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Simultaneous Reconstruction of Duplication Episodes and Gene-Species Mappings

Authors Paweł Górecki , Natalia Rutecka, Agnieszka Mykowiecka , Jarosław Paszek

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Author Details

Paweł Górecki
  • Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland
Natalia Rutecka
  • Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland
Agnieszka Mykowiecka
  • Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland
Jarosław Paszek
  • Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland

Funding

  • Górecki, Paweł: National Science Centre grant nr 2017/27/B/ST6/02720.
  • Rutecka, Natalia: National Science Centre grant nr 2017/27/B/ST6/02720.
  • Mykowiecka, Agnieszka: National Science Centre grant nr 2017/27/B/ST6/02720.
  • Paszek, Jarosław: National Science Centre grant nr 2020/39/D/ST6/03321.

Cite AsGet BibTex

Paweł Górecki, Natalia Rutecka, Agnieszka Mykowiecka, and Jarosław Paszek. Simultaneous Reconstruction of Duplication Episodes and Gene-Species Mappings. In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 6:1-6:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.WABI.2023.6

Abstract

We present a novel problem, called MetaEC, which aims to infer gene-species assignments in a collection of gene trees with missing labels by minimizing the size of duplication episode clustering (EC). This problem is particularly relevant in metagenomics, where incomplete data often poses a challenge in the accurate reconstruction of gene histories. To solve MetaEC, we propose a polynomial time dynamic programming (DP) formulation that verifies the existence of a set of duplication episodes from a predefined set of episode candidates. We then demonstrate how to use DP to design an algorithm that solves MetaEC. Although the algorithm is exponential in the worst case, we introduce a heuristic modification of the algorithm that provides a solution with the knowledge that it is exact. To evaluate our method, we perform two computational experiments on simulated and empirical data containing whole genome duplication events, showing that our algorithm is able to accurately infer the corresponding events.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Combinatorial optimization
  • Applied computing → Computational genomics
Keywords
  • Genomic Duplication
  • Gene-Species Mapping
  • Duplication Episode
  • Gene Tree
  • Species Tree

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Supplementary Materials

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