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InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data

Author

Listed:
  • Xiaochuan Liu

    (Tianjin Medical University)

  • Hao Chen

    (Tianjin Medical University)

  • Zekun Li

    (Tianjin Medical University)

  • Xiaoxiao Yang

    (Tianjin Medical University
    Tianjin Medical University)

  • Wen Jin

    (Tianjin Medical University
    Tianjin Medical University)

  • Yuting Wang

    (Tianjin Medical University
    Tianjin Medical University)

  • Jian Zheng

    (Tianjin Medical University)

  • Long Li

    (Tianjin Medical University)

  • Chenghao Xuan

    (Tianjin Medical University)

  • Jiapei Yuan

    (Chinese Academy of Medical Sciences and Peking Union Medical College
    Tianjin Institutes of Health Science)

  • Yang Yang

    (Tianjin Medical University
    Tianjin Medical University)

Abstract

Alternative polyadenylation can occur in introns, termed intronic polyadenylation (IPA), has been implicated in diverse biological processes and diseases, as it can produce noncoding transcripts or transcripts with truncated coding regions. However, a reliable method is required to accurately characterize IPA. Here, we propose a computational method called InPACT, which allows for the precise characterization of IPA from conventional RNA-seq data. InPACT successfully identifies numerous previously unannotated IPA transcripts in human cells, many of which are translated, as evidenced by ribosome profiling data. We have demonstrated that InPACT outperforms other methods in terms of IPA identification and quantification. Moreover, InPACT applied to monocyte activation reveals temporally coordinated IPA events. Further application on single-cell RNA-seq data of human fetal bone marrow reveals the expression of several IPA isoforms in a context-specific manner. Therefore, InPACT represents a powerful tool for the accurate characterization of IPA from RNA-seq data.

Suggested Citation

  • Xiaochuan Liu & Hao Chen & Zekun Li & Xiaoxiao Yang & Wen Jin & Yuting Wang & Jian Zheng & Long Li & Chenghao Xuan & Jiapei Yuan & Yang Yang, 2024. "InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46875-8
    DOI: 10.1038/s41467-024-46875-8
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    References listed on IDEAS

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