Visual Phenotype Database
http://phenomicsworld.org

Discoveries in biology often require extensive knowledge of the genetics of an organism, a keen eye for phenotypes, a deep understanding of related species, and efficient strategies for collecting, combining, analyzing, and comparing data. Currently, public database tools that retrieve phenotypic and genomic information allow only relatively simplistic queries, and viable software tools to capture, parse, and return information from digital images are lacking. We hope to enable biologists to simultaneously query phenotype data by image example, sequence, ontology, genetic and physical map information, and text annotations by developing the first web-based visual phenotypic information management system to allow such complex queries.

The database framework will consist of five modules:

(1) A system to extract and quantify low-level features from phenotypic images
(2) A high-dimensional database indexing system to manage and cluster images for real-time retrievals
(3) A linking hub to correlate visual features already attributed to a given locus with relevant genetic and physical maps
(4) A text mining and ontology utilization system for parsing annotations
(5) A results visualization system.

• Status: Active
• Funding: National Science Foundation (#DBI-0447794)
• Members: Adrian Barb Jia-Fu Chang Thomaz DeSouza Cynthia Farmer Jason Green Juan Gutierrez Jaturon Harnsomburana Dmitry Korkin Heather Nelson Chi-Ren Shyu

• Publications:
  Chi-Ren Shyu, Jason Green, Cynthia Farmer, Toni Kazic, Ed Coe, M. Schaeffer, M. Millard, P. Cyr, and C. Gardner. A Computational Approach for Characterizing Standardized Phenotypic Images for Maize, in The 48th Maize Genetics Conference 2006
  
  Chi-Ren Shyu, Jason Green, Cynthia Farmer, Toni Kazic, Ed Coe, M. Schaeffer, C. Lawerence, M. Millard, P. Cyr, and C. Gardner. A Framework of Complex Queries For Plant Phenotype Image Databases, in The International Conference on Plant, Animal, and Microbe Genomes XIV 2006
  
  Grant Scott, and Chi-Ren Shyu. Knowledge Driven Multidimensional Indexing Structure for Biomedical Media Database Retrieval, in IEEE Transactions on Information Technology in Biomedicine, Vol. 11, No. 3 , May 2007; 320-331
  
  Chi-Ren Shyu, Jason Green, D. P. K. Lun, Toni Kazic, M. Schaeffer, and Ed Coe. Image Analysis for Mapping Immeasurable Phenotypes in Maize, in IEEE Signal Processing Magazine, Vol. 24, No. 3 , May 2007; 116-119
  
  Jason Green, and Chi-Ren Shyu. A Computational Approach for Scoring Visually Observed Phenotypic Expression in Maize, in The 49th Maize Genetics Conference, March 2007
  
  Toni Kazic, Jason Green, Jaturon Harnsomburana, and Chi-Ren Shyu. Quantification of Lesion Phenotypes as a Function of Inbred Background, in The 49th Maize Genetics Conference, March 2007
  
  Chi-Ren Shyu, Jaturon Harnsomburana, Jason Green, Adrian Barb, Toni Kazic, M. Schaeffer, and Ed Coe. Searching and mining visually-observed phenotypes of maize mutants, in Journal of Bioinformatics and Computational Biology, Special Issue on Making Sense of Mutations Requires Knowledge Management, Vol. 5, No. 6 , December 2007; 1193-1213
  
  

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