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* '''Try the Beta version of the [http://kb.phenoscape.org Phenoscape Knowledgebase].''' Development is very much in progress, and your feedback is welcome!
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* Try the [http://kb.phenoscape.org Phenoscape Knowledgebase].  Your feedback is welcome!
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* Check out the latest news on the [http://blog.phenoscape.org/ Phenoscape blog], including the latest changes to PATO, a new bridging Vertebrate Anatomy Ontology, and the launch of a new Phenotype Ontology Research Coordination Network.}}
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* Check out the latest news on the [http://blog.phenoscape.org/ Phenoscape blog]
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* Learn more in one of our upcoming [[Training_and_Workshops | training workshops]]
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== Ontology-enabled reasoning across phenotypes from evolution and model organisms ==
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== Enabling Machine-actionable Semantics for Comparative Analysis of Trait Evolution ==
  
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=== About this project ===
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The current project of the Phenoscape team is Enabling Machine-actionable '''S'''emantics for '''C'''omparative '''A'''nalysis of '''T'''rait '''E'''volution (SCATE).
  
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Our overall objective is to create a scalable infrastructure that enables linking descriptive phenotype observations across different fields of biology by the semantic similarity of their free-text descriptions. In other words, we are trying to make descriptive observations amenable to large-scale computation so that they can be subjected to computational data integration and knowledge discovery techniques in ways similarly powerful as the techniques we are used to for numeric, quantitative observations.
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The objective is to create infrastructure that will provide comparative trait analysis tools easy access to algorithms powered by machine reasoning with the semantics of trait descriptions. Similar to how Google, IBM Watson, and others have enabled developers of smartphone apps to incorporate, with only a few lines of code, complex machine-learning and artificial intelligence capabilities such as sentiment analysis, we aim to demonstrate how easy access to knowledge computing opens up new opportunities for analysis, tools, and research in comparative trait analysis.  
  
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Our approach to accomplish this centers on transforming descriptive observations from the natural language text form in which they are typically reported, to fully computable logic expressions that utilize terms from shared ontologies. [[Guide to Character Annotation| We create these expressions]] (which we also call "annotations") for evolutionary phenotypes reported in the systematics literature, typically in the form of character state matrices. We use the [[EQ for character matrices| Entity-Quality (EQ) formalism]] to compose these expressions, which was initially conceived for making biomedical and mutant model organism phenotype observations interoperable.
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As driving biological research questions, we focus on addressing three long-standing limitations in comparative studies of trait evolution: recombining trait data, modeling trait evolution, and generating testable hypotheses for the drivers of trait adaptation.
  
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We combine the EQ annotations we create for evolutionary phenotypes with the EQ annotations created for the myriad of phenotypes observed for mutant model organisms in an [http://kb.phenoscape.org integrated knowledgebase] (essentially a triple-store).  We then apply [[:Category:Reasoning| Description Logic-reasoning]] to evaluate which evolutionary phenotype transitions can be inferred as semantically similar to which mutant model organism phenotypes, and vice versa.  Since the genetic cause of a mutant phenotype is usually known, the links between evolutionary and mutant phenotypes identified in this way can be used to construct testable hypotheses about the genetic correlates or causes of evolutionary transitions.
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SCATE is funded by NSF collaborative grants DBI-1661456 (Duke University), DBI-1661529 (Virginia Tech), DBI-1661516 (University of South Dakota), and DBI-1661356 (UNC Chapel Hill and RENCI) from Sep 1, 2017 to Aug 31, 2020.  
  
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[[Image:Phenoscape II tree view.jpg|right|380px]] In a previous project, titled [[Linking Evolution to Genomics Using Phenotype Ontologies]], we developed a working prototype as a successful proof-of-concept, using teleost fishes for evolutionary phenotypes and the [http://zfin.org zebrafish model organism] as a source of mutant phenotypes. Here, we aim to make the components of the prototype, including tools and workflows, sufficiently scalable so that they are adequate for the much more extensive volume and more diverse nature of skeletal phenotypes across all vertebrates, fossil and modern. Specifically, our aims encompass the following:
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The grant proposal text with references is publicly available: ''W. Dahdul, J.P. Balhoff, H. Lapp, P. M. Mabee, J. Uyeda, & T.J. Vision. (2017). Enabling machine-actionable semantics for comparative analyses of trait evolution. Zenodo. http://doi.org/10.5281/zenodo.885538''.
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# Develop a fast semantic similarity engine so that the integrated knowledgebase can be searched on-the-fly for biological taxa or genotypes bearing a profile of phenotypes that is similar, but not necessarily identical, to a query profile.
 
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# Develop an [[Reasoning over homology statements| ontological framework for reasoning over homology]] that can be scaled to a large number of anatomically diverse evolutionary lineages.
 
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# Reduce the time and cost of obtaining EQ statements from the literature, while at the same time improving the quality and consistency of those statements, by incorporating natural language processing tools and by improving curation software to allow for on-demand augmentation of community ontologies. [[Image:Phenoscape II architecture.png|right|430px]]
 
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# Build umbrella [[Ontologies#Vertebrate Taxonomy Ontology| taxonomic]] and [[Anatomy Ontology Development Plan| anatomical ontologies]] for the vertebrates, the latter to be supplemented by explicit homology relations among anatomical structures.
 
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# Create a knowledgebase that integrates evolutionary phenotypes for vertebrate fin and limb characters with genetic and phenotype data from three vertebrate model organisms: [http://zfin.org zebrafish] (Danio rerio), [http://xenbase.org frog] (Xenopus laevis), and [http://www.informatics.jax.org/ mouse] (Mus musculus).
 
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# As a capstone, we will assess the results of our work by how well we can apply machine reasoning to retrieve candidate genes for the well-studied vertebrate fin-limb transition and other major events in skeletal evolution of vertebrates.
 
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In addition to a web-based interface, we will make all data, including the integrated knowledgebase, available in the Ontology Web Language (OWL), so that other researchers can reuse the data in as many ways as possible.
 
  
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=== The vertebrate fin/limb transition ===
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See the [http://scate.phenoscape.org project website] for more information.  For a little more background on the how and why of incorporating ontologies into comparative analysis, see [[ComparativeAnalysis]].
  
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=== Project Exploration ===
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== Previous Phenoscape projects and Acknowledgements ==
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* Phenoscape II ("[[Ontology-enabled reasoning across phenotypes from evolution and model organisms]]") was funded by NSF collaborative grants DBI-1062404 and DBI-1062542 from July 1, 2011, to June 30, 2018, and supported by the National Evolutionary Synthesis Center (NESCent), NSF #EF-0905606.  The original Project Description for this grant is available [[:File:Phenoscape_Project_description_refs.pdf| here]].
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* Phenoscape I ("[[Linking Evolution to Genomics Using Phenotype Ontologies]]") was funded by NSF grant BDI-0641025 from June 1, 2007, to Jun 30, 2011, and was supported by NESCent, NSF #EF-0423641.
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* The original ideas for Phenoscape arose from a NESCent <span class="plainlinks">[http://www.nescent.org/science/workinggroup.php Working Group]</span> led by Paula Mabee and Monte Westerfield, "[[Fish Evolution Working Group|Towards an Integrated Database for Fish Evolution]]."
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* These projects have bene made possible possible by the hard work of [[Acknowledgments#Contributors| numerous contributors]].
  
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=== Contact ===
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https://www.nescent.org/about/images/nsf_logo.jpg
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Paula Mabee (University of South Dakota) is the Principal Investigator. Co-principal investigators are David Blackburn (California Academy of Sciences), Judith Blake (Mouse Genome Informatics, Jackson Laboratories), Hilmar Lapp (NESCent), Paul Sereno (University of Chicago), Todd Vision (University of North Carolina, Chapel Hill), Monte Westerfield (University of Oregon, ZFIN), and Aaron Zorn (Xenbase, Cincinnati's Children's Hospital and Medical Center) (see their [[Contact| contact addresses]]).
 
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== Acknowledgements ==
 
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{|
 
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| This project is funded by NSF grant BDI<nowiki>-</nowiki>1062404 from July 1, 2011, to Jun 30, 2015, and is supported by the National Evolutionary Synthesis Center (NESCent), NSF #EF-0905606.
 
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This project would not have been possible without the hard work of [[Acknowledgments#Contributors| numerous contributors]] and the results obtained in the [[Linking Evolution to Genomics Using Phenotype Ontologies]] project, which was funded by NSF grant BDI<nowiki>-</nowiki>0641025 from June 1, 2007, to Jun 30, 2011, and was supported by NESCent, NSF #EF-0423641. This earlier project in turn arose from a NESCent <span class="plainlinks">[http://www.nescent.org/science/workinggroup.php Working Group]</span> led by Paula Mabee and Monte Westerfield, "[[Fish Evolution Working Group|Towards an Integrated Database for Fish Evolution]]."
 
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| http://www.nescent.org/about/images/nsf_logo.jpg
 
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|}
 
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==Pages of public interest==
 
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* [[Training and Workshops]]
 

Latest revision as of 22:31, 13 December 2019

Enabling Machine-actionable Semantics for Comparative Analysis of Trait Evolution

The current project of the Phenoscape team is Enabling Machine-actionable Semantics for Comparative Analysis of Trait Evolution (SCATE).

The objective is to create infrastructure that will provide comparative trait analysis tools easy access to algorithms powered by machine reasoning with the semantics of trait descriptions. Similar to how Google, IBM Watson, and others have enabled developers of smartphone apps to incorporate, with only a few lines of code, complex machine-learning and artificial intelligence capabilities such as sentiment analysis, we aim to demonstrate how easy access to knowledge computing opens up new opportunities for analysis, tools, and research in comparative trait analysis.

As driving biological research questions, we focus on addressing three long-standing limitations in comparative studies of trait evolution: recombining trait data, modeling trait evolution, and generating testable hypotheses for the drivers of trait adaptation.

SCATE is funded by NSF collaborative grants DBI-1661456 (Duke University), DBI-1661529 (Virginia Tech), DBI-1661516 (University of South Dakota), and DBI-1661356 (UNC Chapel Hill and RENCI) from Sep 1, 2017 to Aug 31, 2020.

The grant proposal text with references is publicly available: W. Dahdul, J.P. Balhoff, H. Lapp, P. M. Mabee, J. Uyeda, & T.J. Vision. (2017). Enabling machine-actionable semantics for comparative analyses of trait evolution. Zenodo. http://doi.org/10.5281/zenodo.885538.

See the project website for more information. For a little more background on the how and why of incorporating ontologies into comparative analysis, see ComparativeAnalysis.

Previous Phenoscape projects and Acknowledgements

nsf_logo.jpg

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