Journal Article

DKFZ-2026-00354

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Umite: fast quantification of smart-seq3 libraries with improved UMI retrieval.

Foerster, L. C. (First author)DKFZ* ; Frigoli, E.DKFZ* ; Sun, X.DKFZ* ; Hooli, J. H.DKFZ* ; Goncalves, A.DKFZ* ; Martin-Villalba, A. (Last author)DKFZ*

2026
Oxford Univ. Press Oxford

Abstract: Commercial solutions like 10X cellranger provide robust UMI quantification for their proprietary single-cell protocols, but open methods such as Smart-seq3 lack comparable support.Here, we introduce umite, a Smart-seq3 UMI counting pipeline with a focus on speed and a light memory footprint. Unlike existing tools, umite offers efficient mismatch-tolerant UMI detection, boosting UMI retrieval by 5-15% in benchmarks. It also outperforms current Smart-seq3 quantification tools in runtime, disk usage, and memory footprint, offering better scalability on large datasets.umite is available at https://github.com/leoforster/umite (or via Zenodo: https://doi.org/10.5281/zenodo.18166431) and includes a Snakemake workflow for Smart-seq3 quantification. Single cell libraries of the mouse nasal vasculature dataset (GSE207085) and human CD4+ T-cell dataset (GSE270928) used in benchmarking were downloaded from NCBI (see Supplement for details).Supplementary data are available at Bioinformatics online.

Note: #EA:A290#LA:A290# / epub

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Contributing Institute(s):

1. A290 Molekulare Neurobiologie (A290)
2. Abteilung Computergestützte und Molekulare Prävention (C220)

Research Program(s):

1. 311 - Zellbiologie und Tumorbiologie (POF4-311) (POF4-311)

Appears in the scientific report 2026

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