PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Lucarelli, D. ; Kos, T.* ; Shull, C.* ; Jimenez, S. ; Öllinger, R.* ; Rad, R.* ; Saur, D.* ; Theis, F.J.

QuiCAT: A scalable and flexible framework for mapping synthetic sequences.

Bioinformatics, DOI: 10.1093/bioinformatics/btaf607:btaf607 (2025)
Postprint Forschungsdaten DOI PMC
Creative Commons Lizenzvertrag
Open Access Gold möglich sobald Verlagsversion bei der ZB eingereicht worden ist.
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.
Impact Factor
Scopus SNIP
Altmetric
0.000
0.000
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
e-ISSN 1367-4811
Zeitschrift Bioinformatics
Quellenangaben Band: , Heft: , Seiten: , Artikelnummer: btaf607 Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
DOI 10.1093/bioinformatics/btaf607
PubMed ID 41217759
Erfassungsdatum 2025-11-12
[➜Korrektur melden]