PASSpedia: A Polyadenylation Site Database Across Different Species at Single-cell Resolution.

Pei-Hong Zhang, Hua Feng, Xu-Kai Ma, Fang Nan, Li Yang

Abstract read

In one paragraph

Article in Genomics, proteomics & bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

PolyA_DB v4: systematic polyA site identification and isoform annotation in human and mouse genomes using 3' end and long-read sequencing data.Nucleic acids research · 2026
Article

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5 · Who and what money

Authors and funding

5 authors.

Pei-Hong ZhangCenter for Molecular Medicine, Children's Hospital of Fudan University and Shanghai Key Laboratory of Medical Epigenetics, International Laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China.ORCID 0009-0004-4297-3553

Hua FengCenter for Molecular Medicine, Children's Hospital of Fudan University and Shanghai Key Laboratory of Medical Epigenetics, International Laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China.ORCID 0009-0003-0762-1854

Xu-Kai MaCenter for Molecular Medicine, Children's Hospital of Fudan University and Shanghai Key Laboratory of Medical Epigenetics, International Laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China.ORCID 0000-0003-3779-4070

Fang NanCenter for Molecular Medicine, Children's Hospital of Fudan University and Shanghai Key Laboratory of Medical Epigenetics, International Laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China.ORCID 0009-0004-5741-8122

Li YangCenter for Molecular Medicine, Children's Hospital of Fudan University and Shanghai Key Laboratory of Medical Epigenetics, International Laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China.ORCID 0000-0001-8833-7473

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polyadenylation site (PAS) selection plays important roles in gene expression regulation and function. RNA sequencing (RNA-seq) data derived from 3' tag sequencing contain intrinsic information about PAS usage and have been analyzed for alternative polyadenylation (APA) isoform expression in both bulk and single-cell samples. Here, we upgraded our previously developed deep learning-based PAS analysis pipeline SCAPTURE v2 to profile PASs from 1330 published 3' tag-based single-cell RNA-seq (scRNA-seq) datasets across seven species, resulting in a comprehensive PAS landscape across species. Validation with long-read sequencing data from matched human tissues showed high accuracy of single-cell PAS profiling by SCAPTURE, including previously unannotated ones. Further comparisons revealed distinct PAS usage preferences in different species, such as human versus mouse, independent of conservation of gene expression. Finally, we present PASSpedia, a comprehensive database for PAS analysis and comparison across seven species at single-cell resolution, which is freely accessible online at https://bits.fudan.edu.cn/PASSpedia/.

Indexed as

Databases, GeneticPolyadenylationSingle-Cell AnalysisAnimalsHumansMiceSingle-Cell Gene Expression AnalysisSpecies SpecificityAlternative polyadenylationDatabaseMulti-species conservationPolyadenylationSingle-cell RNA-seq

Identifiers

PMID40986375

DOI10.1093/gpbjnl/qzaf089

PMCPMC13309242

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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.