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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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Author Details

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

Funding

Supported by ISF grants no. 1176/18 and no. 939/18. This work was supported by the Lynn and William Frankel Center for Computer Science.

Acknowledgements

We thank the anonymous WABI reviewers for very helpful comments.

Cite AsGet BibTex

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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Supplementary Materials

References

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