ArticleGenome biology2023
ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 25 citations in OpenAlex.
- Long-read sequencing of single cell-derived melanoma sublines reveals divergent and parallel genomic and epigenomic evolutionary trajectories.Nature communications · 2026Article
- Phylogenetic tree inference from single-cell RNA sequencing data with SCITE-RNA.Genome biology · 2026Article
- Dolphyin: a combinatorial algorithm for identifying 1-Dollo phylogenies in cancer.Algorithms for molecular biology : AMB · 2026Article
- Single-cell technologies drive mechanistic insights and therapeutic translation in skeletal system research.iScience · 2026Review
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- Acquired High Tumor Mutational Burden and Activity of Immunotherapy after Targeted Therapy in Microsatellite Stable Colorectal Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Genomic evolution of pancreatic cancer at single-cell resolution.Nature genetics · 2026Article
- Canopy2: Tumor Phylogeny Inference by Bulk DNA and Single-Cell RNA Sequencing.Statistics in biosciences · 2026Article
- Resolving intra-tumor heterogeneity and clonal evolution of core-binding factor acute myeloid leukemia patients with single-cell resolution.Experimental hematology & oncology · 2025Article
- Long-read sequencing of single cell-derived melanoma subclones reveals divergent and parallel genomic and epigenomic evolutionary trajectories.bioRxiv : the preprint server for biology · 2025Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- Sc-TUSV-Ext: Single-Cell Clonal Lineage Inference from Single Nucleotide Variants, Copy Number Alterations, and Structural Variants.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- Bayesian inference of fitness landscapes via tree-structured branching processes.Bioinformatics (Oxford, England) · 2025Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- Article
- Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints.bioRxiv : the preprint server for biology · 2025Article
- A regression based approach to phylogenetic reconstruction from multi-sample bulk DNA sequencing of tumors.PLoS computational biology · 2024Article
- Mechanism of neurodegeneration mediated by clonal inflammatory microglia.bioRxiv : the preprint server for biology · 2024Article
- Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data.Bioinformatics (Oxford, England) · 2024Article
- ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model.Genome biology · 2023Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.