Informational blueprints reveal condition-dependent gene regulatory architectures

TL;DR

This paper introduces an information blueprint algorithm that identifies condition-dependent transcription factor binding sites in genomes by compressing and analyzing global sequence information, validated on E. coli data.

Contribution

The study presents a novel computational method inspired by renormalisation-group techniques to detect gene regulatory elements across different environmental conditions.

Findings

Successfully identified known TF binding sites in E. coli.

Discovered novel regulatory elements responsive to environmental changes.

Validated the approach's scalability across multiple growth conditions.

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.