On Variants of the Genome Rearrangement Distance Problem

In comparative genomics, evolutionary distance can be estimated by determining a minimum sequence of genome rearrangements required to transform one genome into another. The length of this sequence is called the rearrangement distance. Traditionally, most studies assumed that compared genomes shared the same set of genes, with only their relative gene order being used for comparison. Recent studies indicate that incorporating intergenic region sizes leads to more accurate distance estimates when analyzing real genomes. Furthermore, they started to consider genomes having a distinct set of genes. In this work, we investigate genome rearrangement problems by incorporating additional information and complexity into the models, in order to achieve more realistic practical results that can benefit several scientific fields.

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Published
2025-07-20
DOI
https://doi.org/10.5753/ctd.2025.7128
Primary Topic
Genome Rearrangement Algorithms
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article
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article

On Variants of the Genome Rearrangement Distance Problem

Alexsandro Oliveira Alexandrino, Zanoni Dias, Ulisses Dias
Genome Rearrangement Algorithms
article

On Variants of the Genome Rearrangement Distance Problem

Alexsandro Oliveira Alexandrino, Zanoni Dias, Ulisses Dias
article en

Abstract

In comparative genomics, evolutionary distance can be estimated by determining a minimum sequence of genome rearrangements required to transform one genome into another. The length of this sequence is called the rearrangement distance. Traditionally, most studies assumed that compared genomes shared the same set of genes, with only their relative gene order being used for comparison. Recent studies indicate that incorporating intergenic region sizes leads to more accurate distance estimates when analyzing real genomes. Furthermore, they started to consider genomes having a distinct set of genes. In this work, we investigate genome rearrangement problems by incorporating additional information and complexity into the models, in order to achieve more realistic practical results that can benefit several scientific fields.

Hospital de Clínicas da Unicamp (BR)
Openalex Percentile: Top 75%
Genome Rearrangement Algorithms
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