ReviewNAR genomics and bioinformatics2025
Benchmarking of methods that identify alternative polyadenylation events in single-/multiple-polyadenylation site genes.
Review in NAR genomics and bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Benchmarking computational methods for identifying and quantifying polyadenylation sites from 3' tag-based single-cell RNA-seq data.Nucleic acids research · 2026Article
- Multiomics profiling of zebrafish embryonic cell line PAC2 across growth phases to assess its relevance for toxicological studies.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alternative polyadenylation (APA) is a widespread post-transcriptional mechanism that diversifies gene expression by generating messenger RNA isoforms with varying 3' untranslated regions. Accurate identification and quantification of transcriptome-wide polyadenylation site (PAS) usage are essential for understanding APA-mediated gene regulation and its biological implications. In this review, we first review the landscape of computational tools developed to identify APA events from RNA sequencing (RNA-seq) data. We then benchmarked five PAS prediction tools and seven APA detection algorithms using five RNA-seq datasets derived from clear cell renal cell carcinoma (ccRCC) and adjacent normal tissues. By evaluating tool performance across genes with either single or multiple PASs, we revealed substantial variation in accuracy, sensitivity, and consistency among the tools. Based on this comparative analysis, we offer practical guidelines for tool selection and propose considerations for improving APA detection accuracy. Additionally, our analysis identified CCNL2 as a candidate gene exhibiting significant APA regulation in ccRCC, highlighting its potential as a disease-associated biomarker.
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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 Socratic graph.