Super short operations on both gene order and intergenic sizes

Autor(es)

Oliveira, Andre R. ^[1] ; Jean, Geraldine ^[2] ; Fertin, Guillaume ^[2] ; Dias, Ulisses ^[3] ; Dias, Zanoni ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil

^[2] Univ Nantes, UMR CNRS 6004, LS2N, Nantes - France

^[3] Univ Estadual Campinas, Sch Technol, Limeira - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Algorithms for Molecular Biology; v. 14, n. 1 NOV 5 2019.
• Citações Web of Science: 0

Resumo

Background The evolutionary distance between two genomes can be estimated by computing a minimum length sequence of operations, called genome rearrangements, that transform one genome into another. Usually, a genome is modeled as an ordered sequence of genes, and most of the studies in the genome rearrangement literature consist in shaping biological scenarios into mathematical models. For instance, allowing different genome rearrangements operations at the same time, adding constraints to these rearrangements (e.g., each rearrangement can affect at most a given number of genes), considering that a rearrangement implies a cost depending on its length rather than a unit cost, etc. Most of the works, however, have overlooked some important features inside genomes, such as the presence of sequences of nucleotides between genes, called intergenic regions. Results and conclusions In this work, we investigate the problem of computing the distance between two genomes, taking into account both gene order and intergenic sizes. The genome rearrangement operations we consider here are constrained types of reversals and transpositions, called super short reversals (SSRs) and super short transpositions (SSTs), which affect up to two (consecutive) genes. We denote by super short operations (SSOs) any SSR or SST. We show 3-approximation algorithms when the orientation of the genes is not considered when we allow SSRs, SSTs, or SSOs, and 5-approximation algorithms when considering the orientation for either SSRs or SSOs. We also show that these algorithms improve their approximation factors when the input permutation has a higher number of inversions, where the approximation factor decreases from 3 to either 2 or 1.5, and from 5 to either 3 or 2. (AU)

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