A potential disadvantage of our weighted method is the inherited restriction (via the weighting scheme employed) to AASs falling within conserved protein domains. However, protein domain annotations from the SUPERFAMILY and Pfam databases encompass around 80% of the SwissProt/TrEMBL database [Punta et al., 2012]. In our analysis, we were able to analyse a large proportion (>70%) of the VariBench and SwissVar benchmarking datasets. On the other hand, unlike other sequence-based prediction methods (including our own unweighted method), which are too slow for practical use in large-scale sequencing projects, our weighted method uses computationally inexpensive domain assignments. Therefore, FATHMM can be efficiently applied to all foreseeable high-throughput large-scale genomic datasets with minimal reduction in coverage. In addition, our method advances the field with its unique ability to annotate the molecular and phenotypic consequences of AASs using several domain-centric ontologies [de Lima Morais et al., 2011] including the Human Phenotype Ontology [Robinson et al., 2008] and the Mammalian Phenotype Ontology [Smith and Eppig, 2009]. Thus, by coupling the functional predictions generated by FATHMM with domain-based ontological associations, as opposed to protein level
