The major challenges for data repositories include the initial and continuous input of data into the database and their long-term sustainability. As described by Merali and Giles [23
] community-based and driven databases are generally more successful than projects initiated and maintained by single labs or small research groups. The Evo-Devo field is an interactive and growing community and members are invited to submit their gene expression patterns as well as suggestions for improvement to this new database. Currently, the database contains only data from marine invertebrates, but contributions for terrestrial organisms are welcome. Our hope is that this will encourage researchers to share their data with the Evo-Devo community through a community platform that acts in parallel to peer-to-peer publications, improves the visibility of the published work and fosters scientific interactions.
We hope that community driven projects like this one will help improve the way we publish gene expression data today. Instead of collecting all information in static PDF's for print, all image data should primarily be annotated and published in a searchable and standardized format that then can be summarized for print (with automatic creation of hyperlinks to the online data).
The Kahi Kai non-profit organization is in the process of assembling an international scientific committee from different laboratories. We are considering using part of this committee to screen all added genes to minimize erroneous additions to the database in regard to naming conventions, duplicate entries, orthologies and image orientations. This review process will require all new additions to be put in a queue causing a slight delay (the reviewing time) before the expression data is visible to all users.
To make this tool even more useful, we are planning to add additional features including possibilities to implement qPCR and other quantified expression data such as RNA-seq and microarray that will enable gene regulatory network predictions. Therefore, we will develop tools to organize this information in gene regulatory networks where each node can be linked to the corresponding data, making it easy to check, confirm and compare relations.
Gene expression patterns are defined by precise developmental stages and embryonic regions/or germ layers of a given species. This information is usually only known by specialists, making comparison between species for non-specialists sometimes difficult. To facilitate this, we will associate each species present in the database with detailed information about their embryonic/larval development (database in progress). In addition, we anticipate adding illustrations of the developmental stages high-lighting the various domains relevant for the gene expression data (refer to Figure ). We also consider that each species present in the database will be associated with information about habitat, life cycle, feeding behavior, spawning season and advice for laboratory cultures. This information will also be illustrated with high-resolution pictures of the animals, which can be used for outreach, education, scientific presentations and publications for example.
We encourage independent Principal Investigators who submit data to open access journals such as EvoDevo to also submit expression data to the Kahi Kai gene expression database.