umite: fast quantification of Smart-seq3 libraries with improved UMI retrieval.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41692984.
- Also identified by DOI 10.1093/bioinformatics/btag075 and PMC identifier 12989134.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Software
- Single-Cell Analysis
- High-Throughput Nucleotide Sequencing
- Computational Biology