RT Journal
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
JF Genome Research 
JO Genome Research 
YR 2025 
FD May 01 
VO 35 
IS 5 
SP 1138 
OP 1153 
DO 10.1101/gr.279652.124 
UL http://genome.cshlp.org/content/35/5/1138.abstract 
AB 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.