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.

Methods
We tested seven variations of four orthology inference algorithms - OrthoFinder, SonicParanoid, Broccoli, and OrthNet. We used publicly available genomes and associated protein files from eight Brassicaceae species and compared the results from these algorithms on two sets of species: one consisting of five diploid species and one consisting of eight species - five of the diploids, two mesopolyploids, and one hexaploid. We examined the similarities and differences in the results to understand the performance of these algorithms in inferring orthologs and orthogroups with and without genomically complex species.