Polygraph: a software framework for the systematic assessment of synthetic regulatory DNA elements.

Area of Science:

• Molecular Biology
• Bioinformatics
• Gene Therapy

Background:

• Designing regulatory elements is crucial for gene and cell therapies to control gene expression.
• Systematic assessment of synthetic DNA sequences is challenging due to a lack of robust metrics and user-friendly software.

Purpose of the Study:

• Introduce Polygraph, a novel Python framework for evaluating synthetic DNA elements.
• Provide researchers with a tool to systematically assess synthetic DNA sequences for therapeutic applications.

Main Methods:

• Polygraph analyzes synthetic DNA elements based on sequence diversity, motif and k-mer composition.
• It compares synthetic sequences to endogenous ones and uses predictive and foundational models for screening.

Main Results:

• Polygraph offers a comprehensive approach to evaluating synthetic DNA elements.
• The framework provides metrics for assessing sequence features relevant to gene regulation.

Conclusions:

• Polygraph is the first instrument designed for assessing synthetic regulatory sequences.
• This tool can accelerate progress in gene and cell therapy development and enhance understanding of gene regulatory mechanisms.