ArticleBioinformatics (Oxford, England)2018
Deconvolution and phylogeny inference of structural variations in tumor genomic samples.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 61 citations in OpenAlex.
- Theoretical estimates on the expected number of mutations needed to reconstruct clonal lineage trees.Bioinformatics advances · 2026Article
- 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
- Deconvolution and Phylogeny Inference of Diverse Variant Types Integrating Bulk DNA-seq with Single-cell RNA-seq.bioRxiv : the preprint server for biology · 2025Article
- Cancer phylogenetic inference using copy number alterations detected from DNA sequencing data.Cancer pathogenesis and therapy · 2025Review
- Computational strategies in tumor phylogenetics: evaluating multimodal integration and methodological trade-offs across study designs.Bioinformatics advances · 2025Review
- Deconvolution and phylogeny inference of diverse variant types integrating bulk DNA-seq with single-cell RNA-seq.Bioinformatics advances · 2025Article
- Computationally reconstructing the evolution of cancer progression risk.bioRxiv : the preprint server for biology · 2024Article
- ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model.Genome biology · 2023Article
- ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model.bioRxiv : the preprint server for biology · 2023Article
- bmVAE: a variational autoencoder method for clustering single-cell mutation data.Bioinformatics (Oxford, England) · 2023Article
- Reconstructing tumor clonal lineage trees incorporating single-nucleotide variants, copy number alterations and structural variations.Bioinformatics (Oxford, England) · 2022Article
- CONET: copy number event tree model of evolutionary tumor history for single-cell data.Genome biology · 2022Article
- Computational Approaches for the Investigation of Intra-tumor Heterogeneity and Clonal Evolution from Bulk Sequencing Data in Precision Oncology Applications.Advances in experimental medicine and biology · 2022Review
- SCClone: Accurate Clustering of Tumor Single-Cell DNA Sequencing Data.Frontiers in genetics · 2022Article
- Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data.Bioinformatics (Oxford, England) · 2021Article
- PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity.BMC bioinformatics · 2021Article
- The Bourque distances for mutation trees of cancers.Algorithms for molecular biology : AMB · 2021Article
- Integrative reconstruction of cancer genome karyotypes using InfoGenomeR.Nature communications · 2021Article
- Assessing the contribution of tumor mutational phenotypes to cancer progression risk.PLoS computational biology · 2021Article
- A practical guide to cancer subclonal reconstruction from DNA sequencing.Nature methods · 2021Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Motivation: Phylogenetic reconstruction of tumor evolution has emerged as a crucial tool for making sense of the complexity of emerging cancer genomic datasets. Despite the growing use of phylogenetics in cancer studies, though, the field has only slowly adapted to many ways that tumor evolution differs from classic species evolution. One crucial question in that regard is how to handle inference of structural variations (SVs), which are a major mechanism of evolution in cancers but have been largely neglected in tumor phylogenetics to date, in part due to the challenges of reliably detecting and typing SVs and interpreting them phylogenetically. Results: We present a novel method for reconstructing evolutionary trajectories of SVs from bulk whole-genome sequence data via joint deconvolution and phylogenetics, to infer clonal sub-populations and reconstruct their ancestry. We establish a novel likelihood model for joint deconvolution and phylogenetic inference on bulk SV data and formulate an associated optimization algorithm. We demonstrate the approach to be efficient and accurate for realistic scenarios of SV mutation on simulated data. Application to breast cancer genomic data from The Cancer Genome Atlas shows it to be practical and effective at reconstructing features of SV-driven evolution in single tumors. Availability and implementation: Python source code and associated documentation are available at https://github.com/jaebird123/tusv.
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Registered trials
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.