UC San Diego

Data integration continues to remain a difficult and escalating problem in bioinformatics. The goal of this thesis is to develop a data integration platform that addresses two recurring issues in current data integration methods: 1) the issue of naming and identity and 2) the barrier of entry for general researchers to contribute and perform analysis of data. We have developed a web tool and warehousing system, Booly, that features a simple yet flexible data model coupled with the ability to perform powerful comparative analysis, including the use of Boolean logic to merge datasets together, and an integrated aliasing system to decipher differing names of the same gene or protein. We applied Booly across heterogeneous data sources and identified genes useful in comparing avian and mammalian brain architecture, which were validated by comprehensive in situ hybridization experiments. The Booly paradigm for data storage and analysis should facilitate integration between disparate biological and medical fields and result in novel discoveries that can then be validated experimentally