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This project arose from a NESCent Working Group led by Paula Mabee and Monte Westerfield, "towards an Intregrated Database for Fish Evolution". Goals and summaries of the group are archived on this wiki.

Linking Evolution to Genomics Using Phenotype Ontologies

About this project

What are the developmental and genetic bases of evolutionary differences in morphology across species? Currently it is difficult to approach this question due to a lack of computational tools that allow researchers to integrate developmental genetic and comparative morphological/anatomical data.

We will develop EQSYTE (Entity-Quality System for Trait Evolution) as the database, tools and data to integrate evolutionary and developmental/genomic data for fishes. We are prototyping this approach by developing a database of morphological characters for a large clade of fishes (the Ostariophysi) that will be connected to the existing zebrafish database, through common ontologies. Because the ontologies will be developed and shared with model organism databases, they will allow immediate integration with genetic and developmental data. EQSYTE will be a community research tool that will be used to integrate data to address questions about the genetic and developmental regulation of evolutionary morphological transitions.

Tool and database development will be guided by use cases that are defined by the devo-evo community. We will demonstrate the feasibility of our approach by implementing a select number of these use-case queries as a proof-of-concept. The queries will be implemented via a web-based user interface for searching and analyzing the data content. These tools will be designed to be generalized such that other taxon-model organism integrations can be made.

The Role of Ontologies

Background

Ontologies are constrained, structured vocabularies with well defined relationships among terms. Ontologies represent a knowledge-base of a particular discipline, and provide not only a mechanism for consistent annotation of data, but also greater interoperability among people and machines. The most widely used biological ontology is the Gene Ontology, which is utilized to annotate molecular function, biological processes and subcellular localization to gene products from different organisms.

Phenotype ontologies

For model organisms 

Approximately 500 mutant zebrafish lines (alleles) with over 660 annotated phenotypic characters from the jaw or gill arches (n=250), fins (n=210), axial skeleton (n=190) and other features (n=10) of the skeleton have been described. Researchers in the Zebrafish Information Network (ZFIN) are annotating mutant phenotypes using the zebrafish anatomy ontology and the Phenotype And Trait Ontology (PATO). PATO is a “universal” ontology of terms describing qualities (e.g. shape, color, size) that may be applied to any organism.

For multiple species (for evolutionary biology) 

Representing anatomical character (and character state) data in an ontology-based framework is a new, forward-looking, and integrative move for phylogenetic systematic studies.

Anatomical ontologies

A multi-species ontology for ostariophysan fishes, The Anatomy of Ostariophysi (TAO), will be developed by expanding on the terms in the zebrafish anatomical ontology. To begin with, development of the TAO will concentrate on the skeletal system because it varies significantly across the Ostariophysi, is well-preserved in fossil specimens, and it is often the focus of morphologically-based evolutionary studies in ichthyology. The zebrafish anatomical ontology currently contains 236 skeletal system entities.

The multi-species anatomy ontology for ostariophysan fishes will be used in combination with the PATO ontology (see EQ format) to describe the naturally occurring phenotypes in non-model species (i.e. various ostariophysan fish species).

Taxonomic ontology

We will develop a taxonomic ontology based on the Catalog of Fishes and taxonomic experts in order to relate species with particular characters and states. The taxonomic ontology will include nodes ancestral to the Ostariophysi as far back as the Vertebrata in order to associate certain anatomical terms with more inclusive clades than the Ostariophysi. The taxonomic ontology will be edited using OBO-Edit, similar to the taxonomic ontologies based on NCBI.

As part of this process, we will store statements of homology between entities, so that individual investigators may select particular relationships based on evidence. Thus, for the first time, research questions can be addressed (for example, using the web-interface and query tools that we will build) that require simultaneous data-mining of phylogenetic and genetic data. This approach will also promote integration across morphological systematic studies. Our study, which matches zebrafish model organism genetic data with phylogenetic data from the lineage to which zebrafish belongs (Cypriniformes and ostariophysan relatives), can be generalized to other model organisms and their respective clades. We bring to this study a unique collaboration between evolutionary and model organism biologists that builds upon the strengths of two national centers, a model organism database, a Tree of Life study, a Research Coordination Network, and several community image databases.

Fish Morphology

Although the comparative anatomy of fishes has been documented in the literature for several hundred years, it is not available in a computable format. A Data Curator will input morphological character data that is gleaned from the literature, culled by experts (see table below) and the ichthyological community. The model of a “curated” database is one that has proven effective for model organism databases such as zebrafish, Drosophila, and mouse. EQSYTE (Entity-Quality System for Trait Evolution) will consist of a database and user interface in which the ontologies and data for evolutionary phenotypes are integrated with the zebrafish mutant phenotypes and associated genetic data from ZFIN.

Our goal is to input approximately 4,000 morphological features in an “EQ” format (Mabee et al. 2007a) using a combination of ontologies.

Year	Taxon	PI	# Papers	# Characters	# Taxa	# Species, Genera, Families
1	Cypriniformes	Mayden	72	1125*	1000	3268, 321, 5
1	Siluriformes	Lundberg	150	1200**	1000	2867, 446, 35
2	Characiformes	Coburn	81	800	550	1674, 270, 18
2	Gymnotiformes	Arratia	20	200	50	134, 30, 5
3	Gonorhynchiformes	Arratia	40	75	20	37, 7, 4
3	Clupeiformes	Hilton	60	380	125	364, 84, 5
	TOTAL		423	37802	2745	8344, 1158, 72

* includes original characters from CToL
** All Catfish project (J. Lundberg)

Help needed

We are hiring two research programmers. See the NESCent employment page for more details.

Contact

Paula Mabee (University of South Dakota) is the Principal Investigator. Co-principal investigators are Todd Vision (University of North Carolina, Chapel Hill), Monte Westerfield (University of Oregon, ZFIN), and Hilmar Lapp (NESCent) (see their contact addresses).

Pages of public interest

• Ontology Data Service API Description