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<p>Long-read sequencing (LRS) technologies have enhanced molecular diagnostics by enabling comprehensive analysis of genetic, epigenetic and transcriptomic alterations in various genetic diseases and cancer. Emerging applications of LRS in central nervous system (CNS) tumour diagnostics demonstrate its ability to detect clinically relevant mutations, gene fusions, copy number variations, and epigenetic changes. With ongoing advancements in sequencing chemistry and data analysis tools, LRS has the potential to improve the molecular diagnosis and management of CNS tumours ultimately leading to improved patient outcomes. However, despite numerous advantages, several challenges remain, including the need for high-quality nucleic acids and the lack of standardised bioinformatics tools. Integrating LRS into routine diagnostics requires further improvement of computational pipelines and the development of standardised analytical workflows. Our review aims to provide insight into novel applications of LRS in CNS tumour diagnostics and a comprehensive overview of available bioinformatics tools to support the analysis and interpretation of LRS data.</p>

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Long-read sequencing enhances molecular and epigenetic characterisation in brain tumour diagnostics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月18日

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  • Sara Petrin,
  • Špela Kert,
  • Andrej Zupan,
  • Alja Videtič Paska,
  • Katarina Kouter,
  • Alenka Matjašič

摘要

Long-read sequencing (LRS) technologies have enhanced molecular diagnostics by enabling comprehensive analysis of genetic, epigenetic and transcriptomic alterations in various genetic diseases and cancer. Emerging applications of LRS in central nervous system (CNS) tumour diagnostics demonstrate its ability to detect clinically relevant mutations, gene fusions, copy number variations, and epigenetic changes. With ongoing advancements in sequencing chemistry and data analysis tools, LRS has the potential to improve the molecular diagnosis and management of CNS tumours ultimately leading to improved patient outcomes. However, despite numerous advantages, several challenges remain, including the need for high-quality nucleic acids and the lack of standardised bioinformatics tools. Integrating LRS into routine diagnostics requires further improvement of computational pipelines and the development of standardised analytical workflows. Our review aims to provide insight into novel applications of LRS in CNS tumour diagnostics and a comprehensive overview of available bioinformatics tools to support the analysis and interpretation of LRS data.