ArticleGenome research2021
Analysis of alternative polyadenylation from single-cell RNA-seq using scDaPars reveals cell subpopulations invisible to gene expression.
Article in Genome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 52 citations in OpenAlex.
- Benchmarking computational methods for identifying and quantifying polyadenylation sites from 3' tag-based single-cell RNA-seq data.Nucleic acids research · 2026Article
- APAdeg enhances differentially expressed gene inference by leveraging site-specific signals in APA-seq data.Briefings in bioinformatics · 2026Article
- scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.Briefings in bioinformatics · 2026Article
- Reference-free discovery with barcoded single-cell sequencing.Nature biotechnology · 2026Article
- metaAPA: a tool for integration of PolyA site predictions from single-cell and spatial transcriptomics.Bioinformatics advances · 2026Article
- Single-nucleus transcriptome atlas of orbitofrontal cortex in ALS with a deep learning-based decoding of alternative polyadenylation mechanisms.Cell genomics · 2025Article
- Alternative cleavage and polyadenylation: key regulatory mechanisms in health and disease.RNA biology · 2025Review
- RNA Alternative Splicing and Polyadenylation and Regulation of the Glomerular Filtration Barrier.Journal of the American Society of Nephrology : JASN · 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
- Quantification of transcript isoforms at the single-cell level using SCALPEL.Nature communications · 2025Article
- The quantitative impact of 3'UTRs on gene expression.Nucleic acids research · 2025Article
- Review
- Predicting and comparing transcription start sites in single cell populations.PLoS computational biology · 2025Article
- Review
- Supervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA.Briefings in bioinformatics · 2024Article
- Article
- Pan-cancer transcriptome analysis reveals widespread regulation through alternative tandem transcription initiation.Science advances · 2024Article
- Dynamics of alternative polyadenylation in single root cells ofFrontiers in plant science · 2024Article
- Infernape uncovers cell type-specific and spatially resolved alternative polyadenylation in the brain.Genome research · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Alternative polyadenylation (APA) is a major mechanism of post-transcriptional regulation in various cellular processes including cell proliferation and differentiation, but the APA heterogeneity among single cells remains largely unknown. Single-cell RNA sequencing (scRNA-seq) has been extensively used to define cell subpopulations at the transcription level. Yet, most scRNA-seq data have not been analyzed in an "APA-aware" manner. Here, we introduce dynamic analysis of APA from single-cell RNA-seq (scDaPars), a bioinformatics algorithm to accurately quantify APA events at both single-cell and single-gene resolution using either 3'-end (10x Chromium) or full-length (Smart-seq2) scRNA-seq data. Validations in both real and simulated data indicate that scDaPars can robustly recover missing APA events caused by the low amounts of mRNA sequenced in single cells. When applied to cancer and human endoderm differentiation data, scDaPars not only revealed cell-type-specific APA regulation but also identified cell subpopulations that are otherwise invisible to conventional gene expression analysis. Thus, scDaPars will enable us to understand cellular heterogeneity at the post-transcriptional APA level.
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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.