Scale-Free Spanning Trees and Their Application in Genomic Epidemiology

We study the algorithmic problem of finding the most "scale-free-like"spanning tree of a connected graph. This problem is motivated by the fundamental problem of genomic epidemiology: given viral genomes sampled from infected individuals, reconstruct the transmission network ("who inf...

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Bibliographic Details
Main Authors: Kaibel, V. (Author), Kukharenko, K. (Author), Orlovich, Y. (Author), Skums, P. (Author)
Format: Article
Language:English
Published: Mary Ann Liebert Inc. 2021
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Online Access:View Fulltext in Publisher
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Summary:We study the algorithmic problem of finding the most "scale-free-like"spanning tree of a connected graph. This problem is motivated by the fundamental problem of genomic epidemiology: given viral genomes sampled from infected individuals, reconstruct the transmission network ("who infected whom"). We use two possible objective functions for this problem and introduce the corresponding algorithmic problems termed m-SF (-scale free) and s-SF Spanning Tree problems. We prove that those problems are APX- and NP-hard, respectively, even in the classes of cubic and bipartite graphs. We propose two integer linear programming (ILP) formulations for the s-SF Spanning Tree problem, and experimentally assess its performance using simulated and experimental data. In particular, we demonstrate that the ILP-based approach allows for accurate reconstruction of transmission histories of several hepatitis C outbreaks. © Yury Orlovich, et al., 2021. Published by Mary Ann Liebert, Inc. 2021.
ISBN:10665277 (ISSN)
DOI:10.1089/cmb.2020.0500