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Updated: Sep 15, 2025

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement
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Soffritto: a deep learning model for predicting high-resolution replication timing.

Dante Bolzan1,2, Ferhat Ay1,2,3

  • 1Center for Autoimmunity and Inflammation, La Jolla Institute for Immunology, La Jolla, CA 92037, United States.

Bioinformatics (Oxford, England)
|July 15, 2025
PubMed
Summary
This summary is machine-generated.

Soffritto, a deep learning model, accurately predicts high-resolution DNA replication timing (RT) using standard two-fraction RT data. This method overcomes limitations of current techniques, enabling detailed RT pattern detection across cell types.

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Area of Science:

  • Genomics and Molecular Biology
  • Computational Biology and Bioinformatics

Background:

  • Replication timing (RT) describes the order of DNA replication during S phase and is cell-type specific, influencing gene expression and cellular processes.
  • Current genome-wide RT quantification (e.g., Repli-Seq) uses two-fraction assays, providing limited resolution.
  • High-resolution Repli-Seq (16 fractions) offers greater detail but is costly and technically demanding, with scarce data.

Purpose of the Study:

  • To develop a computational method for predicting high-resolution (16-fraction) RT data from readily available low-resolution (two-fraction) RT data.
  • To enable accurate and accessible analysis of detailed RT patterns across diverse cell types.

Main Methods:

  • Developed Soffritto, a deep learning model utilizing a Long Short-Term Memory (LSTM) module and a prediction module.
  • Input features include two-fraction RT data, histone ChIP-seq data, GC content, and gene density.
  • Trained and tested Soffritto on five human and mouse cell lines, performing both within and cross-cell line analyses.

Main Results:

  • Soffritto accurately predicts 16-fraction RT signals, capturing experimental high-resolution RT patterns.
  • The model demonstrates high accuracy in both within-cell line and cross-cell line predictions.
  • Predicted RT signals facilitate the detection of intricate, high-resolution replication timing features.

Conclusions:

  • Soffritto effectively predicts high-resolution replication timing from standard two-fraction data, overcoming current limitations.
  • The model provides a powerful, accessible tool for detailed RT analysis in various cell types.
  • This approach can advance research into the functional roles of replication timing in cellular processes and disease.