Pré-Publication, Document De Travail Année : 2024

Contextualising transcription factor binding during embryogenesis using natural sequence variation

Résumé

Understanding how genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associated variants. Here, we used a highly controlled system of F1 crosses with extensive genetic diversity to profile allele-specific binding of four TFs at several embryonic time-points, using Drosophila as a model. Using a combined haplotype test, we identified 9-18% of TF bound regions impacted by genetic variation. By expanding WASP (a tool for allele-specific read mapping) to examine INDELs, we increased detection of allele imbalanced (AI) peaks by 30-50%. This fine-grained ‘mutagenesis’ could reconstruct functionalized binding motifs of all factors. To prioritise potential causal variants, we trained a convolutional neural network (Basenji) to predict TF binding from DNA sequence. The model could accurately predict experimental AI for strong effect variants, providing a mechanistic interpretation for how genetic variation impacted TF binding. This revealed unexpected relationships between TFs, including potential cooperative pairs, and mechanisms of tissue specific recruitment of the ubiquitous factor CTCF.

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Dates et versions

hal-04894851 , version 1 (17-01-2025)

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Olga M Sigalova, Mattia Forneris, Frosina Stojanovska, Bingqing Zhao, Rebecca R Viales, et al.. Contextualising transcription factor binding during embryogenesis using natural sequence variation. 2025. ⟨hal-04894851⟩

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