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StarCDP: Dynamic Programming Algorithms for Fast and Accurate Cell Lineage Tree Reconstruction from CRISPR-Based

Junyan Dai1,2, Erin K Molloy1,2

  • 1Department of Computer Science, University of Maryland, College Park, Maryland, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|October 31, 2025
PubMed
Summary
This summary is machine-generated.

Star-CDP enhances cell lineage tree reconstruction for CRISPR-based lineage tracing. This new method improves accuracy and scalability, addressing limitations in phylogenetic signal consistency for developmental and disease studies.

Keywords:
CRISPRCamin-Sokallineage tracingparsimonyphylogeneticsstar homoplasy

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Area of Science:

  • Computational Biology
  • Genomics
  • Developmental Biology

Background:

  • CRISPR-based lineage tracing combined with single-cell RNA sequencing offers insights into cellular development and disease.
  • Cell lineage tree (CLT) reconstruction is crucial but faces challenges with phylogenetic signal consistency.
  • Existing methods like Star Homoplasy Parsimony (SHP) and Startle family have limitations.

Purpose of the Study:

  • To introduce Star-CDP, a novel dynamic programming algorithm for CLT reconstruction.
  • To address the limitations of existing methods in handling CRISPR mutation properties and scalability.
  • To improve the accuracy and efficiency of phylogenetic signal reconstruction in lineage tracing.

Main Methods:

  • Developed Star-CDP, a collection of dynamic programming algorithms for SHP solutions within a constrained search space.
  • Implemented clade constraints for polynomial-time computation, enabling scalability.
  • Benchmarked Star-CDP against leading parsimony methods (PAUP*, Startle-NNI) using simulations and mouse lung adenocarcinoma data.

Main Results:

  • Star-CDP demonstrated comparable or higher accuracy (f1-score) than existing methods, especially with limited phylogenetic signals.
  • Achieved the lowest SHP score on lung adenocarcinoma data, with performance comparable to established methods for metastatic reseeding.
  • Star-CDP offers polynomial-time scalability, outperforming Startle-ILP for larger cell numbers.

Conclusions:

  • Star-CDP provides a scalable and accurate solution for cell lineage tree reconstruction in CRISPR-based lineage tracing.
  • The method effectively models CRISPR mutation properties and improves phylogenetic signal consistency.
  • Star-CDP represents a significant advancement for studying complex biological processes at the cellular level.