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February 7, 2026NatureOpen Access

Regulatory grammar in human promoters uncovered by MPRA-based deep learning

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Authors

LBLucía Barbadilla-MartínezNKNoud H.M. KlaassenVFVinícius H. Franceschini-Santos

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Overview

Promoter activity regulatory model predicts gene expression in humans, highlighting transcription factors' roles.

Key Points

  • To develop a model that predicts genome-wide gene expression from promoter sequences.
  • Developed promoter activity regulatory model (PARM) using massively parallel reporter assays (MPRAs)
  • Trained a deep-learning model on human promoter sequences
  • Identified transcription factor binding sites and their regulatory interactions
  • Analyzed positional preferences of transcription factors with activating and repressive functions.
  • Successfully predicted autonomous promoter activity across the genome
  • Identified substantial differences in transcription factor interactions
  • Uncovered a complex grammar of motif–motif interactions.

Cite This Study

Barbadilla-Martínez et al. (2026) studied this question.

synapsesocial.com/papers/698692e89d267392364c994bhttps://doi.org/10.1038/s41586-025-10093-z
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