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Theoretical Computer Science
Article . 2025 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2024
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Pairwise rearrangement is fixed-parameter tractable in the Single Cut-and-Join model

Authors: Lora Bailey; Heather C. Smith Blake; Garner Cochran; Nathan Fox; Michael Levet; Reem Mahmoud; Inne Singgih; +2 Authors

Pairwise rearrangement is fixed-parameter tractable in the Single Cut-and-Join model

Abstract

Genome rearrangement is a common model for molecular evolution. In this paper, we consider the Pairwise Rearrangement problem, which takes as input two genomes and asks for the number of minimum-length sequences of permissible operations transforming the first genome into the second. In the Single Cut-and-Join model (Bergeron, Medvedev, & Stoye, J. Comput. Biol. 2010), Pairwise Rearrangement is $\#\textsf{P}$-complete (Bailey, et. al., COCOON 2023), which implies that exact sampling is intractable. In order to cope with this intractability, we investigate the parameterized complexity of this problem. We exhibit a fixed-parameter tractable algorithm with respect to the number of components in the adjacency graph that are not cycles of length $2$ or paths of length $1$. As a consequence, we obtain that Pairwise Rearrangement in the Single Cut-and-Join model is fixed-parameter tractable by distance. Our results suggest that the number of nontrivial components in the adjacency graph serves as the key obstacle for efficient sampling.

Full version of paper that appeared in SWAT 2024; arXiv admin note: text overlap with arXiv:2305.01851

Country
Germany
Keywords

Genomics (q-bio.GN), FOS: Computer and information sciences, Computational Complexity, Genome Rearrangement, 004, Phylogenetics, 92-08, 92D10, 92D20, 68Q17, FOS: Biological sciences, Computer Science - Data Structures and Algorithms, FOS: Mathematics, Mathematics - Combinatorics, Quantitative Biology - Genomics, Data Structures and Algorithms (cs.DS), Combinatorics (math.CO), F.2.2, Single Cut-and-Join, ddc: ddc:004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
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