ArticlePLoS computational biology2023
Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Prognostic factors and outcomes in pediatric acute myeloid leukemia: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2025Pooled it
- The glioblastoma ecosystem: clonal evolution, heterogeneity, and therapeutic resistance.Frontiers in cell and developmental biology · 2026Review
- Canopy2: Tumor Phylogeny Inference by Bulk DNA and Single-Cell RNA Sequencing.Statistics in biosciences · 2026Article
- Fast tumor phylogeny regression via tree-structured dual dynamic programming.Bioinformatics (Oxford, England) · 2025Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.Genome biology · 2025Article
- Isotype-aware inference of B cell clonal lineage trees from single-cell sequencing data.Cell genomics · 2024Article
- Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data.Genome research · 2023Article
- ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to also analyze the evolution of SNVs from the same set of tumor cells. We present Phertilizer, a method to infer a clonal tree from ultra-low coverage scDNA-seq data of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance of Phertilizer on simulated data as well as on two real datasets, finding that Phertilizer effectively utilizes the copy-number signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods.
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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.