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

Area of Science:

• Synthetic biology
• Bioinformatics
• Computational biology

Background:

• Formal standards like the Synthetic Biology Open Language (SBOL) are crucial for data exchange in synthetic biology.
• However, the complexity of these standards and associated tools can hinder adoption by researchers.
• Integrating SBOL into existing research workflows often requires specialized expertise or custom software development.

Purpose of the Study:

• To develop a user-friendly tool that facilitates the adoption and use of the Synthetic Biology Open Language (SBOL) standard.
• To enable researchers to seamlessly integrate SBOL data into their existing workflows without requiring deep expertise in the standard.
• To lower the barrier to entry for utilizing formal standards in synthetic biology research.

Main Methods:

• Development of two Python libraries for bidirectional conversion between Excel and SBOL formats.
• Implementation of a converter from Excel templates to SBOL (Synthetic Biology Open Language) files.
• Implementation of a converter from SBOL files to Excel workbooks.
• Integration capability with SynBioHub via a plugin for enhanced accessibility.

Main Results:

• The Excel-SBOL Converter provides a straightforward method for managing synthetic biology components using familiar spreadsheet software.
• Successfully demonstrated conversion of Excel data to SBOL and vice versa, validating the tool's functionality.
• The tool supports integration into existing bioinformatics pipelines and SynBioHub, enhancing usability.

Conclusions:

• The Excel-SBOL Converter effectively democratizes the use of the SBOL standard in synthetic biology research.
• By leveraging familiar Excel interfaces, the tool significantly reduces the technical expertise required for SBOL data management.
• This facilitates broader adoption of standardized data formats, promoting reproducibility and collaboration in synthetic biology.