Sorting Permutations by Intergenic Operations

Oliveira, André Rodrigues; Jean, Géraldine; Fertin, Guillaume; Brito, Klairton Lima; Dias, Ulisses; Dias, Zanoni

doi:10.1109/tcbb.2021.3077418

Cited by 13 publications

(24 citation statements)

References 27 publications

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“…al. [ 17 ]. We hereafter refer to the 3-approximation algorithm [ 17 ], 2.5-approximation algorithm [ 17 ], Algorithm 3, and Algorithm 4 by 3 , 2.5 , 4 , and 3 , respectively.…”

Section: Practical Resultsmentioning

confidence: 99%

“…[ 17 ]. We hereafter refer to the 3-approximation algorithm [ 17 ], 2.5-approximation algorithm [ 17 ], Algorithm 3, and Algorithm 4 by 3 , 2.5 , 4 , and 3 , respectively. The results of the 4 and 3 algorithms were obtained adopting the greedy strategy.…”

Section: Practical Resultsmentioning

confidence: 99%

“…al. [ 17 ], and they have the following characteristics: Each dataset started with 100 target genomes , such that has 100 elements, and each value of , with , being chosen randomly and uniformly in interval [0..100]. After that, from each source genome were generated 100 instances by applying: DB SIRIT : d random operations of reversals and transpositions (being 50% of each) in each source genome .…”

Section: Practical Resultsmentioning

confidence: 99%

“…Considering the reversal event, we have the sorting by intergenic reversals problem, which is NP-hard on signed and unsigned permutations [ 15 , 16 ] and the best algorithms have approximation factors of 2 [ 15 ] and 4 [ 16 ], respectively. The sorting by intergenic transpositions is NP-hard and the best algorithm has an approximation factor of 3.5 [ 17 ]. The sorting by intergenic reversals and transpositions (SbIRT) is NP-hard on signed and unsigned permutations [ 16 , 17 ] and the best algorithms have the approximation factors of 3 [ 17 ] and 4.5 [ 16 ], respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The sorting by intergenic transpositions is NP-hard and the best algorithm has an approximation factor of 3.5 [ 17 ]. The sorting by intergenic reversals and transpositions (SbIRT) is NP-hard on signed and unsigned permutations [ 16 , 17 ] and the best algorithms have the approximation factors of 3 [ 17 ] and 4.5 [ 16 ], respectively. The problem with the generalized definition of the transposition event on signed permutations has an approximation algorithm with a factor of 2.5 [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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

Brito

Oliveira

Alexandrino

et al. 2021

Algorithms Mol Biol

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Background In 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 conclusions In 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.

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“…al. [ 17 ]. We hereafter refer to the 3-approximation algorithm [ 17 ], 2.5-approximation algorithm [ 17 ], Algorithm 3, and Algorithm 4 by 3 , 2.5 , 4 , and 3 , respectively.…”

Section: Practical Resultsmentioning

confidence: 99%