Deciphering Sequence Determinants of Zygotic Genome Activation Genes: Insights From Machine Learning and the ZGAExplorer Platform DOI Creative Commons

Jixiang Xing,

Siqi Yang,

Yuchao Liang

et al.

Cell Proliferation, Journal Year: 2025, Volume and Issue: unknown

Published: April 18, 2025

ABSTRACT The mammalian life cycle initiates with the transition of genetic control from maternal to embryonic genome during zygotic activation (ZGA), which becomes pivotal for development. Nevertheless, understanding conservation genes and transcription factors (TFs) that underlie ZGA remains limited. Here, we compiled a comprehensive set mice, humans, pigs, bovines goats, including Nr5a2 TPRX1/2 . identification 111 homologous through comparative analyses was followed by discovery conserved coding region, suggesting potential sequence preferences genes. Notably, an interpretable machine learning model based on k ‐mer core features showed excellent performance in predicting (area under ROC curve [AUC] > 0.81), revealing abundant intricate 6‐base specific patterns binding TFs, motifs NR5A2 TPRX1/2. Further analysis demonstrated gene epigenetic modification play equally important roles regulating Ultimately, developed ZGAExplorer platform provide invaluable resource screening Our study unravels determinants across species multi‐omics data integration learning, yielding insights into regulatory mechanisms developmental arrest.

Language: Английский

Deciphering Sequence Determinants of Zygotic Genome Activation Genes: Insights From Machine Learning and the ZGAExplorer Platform DOI Creative Commons

Jixiang Xing,

Siqi Yang,

Yuchao Liang

et al.

Cell Proliferation, Journal Year: 2025, Volume and Issue: unknown

Published: April 18, 2025

ABSTRACT The mammalian life cycle initiates with the transition of genetic control from maternal to embryonic genome during zygotic activation (ZGA), which becomes pivotal for development. Nevertheless, understanding conservation genes and transcription factors (TFs) that underlie ZGA remains limited. Here, we compiled a comprehensive set mice, humans, pigs, bovines goats, including Nr5a2 TPRX1/2 . identification 111 homologous through comparative analyses was followed by discovery conserved coding region, suggesting potential sequence preferences genes. Notably, an interpretable machine learning model based on k ‐mer core features showed excellent performance in predicting (area under ROC curve [AUC] > 0.81), revealing abundant intricate 6‐base specific patterns binding TFs, motifs NR5A2 TPRX1/2. Further analysis demonstrated gene epigenetic modification play equally important roles regulating Ultimately, developed ZGAExplorer platform provide invaluable resource screening Our study unravels determinants across species multi‐omics data integration learning, yielding insights into regulatory mechanisms developmental arrest.

Language: Английский

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