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A New Paradigm for Identifying Reconciliation-Scenario Altering Mutations Conferring Environmental Adaptation

Authors Roni Zoller, Meirav Zehavi, Michal Ziv-Ukelson



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Roni Zoller
  • Ben Gurion University of the Negev, Israel
Meirav Zehavi
  • Ben Gurion University of the Negev, Israel
Michal Ziv-Ukelson
  • Ben Gurion University of the Negev, Israel

Acknowledgements

We thank the anonymous WABI reviewers for very helpful comments.

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Roni Zoller, Meirav Zehavi, and Michal Ziv-Ukelson. A New Paradigm for Identifying Reconciliation-Scenario Altering Mutations Conferring Environmental Adaptation. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 9:1-9:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.WABI.2019.9

Abstract

An important goal in microbial computational genomics is to identify crucial events in the evolution of a gene that severely alter the duplication, loss and mobilization patterns of the gene within the genomes in which it disseminates. In this paper, we formalize this microbiological goal as a new pattern-matching problem in the domain of Gene tree and Species tree reconciliation, denoted "Reconciliation-Scenario Altering Mutation (RSAM) Discovery". We propose an O(m * n * k) time algorithm to solve this new problem, where m and n are the number of vertices of the input Gene tree and Species tree, respectively, and k is a user-specified parameter that bounds from above the number of optimal solutions of interest. The algorithm first constructs a hypergraph representing the k highest scoring reconciliation scenarios between the given Gene tree and Species tree, and then interrogates this hypergraph for subtrees matching a pre-specified RSAM Pattern. Our algorithm is optimal in the sense that the number of hypernodes in the hypergraph can be lower bounded by Omega(m * n * k). We implement the new algorithm as a tool, denoted RSAM-finder, and demonstrate its application to the identification of RSAMs in toxins and drug resistance elements across a dataset spanning hundreds of species.

Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
Keywords
  • Gene tree
  • Species tree
  • Reconciliation

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