Complexity and algorithms for copy-number evolution problems

Background Cancer is an evolutionary process characterized by the accumulation of somatic mutations in a population of cells that form a tumor. One frequent type of mutations is copy number aberrations, which alter the number of copies of genomic regions. The number of copies of each position along...

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Published in:Algorithms for molecular biology Vol. 12; no. 1; pp. 13 - 11
Main Authors: El-Kebir, Mohammed, Raphael, Benjamin J., Shamir, Ron, Sharan, Roded, Zaccaria, Simone, Zehavi, Meirav, Zeira, Ron
Format: Journal Article
Language:English
Published: London BioMed Central 16.05.2017
Springer Nature B.V
BMC
Subjects:
Aberration
Algorithms
Bioinformatics
Biomedical and Life Sciences
Cancer
Cellular and Medical Topics
Children
Chromosomes
Complexity
Computational Biology/Bioinformatics
Computer simulation
Computing time
Copy number
Copy-number variant
Diagnosis
Dynamic programming
Evolutionary algorithms
Genes
Graph theory
Leaves
Life Sciences
Maximum parsimony
Mutation
Phylogenetics
Phylogeny
Physiological
Prognosis
Quality assessment
Selected papers from WABI 2016
Somatic mutation
Somatic mutation
Phylogeny
Maximum parsimony
Copy-number variant
Cancer
ISSN:1748-7188, 1748-7188
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Summary:Background Cancer is an evolutionary process characterized by the accumulation of somatic mutations in a population of cells that form a tumor. One frequent type of mutations is copy number aberrations, which alter the number of copies of genomic regions. The number of copies of each position along a chromosome constitutes the chromosome’s copy-number profile. Understanding how such profiles evolve in cancer can assist in both diagnosis and prognosis. Results We model the evolution of a tumor by segmental deletions and amplifications, and gauge distance from profile a to b by the minimum number of events needed to transform a into b . Given two profiles, our first problem aims to find a parental profile that minimizes the sum of distances to its children. Given k profiles, the second, more general problem, seeks a phylogenetic tree, whose k leaves are labeled by the k given profiles and whose internal vertices are labeled by ancestral profiles such that the sum of edge distances is minimum. Conclusions For the former problem we give a pseudo-polynomial dynamic programming algorithm that is linear in the profile length, and an integer linear program formulation. For the latter problem we show it is NP-hard and give an integer linear program formulation that scales to practical problem instance sizes. We assess the efficiency and quality of our algorithms on simulated instances. Availability https://github.com/raphael-group/CNT-ILP
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ISSN:1748-7188
1748-7188
DOI:10.1186/s13015-017-0103-2