{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T22:55:15Z","timestamp":1778108115128,"version":"3.51.4"},"reference-count":36,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":97,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 22050004"],"award-info":[{"award-number":["No. 22050004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>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\u00a0SeuratObject, 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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/gigascience\/giag039","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:35:32Z","timestamp":1775561732000},"source":"Crossref","is-referenced-by-count":1,"title":["<i>GEfetch2R<\/i>\n                    : fetching single-cell\/bulk RNA-seq data from public repositories to R and benchmarking the subsequent format conversion tools"],"prefix":"10.1093","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1352-2994","authenticated-orcid":false,"given":"Yabing","family":"Song","sequence":"first","affiliation":[{"name":"State Key Laboratory of Microbial Diversity and Innovative Utilization, Institute of Microbiology, Chinese Academy of Sciences , Beijing 100101 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6725-7925","authenticated-orcid":false,"given":"Jianbin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Tsinghua University , Beijing 100084 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6161-4629","authenticated-orcid":false,"given":"Jiaxin","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Microbial Diversity and Innovative Utilization, Institute of Microbiology, Chinese Academy of Sciences , Beijing 100101 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"2026050606513515500_bib1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13073-017-0467-4","article-title":"A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications","volume":"9","author":"Haque","year":"2017","journal-title":"Genome Med"},{"key":"2026050606513515500_bib2","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1038\/s42003-023-04928-6","article-title":"A scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data","volume":"6","author":"Cheng","year":"2023","journal-title":"Commun Biol"},{"key":"2026050606513515500_bib3","doi-asserted-by":"publisher","first-page":"abe6474","DOI":"10.1126\/science.abe6474","article-title":"Pan-cancer single-cell landscape of tumor-infiltrating T cells","volume":"374","author":"Zheng","year":"2021","journal-title":"Science"},{"key":"2026050606513515500_bib4","doi-asserted-by":"publisher","first-page":"D387","DOI":"10.1093\/nar\/gkab1053","article-title":"The sequence read archive: a decade more of explosive growth","volume":"50","author":"Katz","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2026050606513515500_bib5","doi-asserted-by":"publisher","first-page":"D121","DOI":"10.1093\/nar\/gkac1051","article-title":"The European Nucleotide Archive in 2022","volume":"51","author":"Burgin","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2026050606513515500_bib6","doi-asserted-by":"publisher","first-page":"D991","DOI":"10.1093\/nar\/gks1193","article-title":"NCBI GEO: archive for functional genomics data sets\u2014update","volume":"41","author":"Barrett","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2026050606513515500_bib7","doi-asserted-by":"publisher","first-page":"4578","DOI":"10.1093\/bioinformatics\/btab503","article-title":"UCSC cell browser: visualize your single-cell data","volume":"37","author":"Speir","year":"2021","journal-title":"Bioinformatics"},{"key":"2026050606513515500_bib8","doi-asserted-by":"publisher","first-page":"baz046","DOI":"10.1093\/database\/baz046","article-title":"PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data","volume":"2019","author":"Franz\u00e9n","year":"2019","journal-title":"Database"},{"key":"2026050606513515500_bib9","unstructured":"Zenodo. \u00a0CERN, Gen\u00e8ve. 2013. https:\/\/www.zenodo.org\/. 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