Recent high-throughput transcription factor (TF) binding assays revealed that TF cooperativity is a widespread phenomenon. However, a global mechanistic and functional understanding of TF cooperativity is still lacking. To address this, here we introduce a statistical learning framework that provides structural insight into TF cooperativity and its functional consequences based on next generation sequencing data. We identify DNA shape as driver for cooperativity, with a particularly strong effect for Forkhead-Ets pairs. Follow-up experiments reveal a local shape preference at the Ets-DNA-Forkhead interface and decreased cooperativity upon loss of the interaction. Additionally, we discover many functional associations for cooperatively bound TFs. Examination of the link between FOXO1:ETV6 and lymphomas reveals that their joint expression levels improve patient clinical outcome stratification. Altogether, our results demonstrate that inter-family cooperative TF binding is driven by position-specific DNA readout mechanisms, which provides an additional regulatory layer for downstream biological functions.

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• Source code available here GitHub repository
• Relevant git input and output data:
  • Processed CAP-SELEX data (k-mer enrichments).
    • Files are divided into multiple directories of ca. ~80MB each). You can download them them all with this bash script download
  • In vivo motif TF-TF k-mer motif mapping in [~5-10GB each file]
    • ChIP-seq peaks ReMap
  • Ontology Association Probabilities for all studied TF-TF terms and terms in this work .
    • All predictions (including negatives) download
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