ArticleBMC bioinformatics2020
PyClone-VI: scalable inference of clonal population structures using whole genome data.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
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93 citing papers in PubMed.
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- Splicing-associated network PAK1-CLK1/4-SRRM1 is a vulnerability to overcome chemoresistance in human and mouse acute myeloid leukemia.Science translational medicine · 2026Article
- Patient-derived organoids across cancers reveal conserved tumor heterogeneity and actionable therapeutic vulnerabilities.Science advances · 2026Article
- Divergent clonal evolution and tumor microenvironment remodeling shape gastric cancer peritoneal metastasis.Communications biology · 2026Article
- Metapipeline-DNA: A comprehensive germline and somatic genomics Nextflow pipeline.Cell reports methods · 2026Article
- Cancer evolution and multi-omic profile of relapsed colorectal liver metastases after treatment.Genome medicine · 2026Article
- Discover personalized drug opportunity and optimized tumor-infiltrating lymphocyte therapy for rare cancer: an organoid-based whole-journey clinical mapping study in a neuroendocrine cancer patient.Cellular oncology (Dordrecht, Netherlands) · 2026Article
- Multi-omic analysis identifies a multi-step pathology in a case of multiple chorangioma syndrome in monochorionic twins.Orphanet journal of rare diseases · 2026Article
- Full spatio-temporal analyses of migration and colonization in evolution-dense 3D mapping of cancer metastases provides new insights.Molecular biology and evolution · 2026Article
- Deciphering brain metastasis in epithelial ovarian cancer: multimodal analysis and potential biomarkers.NPJ precision oncology · 2026Article
- AI/ML-Driven Gene Analysis: New Perspectives on Variant Calling in Normal Human Tissue Using scRNA-seq Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
33 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
backgroundAt diagnosis tumours are typically composed of a mixture of genomically distinct malignant cell populations. Bulk sequencing of tumour samples coupled with computational deconvolution can be used to identify these populations and study cancer evolution. Existing computational methods for populations deconvolution are slow and/or potentially inaccurate when applied to large datasets generated by whole genome sequencing data.
resultsWe describe PyClone-VI, a computationally efficient Bayesian statistical method for inferring the clonal population structure of cancers. We demonstrate the utility of the method by analyzing data from 1717 patients from PCAWG study and 100 patients from the TRACERx study.
conclusionsOur proposed method is 10-100× times faster than existing methods, while providing results which are as accurate. Software implementing our method is freely available https://github.com/Roth-Lab/pyclone-vi .
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