Toward synthesizing our knowledge of morphology: using ontologies and machine reasoning to extract presence/absence evolutionary phenotypes across studies

Abstract

The reality of larger and larger molecular databases and the need to integrate data scalably have presented a major challenge for the use of phenotypic data. Morphology is currently primarily described in discrete publications, entrenched in non-computer readable text, and requires enormous investments of time and resources to integrate across large numbers of taxa and studies. Here we present a new methodology, using ontology-based reasoning systems working with the Phenoscape Knowledgebase (KB), to automatically integrate large amounts of evolutionary character state descriptions into a synthetic character matrix of neomorphic (presence/absence) data. Using the KB, which includes more than 55 studies of sarcopterygian taxa, we generated a synthetic supermatrix of 1051 variable characters scored for 639 taxa resulting in over 145,000 populated cells. Of these characters, over 76% were made variable through the addition of inferred presence/absence states derived by machine reasoning over the formal semantics of the source ontologies. Inferred data reduced the missing data in the variable character-subset from 98.5% to 78.2%. Machine reasoning also enables the isolation of conflicts in the data, i.e., cells where both presence and absence are indicated; reports regarding conflicting data provenance can be generated automatically. Further, reasoning enables quantification and new visualizations of the data, here for example, allowing identification of character space that has been undersampled across the fin to limb transition. The approach and methods demonstrated here to compute synthetic presence/absence supermatrices are applicable to any taxonomic and phenotypic slice across the tree of life, providing the data are semantically annotated. Because such data can also be linked to model organism genetics through computational scoring of phenotypic similarity, they open a rich set of future research questions into phenotype to genome relationships.

Extraction of phenotypic traits from taxonomic descriptions for the tree of life using natural language processing

“…Processing and analyzing new collections of taxonomic descriptions from across the tree of life with the NLP pipeline leads us to discover new expressions and grammatical constructions that help to optimize the NLP pipeline and its components, like the Plant Glossary. In the future, the ETC pipeline will allow users to import and create ontologies, hierarchical organizations of terms that establish relationships among structures, entities, and qualities and enable the computer to have reasoning capabilities (Dececchi et al, 2015). Using ontologies will likely increase the number of usable phenotypic characters obtained by the NLP pipeline.…”

Crowdsourced geometric morphometrics enable rapid large‐scale collection and analysis of phenotypic data

“…One such example is the Phenoscaping project (http://kb.phenoscape.org; Deans et al 2015), and related efforts in the Vertebrate Taxonomy Ontogeny (Midford et al 2013) and Hymenoptera Anatomy Ontology (Yoder et al 2010), which require large amounts of researcher effort to collate. Other approaches include using machine learning (Dececchi et al 2015), machine vision (Corney et al 2012a, b) or natural language processing (Cui 2012) to identify or infer phenotypes. These statistical techniques function ideally with either a large training data set (e.g., a predefined ontogeny data base) or a complex model (Brill 2003;Halevy, Norvig & Pereira 2009;Hastie, Tibshirani & Friedman 2009), both of which also require intensive researcher effort to build and validate.…”

Crowdsourced geometric morphometrics enable rapid large-scale collection and analysis of phenotypic data

“…One such example is the Phenoscaping project (http://kb.phenoscape.org; Deans et al 2015), and related efforts in the Vertebrate Taxonomy Ontogeny (Midford et al 2013) and Hymenoptera Anatomy Ontology (Yoder et al 2010), which require large amounts of researcher effort to collate. Other approaches include using machine learning (Dececchi et al 2015), machine vision (Corney et al 2012a; b), or natural language processing (Cui 2012) to identify or infer phenotypes. These statistical techniques function ideally with either a large training dataset (e.g., a predefined ontogeny database) or a complex model (Brill 2003; Halevy et al 2009; Hastie et al 2009), both of which also require intensive researcher effort to build and validate.…”