Dataset Information

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data.

ABSTRACT: BACKGROUND:A 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. RESULTS:We 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. CONCLUSIONS:We 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.

SUBMITTER: Ramazzotti D

PROVIDER: S-EPMC6485126 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data.

Ramazzotti Daniele D Graudenzi Alex A De Sano Luca L Antoniotti Marco M Caravagna Giulio G

BMC bioinformatics 20190425 1

<h4>Background</h4>A 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.<h4>Results</h4>We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple type ...[more]

PMID: 31023236

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Dataset Information

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data.

Publications

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data.

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