ReviewGenomics, proteomics & bioinformatics2023
A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq.
Review in Genomics, proteomics & bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- scPASU: A computational protocol for quantifying polyadenylation site usage and alternative polyadenylation from 3' scRNA-seq data.STAR protocols · 2026Article
- Benchmarking computational methods for identifying and quantifying polyadenylation sites from 3' tag-based single-cell RNA-seq data.Nucleic acids research · 2026Article
- Identifying Candidate Genes for Cotton Fruit Branch Length Using BSA-Seq and RNA-Seq.Plants (Basel, Switzerland) · 2026Article
- Transcript diversity reflects deleterious RNA processing errors shaped by population size in metazoans.PLoS biology · 2026Article
- PolyAseqTrap: a universal tool for genome-wide identification and quantification of polyadenylation sites from different 3' end sequencing data.Genome biology · 2026Article
- mRNALocator-imb: an imbalance-tolerant ensemble framework integrating random forest and transformer for mRNA subcellular localization prediction.Frontiers in genetics · 2026Article
- Systematic Comparison of Droplet-Based and Microwell-Based Platforms for Comprehensive Single-Cell Transcriptomic Analysis in Clinical Samples.IET nanobiotechnology · 2026Article
- Alternative cleavage and polyadenylation: key regulatory mechanisms in health and disease.RNA biology · 2025Review
- Multiscale Construction, Evaluation, and Application of Organoids.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Benchmarking of methods that identify alternative polyadenylation events in single-/multiple-polyadenylation site genes.NAR genomics and bioinformatics · 2025Review
- Improved characterization of 3' single-cell RNA-seq libraries with paired-end avidity sequencing.NAR genomics and bioinformatics · 2024Article
- Improved characterization of single-cell RNA-seq libraries with paired-end avidity sequencing.bioRxiv : the preprint server for biology · 2024Article
- vizAPA: visualizing dynamics of alternative polyadenylation from bulk and single-cell data.Bioinformatics (Oxford, England) · 2024Article
- Dynamics of alternative polyadenylation in single root cells ofFrontiers in plant science · 2024Article
- Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data.RNA (New York, N.Y.) · 2023Review
- Alternative Transcripts Diversify Genome Function for Phenome Relevance to Health and Diseases.Genes · 2023Review
- Stress responses of plants through transcriptome plasticity by mRNA alternative polyadenylation.Molecular horticulture · 2023Review
- Regulation and function of alternative polyadenylation in development and differentiation.RNA biology · 2023Review
- Cellular dynamics of distinct skeletal cells and the development of osteosarcoma.Frontiers in endocrinology · 2023Review
- The Advances in Deep Learning Modeling of Polyadenylation Codes.Wiley interdisciplinary reviews. RNAReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alternative polyadenylation (APA) plays important roles in modulating mRNA stability, translation, and subcellular localization, and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity. Identification of poly(A) sites (pAs) on a genome-wide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation. A number of established computational tools have been proposed to predict pAs from diverse genomic data. Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences, bulk RNA sequencing (RNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Particularly, we examined several representative tools using bulk RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools. We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios. Moreover, we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods. Additionally, we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques, and provided our perspective on how computational methodologies might evolve in the future for non-3' untranslated region, tissue-specific, cross-species, and single-cell pA prediction.
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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.