Synthetic Biology Curation Tools (SYNBICT).

Area of Science:

• Synthetic Biology
• Computational Biology
• Bioinformatics

Background:

• Component-based design representations are crucial for biological systems, but many designs remain sequence-level.
• A gap exists between sequence data and higher-level design tools, hindering automated workflows.
• Minimal annotation in sequences complicates the inference of design representations.

Purpose of the Study:

• To introduce a tool for automatic inference of component-based design representations from genetic sequences.
• To assist computational synthetic biologists in automating design curation and quality control.
• To bridge the gap between sequence editors and advanced biological design tools.

Main Methods:

• Development of Synthetic Biology Curation Tools (SYNBICT), a Python tool suite.
• Implementation of automation-assisted annotation, curation, and functional inference for genetic designs.
• Validation using genetic designs from the DARPA Synergistic Discovery & Design (SD2) program and iGEM 2018 distribution.

Main Results:

• SYNBICT successfully automates annotation, curation, and functional inference for genetic designs.
• The tool was validated on diverse genetic design datasets.
• SYNBICT offers greater automation and parallelization compared to manual design editors.

Conclusions:

• SYNBICT effectively infers component-based design representations from sequence data, even with minimal annotation.
• The tool facilitates automated workflows in synthetic biology, enhancing design curation and quality control.
• SYNBICT can interpret existing designs, expanding its utility beyond new design generation.