A quantitative approach to modeling transcription factor binding specificity from DNA sequences

Understanding the mechanisms governing transcription factor (TF) binding specificity remains a fundamental challenge in computational biology. This study aims to improve the prediction of TF binding specificity, addressing key limitations in current computational models that often struggle to accurately predict TF binding affinities due to issues such as batch effects and non-uniform data distributions. Here, we present a novel approach that utilizes regression models trained on high-throughput SELEX (HT-SELEX) data, employing a deep forest algorithm to model the binding specificity of 215 mammalian TFs, integrating DNA sequences, composition, physicochemical properties, and structural features. The predicted binding affinities show strong concordance with experimental HT-SELEX measurements, confirming the model’s accuracy and predictive reliability. Additionally, we apply this method to analyze the effects of genetic variations on TF binding, as well as to identify TF binding sites across the genome. Both applications rely on the same underlying methodology, with cumulative density function-based regression calibration improving the precision of predictions. This ensures reliable predictions of TF binding changes, both for sequence variations and genome-wide binding patterns. This study presents a novel approach that enhances the accuracy of TF binding specificity predictions and provides deeper insights into gene regulation and the impact of genetic variations on TF binding, with important implications for understanding gene regulation and disease mechanisms.