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  3. Iceqream: Quantitative Chromosome Accessibility Analysis Using Physical Tf Models.
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  3. Iceqream: Quantitative Chromosome Accessibility Analysis Using Physical Tf Models.

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IceQream: Quantitative chromosome accessibility analysis using physical TF models.

Akhiad Bercovich1,2, Aviezer Lifshitz1,2, Michal Eldar1,2

  • 1Weizmann Institute of Science, Department of Computer Science and Applied Mathematics, Rehovot, Israel.

Nature Communications
|October 9, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We developed IceQream (IQ), a new method for predicting gene regulation from DNA sequence using physical models of transcription factor binding. IQ offers a mechanistic and interpretable alternative to deep learning, achieving comparable performance.

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Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • Single-cell sequencing reveals chromosomal accessibility patterns, improving epigenomic prediction from DNA sequence.
  • Current quantitative models lack interpretable components for epigenome explanation.
  • Predictive modeling of epigenomic activity from sequence is an active research area.

Purpose of the Study:

  • To develop a novel modeling strategy and inference algorithm for regressing accessibility from sequences using physical models of transcription factor (TF) binding.
  • To create a mechanistic and interpretable baseline for understanding gene and genome regulation from sequence.
  • To infer TF effective concentrations and their interactions.

Main Methods:

  • IceQream (IQ) employs spatial integration of sequences across TF-DNA affinities and localization relative to target loci.
  • IQ infers TF effective concentrations as latent variables, modeling non-linear activation/repression of regulatory elements.
  • Synergistic and antagonistic pairwise TF interactions are incorporated.

    • Main Results:

    • IQ demonstrates performance comparable or superior to state-of-the-art deep neural network models on human and mouse data.
    • The model successfully infers TF effective concentrations and their interactions.
    • IQ provides interpretable components for epigenome modeling.

    Conclusions:

    • IQ offers a mechanistic and explicable approach to modeling epigenomic activity from sequence.
    • This method serves as a valuable baseline for future research in gene regulation.
    • IQ advances the understanding of how DNA sequence dictates regulatory element function.