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

Xiaochuan Liu, Hao Chen, Zekun Li, Xiaoxiao Yang, Wen Jin, Yuting Wang, Jian Zheng, Long Li, Chenghao Xuan (), Jiapei Yuan () and Yang Yang ()
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Xiaochuan Liu: Tianjin Medical University
Hao Chen: Tianjin Medical University
Zekun Li: Tianjin Medical University
Xiaoxiao Yang: Tianjin Medical University
Wen Jin: Tianjin Medical University
Yuting Wang: 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
Yang Yang: Tianjin Medical University

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-46875-8

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