Umite: fast quantification of Smart-seq3 libraries with improved UMI retrieval
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...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
March 2026
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| In: |
Bioinformatics
Year: 2026, Volume: 42, Issue: 3, Pages: 1-5 |
| ISSN: | 1367-4811 |
| DOI: | 10.1093/bioinformatics/btag075 |
| Online Access: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.1093/bioinformatics/btag075 Verlag, kostenfrei, Volltext: https://academic.oup.com/bioinformatics/article/42/3/btag075/8487129 
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| Author Notes: | Leo Carl Foerster, Enrico Frigoli, Xiaoyu Sun, Jooa Hooli, Angela Goncalves, Ana Martin-Villalba |
| Summary: | 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. |
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| Item Description: | Online veröffentlicht: 15. Februar 2026 Gesehen am 13.04.2026 |
| Physical Description: | Online Resource |
| ISSN: | 1367-4811 |
| DOI: | 10.1093/bioinformatics/btag075 |