ArticleNature communications2024
InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 7 citations in OpenAlex.
- Mechanisms of transcription termination across the coding and noncoding loci of the genome.Nature reviews. Molecular cell biology · 2026Review
- Dynamics of intronic polyadenylation in the hematopoietic lineage and its regulation by DNA methylation.Genome research · 2026Article
- Alternative cleavage and polyadenylation: key regulatory mechanisms in health and disease.RNA biology · 2025Review
- IPScan: Detecting novel intronic PolyAdenylation events with RNA-seq data.PLoS computational biology · 2025Article
- Computational methods for alternative polyadenylation and splicing in post-transcriptional gene regulation.Experimental & molecular medicine · 2025Review
- Exploring transcriptome plasticity at the intersection of cell signaling, metabolism and computational biology.Experimental & molecular medicine · 2025Article
- DIPAN: Detecting personalized intronic polyadenylation derived neoantigens from RNA sequencing data.Computational and structural biotechnology journal · 2024Article
- InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data.Nature communications · 2024Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
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.
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Registered trials
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