ArticleBMC bioinformatics2019
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Somatic mutation phasing and haplotype extension using linked-reads in multiple myeloma.bioRxiv : the preprint server for biology · 2024Article
- Order-of-Mutation Effects on Cancer Progression: Models for Myeloproliferative Neoplasm.Bulletin of mathematical biology · 2024Article
- Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients.Nature communications · 2023Article
- Phylogenetic inference from single-cell RNA-seq data.Scientific reports · 2023Article
- LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution.BMC bioinformatics · 2023Article
- J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments.BMC bioinformatics · 2022Article
- Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines.Nature communications · 2022Article
- Studying the History of Tumor Evolution from Single-Cell Sequencing Data by Exploring the Space of Binary Matrices.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses.Bioinformatics (Oxford, England) · 2021Article
- Multiregion Sequence Analysis to Predict Intratumor Heterogeneity and Clonal Evolution.Methods in molecular biology (Clifton, N.J.) · 2021Article
- gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data.BMC bioinformatics · 2020Article
- Tumor Phylogeny Topology Inference via Deep Learning.iScience · 2020Article
- A multi-labeled tree dissimilarity measure for comparing "clonal trees" of tumor progression.Algorithms for molecular biology : AMB · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types.
resultsWe introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods.
conclusionsWe show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses.
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