Phenoscape is a collaboration among researchers at universities in the USA on projects that aim to create a scalable infrastructure for linking phenotypes across different fields of biology through the semantic similarity of their descriptions. The original ideas for Phenoscape came from a National Evolutionary Synthesis Center (NESCent) Working Group led by Paula Mabee and Mone Westerfield called “Towards an Integrated Database for Fish Evolution”. Phenoscape projects include their online resource the Phenoscape Knowledgebase, Phenoscape I "Linking Evolution to Genomics Using Phenotype Ontologies" (2007-2011), Phenoscape II “Ontology-enabled reasoning across phenotypes from evolution and model organisms” (2011-2018) and SCATE (Enabling Machine-actionable Semantics for Comparative Analysis of Trait Evolution). Phenoscape projects have been funded by the National Science Foundation (NSF).
The Phenoscape Knowledgebase contains computable phenotypes for research in evolution and genetics. Annotations come from reports in the literature that a taxon or genotype shows variation in a quality such as shape or size for some entity such as an anatomical part and use controlled vocabulary terms from community ontologies that allow for semantic reasoning across diverse phenotypes from different organisms. Phenotype annotations from model organisms databases are combined with new phenotype annotations from evolutionary literature. Phenoscape curators define computable phenotype concepts in the form of Entity-Quality (EQ) compositions which use terms from Uberon anatomy ontology, the Biospatial Ontology (BSPO) and the Phenotype and Trait Ontology (PATA) and taxonomic concept from the Vertebrate Taxonomy Ontology (VTO). Comparative biodiversity is linked to developmental genetic mechanisms by importing information about associations between genotype and phenotype and localization of gene expression from model organisms such as zebrafish (ZFIN), mouse (MGI), Xenopus (Xenbase) and human (Human Phenotype Ontology project).
The most recent project is Enabling Machine-actionable Semantics for Comparative Analysis of Trait Evolution (SCATE) is funded by NSF grants. The project objective is to create infrastructure for comparative trait analysis tools to easily access algorithms powered by machine reasoning with the semantics of trait descriptions. SCATE will focus on addressing three long-standing limitations in comparative studies in trait evolution which are recombining trait data, modeling trait evolution and generating testable hypotheses for the drivers of trait adaptation.