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Open Access Gold as soon as Publ. Version/Full Text is submitted to ZB.
QuiCAT: A scalable and flexible framework for mapping synthetic sequences.
Bioinformatics, DOI: 10.1093/bioinformatics/btaf607:btaf607 (2025)
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MOTIVATION: Synthetic cellular tagging technologies play a crucial role in cell fate and lineage-tracing studies. Their integration with single-cell and spatial transcriptomics assays has heightened the need for scalable software solutions to analyze such data. However, previous methods are either designed for a subset of tagging technologies, or lack the performance needed for large-scale applications. RESULTS: To address these challenges, we developed Quick Clonal Analysis Toolkit (QuiCAT), an end-to-end Python-based package that streamlines the extraction, clustering, and analysis of synthetic tags from sequencing data. QuiCAT outperforms existing pipelines in both speed and accuracy. Its outputs are widely compatible with the Python ecosystem for single-cell and spatial transcriptomics data analysis packages allowing seamless integrations and downstream analyses. QuiCAT provides users with two workflows: a reference-free approach for extracting and mapping synthetic tags, and a reference-based approach for aligning tags against known sequences. We validate QuiCAT across diverse datasets, including population-level data, single-cell and spatially resolved transcriptomics, and benchmarked it against the two most recently published tools. Our computational optimizations enhance performance while improving accuracy.. AVAILABILITY: QuiCAT is available as a Python package to be installed. The source code is available at https://github.com/theislab/quicat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Publication type
Article: Journal article
Document type
Scientific Article
Language
english
Publication Year
2025
HGF-reported in Year
2025
e-ISSN
1367-4811
Journal
Bioinformatics
Quellenangaben
Article Number: btaf607
Publisher
Oxford University Press
Publishing Place
Oxford
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-503800-001
PubMed ID
41217759
Erfassungsdatum
2025-11-12