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Published on: August 3, 2018
Umite: cuantificación rápida de bibliotecas smart-seq3 con recuperación mejorada de UMI
Leo Carl Foerster1,2, Enrico Frigoli1,2, Xiaoyu Sun1,2
1Molecular Neurobiology, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany.
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:
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 Information:
Supplementary data are available at Bioinformatics online.
