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Updated: Jan 25, 2026
11:15
Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
Published on: September 20, 2016
25.0K
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
Daniele Ramazzotti1, Alex Graudenzi2,3, Luca De Sano4
1Department of Pathology, Stanford University, Palo Alto, 94305, CA, USA.
BMC Bioinformatics
|April 27, 2019
Summary
TRaIT is a new computational framework for reconstructing tumor evolution. It accurately models somatic alterations from both single-cell and multi-region sequencing data, improving upon existing methods.
Area of Science:
- Computational biology
- Cancer genomics
- Evolutionary modeling
Background:
- Numerous algorithms reconstruct tumor evolution from genomic data.
- Existing methods typically analyze either bulk multi-region or single-cell sequencing data, but not both.
- A unified approach for diverse cancer sequencing data is lacking.
Purpose of the Study:
- To introduce TRaIT, a novel computational framework for inferring tumor evolutionary models.
- To enable the analysis of both multi-region and single-cell sequencing data within a single statistical framework.
- To develop expressive models capturing complex evolutionary phenomena in tumors.
Main Methods:
- TRaIT infers mutational graphs to model somatic alteration accumulation.
- It employs a unified statistical framework for diverse sequencing data types.
- The framework is designed for accuracy, robustness, and computational efficiency.
Main Results:
- TRaIT successfully supports both multi-region and single-cell sequencing data.
- It generates expressive models capturing complex tumor evolutionary dynamics.
- TRaIT demonstrates improved accuracy and robustness compared to existing methods.
Conclusions:
- TRaIT provides accurate and reliable models of single-tumor evolution.
- The framework quantifies intra-tumor heterogeneity effectively.
- TRaIT facilitates the generation of testable experimental hypotheses in cancer research.
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