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This study introduces an algorithm called "information blueprint" that identifies where regulatory proteins called transcription factors bind to non-coding regions of DNA, without relying on a predefined lookup table. The method compresses genomic sequence data into collective variables called "hyperletters" by scanning entire promoter sequences and grouping correlated mutations that collectively influence gene expression. Validated on experimental E. coli data, the approach successfully identifies transcription factor binding sites across different growth conditions and uncovers novel regulatory elements.
Why it matters
A reliable method for decoding non-coding regulatory DNA could advance our understanding of gene regulation in health and disease, with potential applications in synthetic biology, antibiotic resistance research, and the design of genetically engineered organisms.
arXiv:2605.19071v1 Announce Type: new
Abstract: While coding regions in the genome have a direct interpretation in terms of protein products, significant fractions are non-coding and yet control essential biological functions. Unlike the genetic code, there is no “lookup table” that identifies where regulatory proteins, known as transcription factors (TFs), bind. Here, we extract these binding sites by distilling sequences of nucleotide letters into collective coordinates (hyperletters) representing the binding sites that are active under specific environmental conditions. Going beyond local information footprints between individual bases and expression levels, our $textit{information blueprint}$ algorithm compresses the global information by optimising filters that simultaneously scan an entire promoter sequence. Inspired by renormalisation-group techniques, we identify TF binding sites as coarse-grained variables combining groups of correlated mutations with the highest collective impact on gene expression. We validate our approach on experimental data for $textit{E. coli}$ and discover novel regulatory elements illustrating its deployment at scale across growth conditions.
Source: Informational blueprints reveal condition-dependent gene regulatory architectures