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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.
QH301-705.5, DNA, QH426-470, Regulatory Sequences, Nucleic Acid, Sequence modeling, machine learning, regulatory genomics, Sequence design, Machine learning, Genetics, Regulatory genomics, Humans, Biology (General), sequence design, Synthetic biology, Software
QH301-705.5, DNA, QH426-470, Regulatory Sequences, Nucleic Acid, Sequence modeling, machine learning, regulatory genomics, Sequence design, Machine learning, Genetics, Regulatory genomics, Humans, Biology (General), sequence design, Synthetic biology, Software
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| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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