Bioinformatics Resource Manager
Click for a larger version. BRM connects datasets from a variety of sources to visualization applications. An article about BRM was recently featured in the March issue of Scientific Computing. Read more..
Katrina Waters, Principal Investigator
BRM Contact Information: brm@pnl.gov
To allow biologists to address complex biological questions, tools must be created that allow them to perform computational analyses. The Bioinformatics Resource Manager (BRM), developed at Pacific Northwest National Laboratory (PNNL), is a software program (download BRM) that provides robust middleware architecture to link scientific applications and heterogeneous data sources. Our bioinformaticists are using this framework to automate multidisciplinary data mining processes used for high-throughput molecular profiling. The software created by our research team will allow communication between different software tools from within BRM.
Through these efforts, an environment will be provided in which biologists can take advantage of techniques that are otherwise costly, time-consuming, require expertise in bioinformatics and statistics, and require knowledge of data sources. The tools developed as part of this project will provide a variety of capabilities to automate molecular profiling data analysis.
- Automated data gathering - New data services and sources are being registered within the BRM framework. This includes support to import microarray data in a variety of formats, including Affymetrics, NimbleGen, and custom in-house formats, and relevant protein databases, such as SwissProt. As required, this project is also building and maintaining cross-reference information between data sources.
- Bioinformatics tool interfaces - Commercial programs, private visualization tools, and statistical algorithms are being registered within the framework. Interfaces between the data repository and the analysis tools are being constructed. Our goal is to make the environment between the data and tools as seamless as the particular analysis program allows.
- Data Extraction and Integration - BRM also provides data integration and merging capabilities by being able to extract relevant identifiers from an Affymetrix or Nimblegen file and adding additional information from public datasources such as NCBI, KEGG, DIP and BIND. It also has tools to facilitate merging two files using identifier matching or advanced merging using the cross-reference information.
Related publications
Shah AR, M Singhal, KR Klicker, EG Stephan, HS Wiley, KM Waters. 2007. "Enabling high-throughput data management for systems biology: The Bioinformatics Resource Manager." Bioinformatics 2007 23(7):906-909; doi:10.1093/bioinformatics/btm031.
Singhal M, EG Stephan, KR Klicker, LL Trease, G Chin, JR, DK Gracio, and DA Payne. 2004. "Enabling Systems Biology: A Scientific Problem-Solving Environment." In International Conference on Computational Science, June 6-9, 2004, Krakow, Poland, pp. 540-547. Springer-Verlag, Berlin, Germany.
Singhal M, KR Klicker, LL Trease, G Chin, JR, EG Stephan, DK Gracio, 2004. "Bioinformatics approach for exploring MS/MS proteomics data" In Hawaii International Conference on System Sciences, HICSS-38, January 3-6, 2005, Big Island, Hawaii, pp. Track 9 - Volume 09, pp: 279.3, IEEE Computer Society, Washington, DC, USA.
Havre SL, M Singhal, B Gopalan, DA Payne, KR Klicker, GR Kiebel, KJ Auberry, EG Stephan, BM Webb-Robertson, and DK Gracio. 2004. "Integrating Evolving Tools for Proteomics Research."In Proceedings of The 2004 International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences. CSREA Press, Las Vegas, NV.
Klicker KR, M Singhal, EG Stephan, LL Trease, and DK Gracio. 2004. "Computational Cell Environment: A Problem Solving Environment for integrating diverse biological data." In Proceedings of the 2004 International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, ed. Valafar, Faramarz and Homayoun Valafar, pp. 422-428. CSREA Press, Las Vegas, NV.
Waters KM, M Singhal, BM Webb-Robertson, EG Stephan, and JM Gephart. 2006. "Breaking the High-Throughput Bottleneck." Scientific Computing. 23(5) April 2006.
