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<center><big>'''Towards an Integrated Database for Fish Evolution'''</big><p>A NESCent Working Group</p></center>
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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]
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* Learn more in one of our upcoming [[Training_and_Workshops | training workshops]]
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Model systems provide a way to untangle complex problems. Often concepts worked out in organisms such as fruit flies or frogs can be applied to other systems, including humans. Zebrafish have become a primary model for studying developmental genetics and evolutionary developmental biology, or "EvoDevo". Ichthyologists (fish biologists) have also studied zebrafish, among other cypriniforms (generally small, tropical fish like minnows, barbs, carps and goldfish), to understand their evolutionary relationships. Groups that work on the same organism, but from different viewpoints can learn a lot from each other and potentially advance their science dramatically by combining their information. However, developmental biologists and fish evolutionists have not historically interacted to a great extent, and generally have not "spoken the same language", making collaboration and interaction challenging.
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== Enabling Machine-actionable Semantics for Comparative Analysis of Trait Evolution ==
  
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This working group brings together members of the zebrafish research community (including those involved with the Zebrafish Information Network - [http://zfin.org ZFIN]) and the Cypriniform research community (including those involved with the Cypriniform Tree of Life, or [http://bio.slu.edu/mayden/cypriniformes/home.html CToL], project) with the goal of linking information from both fields. The group proposes to join databases to combine the genetic information from developmental biology with the morphological data from evolutionary biology. One of the key challenges for this group is finding a common language, since every field has its own jargon and the same term can mean different things in different fields. One way of accomplishing this is by using ontologies, which are hierarchical constrained vocabularies. Another challenge will be determining how best to use the available data. What kinds of information would be most useful? How should one kind of information be linked to another? The group will tackle the logistics of developing such a database, from basic data entry curation through programming, beginning with a prototype.
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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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The Fish Database Workgroup had their first meeting at NESCent November 4-7, 2005. The focus of our second meeting from April 9 to 11 was on tying homology relationships into anatomy, taxonomy, and phylogeny ontologies. We began technical discussions of developing taxonomy ontologies and how to reference anatomy using such ontologies. We talked through the coding of multiple systematic characters using two ontologies (Anatomy and PATO).
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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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Participants are:
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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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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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|Paula Mabee, University of South Dakota
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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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|Monte Westerfield, University of Oregon
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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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|Miles Coburn, John Carroll University
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== Previous Phenoscape projects and Acknowledgements ==
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|Melissa Haendel, University of Oregon
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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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|Erik Hilton, Field Museum of Natural History
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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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|John Lundberg, Academy of Natural Sciences
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https://www.nescent.org/about/images/nsf_logo.jpg
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|Carol Lushbough, University of South Dakota
 
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|Rick Mayden, St. Louis University
 
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|Nelson Rios, Tulane University
 
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|Gloria Arratia, University of Kansas
 
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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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