Polygraph: A Software Framework for the Systematic Assessment of Synthetic Regulatory DNA Elements
Polygraph: A Software Framework for the Systematic Assessment of Synthetic Regulatory DNA Elements
Abstract The design of regulatory elements is pivotal in gene and cell therapy, where DNA sequences are engineered to drive elevated and cell-type specific expression. However, the systematic assessment of synthetic DNA sequences without robust metrics and easy-to-use software remains challenging. Here, we introduce Polygraph, a Python framework that evaluates synthetic DNA elements, based on features like diversity, motif and k-mer composition, similarity to endogenous sequences, and screening with predictive and foundational models. Polygraph is the first instrument for assessing synthetic regulatory sequences, enabling faster progress in therapeutic interventions and improving our understanding of gene regulatory mechanisms.
Eraslan Gokcen、Gupta Anay、Biancalani Tommaso、Lal Avantika、Gunsalus Laura
Biology Research | AI Development, gRED Computational SciencesCollege of Computing, Georgia Institute of TechnologyBiology Research | AI Development, gRED Computational SciencesBiology Research | AI Development, gRED Computational SciencesBiology Research | AI Development, gRED Computational Sciences
生物科学研究方法、生物科学研究技术计算技术、计算机技术分子生物学
sequence designsynthetic biologymachine learningsequence modelingregulatory genomics
Eraslan Gokcen,Gupta Anay,Biancalani Tommaso,Lal Avantika,Gunsalus Laura.Polygraph: A Software Framework for the Systematic Assessment of Synthetic Regulatory DNA Elements[EB/OL].(2025-03-28)[2025-06-23].https://www.biorxiv.org/content/10.1101/2023.11.27.568764.点此复制
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