ArticleGenome research2023
Accurate transcriptome-wide identification and quantification of alternative polyadenylation from RNA-seq data with APAIQ.
Article in Genome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- IPScan: Detecting novel intronic PolyAdenylation events with RNA-seq data.PLoS computational biology · 2025Article
- Genome-wide CRISPR screen for human factors involved in alternative polyadenylation based on differential localization of CD47.Scientific reports · 2025Article
- scTail: precise polyadenylation site detection and its alternative usage analysis from reads 1 preserved 3' scRNA-seq data.Genome biology · 2025Article
- Computational methods for alternative polyadenylation and splicing in post-transcriptional gene regulation.Experimental & molecular medicine · 2025Review
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Benchmarking of methods that identify alternative polyadenylation events in single-/multiple-polyadenylation site genes.NAR genomics and bioinformatics · 2025Review
- Strategies for the drug development of cancer therapeutics.Frontiers in pharmacology · 2025Review
- An AI Agent for Fully Automated Multi-Omic Analyses.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data.Nature communications · 2024Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alternative polyadenylation (APA) enables a gene to generate multiple transcripts with different 3' ends, which is dynamic across different cell types or conditions. Many computational methods have been developed to characterize sample-specific APA using the corresponding RNA-seq data, but suffered from high error rate on both polyadenylation site (PAS) identification and quantification of PAS usage (PAU), and bias toward 3' untranslated regions. Here we developed a tool for APA identification and quantification (APAIQ) from RNA-seq data, which can accurately identify PAS and quantify PAU in a transcriptome-wide manner. Using 3' end-seq data as the benchmark, we showed that APAIQ outperforms current methods on PAS identification and PAU quantification, including DaPars2, Aptardi, mountainClimber, SANPolyA, and QAPA. Finally, applying APAIQ on 421 RNA-seq samples from liver cancer patients, we identified >540 tumor-associated APA events and experimentally validated two intronic polyadenylation candidates, demonstrating its capacity to unveil cancer-related APA with a large-scale RNA-seq data set.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.