Phylogenetic Comparative Analyses using Ontologies
A major goal of the SCATE project is to leverage the power of ontologies and the Phenoscape Knowledgebase to assist in evolutionary analyses of trait evolution. For example, researchers may wish to estimate phylogenies from phenotypic data, reconstruct ancestral states, or estimate correlations between phenotypes. Borrowing from molecular sequence data, the methods used for conducting such analyses typically make a series of assumptions that are very poorly suited for phenotypic data. For example, a common assumption is that every character in a character matrix is independent of each other. Phenotypic characters regularly violate this principle. By leveraging the information in phenotypic ontologies, we can correctly model character evolution by accounting for the dependencies of structures among each other. Furthermore, metrics such as semantic similarity can provide useful data that can be integrated into many steps in a phylogenetic comparative analysis.
Structured Markov Models
Ontologies provide knowledge of dependencies among traits. For example, the humerus is a bone that is part of the forelimb. Thus, the presence of a humerus depends on the presence of a forelimb. Treating these as independent characters can result in, for example, ancestral reconstructions in which an organism has a humerus, but lacks a forelimb. Such dependencies can be built into how we model traits by making use of structured markov models (Tarasov, 2018). By structuring the dependencies among traits (as described by Tarasov, 2018), we can reconstruct not only individual traits, but entire ancestral anatomies in a logically consistent framework. We have developed a stochastic mapping pipeline called _PARAMO_ (Tarasov et al. 2019) that allows users to reconstruct ancestral anatomies, seamlessly moving between levels of anatomical hierarchy to query the phenome and ask questions about evolutionary rates, ancestral states, and character evolution.
Hidden State Models & Gene Regulatory Networks
In addition to modeling the dependencies of characters on each other, proper modeling of dependent phenotypic characters requires inclusion of hidden states (Tarasov, 2018, 2019), that is a character state in which the observable phenotype corresponds to different genetic states. Hidden states add the possibility of incorporating more knowledge regarding the evo-devo of traits and account for evolution of novel phenotypic traits.
Character construction is the first step of any comparative analysis, and involves categorizing different free-text semantic descriptions (e.g. pectoral fin curved, pectoral fin round, pectoral fin absent, pectoral fin elongate, pectoral fin circular). Some of these phenotypes may be different ways of describing the same phenotypic state, while others may represent mutually exclusive states. This step of character construction has traditionally relied on expert opinion and reasoning. However, with phenotypic ontologies, such information can be automated to allow machine-reasoning to accomplish similar ends, while improving reproducibility. Furthermore, the distinction between character and character state disappears when using properly structured and hidden state models (Tarasov, 2018). We are exploring how this principle can be leveraged in character construction together with semantic similarity (see below) to construct "well-behaved" character codings in phylogenetic analysis.
Evolutionary Enrichment Analyses
Phylogenetic analysis allows researchers to estimate the evolutionary history of phenotypic evolution. As with gene ontologies, we can ask question such as which branches and at which time periods in evolutionary history has there been a particularly rapid rate of change. Ontologies allow to not only ask which characters are enriched on a given branch, but also which concepts are enriched. For example, are features associated with the fin-to-limb developmental transition particularly evolvable during the Devonian? Does a suite of traits related to a specific gene regulatory network demonstrate concerted evolution across the phylogeny?
Semantic similarity metrics measure concept similarity. We are working to integrate semantic similarity metrics into phylogenetic comparative models, as these contain information that allows users to partition, cluster and amalgamate different characters and character states to make sense of evolutionary patterns.