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Bioinformatics. 2015 Jun 15;31(12):i62-70. doi: 10.1093/bioinformatics/btv261.

Reconstruction of clonal trees and tumor composition from multi-sample sequencing data.

Author information

1
Center for Computational Molecular Biology and Department of Computer Science, Brown University, Providence, RI 02912, USA.

Abstract

MOTIVATION:

DNA sequencing of multiple samples from the same tumor provides data to analyze the process of clonal evolution in the population of cells that give rise to a tumor.

RESULTS:

We formalize the problem of reconstructing the clonal evolution of a tumor using single-nucleotide mutations as the variant allele frequency (VAF) factorization problem. We derive a combinatorial characterization of the solutions to this problem and show that the problem is NP-complete. We derive an integer linear programming solution to the VAF factorization problem in the case of error-free data and extend this solution to real data with a probabilistic model for errors. The resulting AncesTree algorithm is better able to identify ancestral relationships between individual mutations than existing approaches, particularly in ultra-deep sequencing data when high read counts for mutations yield high confidence VAFs.

AVAILABILITY AND IMPLEMENTATION:

An implementation of AncesTree is available at: http://compbio.cs.brown.edu/software.

PMID:
26072510
PMCID:
PMC4542783
DOI:
10.1093/bioinformatics/btv261
[Indexed for MEDLINE]
Free PMC Article

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