ARDAS™
Array Repository and Data Analysis System

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Key Features • LIMS for 2-color spotted workflows • Flexible & searchable data warehouse • Supports Affymetrix & 2-color spotted data • Powerful analyses based on BioConductor & R • Support for publishing to ArrayExpress • MIAME compliant Key Benefits • Enterprise system based on Oracle • Distributed web technology • Highly flexible & scalable • Intuitive user interface • Sophisticated security model • DoD Quality award winner

Track, Organize, and Analyze microarray data
The award winning Array Repository and Data Analysis System (ARDAS™) provides a complete and fully integrated solution to microarray data acquisition, management, and analysis. ARDAS includes a Laboratory Information Management System (LIMS), a repository and data warehouse, and an Analysis Information Management System (AIMS).

ARDAS is based on robust and scalable enterprise technologies, including an Oracle relational database. The web-based user interface provides consistent and intuitive navigation through the system. ARDAS was recognized by the US Department of Defense for its technological innovation through a prestigious quality award.

LIMS
The LIMS provides support for laboratory workflows for 2-color spotted microarrays. It records RNA extraction, RNA concentration, RNA labeling, hybridization of labeled RNA to arrays, scanning of arrays, printing arrays from oligo or cDNA libraries, and post-processing of arrays.

The LIMS includes the ability to support multiple laboratories. It provides means to manage the location of samples, standard operating procedures, and equipment in the lab. Scans from hybridized arrays can be published directly from the LIMS into the repository.

Repository
The repository supports Affymetrix® and 2-color spotted data. Array designs are defined either through Affymetrix annotation files and CDF files, or GAL files. The repository organizes data in projects and experiment designs. Sharing of data between scientists is enabled through a sophisticated security model.

The repository provides a simple yet powerful means to annotate samples and arrays with the relevant biological, analytical, and methodological context through predefined and user-defined parameters. This annotation model for the repository is compatible with the MIAME standard.

The repository includes manual and automated updating of annotations for transcripts represented on arrays including links to a many external resources, summaries of function, and GO terms. It also includes a document management feature for organizing and sharing files related to microarray experiments such as publications or protocols. The repository provides exports of data from experiments to flat files and can exchange data with 3rd party software.

Scientists can define and store sets of transcripts in the repository. The repository can seamlessly match transcripts in sets to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and import them in the Database for Annotation, Visualization and Integrated Discovery (DAVID), which supports a broad range of analyses for extracting biological relevance from such sets.

AIMS
The AIMS includes analytical workflows based on R and BioConductor for the normalization and quality control, and linear modeling of Affymetrix and 2-color spotted data. The AIMS supports the publication of normalized data into the warehouse.

The normalization and quality control of 2-color spotted data provides extensive flagging of spots based on single channel and dual channel intensities. It includes many plots and graphs including MA plots, intensity distributions and histograms, and image maps of arrays. The normalization methods supported are loess, loess per print tip, loess per print tip with standardization across print tips, and 2D spatial location loess normalization.

The normalization and quality control of Affymetrix data includes intensity images maps, PM and MM probe intensity distribution and boxplots, Present/Absent metrics from the MAS5 algorithm output, RNA degradation plots, and MA plots. The AIMS provides several algorithms for background correction, normalization, PM probe correction and summarization, including algorithms implementing the MAS5, RMA and d-Chip methods.

The AIMS support linear modeling of two-color spotted data through the maanova R package and BioConductor's LIMMA package. Linear modeling of Affymetrix data is supported through LIMMA. The AIMS guides scientists in formulating linear models for their data with simple and intuitive yet powerful user interfaces. Models for simple or complex (e.g., factorial) experiment designs are formulated with equal ease. The linear modeling workflows include several methods for fitting the data to the model and adjusting statistical significance for multiple testing. The results from the fit of the data to a linear model include volcano plots, Venn diagrams, and heat maps.