Minimizing genomic duplication episodes

Paszek, Jarosław; Tiuryn, Jerzy; Górecki, Paweł

doi:10.1016/j.compbiolchem.2020.107260

Cited by 3 publications

(5 citation statements)

References 45 publications

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“…Here, EC ( G, S ) as depicted in the three rightmost scenarios with episode sets for and for and . The example trees are partially adapted from [ 24 ] …”

Section: Basic Definitionsmentioning

confidence: 99%

“…Alternative approaches include generalization to unrooted gene trees; however, such approaches are often computationally complex [ 21 , 22 ]. Other approaches include variants of clustering rules that depend on the maximal number of duplication episodes placed in one path [ 23 , 24 ]. A comprehensive analysis of various models is available in [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Unifying duplication episode clustering and gene-species mapping inference

Górecki,

Rutecka,

Mykowiecka

et al. 2024

Algorithms Mol Biol

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We present a novel problem, called MetaEC, which aims to infer gene-species assignments in a collection of partially leaf-labeled gene trees 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. In addition, we design a method to infer distributions of gene-species mappings. 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.

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“…Here, EC ( G, S ) as depicted in the three rightmost scenarios with episode sets for and for and . The example trees are partially adapted from [ 24 ] …”

Section: Basic Definitionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unifying duplication episode clustering and gene-species mapping inference

Górecki,

Rutecka,

Mykowiecka

et al. 2024

Algorithms Mol Biol

Self Cite

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Section: Declarationsmentioning

confidence: 99%

“…Here, EC(G, S) = 3 as indicted in the three rightmost scenarios with episode sets {a, b, abcd} for E5 and {a, abc, abcd} for E6 and E7. The example trees are partially adopted from [24]. Locations of the simulated whole-genome duplication (WGD) events are denoted by Greek letters.…”

Section: Declarationsmentioning

confidence: 99%

“…Alternative approaches include generalization to unrooted gene trees; however, such approaches are often computationally complex [21,22]. Other approaches include variants of clustering rules that depend on the maximal number of duplication episodes placed in one path [23,24]. A comprehensive analysis of various models is available in [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unifying Duplication Episode Clustering andGene-Species Mapping Inference

Górecki,

Rutecka,

Mykowiecka

et al. 2023

Preprint

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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. In addition, we design a method to infer distributions of gene-species mappings. 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.

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