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. 2025 Sep 22;26:807. doi: 10.1186/s12864-025-12061-3

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© The Author(s) 2025

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 5 — Comparison of the runtime performance of global alignment and phylogenetic tree construction. Shown are the runtime performances of the global alignment step combined with the construction of phylogenetic trees. Three representative sequence collections were selected (containing 43, 113, and 212 sequences) and processed in parallel (dark green) or subsequently (light green). The global alignment was conducted either with MAFFT v7.490 [43, 44] or MUSCLE v3.8.1551 [45], the phylogenetic tree was constructed with FastTree v2.1.11 [46, 47], RAxML-NG v1.2.2 [48], or IQ-TREE v2.0.7 [49]. The vertical axis was split between 6 and 100 h in order to visualize all data points