gReLU advances deep-learning-based modeling and analysis of DNA sequences with comprehensive toolsets and versatile applications.

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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.

Sleep is an essential component of our daily life. A mutation in human salt induced kinase 3 (hSIK3), which is critical for regulating sleep durati...