Paper Info
Title
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data.
Abstract
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. 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. 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.
Year
DOI
Venue
2019
10.1186/s12859-019-2795-4
BMC Bioinformatics
Keywords
Field
DocType
Single-tumour evolution, Single-cell sequencing, Multi-region sequencing, Mutational graphs, Cancer evolution, Tumour phylogeny
Graph,Biology,Trait,Robustness (computer science),Single cell sequencing,Data type,DNA sequencing,Computational biology,Genetics,DNA microarray,Computational complexity theory
Journal
Volume
Issue
ISSN
20
1
1471-2105
Citations 
PageRank 
References 
1
0.35
0
Authors
5
Name
Order
Citations
PageRank
Daniele Ramazzotti13610.54
Alex Graudenzi29017.99
luca de sano331.07
M. Antoniotti4967.36
Giulio Caravagna515616.46