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

## TL;DR

The paper introduces umite, a fast and efficient tool for UMI quantification in Smart-seq3 single-cell RNA sequencing data.

## Contribution

umite introduces a new UMI counting pipeline for Smart-seq3 with improved UMI retrieval and reduced resource usage.

## Key findings

- umite improves UMI retrieval by 5%–15% compared to existing tools.
- umite outperforms other Smart-seq3 quantification tools in runtime, memory usage, and disk usage.
- umite provides a scalable solution for large Smart-seq3 datasets.

## 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.