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000309869 1001_ $$0P:(DE-HGF)0$$aFoerster, Leo Carl$$b0$$eFirst author
000309869 245__ $$aUmite: fast quantification of smart-seq3 libraries with improved UMI retrieval.
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000309869 520__ $$aCommercial 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.
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000309869 7001_ $$0P:(DE-He78)eb01f0ec1678a1bb15746c1ba84e7845$$aFrigoli, Enrico$$b1$$udkfz
000309869 7001_ $$0P:(DE-He78)ea0332b0f3d1398f686e6c11ff3ed2e6$$aSun, Xiaoyu$$b2$$udkfz
000309869 7001_ $$0P:(DE-He78)e12f82005cd8d0ea685f6e988bd73256$$aHooli, Jooa Hermanni$$b3
000309869 7001_ $$0P:(DE-HGF)0$$aGoncalves, Angela$$b4
000309869 7001_ $$0P:(DE-He78)9f92f3ae36d92f7d6f070602e0e8585c$$aMartin-Villalba, Ana$$b5$$eLast author$$udkfz
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