2021 publications

content/publication/mante-curation-principles-2021/cite.bib

@@ -0,0 +1,24 @@
+@article{doi:10.1021/acssynbio.1c00225,
+ abstract = { As an engineering endeavor, synthetic biology requires effective sharing of genetic design information that can be reused in the construction of new designs. While there are a number of large community repositories of design information, curation of this information has been limited. This in turn limits the ways in which design information can be put to use. The aim of this work was to improve this situation by creating a curated library of parts from the International Genetically Engineered Machines (iGEM) registry data set. To this end, an analysis of the Synthetic Biology Open Language (SBOL) version of the iGEM registry was carried out using four different approaches—simple statistics, SnapGene autoannotation, SYNBICT autoannotation, and expert analysis—the results of which are presented herein. Key challenges encountered include the use of free text, insufficient part provenance, part duplication, lack of part removal, and insufficient continuous curation. On the basis of these analyses, the focus has shifted from the creation of a curated iGEM part library to instead the extraction of a set of lessons, which are presented here. These lessons can be exploited to facilitate the creation and curation of other part libraries using a simpler and less labor intensive process. },
+ author = {Mante, Jeanet and Roehner, Nicholas and Keating, Kevin and McLaughlin, James Alastair and Young, Eric and Beal, Jacob and Myers, Chris J.},
+ doi = {10.1021/acssynbio.1c00225},
+ eprint = { 
+
+https://doi.org/10.1021/acssynbio.1c00225
+
+
+},
+ journal = {ACS Synthetic Biology},
+ note = {PMID: 34546707},
+ number = {10},
+ pages = {2592-2606},
+ title = {Curation Principles Derived from the Analysis of the SBOL iGEM Data Set},
+ url = { 
+
+https://doi.org/10.1021/acssynbio.1c00225
+
+
+},
+ volume = {10},
+ year = {2021}
+}

content/publication/mante-curation-principles-2021/index.md

@@ -0,0 +1,36 @@
+---
+title: Curation Principles Derived from the Analysis of the SBOL iGEM Data Set
+authors:
+- Jeanet Mante
+- Nicholas Roehner
+- Kevin Keating
+- James Alastair McLaughlin
+- Eric Young
+- Jacob Beal
+- Chris J. Myers
+date: '2021-10-15'
+publishDate: '2024-12-13T21:50:56.610578Z'
+publication_types:
+- '2'
+publication: '*ACS Synthetic Biology*'
+doi: 10.1021/acssynbio.1c00225
+abstract: As an engineering endeavor, synthetic biology requires effective sharing
+  of genetic design information that can be reused in the construction of new designs.
+  While there are a number of large community repositories of design information,
+  curation of this information has been limited. This in turn limits the ways in which
+  design information can be put to use. The aim of this work was to improve this situation
+  by creating a curated library of parts from the International Genetically Engineered
+  Machines (iGEM) registry data set. To this end, an analysis of the Synthetic Biology
+  Open Language (SBOL) version of the iGEM registry was carried out using four different
+  approaches—simple statistics, SnapGene autoannotation, SYNBICT autoannotation, and
+  expert analysis—the results of which are presented herein. Key challenges encountered
+  include the use of free text, insufficient part provenance, part duplication, lack
+  of part removal, and insufficient continuous curation. On the basis of these analyses,
+  the focus has shifted from the creation of a curated iGEM part library to instead
+  the extraction of a set of lessons, which are presented here. These lessons can
+  be exploited to facilitate the creation and curation of other part libraries using
+  a simpler and less labor intensive process.
+links:
+- name: URL
+  url: 'https://doi.org/10.1021/acssynbio.1c00225'
+---

content/publication/mante-sbks-2021/cite.bib

@@ -0,0 +1,24 @@
+@article{doi:10.1021/acssynbio.1c00188,
+ 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. },
+ author = {Mante, Jeanet and Hao, Yikai and Jett, Jacob and Joshi, Udayan and Keating, Kevin and Lu, Xiang and Nakum, Gaurav and Rodriguez, Nicholas E. and Tang, Jiawei and Terry, Logan and Wu, Xuanyu and Yu, Eric and Downie, J. Stephen and McInnes, Bridget T. and Nguyen, Mai H. and Sepulvado, Brandon and Young, Eric M. and Myers, Chris J.},
+ doi = {10.1021/acssynbio.1c00188},
+ eprint = { 
+
+https://doi.org/10.1021/acssynbio.1c00188
+
+
+},
+ journal = {ACS Synthetic Biology},
+ note = {PMID: 34387462},
+ number = {9},
+ pages = {2276-2285},
+ title = {Synthetic Biology Knowledge System},
+ url = { 
+
+https://doi.org/10.1021/acssynbio.1c00188
+
+
+},
+ volume = {10},
+ year = {2021}
+}

content/publication/mante-sbks-2021/index.md

@@ -0,0 +1,42 @@
+---
+title: Synthetic Biology Knowledge System
+authors:
+- 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
+date: '2021-09-17'
+publishDate: '2024-12-13T21:53:42.026108Z'
+publication_types:
+- '2'
+publication: '*ACS Synthetic Biology*'
+doi: 10.1021/acssynbio.1c00188
+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.
+links:
+- name: URL
+  url: 'https://doi.org/10.1021/acssynbio.1c00188'
+---

content/publication/roehner-synbict-2021/cite.bib

@@ -0,0 +1,24 @@
+@article{doi:10.1021/acssynbio.1c00220,
+ abstract = { Much progress has been made in developing tools to generate component-based design representations of biological systems from standard libraries of parts. Most biological designs, however, are still specified at the sequence level. Consequently, there exists a need for a tool that can be used to automatically infer component-based design representations from sequences, particularly in cases when those sequences have minimal levels of annotation. Such a tool would assist computational synthetic biologists in bridging the gap between the outputs of sequence editors and the inputs to more sophisticated design tools, and it would facilitate their development of automated workflows for design curation and quality control. Accordingly, we introduce Synthetic Biology Curation Tools (SYNBICT), a Python tool suite for automation-assisted annotation, curation, and functional inference for genetic designs. We have validated SYNBICT by applying it to genetic designs in the DARPA Synergistic Discovery & Design (SD2) program and the International Genetically Engineered Machines (iGEM) 2018 distribution. Most notably, SYNBICT is more automated and parallelizable than manual design editors, and it can be applied to interpret existing designs instead of only generating new ones. },
+ author = {Roehner, Nicholas and Mante, Jeanet and Myers, Chris J. and Beal, Jacob},
+ doi = {10.1021/acssynbio.1c00220},
+ eprint = { 
+
+https://doi.org/10.1021/acssynbio.1c00220
+
+
+},
+ journal = {ACS Synthetic Biology},
+ note = {PMID: 34757736},
+ number = {11},
+ pages = {3200-3204},
+ title = {Synthetic Biology Curation Tools (SYNBICT)},
+ url = { 
+
+https://doi.org/10.1021/acssynbio.1c00220
+
+
+},
+ volume = {10},
+ year = {2021}
+}

content/publication/roehner-synbict-2021/index.md

@@ -0,0 +1,33 @@
+---
+title: Synthetic Biology Curation Tools (SYNBICT)
+authors:
+- Nicholas Roehner
+- Jeanet Mante
+- Chris J. Myers
+- Jacob Beal
+date: '2021-11-19'
+publishDate: '2024-12-13T21:43:22.712475Z'
+publication_types:
+- '2'
+publication: '*ACS Synthetic Biology*'
+doi: 10.1021/acssynbio.1c00220
+abstract: Much progress has been made in developing tools to generate component-based
+  design representations of biological systems from standard libraries of parts. Most
+  biological designs, however, are still specified at the sequence level. Consequently,
+  there exists a need for a tool that can be used to automatically infer component-based
+  design representations from sequences, particularly in cases when those sequences
+  have minimal levels of annotation. Such a tool would assist computational synthetic
+  biologists in bridging the gap between the outputs of sequence editors and the inputs
+  to more sophisticated design tools, and it would facilitate their development of
+  automated workflows for design curation and quality control. Accordingly, we introduce
+  Synthetic Biology Curation Tools (SYNBICT), a Python tool suite for automation-assisted
+  annotation, curation, and functional inference for genetic designs. We have validated
+  SYNBICT by applying it to genetic designs in the DARPA Synergistic Discovery & Design
+  (SD2) program and the International Genetically Engineered Machines (iGEM) 2018
+  distribution. Most notably, SYNBICT is more automated and parallelizable than manual
+  design editors, and it can be applied to interpret existing designs instead of only
+  generating new ones.
+links:
+- name: URL
+  url: 'https://doi.org/10.1021/acssynbio.1c00220'
+---

content/publication/schreiber-specifications-2021/cite.bib

@@ -0,0 +1,10 @@
+@article{article,
+ author = {Schreiber, Falk and Gleeson, Padraig and Golebiewski, Martin and Gorochowski, Thomas and Hucka, Michael and Keating, Sarah and König, Matthias and Myers, Chris and Nickerson, David and Sommer, Björn and Waltemath, Dagmar},
+ doi = {10.1515/jib-2021-0026},
+ journal = {Journal of Integrative Bioinformatics},
+ month = {10},
+ pages = {000010151520210026},
+ title = {Specifications of standards in systems and synthetic biology: status and developments in 2021},
+ volume = {18},
+ year = {2021}
+}

content/publication/schreiber-specifications-2021/index.md

@@ -0,0 +1,23 @@
+---
+title: 'Specifications of standards in systems and synthetic biology: status and developments
+  in 2021'
+authors:
+- Falk Schreiber
+- Padraig Gleeson
+- Martin Golebiewski
+- Thomas Gorochowski
+- Michael Hucka
+- Sarah Keating
+- Matthias König
+- Chris Myers
+- David Nickerson
+- Björn Sommer
+- Dagmar Waltemath
+date: '2021-09-01'
+publishDate: '2024-12-13T22:02:41.692965Z'
+publication_types:
+- '2'
+publication: '*Journal of Integrative Bioinformatics*'
+doi: 10.1515/jib-2021-0026
+abstract: 'This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2021 special issue presents four updates of standards: Synthetic Biology Open Language Visual Version 2.3, Synthetic Biology Open Language Visual Version 3.0, Simulation Experiment Description Markup Language Level 1 Version 4, and OMEX Metadata specification Version 1.2. This document can also be consulted to identify the latest specifications of all COMBINE standards.'
+---