TY  - JOUR
A1  - Sigalova, Olga M.
A1  - Forneris, Mattia
A1  - Stojanovska, Frosina
A1  - Zhao, Bingqing
A1  - Viales, Rebecca R.
A1  - Rabinowitz, Adam
A1  - Hammal, Fayrouz
A1  - Ballester, Benoît
A1  - Zaugg, Judith B.
A1  - Furlong, Eileen E.M.
T1  - Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis
Y1  - 2025/05/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1138 
EP  - 1153 
DO  - 10.1101/gr.279652.124 
VL  - 35 
IS  - 5 
UR  - http://genome.cshlp.org/content/35/5/1138.abstract 
N2  - 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 time points during Drosophila embryogenesis. Using a combined haplotype test, we identified 9%–18% of TF-bound regions impacted by genetic variation even for essential regulators. By expanding WASP (a tool for allele-specific read mapping) to examine indels, we increased detection of allelically imbalanced peaks by 30%–50%. This fine-grained “mutagenesis” can reconstruct functionalized binding motifs for all factors. To prioritize causal variants, we trained a convolutional neural network (Basenji) to accurately predict binding from DNA sequence. The model can also predict measured allelic imbalance for strong effect variants, providing a mechanistic interpretation for how the variant impacts binding. This reveals unexpected relationships between TFs, including potential cooperative pairs, and mechanisms of tissue-specific recruitment of the ubiquitous factor CTCF. 
ER  -