• Corpus ID: 53215187

Efficient Projection onto the Perfect Phylogeny Model

  title={Efficient Projection onto the Perfect Phylogeny Model},
  author={Bei Jia and Surjyendu Ray and Sam Safavi and Jos{\'e} Bento},
Several algorithms build on the perfect phylogeny model to infer evolutionary trees. This problem is particularly hard when evolutionary trees are inferred from the fraction of genomes that have mutations in different positions, across different samples. Existing algorithms might do extensive searches over the space of possible trees. At the center of these algorithms is a projection problem that assigns a fitness cost to phylogenetic trees. In order to perform a wide search over the space of… 

Figures from this paper

Reconstructing complex cancer evolutionary histories from multiple bulk DNA samples using Pairtree
Pairtree uses DNA sequencing data from many samples of the same cancer to reconstruct a cancer’s evolutionary history with much greater detail and accuracy than previously possible.
Pairtree: fast reconstruction of cancer evolutionary history using pairwise mutation relationships
Pairtree is a new algorithm for constructing evolutionary trees that reveal relationships between genetically distinct cell subpopulations composing a patient9s cancer, and produces a useful visual representation of the degree of support underlying evolutionary relationships present in the user9s data, allowing users to make accurate inferences from its results.


Efficient algorithms for inferring evolutionary trees
These problems of inferring the evolutionary history of n objects, either from present characters of the objects or from several partial estimates of their evolutionary history, can be solved by graph theoretic methods in linear time, which is time optimal, and which is a significant improvement over existing methods.
When and How the Perfect Phylogeny Model Explains Evolution
It is shown that, in this setting, some graph-theoretical notions can provide a characterization of the relationships between characters, playing a crucial role in developing algorithmic solutions to the problem of reconstructing a maximum parsimony tree.
Tumor phylogeny inference using tree-constrained importance sampling
On real data from a chronic lymphocytic leukemia (CLL) patient, it is demonstrated on simulated data that PASTRI outperforms other cancer phylogeny algorithms in terms of runtime and accuracy, and a simple linear phylogeny better explains the data the complex branching phylogeny that was previously reported.
The Perfect Phylogeny Problem
This work is concerned here with taxa described by the states they exhibit on a set of characters, and assumes that the taxa descend from a common ancestor where all characters are absent.
A Linear-Time Algorithm for the Perfect Phylogeny Haplotyping (PPH) Problem
This paper solves the open problem of finding a linear-time solution to the Perfect Phylogeny Haplotyping problem, giving a practical, deterministiclinear-time algorithm based on a simple data structure and simple operations on it.
Reconstruction of clonal trees and tumor composition from multi-sample sequencing data
The problem of reconstructing the clonal evolution of a tumor using single-nucleotide mutations as the variant allele frequency (VAF) factorization problem is formalized and a combinatorial characterization of the solutions is derived, showing that the problem is NP-complete.
Inferring clonal evolution of tumors from single nucleotide somatic mutations
A new statistical model, PhyloSub, is introduced that infers the phylogeny and genotype of the major subclonal lineages represented in the population of cancer cells and can be applied to frequencies of any “binary” somatic mutation, including SNVs as well as small insertions and deletions.
Proximal Algorithms
The many different interpretations of proximal operators and algorithms are discussed, their connections to many other topics in optimization and applied mathematics are described, some popular algorithms are surveyed, and a large number of examples of proxiesimal operators that commonly arise in practice are provided.
Fast and scalable inference of multi-sample cancer lineages
A novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples by using variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples.