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Standard Biological Parts Knowledgebase

Overview of attention for article published in PLOS ONE, February 2011
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Title
Standard Biological Parts Knowledgebase
Published in
PLOS ONE, February 2011
DOI 10.1371/journal.pone.0017005
Pubmed ID
Authors

Michal Galdzicki, Cesar Rodriguez, Deepak Chandran, Herbert M. Sauro, John H. Gennari

Abstract

We have created the Knowledgebase of Standard Biological Parts (SBPkb) as a publically accessible Semantic Web resource for synthetic biology (sbolstandard.org). The SBPkb allows researchers to query and retrieve standard biological parts for research and use in synthetic biology. Its initial version includes all of the information about parts stored in the Registry of Standard Biological Parts (partsregistry.org). SBPkb transforms this information so that it is computable, using our semantic framework for synthetic biology parts. This framework, known as SBOL-semantic, was built as part of the Synthetic Biology Open Language (SBOL), a project of the Synthetic Biology Data Exchange Group. SBOL-semantic represents commonly used synthetic biology entities, and its purpose is to improve the distribution and exchange of descriptions of biological parts. In this paper, we describe the data, our methods for transformation to SBPkb, and finally, we demonstrate the value of our knowledgebase with a set of sample queries. We use RDF technology and SPARQL queries to retrieve candidate "promoter" parts that are known to be both negatively and positively regulated. This method provides new web based data access to perform searches for parts that are not currently possible.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 11 6%
United Kingdom 5 3%
Germany 1 <1%
Latvia 1 <1%
Australia 1 <1%
Italy 1 <1%
Portugal 1 <1%
Mexico 1 <1%
Belgium 1 <1%
Other 5 3%
Unknown 154 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 51 28%
Researcher 39 21%
Student > Master 19 10%
Student > Bachelor 15 8%
Other 10 5%
Other 32 18%
Unknown 16 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 76 42%
Biochemistry, Genetics and Molecular Biology 37 20%
Computer Science 19 10%
Engineering 15 8%
Economics, Econometrics and Finance 2 1%
Other 14 8%
Unknown 19 10%