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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Updated: Feb 17, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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umite: fast quantification of Smart-seq3 libraries with improved UMI retrieval.

Leo Carl Foerster1,2, Enrico Frigoli1,2, Xiaoyu Sun1,2

  • 1Molecular Neurobiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.

Bioinformatics (Oxford, England)
|February 15, 2026
PubMed
Summary

We developed umite, a fast and memory-efficient pipeline for Smart-seq3 Uniquely Identifiable Molecule (UMI) counting. Umite improves UMI retrieval and outperforms existing tools in speed and resource usage for single-cell RNA sequencing analysis.

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Area of Science:

  • Single-cell RNA sequencing
  • Bioinformatics tools
  • Computational genomics

Background:

  • Commercial single-cell RNA sequencing (scRNA-seq) solutions offer robust Uniquely Identifiable Molecule (UMI) quantification.
  • Open-source protocols like Smart-seq3 currently lack comparable UMI quantification support.
  • Efficient UMI counting is crucial for accurate gene expression analysis in scRNA-seq.

Purpose of the Study:

  • To introduce umite, a novel UMI counting pipeline specifically designed for Smart-seq3.
  • To enhance the speed and reduce the memory footprint of UMI quantification for Smart-seq3.
  • To improve UMI retrieval rates through mismatch-tolerant detection.

Main Methods:

  • Development of the umite pipeline utilizing a Snakemake workflow.
  • Implementation of efficient, mismatch-tolerant UMI detection algorithms.
  • Benchmarking against existing Smart-seq3 quantification tools using public datasets (GSE207085, GSE270928).

Main Results:

  • Umite demonstrates superior performance in runtime, disk usage, and memory footprint compared to current Smart-seq3 quantification tools.
  • Mismatch-tolerant UMI detection in umite boosts UMI retrieval by 5-15% in benchmark tests.
  • Umite exhibits enhanced scalability for analyzing large single-cell datasets.

Conclusions:

  • Umite provides a fast, memory-efficient, and scalable solution for Smart-seq3 UMI quantification.
  • The pipeline improves UMI detection accuracy and overall analytical efficiency.
  • Umite is readily available as an open-source tool with a Snakemake workflow for seamless integration.