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Excel–SBOL Converter: Creating SBOL from Excel Templates and Vice Versa

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    Excel–SBOL Converter: Creating SBOL from Excel Templates and Vice Versa
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    ACS Synthetic Biology

    Cite this: ACS Synth. Biol. 2023, 12, 1, 340–346
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    https://doi.org/10.1021/acssynbio.2c00521
    Published January 3, 2023
    Copyright © 2023 American Chemical Society
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    Abstract

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    Standards support synthetic biology research by enabling the exchange of component information. However, using formal representations, such as the Synthetic Biology Open Language (SBOL), typically requires either a thorough understanding of these standards or a suite of tools developed in concurrence with the ontologies. Since these tools may be a barrier for use by many practitioners, the Excel–SBOL Converter was developed to facilitate the use of SBOL and integration into existing workflows. The converter consists of two Python libraries: one that converts Excel templates to SBOL and another that converts SBOL to an Excel workbook. Both libraries can be used either directly or via a SynBioHub plugin.

    Copyright © 2023 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acssynbio.2c00521.

    • Template for Excel to SBOL conversion (XLSX)

    • Output of SBOL to Excel (XLSX)

    • Additional technical information about the conversion (PDF)

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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

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    This article is cited by 2 publications.

    1. Emilee Holtzapple, Gaoxiang Zhou, Haomiao Luo, Difei Tang, Niloofar Arazkhani, Casey Hansen, Cheryl A. Telmer, Natasa Miskov-Zivanov. The BioRECIPE Knowledge Representation Format. ACS Synthetic Biology 2024, 13 (8) , 2621-2624. https://doi.org/10.1021/acssynbio.4c00096
    2. Sai P. Samineni, Gonzalo Vidal, Carolus Vitalis, Guillermo Yáñez Feliú, Timothy J. Rudge, Chris J. Myers, Jeanet Mante. Experimental Data Connector (XDC): Integrating the Capture of Experimental Data and Metadata Using Standard Formats and Digital Repositories. ACS Synthetic Biology 2023, 12 (4) , 1364-1370. https://doi.org/10.1021/acssynbio.2c00669
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    ACS Synthetic Biology

    Cite this: ACS Synth. Biol. 2023, 12, 1, 340–346
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acssynbio.2c00521
    Published January 3, 2023
    Copyright © 2023 American Chemical Society
    Request reuse permissions

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