Gap in Seattle Hub lineage tracing: Computational Lineage Reconstruction and Uncertainty

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Composite

81%

Novelty

78%

Mechanistic

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Druggability

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Priority

81%

Importance

78%

Tractability

80%

Market price

50%

Description

Expose uncertainty, missing edits, convergent scars, and model assumptions as first-class outputs in lineage reconstruction pipelines. Boundary domains: machine-learning, phylogenetics. Representative papers: A Bayesian phylodynamic inference framework for single-cell CRISPR/Cas9 lineage tracing barcode data with dependent target sites.

Resolution criteria

Resolution requires: (1) Lineage reconstruction pipeline with uncertainty quantification for >=90% of tree edges; (2) Detection of >=95% of simulated convergent scars with FDR <=5%; (3) Benchmark dataset with ground truth for >=5 scenarios; (4) KG edges capturing reconstruction confidence. Deliverable: pipeline + benchmark dataset.

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