Mola: New Sequence-to-Distribution Model Predicts Single-Molecule Chromatin States from DNA
anshulkundaje · x · 2026-11-06
A preprint on bioRxiv, "Predicting single-molecule chromatin distribution from DNA sequence" by Ruoyu Wang, Junru Jin, Sebastian Pott and Jian Zhou (University of Chicago), introduces a sequence-to-distribution (S2d) framework that directly models the full probabilistic distribution of single-molecule chromatin states from DNA sequence alone.
Unlike prior deep-learning models that predict only cell-population averages, the trained model Mola captures single-molecule chromatin accessibility, quantifying sequence-driven nucleosome regulation and revealing distinct modes of transcription factor engagement with nucleosomes. The author also recommends related work from Jian Zhou's and Katie Pollard's labs, with support from the EMBL–Stanford Alliance postdoctoral program.
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