Different orthology inference algorithms generate similar predicted orthogroups among Brassicaceae species

Premise – Orthology inference is crucial for comparative genomics, and
multiple algorithms have been developed to identify putative orthologs for
downstream analyses. Despite the abundance of proposed solutions,
including publicly available benchmarks, it is difficult to assess which
tool to best use for plant species, which commonly have complex genomic
histories. Methods – We explored the performance of four orthology
inference algorithms – OrthoFinder, SonicParanoid, Broccoli, and OrthNet –
on eight Brassicaceae genomes in two groups: one group comprising only
diploids and another set comprising the diploids, two mesopolyploids, and
one recent hexaploid genome. Results – Orthogroup compositions reflect the
species’ ploidy and genomic histories. Additionally, the diploid set had a
higher proportion of identical orthogroups. While the diploid+higher
ploidy set had a lower proportion of orthogroups with identical
compositions, the average degree of similarity between the orthogroups was
not different from the diploid set. Discussion – Three algorithms –
OrthoFinder, SonicParanoid, and Broccoli – are helpful for initial
orthology predictions. Results from OrthNet were generally an outlier but
could provide detailed information about gene colinearity. With our
Brassicaceae dataset, slight discrepancies were found across the orthology
inference algorithms, necessitating additional analyses, such as tree
inference to fine-tune results.