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<p>Long-read transcriptome sequencing (long-RNA-seq) revolutionizes transcriptome research by enabling full-length transcript analysis for comprehensive exploration of isoform diversity. We developed IFDlong, a probabilistic framework and software suite for detecting isoform and fusion transcripts from bulk or single-cell long-RNA-seq data. IFDlong annotates each long read, identifies novel isoforms, quantifies expression via an expectation-maximization algorithm, and profiles fusion transcripts. In large-scale simulation and real data analyses, IFDlong outperforms existing tools and demonstrated high accuracy and robustness across multiple in-house and public datasets, including healthy tissues, cell lines, and different diseases.</p>

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IFDlong: a model-based isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Wenjia Wang,
  • Jia-Jun Liu,
  • Yuzhen Li,
  • Sungjin Ko,
  • Ning Feng,
  • Manling Zhang,
  • Qingqi Lin,
  • Mengying Xia,
  • Yan P. Yu,
  • Jian-Hua Luo,
  • Pedro L. Baldoni,
  • George C. Tseng,
  • Silvia Liu

摘要

Long-read transcriptome sequencing (long-RNA-seq) revolutionizes transcriptome research by enabling full-length transcript analysis for comprehensive exploration of isoform diversity. We developed IFDlong, a probabilistic framework and software suite for detecting isoform and fusion transcripts from bulk or single-cell long-RNA-seq data. IFDlong annotates each long read, identifies novel isoforms, quantifies expression via an expectation-maximization algorithm, and profiles fusion transcripts. In large-scale simulation and real data analyses, IFDlong outperforms existing tools and demonstrated high accuracy and robustness across multiple in-house and public datasets, including healthy tissues, cell lines, and different diseases.