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. 2024 Jun 8;6(2):lqae066. doi: 10.1093/nargab/lqae066

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© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Figure 2. — The ortho2tree analysis pipeline. (A) Gene-Centric groups for Panther orthologs (22) are used to extract orthologous canonical and isoform sequences. Panther Ortholog IDs are used to group genes among organisms; Gene-Centric groups are used to identify canonical and isoform sequences associated with each Panther Ortholog set. (B) Orthologous canonical proteins and orthologs are aligned using muscle (23). (C) A gap-distance tree (see Materials and Methods) is constructed using the BioPython3.8 (24) functions DistanceCalculator, DistanceMatrix and DistanceTreeConstructor. (D) A heuristic clade search script examines the gap-distance tree and identifies low-cost clades with sequences from different proteomes. (E) The list of low-cost clades is scored based on the number of proteomes, the cost of the clade, the number of UniProtKB/Swiss-Prot entries from human or mouse, and length consistency. The highest scoring clades from each distinct gene set is then plotted on a tree (Figure 3).