Table 1.

Data integration challenges and solutions encountered when integrating genomic and post-genomic experiment data related to infectious disease research.

Challenges	Solutions
Tolerance for identification of gene or proteins	Maintaining multiple interpretations of data
Ensuring completeness, validity, accuracy and currency of data	Formalizing data staging and review process; implementing multiple data quality controls
Managing multiple identifier schemes	Short term, use mapping service, but long term use reduced number of identifier schemes and equivalence mappings in Semantic Web
Data synchronization	Use access on demand APIs with canonical data structures when possible and bulk load only when APIs cannot meet requirements
Literature integration	Use APIs to query literature repositories; provide end users with "smart query" capabilities
Maximizing the use of limited post- genomic experimental evidence for gene and protein predictions.	Treat experiments using orthologs as indirect evidence for gene and protein predictions.
Usability	Employ usability engineering to complement and inform software development; include support for integrated information, data and tools, progressive filtering, and context sensitivity