An improved approximation algorithm for the reversal and transposition distance considering gene order and intergenic sizes

Algorithms for Molecular Biology

Klairton L. Brito¹  Zanoni Dias¹  Alexsandro O. Alexandrino¹  Andre R. Oliveira¹  Ulisses Dias² 

[1] Institute of Computing, University of Campinas, 1251 Albert Einstein Ave., 13083-852, Campinas, Brazil;School of Technology, University of Campinas, 1888 Paschoal Marmo St., 13484-332, Limeira, Brazil;

关键词: Genome rearrangement events;    Intergenic regions;    Approximation algorithms;   

DOI  :  10.1186/s13015-021-00203-7

来源: Springer

【 摘 要 】

BackgroundIn the comparative genomics field, one of the goals is to estimate a sequence of genetic changes capable of transforming a genome into another. Genome rearrangement events are mutations that can alter the genetic content or the arrangement of elements from the genome. Reversal and transposition are two of the most studied genome rearrangement events. A reversal inverts a segment of a genome while a transposition swaps two consecutive segments. Initial studies in the area considered only the order of the genes. Recent works have incorporated other genetic information in the model. In particular, the information regarding the size of intergenic regions, which are structures between each pair of genes and in the extremities of a linear genome.Results and conclusionsIn this work, we investigate the sorting by intergenic reversals and transpositions problem on genomes sharing the same set of genes, considering the cases where the orientation of genes is known and unknown. Besides, we explored a variant of the problem, which generalizes the transposition event. As a result, we present an approximation algorithm that guarantees an approximation factor of 4 for both cases considering the reversal and transposition (classic definition) events, an improvement from the 4.5-approximation previously known for the scenario where the orientation of the genes is unknown. We also present a 3-approximation algorithm by incorporating the generalized transposition event, and we propose a greedy strategy to improve the performance of the algorithms. We performed practical tests adopting simulated data which indicated that the algorithms, in both cases, tend to perform better when compared with the best-known algorithms for the problem. Lastly, we conducted experiments using real genomes to demonstrate the applicability of the algorithms.

【 授权许可】

CC BY