title: A semantic problem solving environment for integrative parasite research: identification of intervention targets for Trypanosoma cruzi.
abstract: BACKGROUND
  Research on the biology of parasites requires a sophisticated and integrated computational platform to query and analyze large volumes of data, representing both unpublished (internal) and public (external) data sources. Effective analysis of an integrated data resource using knowledge discovery tools would significantly aid biologists in conducting their research, for example, through identifying various intervention targets in parasites and in deciding the future direction of ongoing as well as planned projects. A key challenge in achieving this objective is the heterogeneity between the internal lab data, usually stored as flat files, Excel spreadsheets or custom-built databases, and the external databases. Reconciling the different forms of heterogeneity and effectively integrating data from disparate sources is a nontrivial task for biologists and requires a dedicated informatics infrastructure. Thus, we developed an integrated environment using Semantic Web technologies that may provide biologists the tools for managing and analyzing their data, without the need for acquiring in-depth computer science knowledge.
  
  
  RESULTS
  We developed a semantic problem-solving environment (SPSE) that uses ontologies to integrate internal lab data with external resources in a Parasite Knowledge Base (PKB), which has the ability to query across these resources in a unified manner. The SPSE includes Web Ontology Language (OWL)-based ontologies, experimental data with its provenance information represented using the Resource Description Format (RDF), and a visual querying tool, Cuebee, that features integrated use of Web services. We demonstrate the use and benefit of SPSE using example queries for identifying gene knockout targets of Trypanosoma cruzi for vaccine development. Answers to these queries involve looking up multiple sources of data, linking them together and presenting the results.
  
  
  CONCLUSIONS
  The SPSE facilitates parasitologists in leveraging the growing, but disparate, parasite data resources by offering an integrative platform that utilizes Semantic Web techniques, while keeping their workload increase minimal.
authors.affiliation: The Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, United States of America.
authors.forename: Priti P
authors.initials: PP
authors.lastname: Parikh
authors.forename: Todd A
authors.initials: TA
authors.lastname: Minning
authors.forename: Vinh
authors.initials: V
authors.lastname: Nguyen
authors.forename: Sarasi
authors.initials: S
authors.lastname: Lalithsena
authors.forename: Amir H
authors.initials: AH
authors.lastname: Asiaee
authors.forename: Satya S
authors.initials: SS
authors.lastname: Sahoo
authors.forename: Prashant
authors.initials: P
authors.lastname: Doshi
authors.forename: Rick
authors.initials: R
authors.lastname: Tarleton
authors.forename: Amit P
authors.initials: AP
authors.lastname: Sheth
mesh_terms: D002965:Classification / Q000379:methods*; D003628:Database Management Systems; D030562:Databases, Protein; D005786:Gene Expression Regulation; D010274:Parasitology; D015800:Protozoan Proteins / Q000235:genetics / Q000378:metabolism*; D012660:Semantics; D018511:Systems Integration; D009626:Terminology as Topic; D014349:Trypanosoma cruzi / Q000378:metabolism*
chemical_list: D015800:Protozoan Proteins