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

doi:10.1021/acssynbio.1c00188

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 journal › Article › peer-review

Abstract

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 language	English (US)
Pages (from-to)	2276-2285
Number of pages	10
Journal	ACS synthetic biology
Volume	10
Issue number	9
DOIs	https://doi.org/10.1021/acssynbio.1c00188
State	Accepted/In press - 2021

Keywords

data mining
SBOL
sequence annotation
SynBioHub
text mining
topic modeling

ASJC Scopus subject areas

Biomedical Engineering
Biochemistry, Genetics and Molecular Biology (miscellaneous)

Online availability

10.1021/acssynbio.1c00188

Cite this

@article{65104377f2ac468894c3c5c115f5af64,

title = "Synthetic Biology Knowledge System",

abstract = "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. ",

keywords = "data mining, SBOL, sequence annotation, SynBioHub, text mining, topic modeling",

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

note = "Funding Information: This work was funded by the National Science Foundation under Grant Nos. 1939892, 1939929, 1939885, 1939887, 1939951, and 1939860. The SynBioHub instance hosting this data ( https://synbioks.org ) is hosted on an Azure server provided by Microsoft Research. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agency. Publisher Copyright: {\textcopyright} 2021 American Chemical Society.",

year = "2021",

doi = "10.1021/acssynbio.1c00188",

language = "English (US)",

volume = "10",

pages = "2276--2285",

journal = "ACS synthetic biology",

issn = "2161-5063",

publisher = "American Chemical Society",

number = "9",

}

TY - JOUR

T1 - Synthetic Biology Knowledge System

AU - Mante, Jeanet

AU - Hao, Yikai

AU - Jett, Jacob

AU - Joshi, Udayan

AU - Keating, Kevin

AU - Lu, Xiang

AU - Nakum, Gaurav

AU - Rodriguez, Nicholas E.

AU - Tang, Jiawei

AU - Terry, Logan

AU - Wu, Xuanyu

AU - Yu, Eric

AU - Downie, J. Stephen

AU - McInnes, Bridget T.

AU - Nguyen, Mai H.

AU - Sepulvado, Brandon

AU - Young, Eric M.

AU - Myers, Chris J.

N1 - Funding Information: This work was funded by the National Science Foundation under Grant Nos. 1939892, 1939929, 1939885, 1939887, 1939951, and 1939860. The SynBioHub instance hosting this data ( https://synbioks.org ) is hosted on an Azure server provided by Microsoft Research. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agency. Publisher Copyright: © 2021 American Chemical Society.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - data mining

KW - SBOL

KW - sequence annotation

KW - SynBioHub

KW - text mining

KW - topic modeling

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Synthetic Biology Knowledge System

Abstract

Keywords

ASJC Scopus subject areas

Online availability

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