ArticleGenome biology2017
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models.
Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.
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Who cites it
94 citing papers in PubMed.
- Phylogenetic and phylodynamic approaches to understanding the evolution of non-small cell lung cancer using single-cell sequencing.Oncogene · 2026Article
- Statistical Inconsistency of Error-correction Objectives for Perfect Phylogenies.bioRxiv : the preprint server for biology · 2026Article
- LeafRank: A phylodynamic framework for inferring relative fitness from single-cell phylogenies in chromosomally unstable tumors.bioRxiv : the preprint server for biology · 2026Article
- A comparison between CARLIN and DNA Typewriter in CRISPR-mediated lineage tracing.BMC bioinformatics · 2026Article
- The Use of Single-Cell Mitochondrial DNA SNP Combinations for Distinguishing Organ-Specific Cell Types.Cells · 2026Article
- Single-cell genomic analysis of cancer cells from one treatment-naïve patient with metastatic prostate cancer.BMC genomic data · 2026Article
- Single-cell omics data-driven decoding of tumor clonal evolution through reinforcement learning.Genome medicine · 2026Article
- Estimating the Growth Rate of a Birth and Death Process Using data From a Small Sample.Bulletin of mathematical biology · 2026Article
- Canopy2: Tumor Phylogeny Inference by Bulk DNA and Single-Cell RNA Sequencing.Statistics in biosciences · 2026Article
- De Novo Detection of Clonal Structure and Evolution in Single-Cell and Spatial Transcriptomes.International journal of molecular sciences · 2025Article
- DelSIEVE: cell phylogeny modeling of single nucleotide variants and deletions from single-cell DNA sequencing data.Genome biology · 2025Article
- 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
- Review
- Computational strategies in tumor phylogenetics: evaluating multimodal integration and methodological trade-offs across study designs.Bioinformatics advances · 2025Review
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Simulation of CRISPR-Cas9 editing on evolving barcode and accuracy of lineage tracing.Scientific reports · 2024Article
- A Probabilistic Approach to Estimate the Temporal Order of Pathway Mutations Accounting for Intra-Tumor Heterogeneity.Cancers · 2024Article
- Reconstructing phylogenetic trees from genome-wide somatic mutations in clonal samples.Nature protocols · 2024Review
- Resolving tumor evolution: a phylogenetic approach.Journal of the National Cancer Center · 2024Review
- Scuphr: A probabilistic framework for cell lineage tree reconstruction.PLoS computational biology · 2024Article
34 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.
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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.