A Real Linear and Parallel Multiple Longest Common Subsequences (MLCS) Algorithm

Li, Yanni; Li, Hui; Duan, Tihua; Wang, Sheng; Wang, Zhi; Yang, Cheng

doi:10.1145/2939672.2939842

Cited by 14 publications

(15 citation statements)

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“…Unfortunately, Yang et al ( 2010 ) is not suitable for the MLCS problem with many sequences due to the large synchronous costs. Recently, Li et al ( 2016b , a ) proposed two algorithms: PTop-MLCS and RLP-MLCS based on dominant points, these algorithms used a novel graph model called Non-redundant Common Subsequence Graph (NCSG) which can greatly reduce the redundant nodes during processing, and adopted a two-passes topological sorting procedure to find the MLCS. The authors claimed that the time and space complexity of their algorithms is linear to the number of nodes in NCSG.…”

Section: Methodsmentioning

confidence: 99%

A Novel Efficient Graph Model for the Multiple Longest Common Subsequences (MLCS) Problem

Zhan

Wang

2017

Front. Genet.

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Searching for the Multiple Longest Common Subsequences (MLCS) of multiple sequences is a classical NP-hard problem, which has been used in many applications. One of the most effective exact approaches for the MLCS problem is based on dominant point graph, which is a kind of directed acyclic graph (DAG). However, the time and space efficiency of the leading dominant point graph based approaches is still unsatisfactory: constructing the dominated point graph used by these approaches requires a huge amount of time and space, which hinders the applications of these approaches to large-scale and long sequences. To address this issue, in this paper, we propose a new time and space efficient graph model called the Leveled-DAG for the MLCS problem. The Leveled-DAG can timely eliminate all the nodes in the graph that cannot contribute to the construction of MLCS during constructing. At any moment, only the current level and some previously generated nodes in the graph need to be kept in memory, which can greatly reduce the memory consumption. Also, the final graph contains only one node in which all of the wanted MLCS are saved, thus, no additional operations for searching the MLCS are needed. The experiments are conducted on real biological sequences with different numbers and lengths respectively, and the proposed algorithm is compared with three state-of-the-art algorithms. The experimental results show that the time and space needed for the Leveled-DAG approach are smaller than those for the compared algorithms especially on large-scale and long sequences.

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Section: Methodsmentioning

confidence: 99%

A Novel Efficient Graph Model for the Multiple Longest Common Subsequences (MLCS) Problem

Zhan

Wang

2017

Front. Genet.

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“…As mining the big sequence data is still a difficult problem [16][17][18][19] . The key challenge is the computational complexity.…”

Section: Similarity Metricsmentioning

confidence: 99%

“…The key challenge is the computational complexity. We have been designing novel algorithms [17,18] to tackle this challenge. Our recent work has proposed several efficient analysis techniques for big sequences, and also developed an automated tool (I-MLCS), which is mainly used in the similarity analysis.…”

Section: Similarity Metricsmentioning

confidence: 99%

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COVID-19 Evolves in Human Hosts

Liu¹,

Liu²,

Cui³

et al. 2020

Preprint

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“…Por más de 40 años se han propuesto diversas alternativas de solución, en el caso particular de colecciones con dos secuencias, las técnicas más implementadas son, Autómatas Finitos y Punto Dominante [?, 3, 5]. Para colecciones de múltiples secuencias, estudios recientes evidencian estrategias conjuntas tales como, Punto Dominante y Paralelismo, entre otros [6,7].…”

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Una aproximación al problema de la Subsecuencia Común más Larga de Múltiples Secuencias usando entropía de Shannon

Sáenz¹

2019

Sel.mat

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Resumen El problema de la Subsecuencia Común más Larga de Múltiples Secuencias (SCLM), ha sido ampliamente estudiado en Ciencias de la Computación por más de 40 años, motivado principalmente por sus diversas aplicaciones en Bioinformática. En este artículo se presenta un algoritmo heurístico que estima una o más soluciones al problema SCLM, utilizando entropía de Shannon como una medida de la información para determinar alineamientos con el mayor numero de coincidencias.

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A Real Linear and Parallel Multiple Longest Common Subsequences (MLCS) Algorithm

Cited by 14 publications

References 14 publications

A Novel Efficient Graph Model for the Multiple Longest Common Subsequences (MLCS) Problem

A Novel Efficient Graph Model for the Multiple Longest Common Subsequences (MLCS) Problem

COVID-19 Evolves in Human Hosts

Una aproximación al problema de la Subsecuencia Común más Larga de Múltiples Secuencias usando entropía de Shannon

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