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

D Lucarelli1,2,3,4,5, T Kos2,3,4,5, C Shull2,3,4

  • 1Institute of Computational Biology, Helmholtz Center, Munich, Germany.

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Quick Clonal Analysis Toolkit (QuiCAT) is a new Python package for analyzing synthetic cellular tags in large-scale biological datasets. It offers faster and more accurate extraction, clustering, and analysis for cell fate and lineage tracing studies.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Synthetic cellular tagging is vital for cell fate and lineage tracing.
  • Scalable software is needed for analyzing these tags with single-cell and spatial transcriptomics.
  • Existing methods lack performance and broad applicability.

Purpose of the Study:

  • To develop a scalable software solution for synthetic tag analysis.
  • To streamline the extraction, clustering, and analysis of synthetic tags.
  • To provide a tool compatible with existing transcriptomics analysis ecosystems.

Main Methods:

  • Developed Quick Clonal Analysis Toolkit (QuiCAT), a Python-based package.
  • Implemented reference-free and reference-based workflows for tag analysis.
  • Optimized computational performance for speed and accuracy.

Main Results:

  • QuiCAT demonstrates superior speed and accuracy compared to existing pipelines.
  • Outputs are compatible with Python-based single-cell and spatial transcriptomics tools.
  • Validated across diverse datasets including population, single-cell, and spatial transcriptomics.

Conclusions:

  • QuiCAT offers an efficient and accurate solution for synthetic tag analysis.
  • Facilitates seamless integration into downstream transcriptomics analyses.
  • Enhances the scalability and performance of cell fate and lineage tracing studies.