Conifer

Conifer infers clonal composition and reconstructs clonal evolutionary trees by integrating bulk and single-cell sequencing data to resolve tumor heterogeneity.

Key Features:

• Integration of Data Types: Combines bulk sequencing variant allele frequencies (VAFs) with single-cell sequencing branching event data to leverage complementary information.
• Enhanced Clone Identification: Integrates VAFs from bulk sequencing with branching information from single-cell sequencing to improve accuracy of clone identification by resolving clones with similar prevalence and clarifying mutation timing.
• Clonal Tree Inference: Constructs clonal trees depicting evolutionary relationships and uses bulk data to refine the temporal order of mutations inferred from single-cell data, reducing ambiguity in inferred clonal history.

Scientific Applications:

• Benchmarking on simulated data: Validated on simulated datasets demonstrating improved accuracy in clone identification and clonal tree inference compared to existing methods.
• Analysis of real cancer sequencing datasets: Produces evolutionary trees that are consistent with both bulk and single-cell sequencing information in real cancer datasets.
• Clinical research on tumor heterogeneity: Enables study of tumor heterogeneity and clonal evolution to inform personalized treatment strategies.

Methodology:

Integrates bulk sequencing VAFs and single-cell branching event data to infer clones and construct clonal trees, using bulk VAFs to refine the temporal ordering of mutations inferred from single-cell data.