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@ARTICLE{Lucarelli:305752,
      author       = {D. Lucarelli$^*$ and T. Kos$^*$ and C. Shull$^*$ and S.
                      Jiménez and R. Öllinger and R. Rad and D. Saur$^*$ and F.
                      J. Theis},
      title        = {{Q}ui{CAT}: {A} {S}calable and {F}lexible {F}ramework for
                      {M}apping {S}ynthetic {S}equences.},
      journal      = {Bioinformatics},
      volume       = {nn},
      issn         = {1367-4803},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2025-02372},
      pages        = {nn},
      year         = {2025},
      note         = {epub},
      abstract     = {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.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.​.QuiCAT is available as a Python
                      package to be installed. The source code is available at
                      https://github.com/theislab/quicat.Supplementary data are
                      available at Bioinformatics online.},
      cin          = {MU01},
      ddc          = {570},
      cid          = {I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41217759},
      doi          = {10.1093/bioinformatics/btaf607},
      url          = {https://inrepo02.dkfz.de/record/305752},
}