Due to the central role of orthology in comparative and functional genomics, there is an extensive literature on accuracy-restricting factors of its assignment 13, 16, 17
. We have already mentioned several caveats of orthology prediction using the mucin family, the majority of which are exemplified by the 70 RefOGs. The families were selected under certain criteria (Box 2), mostly with a view to understanding the impact of a few biological and technical factors, namely duplications (paralogy)/losses, rate of evolution, domain architecture, and alignment quality. All these factors have been reported to affect the quality of orthology prediction 17
. Paralogy as manifested in multi-gene families hamper the accurate orthology prediction 4, 13
. Multiple lineage-specific gene losses and duplications result in complex evolutionary scenarios, which are hard to interpret. Classifying the RefOGs based on their size, we observed that the larger the RefOG, the more mispredictions are introduced by the methods (). For all methods, the numbers of missing genes () and OG fissions (Fig. S2
in Supporting Information) increases significantly with the RefOG size (Table S5
of Supporting Information). Additionally, families with more than 40 members accumulate both fusion and fission events. For instance, GH18-chitinases, a RefOG that consists of 45 members, is characterized by multiple vertebrate-specific duplication events. All graph-based methods split the vertebrate subfamilies of the GH18-chitinases into distinct groups (Table S2
of Supporting Information), and TreeFam lumps the RefOG with insect-specific homologs due to the presence of the glyco-hydro-18 domain, although phylogenetic analysis of the family indicates a general lack of orthology among those groups 32
Figure 3 The impact of biological complexity in orthology assignment. To evaluate the impact of important caveats in orthology prediction, the RefOGs were classified based on their family size, rate of evolution, alignment quality and domain complexity. A: Family (more ...)
Some large-size RefOGs, like ribosomal proteins or SAM-synthetases are, however, predicted accurately by several methods. Since these two well-predicted large families are well conserved, we decided to investigate the impact of the rate of evolution on orthology prediction. We categorized our benchmarking families into fast-, medium-, and slow-evolving based on their MeanID score (described as the “FamID” in 33
), which indicates the rate of evolution (Supporting Information). Fast-evolving families tend to accumulate a larger number of errors (). All graph-based methods miss a larger number of genes and introduce more fission events (Fig. S2
in Supporting Information) in fast-evolving RefOGs compared to the more slowly evolving groups. Since the MeanID score is calculated based on the multiple sequence alignment (MSA), we investigated the impact of MSA quality by calculating the norMD score 34
, an alignment score that depends on the number and the length of aligned sequences as well as their estimated similarity (Supporting Information). We expected TreeFam to be more sensitive to low-quality MSAs compared to graph-based methods, since it uses MSA for tree-building and reconciliation steps to infer orthology. Indeed, it presents the highest deviation for all sources of errors (Table S5
of Supporting Information). We also found that the number of missing genes is also affected by the alignment quality in graph-based methods (). Because MeanID and norMD scores are correlated, many of the fast-evolving families are also poorly aligned. Still, we can see that TreeFam is significantly more affected by MSA quality rather than rate of evolution.
The vast majority of proteins contain only one domain, and the most common multi-domain proteins tend to have few (two or three) domains 35, 36
. Due to a variety of genetic processes (duplication, inversion, recombination, retrotransposition, etc.) proteins consisting of multiple domains with independent evolutionary origin can arise 37–40
. This leads to conceptual but also practical challenges (e.g. alignment) in orthology prediction, as the domains have followed distinct evolutionary trajectories 16
. We identified the domains of each protein in each RefOG through the SMART database 41
. Out of the 70 RefOGs, 75% contain multi-domain (more than two domains) proteins, compared to 62% in the random subset and a report of 40% multi-domain occurrence in metazoans 36
, which illustrates the tendency of the benchmark set toward more challenging families. As expected, the proportion of accurately predicted RefOGs decreases as the number of average domains per family increases (). Interestingly, the rate of erroneously assigned genes presents the most significant correlation with domain complexity, suggesting that protein families with multiple protein domains “attract” non-orthologous proteins due to domain sharing. Repeated domains within proteins, as the Von Willebrand factor (VW) D-C8-VWC repeat in mucins () or the epidermal growth factor (EGF) domains in collagen, also lead to lower quality of OGs. All of the 27 RefOGs containing repeated domains are more error prone than RefOGs without repeated domains (Fig. S3
of Supporting Information).
Taken together, classification of the families from slow-evolving single copy to fast-evolving large families revealed method-specific limitations, but also that all pipelines fail to predict complex families accurately. The rates of missing genes and fissions significantly correlate with the family size and rate of evolution, as expected, whereas the domain complexity seems to affect the recruitment of non-orthologous genes (, Figs. S2 and S4
of Supporting Information).