Abstract
Multiple sequence alignment (MSA) is a crucial step in comparative genomics, structural, and functional studies, and phylogeny estimation. Owing to the NP-complete nature of MSA, several heuristics have developed for suboptimal alignments. The progressive alignment approach constitutes one of the most convenient and effective ways to align multiple sequences. In this study, to improve the sensitivity of progressive multiple sequence alignments, a new method for pairwise distance measurement based on the intuitionistic fuzzy proposed. Reference sequences from BALiBASE 4.0 (hand-aligned), PREFAB 4.0 (structurally supervised), and OXBench systems were employed to evaluate system performance. For checking the quality, the test alignments score in terms of SP-, C-, and TC score evaluated by the Friedman ranks test at the statistically significant level. The unweighted paired group with arithmetic mean hierarchical clustering (PIFD-UHC) and neighbor-joining-based hierarchical clustering (PIFD-NHC) methods were applied to carry out pairwise intuitionistic fuzzy distance measurements to construct a merge tree. The results indicate better performance of the proposed methods in improving the alignment sensitivity and accuracy. Comparatively, where the sequences not equidistant to each other, the PIFD-NHC has a more reliable performance. However, PIFD-UHC was the top performer in aligning all the BALiBASE reference sequence sets. Meanwhile, our approach runs a somewhat greater time complexity with similar memory usage to the ClustalW used pairwise distance measurement method.
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Data availability
The datasets generated during the current study are available from the corresponding author on request.
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Acknowledgements
The authors would like special thanks to the reviewers for their helpful and constructive suggestions and comment to improve the quality of the paper.
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This work has been supported by Jahrom University, Iran in Grant Code: 103/16133.
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Authors make substantial contributions to conceptualization and methodology, data curation, statistical analysis, and validation. BH wrote the manuscript. NF reviewed.
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Hajieghrari, B., Farrokhi, N. & Kamalizadeh, M. Intuitionistic fuzzy approach improve protein multiple sequence alignment. Netw Model Anal Health Inform Bioinforma 10, 45 (2021). https://doi.org/10.1007/s13721-021-00314-6
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DOI: https://doi.org/10.1007/s13721-021-00314-6