PASSpedia:a polyadenylation site database across different species at single cell resolution
Polyadenylation site (PAS) selection plays important roles in gene expression regulation and function. By applying oligo(dT) to enrich RNAs containing 3' polyadenylation tail for 3' end deep sequencing, cell type-specific selection of PAS usage has been widely revealed at both bulk cell and single cell resolutions. Here, we upgraded and applied a series of species-specific SCAPTURE pipelines to profile PASs from ~1,330 published 3' tag-based scRNA-seq datasets across seven species, resulting in a comprehensive PAS landscape across species. We built up PASSpedia, an encyclopedia database for PAS analyses across distinct species at single cell resolution.
7
Species
1,330
Datasets
5,598,466
Single Cells
27,614,915
PASs
Summary of studies
Summary of datasets
Summary of PAS
Related References
For PASSpedia and SCAPTURE v2:
Zhang PH#, Feng H#, Ma XK, Nan F*, Yang L*. PASSpedia: A Polyadenylation Site Database Across Different Species at Single Cell Resolution. Genomics Proteomics Bioinformatics, 2025 Sep 23:qzaf089, https://doi.org/10.1093/gpbjnl/qzaf089
For DeepPASS-embedded SCAPTURE:
Li GW#, Nan F#, Yuan GH, Liu CX, Liu X, Chen LL, Tian B and Yang L*. SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3' tag-based RNA-seq of single cells. Genome Biol, 2021, 22(1): 221, https://doi.org/10.1186/s13059-021-02437-5
Gene panel
APA panel
Correlations
About PASSpedia
About
It has been well known that 3' polyadenylation is essential for mRNA maturation, stability, localization and translation. Importantly, the selection of different polyadenylation sites (PASs), or alternative cleavage and polyadenylation (APA), leads to the production of multiple mRNA isoforms with distinct 3' untranslated regions (3'UTRs) and/or protein coding sequences (CDS). In this scenario, precise profiling of APA is important for the understanding of gene regulation and function. To obtain more comprehensive information of APA events in single cell level, we set to use SCAPTURE pipeline for extensive APA profiling from a broad spectrum of scRNA-seq datasets, since SCAPTURE outperformed similar methods for high-confidence PAS identification from 3′ tag-based scRNA-seq datasets with an embedded deep learning model (called deepPASS). By using species-specific PAS annotation, species-specific SCAPTURE pipelines were modified to profile PAS from ~1,330 of 10x Chromium scRNA-seq datasets in a spectrum of tissues across different species, including human, rhesus, mouse, rat, zebrafish, fruit-fly and worm. A comprehensive landscape of high-confidence PASs, including thousands of previously-unannotated ones, were identified to demonstrate the cell-specific and species-specific APA landscape. An encyclopedia database of PAS across different species, called PASSpedia, was constructe, which enables the researching, browsing and downloading PASs in seven model animals. PASSpedia also provides a user-friendly online analysis platform, and allow users exploring PASs in single cell datasets to identify cell-specific and species-specific PAS selection, expanding insights into the research of PAS selection.
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Related References
For PASSpedia and SCAPTURE v2:
Zhang PH#, Feng H#, Ma XK, Nan F*, Yang L*. PASSpedia: A Polyadenylation Site Database Across Different Species at Single Cell Resolution. Genomics Proteomics Bioinformatics, 2025 Sep 23:qzaf089, https://doi.org/10.1093/gpbjnl/qzaf089
For DeepPASS-embedded SCAPTURE:
Li GW#, Nan F#, Yuan GH, Liu CX, Liu X, Chen LL, Tian B and Yang L*. SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3' tag-based RNA-seq of single cells. Genome Biol, 2021, 22(1): 221, https://doi.org/10.1186/s13059-021-02437-5