Background: The zebrafish is a powerful model vertebrate amenable to high throughput in vivo genetic analyses. Examples include reverse genetic screens using morpholino knockdown, expression-based screening using enhancer trapping and forward genetic screening using transposon insertional mutagenesis. We have created a database to facilitate web-based distribution of data from such genetic studies. Description: The MOrpholino DataBase is a MySQL relational database with an online, PHP interface. Multiple quality control levels allow differential access to data in raw and finished formats. MODBv1 includes sequence information relating to almost 800 morpholinos and their targets and phenotypic data regarding the dose effect of each morpholino (mortality, toxicity and defects). To improve the searchability of this database, we have incorporated a fixed-vocabulary defect ontology that allows for the organization of morpholino affects based on anatomical structure affected and defect produced. This also allows comparison between species utilizing Phenotypic Attribute Trait Ontology (PATO) designated terminology. MODB is also cross-linked with ZFIN, allowing full searches between the two databases. MODB offers users the ability to retrieve morpholino data by sequence of morpholino or target, name of target, anatomical structure affected and defect produced. Conclusion: MODB data can be used for functional genomic analysis of morpholino design to maximize efficacy and minimize toxicity. MODB also serves as a template for future sequence-based functional genetic screen databases, and it is currently being used as a model for the creation of a mutagenic insertional transposon database.
Bibliographical noteFunding Information:
The authors would like to acknowledge the numerous collaborations that have contributed to the development of MODB such as the laboratories of Drs. Farber, Hammerschmidt, Pickart, Schimmenti, Sivasubbu, and Verfail-lie. We would also like to acknowledge the bioinformaticians who have worked on this project including Dr. Eric Klee and Kyong Jin Shim. Funding for this project was obtained from the NIH to SCE (GM63904 and CA65493). This work was supported in part by the University of Minnesota Supercomputing Institute.