General Technique
Data processing, bioinformatics, and integration of gene-expression data with other data types (like proteomics data) is a current major bottleneck in systems biology. The ISB is uniquely strong in this area.
At the ISB, we are using and developing SBEAMS (Systems Biology Experiment Analysis Management System; http://www.sbeams.org/). SBEAMS is a software and database framework for collecting, storing, and accessing data from different types of experimental data. This system combines a relational database management system (RDBMS) back-end; a collection of tools to store, manage, and query experiment information and results in the RDBMS; a web front-end for querying the database and providing integrated access to remote data sources; and an interface to other data processing and analysis programs.
SBEAMS — Microarray provides MIAME (minimum information about a microarry experiment)-compliant microarray database functionality under the SBEAMS framework. Information about experimental design and details about each microarray run are collected as experiments are carried out. SBEAMS — Microarray then provides an interface for processing the arrays in some common ways.
The SBEAMS - Microarray module allows for data collection, storage and analysis of both spotted glass arrays and Affymetrix chips. Data are collected through a combination of automated data loading processes and user forms. Glass slide analysis is based upon internally developed pre-processing and statistical methods, whereas Affymetrix chip preprocessing and statistics leverage methods developed by the large Affymetrix user community.
Purpose/use/application of the technique:
SBEAMS — Microarray allows investigators to manage and annotate their data in a manner compliant with standards (MIAME) for publication. Information about experimental design, sample preparation, labeling, hybridization, washing and scanning are all collected and stored. Investigators also can review quality control data on their samples, such as electropherograms of their RNA and internal microarray control metrics. Data analysis can be performed, starting with pre-processing and continuing through statistics for finding differentially expressed genes, with links to other software packages such as Cytoscape.
Example(s) of projects at ISB that use this technique:
The Microarray Laboratory is the major producer of data for a wide variety of investigations at the ISB. These include pioneering initiatives to understand immunity and infection, diabetes, and cancer. In addition, the Microarray Lab supports large-scale studies of model organisms, such as yeast, that serve as experimental surrogates and scientific proving grounds. All ISB researchers and projects using microarray technology make use of the SBEAMS— Microarray module to keep track of large numbers of microarrays, perform quality checks, normalize data, perform statistical tests, and load the results into interaction maps in Cytoscape.
Ongoing area of technology development:
Development of SBEAMS — Microarray is ongoing, with several projects currently planned or underway. The SBEAMS — Microarray module will be enhanced to allow automated processing and analysis of ChIP-Chip (protein-DNA interaction) assays within the database interface. We are implementing an export mechanism to allow researchers to export their data as MAGE-ML (microarray and gene expression), the XML standard for archiving and exchanging microarray experimental data for submission to public databases or exchange with other systems. In addition, we continue to develop the inter-operability of SBEAMS-Microarray and the Cytoscape system for the integration, visualization, and analysis of biological network data.
Representative publication(s):
Ideker T, Thorsson V, Siegel AF, Hood LE. (2000) Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data. J. Computational Biol. 7:805-817
Marzolf B, Deutsch EW, Moss P, Campbell D, Johnson MH, Galitski T. (2006) SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology. BMC Bioinformatics 7:286.
Read Publication
http://www.sbeams.org/
|
home
