Proceedings ArticleDOI
Beyond Perfect Phylogeny: Multisample Phylogeny Reconstruction via ILP
Paola Bonizzoni,Simone Ciccolella,Gianluca Della Vedova,Mauricio Soto +3 more
- pp 1-10
TLDR
An experimental analysis shows that the ILP approach is able to explain data that do not fit the perfect phylogeny assumption, thereby allowing multiple losses and gains of mutations, and a number of subpopulations that is smaller than the number of input mutations.Abstract:
Most of the evolutionary history reconstruction approaches are based on the infinite site assumption which is underlying the Perfect Phylogeny model. This is one of the most used models in cancer genomics. Recent results gives a strong evidence that recurrent and back mutations are present in the evolutionary history of tumors[19], thus showing that more general models then the Perfect phylogeny are required. To address this problem we propose a framework based on the notion of Incomplete Perfect Phylogeny. Our framework incorporates losing and gaining mutations, hence including the Dollo and the Camin-Sokal models, and is described with an Integer Linear Programming (ILP) formulation. Our approach generalizes the notion of persistent phylogeny[1] and the ILP approach[14,15] proposed to solve the corresponding phylogeny reconstruction problem on character data. The final goal of our paper is to integrate our approach into an ILP formulation of the problem of reconstructing trees on mixed populations, where the input data consists of the fraction of cells in a set of samples that have a certain mutation. This is a fundamental problem in cancer genomics, where the goal is to study the evolutionary history of a tumor. An experimental analysis shows that our ILP approach is able to explain data that do not fit the perfect phylogeny assumption, thereby allowing (1) multiple losses and gains of mutations, and (2) a number of subpopulations that is smaller than the number of input mutations.read more
Citations
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Journal ArticleDOI
SPhyR: tumor phylogeny estimation from single-cell sequencing data under loss and error.
TL;DR: Single‐cell Phylogeny Reconstruction (SPhyR) is introduced, a method for tumor phylogeny estimation from single‐cell sequencing data that employs the k‐Dollo evolutionary model and outperforms existing methods on simulated data and on a metastatic colorectal cancer.
Journal ArticleDOI
PhISCS: A Combinatorial Approach for Subperfect Tumor Phylogeny Reconstruction via Integrative Use of Single-Cell and Bulk Sequencing Data
Salem Malikic,Farid Rashidi Mehrabadi,Farid Rashidi Mehrabadi,Simone Ciccolella,Simone Ciccolella,Md. Khaledur Rahman,Camir Ricketts,Ehsan Haghshenas,Daniel N. Seidman,Faraz Hach,Faraz Hach,Faraz Hach,Iman Hajirasouliha,S. Cenk Sahinalp +13 more
TL;DR: The resulting method, which is named PhISCS, is the first to integrate SCS and bulk sequencing data while accounting for ISA violating mutations and provides a guarantee of optimality in reported solutions.
Posted ContentDOI
Inferring Cancer Progression from Single-cell Sequencing while Allowing Mutation Losses
Simone Ciccolella,Mauricio Soto Gomez,Murray Patterson,Gianluca Della Vedova,Iman Hajirasouliha,Paola Bonizzoni +5 more
TL;DR: The Simulated Annealing Single-Cell inference (SASC) tool as mentioned in this paper is a new and robust approach based on simulated annealing for the inference of cancer progression from SCS data.
Book
Integer Linear Programming in Computational and Systems Biology: An Entry-Level Text and Course
TL;DR: This paper uses in-depth examples from genomics, phylogenetics, RNA, protein folding, network analysis, cancer, ecology, co-evolution, DNA sequencing, sequence analysis, pedigree and sibling inference, haplotyping, and more, to establish the power of ILP.
References
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Journal ArticleDOI
Clonal evolution in cancer
Mel Greaves,Carlo C. Maley +1 more
TL;DR: The inherently Darwinian character of cancer is the primary reason for this therapeutic failure, but it may also hold the key to more effective control.
Journal ArticleDOI
Generating samples under a Wright-Fisher neutral model of genetic variation.
TL;DR: A Monte Carlo computer program is available to generate samples drawn from a population evolving according to a Wright-Fisher neutral model, and the samples produced can be used to investigate the sampling properties of any sample statistic under these neutral models.