We are seeking collaborations in biological database development, genomics data integration, comparative sequence analysis, and development of data mining tools. Potential collaborators may read the K-INBRE Bioinformatics Core policy for guidelines.

Our collaborative research projects include:

• BeetleBase: We are developing a comprehensive genome database for the Tribolium research community. The database is built on the Chado generic data model, and is able to store various types of data, ranging from genome sequences to mutant phenotypes. A web interface is designed to allow public access to the curated and integrated data in the database. BeetleBase is developed as an important community resource for Tribolium genetics, genomics and developmental biology.

• ArthropodEST: K-State Bioinformatics EST analysis pipeline.

• LipidomeDB: For the Kansas Lipidomics Center, we are building a database system to efficiently manage lipid profiles and the related experimental metadata. Since the long-term goal of the database is to support online data analysis and advanced data mining tools, the data model is developed using the warehousing concepts. The web interface will allow users to browse and search the database. We plan to develop advanced statistical tools for pattern discovery in the database. These tools will enhance our ability to extract biologically meaningful information from lipid profiles.

• Development of statistical and data mining tools: Microarray gene expression data are accumulating in both public databases and individual research laboratories. The scale and heterogeneity of the microarray datasets give rise to substantial challenges in data integration and biological knowledge discovery. We are applying statistical meta-analysis to integrate microarray data from different sources/platforms, and use biclustering algorithms to discover coherent patterns in large gene expression datasets. These tools will enable our collaborators to combine their own microarray dataset with related ones in the public domain for understanding specific biological problems, and to select interesting patterns or gene targets for further studies. The statistical framework may also be applied to other profiling data (e.g., lipidomic and proteomic data).

• Comparative sequence analysis: We are collaborating with several laboratories to systematically compare sequences from different genomes. New computational methods are being developed for identification of orthologous gene groups, functional annotation using gene ontology terms, and pathway prediction. We are also interested in comparative analysis of promoter sequences and transcription factors for understanding gene regulatory networks.

• Other potential projects: We are also experienced with proteomic data analysis, protein-protein interaction prediction, protein structure prediction, and genetic marker development. We are thus actively seeking collaborative research opportunities in these areas.