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In this work, we investigate whether motif subsequence features can predict accessible chromatin regions (ACR) in a genome, i.e. regions that are accessible to regulatory proteins, thus enabling transcription of associated genes. We focus on plants, whose agricultural and ecological importance make them interesting and important organisms to study, and whose complex genomes provide important stress tests for our algorithm. We show that regulatory motif sequence similarity can be found efficiently using co-linear chaining. The similarity scores found are then used as features in machine learning models to explore the feasibility of effectively predicting ACRs in genome assemblies.

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PanACRpred: Predicting Accessible Chromatin Regions in Pangenomes Using Motif Chaining

  • 会议论文

  • 发表日期：

    2026年

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  • Madelyn J. Warr,
  • Trung Dinh,
  • Bella Root,
  • Elyse Onstott,
  • Kevin Yu,
  • Joann Mudge,
  • Thiruvarangan Ramaraj,
  • Indika Kahanda,
  • Brendan Mumey

摘要

In this work, we investigate whether motif subsequence features can predict accessible chromatin regions (ACR) in a genome, i.e. regions that are accessible to regulatory proteins, thus enabling transcription of associated genes. We focus on plants, whose agricultural and ecological importance make them interesting and important organisms to study, and whose complex genomes provide important stress tests for our algorithm. We show that regulatory motif sequence similarity can be found efficiently using co-linear chaining. The similarity scores found are then used as features in machine learning models to explore the feasibility of effectively predicting ACRs in genome assemblies.