Erfan Sadeqi Azer1, Farid Rashidi Mehrabadi1,2, Salem Malikić1, Xuan Cindy Li2,3, Osnat Bartok4, Kevin Litchfield5,6, Ronen Levy4, Yardena Samuels4, Alejandro A Schäffer2, E Michael Gertz2, Chi-Ping Day7, Eva Pérez-Guijarro7, Kerrie Marie7, Maxwell P Lee7, Glenn Merlino7, Funda Ergun1, S Cenk Sahinalp2. 1. Department of Computer Science, Indiana University, Bloomington, IN 47408, USA. 2. Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 3. Program in Computational Biology, Bioinformatics and Genomics, University of Maryland, College Park, MD 20742, USA. 4. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel. 5. Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London NW1 1AT, UK. 6. Cancer Research UK Lung Cancer Centre of Excellence London, University College London Cancer Institute, London WC1E 6DD, UK. 7. Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Abstract
MOTIVATION: Recent advances in single-cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program, or a constraint satisfaction program, which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain or alternative heuristics not only offer no such guarantee, but also are not faster in practice. As a result, novel methods that can scale up to handle the size and noise characteristics of emerging SCS data are highly desirable to fully utilize this technology. RESULTS: We introduce PhISCS-BnB (phylogeny inference using SCS via branch and bound), a branch and bound algorithm to compute the most likely perfect phylogeny on an input genotype matrix extracted from an SCS dataset. PhISCS-BnB not only offers an optimality guarantee, but is also 10-100 times faster than the best available methods on simulated tumor SCS data. We also applied PhISCS-BnB on a recently published large melanoma dataset derived from the sublineages of a cell line involving 20 clones with 2367 mutations, which returned the optimal tumor phylogeny in <4 h. The resulting phylogeny agrees with and extends the published results by providing a more detailed picture on the clonal evolution of the tumor. AVAILABILITY AND IMPLEMENTATION: https://github.com/algo-cancer/PhISCS-BnB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2020.
MOTIVATION: Recent advances in single-cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program, or a constraint satisfaction program, which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain or alternative heuristics not only offer no such guarantee, but also are not faster in practice. As a result, novel methods that can scale up to handle the size and noise characteristics of emerging SCS data are highly desirable to fully utilize this technology. RESULTS: We introduce PhISCS-BnB (phylogeny inference using SCS via branch and bound), a branch and bound algorithm to compute the most likely perfect phylogeny on an input genotype matrix extracted from an SCS dataset. PhISCS-BnB not only offers an optimality guarantee, but is also 10-100 times faster than the best available methods on simulated tumor SCS data. We also applied PhISCS-BnB on a recently published large melanoma dataset derived from the sublineages of a cell line involving 20 clones with 2367 mutations, which returned the optimal tumor phylogeny in <4 h. The resulting phylogeny agrees with and extends the published results by providing a more detailed picture on the clonal evolution of the tumor. AVAILABILITY AND IMPLEMENTATION: https://github.com/algo-cancer/PhISCS-BnB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2020.
Authors: Sabine Mueller; Payal Jain; Winnie S Liang; Lindsay Kilburn; Cassie Kline; Nalin Gupta; Eshini Panditharatna; Suresh N Magge; Bo Zhang; Yuankun Zhu; John R Crawford; Anu Banerjee; Kellie Nazemi; Roger J Packer; Claudia K Petritsch; Nathalene Truffaux; Alison Roos; Sara Nasser; Joanna J Phillips; David Solomon; Annette Molinaro; Angela J Waanders; Sara A Byron; Michael E Berens; John Kuhn; Javad Nazarian; Michael Prados; Adam C Resnick Journal: Int J Cancer Date: 2019-04-03 Impact factor: 7.396
Authors: Amit G Deshwar; Shankar Vembu; Christina K Yung; Gun Ho Jang; Lincoln Stein; Quaid Morris Journal: Genome Biol Date: 2015-02-13 Impact factor: 13.583
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