Synthetic Biology Knowledge System

Jeanet Mante, Yikai Hao, Jacob Jett, Udayan Joshi, Kevin Keating, Xiang Lu, Gaurav Nakum, Nicholas E. Rodriguez, Jiawei Tang, Logan Terry, Xuanyu Wu, Eric Yu, J. Stephen Downie, Bridget T. McInnes, Mai H. Nguyen, Brandon Sepulvado, Eric M. Young, Chris J. Myers

Research output: Contribution to journalArticlepeer-review


The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.

Original languageEnglish (US)
Pages (from-to)2276-2285
Number of pages10
JournalACS synthetic biology
Issue number9
StateAccepted/In press - 2021


  • data mining
  • SBOL
  • sequence annotation
  • SynBioHub
  • text mining
  • topic modeling

ASJC Scopus subject areas

  • Biomedical Engineering
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)


Dive into the research topics of 'Synthetic Biology Knowledge System'. Together they form a unique fingerprint.

Cite this