Work

umite: fast quantification of Smart-seq3 libraries with improved UMI retrieval

Year: 2026

Type: article

Abstract: MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: 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.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btag075

Authors: Leo Carl Foerster, Enrico Frigoli, Xiaoyu Sun, Jooa Hooli, Angela Goncalves, Ana Martin-Villalba

Institutions: German Cancer Research Center, Heidelberg University, University Medical Centre Mannheim, Medizinische Fakultät Mannheim

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,989

Funders: Deutsches Krebsforschungszentrum, Deutsche Forschungsgemeinschaft