|Lushbough, Carol - UNIV. OF SOUTH DAKOTA|
|Bergman, Michael - VISUAL METRICS CORP.|
|Jennewein, Doug - UNIV. OF SOUTH DAKOTA|
|Brendel, Volker - IOWA STATE UNIVERSITY|
Submitted to: Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: March 30, 2007
Publication Date: June 25, 2007
Citation: Lushbough, C., Bergman, M.K., Lawrence, C.J., Jennewein, D., Brendel, V. 2007. Creating Bioinformatic Workflows within the BioExtract Server. In: BIOCOMP 07 Proceedings. The 2007 International Conference on Bioinformatics and Computational Biology, June 25-28, 2007, Las Vegas, NV. p. 18. Interpretive Summary: To be able to leverage information toward its use in application, it must be made accessible. For data sets that are large and that derive from multiple places, a need to create interfaces to access, search, and analyze the diverse data as one dataset must be addressed. The BioExtract Server enables researchers to access data from multiple sources and to analyze it with tools simultaneously. This enables researchers to spend less time analyzing existing data and more time testing derived hypotheses.
Technical Abstract: Computational workflows in bioinformatics are becoming increasingly important in the achievement of scientific advances. These workflows generally require access to multiple, distributed data sources and analytic tools. The requisite data sources may include large public data repositories, community databases, and project databases for use in domain-specific research. Similarly, the associated analytic tools often require specific input formats and produce unique outputs that make it difficult to utilize the output from one tool as input to another. The BioExtract Server (http://bioextract.org) is a distributed service designed to consolidate, analyze, and serve data from heterogeneous biomolecular databases. The basic operations of the BioExtract Server allow researchers via their Web browsers to: specify data sources; flexibly query data sources with a range of relational operators; apply analytic tools; download result sets; and store query results for later reuse. As the researcher works with the system, their "steps" are saved in the background. At any time these steps can be saved as a workflow simply by providing a name and description. Once saved, these workflows can be executed and/or modified.