Ministry of Science, ICT & Future Planning (MSIP, Korea).

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Authors and Affiliations

  1. Biomedical Knowledge Engineering Lab. (BIKE), Seoul National University, Seoul, Korea

    Se-Jin Nam, Jinhyun Ahn, Jin-Muk Lim, Jae-Hong Eom & Hong-Gee Kim

  2. Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 110-749, Korea

    Jae-Hong Eom & Hong-Gee Kim

  3. Department of Physiology, College of Medicine, Seoul National University, Seoul, 110-799, Korea

    Ju-Hong Jeon

Authors
  1. Se-Jin Nam
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  2. Jinhyun Ahn
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  3. Jin-Muk Lim
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  4. Jae-Hong Eom
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  5. Ju-Hong Jeon
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  6. Hong-Gee Kim
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Corresponding authors

Correspondence to Jae-Hong Eom or Hong-Gee Kim .

Editor information

Editors and Affiliations

  1. Yonsei University, Seoul, Korea, Republic of (South Korea)

    Wooju Kim

  2. Indiana University, Bloomington, Indiana, USA

    Ying Ding

  3. Seoul National University, Seoul, Korea, Republic of (South Korea)

    Hong-Gee Kim

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© 2014 Springer International Publishing Switzerland

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Cite this paper

Nam, SJ., Ahn, J., Lim, JM., Eom, JH., Jeon, JH., Kim, HG. (2014). Federating Heterogeneous Biological Resources on the Web: A Case Study on TRP Channel Ontology Construction. In: Kim, W., Ding, Y., Kim, HG. (eds) Semantic Technology. JIST 2013. Lecture Notes in Computer Science(), vol 8388. Springer, Cham. https://doi.org/10.1007/978-3-319-06826-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-06826-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06825-1

  • Online ISBN: 978-3-319-06826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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