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Parallel protein multiple sequence alignment approaches: a systematic literature review

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Abstract

Multiple sequence alignment approaches refer to algorithmic solutions for the alignment of biological sequences. Since multiple sequence alignment has exponential time complexity when a dynamic programming approach is applied, a substantial number of parallel computing approaches have been implemented in the last two decades to improve their performance. In this paper, we present a systematic literature review of parallel computing approaches applied to multiple sequence alignment algorithms for proteins, published in the open literature from 1988 to 2022; we extracted articles from four scientific databases: ACM Digital Library, IEEE Xplore, Science Direct and SpringerLink, and four journals: Bioinformatics, PLOS Computational Biology, PLOS ONE, and Scientific Reports. Additionally, in order to cover other potential databases and journals, we performed a transversal search through Google Scholar. We conducted a selection process that yielded 106 research articles; then, we analyzed these articles and defined a classification framework. Additionally, we point out some directions and trends for parallel computing approaches for multiple sequence alignment, as well as some unsolved problems.

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Funding

Sergio H. Almanza-Ruiz is receiving a full-time scholarship for his graduate studies from the Mexican National Council for Science and Technology (CONACyT).

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  1. Department of Information Systems, CUCEA - Universidad de Guadalajara, Periférico Norte 799, Zapopan, 45100, Jalisco, Mexico

    Sergio H. Almanza-Ruiz, Arturo Chavoya & Hector A. Duran-Limon

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Almanza-Ruiz, S.H., Chavoya, A. & Duran-Limon, H.A. Parallel protein multiple sequence alignment approaches: a systematic literature review. J Supercomput 79, 1201–1234 (2023). https://doi.org/10.1007/s11227-022-04697-9

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