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Updated: Apr 13, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
Published on: November 7, 2025
GEfetch2R: fetching single-cell/bulk RNA-seq data from public repositories to R and benchmarking the subsequent
Yabing Song1, Jianbin Wang2, Jiaxin Gao1
1State Key Laboratory of Microbial Diversity and Innovative Utilization, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
Background:
Downloading and reanalyzing the existing single-cell RNA sequencing (scRNA-seq) data provides an efficient choice to gain clues and new insights. However, no tool can fetch the diverse scRNA-seq data types (raw data, count matrix, and processed object) distributed in various repositories, process and load the downloaded data to R, convert formats between scRNA-seq objects, and benchmark the format conversion tools.
Findings:
Here, we present GEfetch2R, an R package with Docker image to (i) download diverse scRNA-seq data types, including raw data (SRA and ENA), count matrix (GEO, UCSC Cell Browser, and PanglaoDB), and processed objects (GEO, Zenodo, CELLxGENE, and HCA); (ii) process the downloaded data, load the count matrices/annotations/rds files to R (SeuratObject/DESeqDataSet), filter the SeuratObject based on cell metadata and genes, and dissect and extract the RData files; and (iii) convert formats between the widely used scRNA-seq objects, including SeuratObject, AnnData, SingleCellExperiment, CellDataSet/cell_data_set, and loom, and benchmark format conversion tools in terms of information kept, usability, running time, and scalability to guide the tool selection. Furthermore, GEfetch2R can also download, process, and load bulk RNA-seq raw data (SRA and ENA) and count matrices (GEO) to R (DESeqDataSet).
Conclusions:
GEfetch2R is an R package that facilitates researchers in accessing and exploring existing gene expression data from various public repositories. It can function as a data downloader (supports all 3 scRNA-seq and 2 bulk RNA-seq data types), a data processor (processes and loads the output/downloaded count matrices and annotations to R), and an object format converter (between the widely used scRNA-seq objects).
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