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OU Web-based Microarray Database Features

Jilin Han, Joe Grissom, Minghua Yao, and Tyrrell Conway

May 26, 2005 The OU Microarray Database has a web-based architecture that was designed, implemented and administered by experienced database programmers in the OU Bioinformatics Core Facility. Key to the successful development of this highly intuitive software package was daily interactions between the programming team and biologists and array technicians working in OU Microarray Core Facility. The OU Microarray Database design (powered by Oracle 9i) is based on the international microarray community standard MIAME (Minimum Information About a Microarray Experiment). The OUMDB web interface (implemented with PHP, JavaScript, Java Applet, HTML on Apache web server) is designed to facilitate and smooth the entire process of loading, retrieving, analyzing and displaying microarray experiment information to and from the database, which includes user/project management, array production management, laboratory information management system (LIMS), data import, data analysis, data visualization and data export, which will be described in details as follows. The modular layout of the database is shown on the right. The OU E. coli Gene Expression Database (E. coli GenExp DB) serves as a model for the front end of the database, that is, data display. E. coli GenExp DB provides public access to our E. coli data. The Presentation module can be tailored to the specific organism(s) of the users’ choice. There are four major components in the DB. The Front End to the DB contains the Presentation and Production Data modules. The Presentation module contains the presentation, display, and analysis tools, i.e., cluster analysis, graphic displays, heatmaps, etc. The Production Data module contains the minimum information required for the Presentation tools and is

Presentation

Production Data

Print Process Mgmt

Array Design

Microarray/GenePix

Upload Data

Raw Data

Affymetrix/MASMembrane

Preprocessed Data

Project

ExpSet

Experiment

Replicate

OU Microarray DB

Proj

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Front End to DB

MicroarrayPlatform

Raw DataStorageNormalizationFiltering

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configured to facilitate integration of DNA array data generated on the three most commonly used technology platforms (membranes, microarrays, and Affymetrix GeneChips). The Project Management section contains four modules that correspond to intuitive levels of microarray experimentation. Replicate slides are Preprocessed (i.e., normalized) and averaged to create an Experiment. Experiments are usually associated in Experiment Sets (i.e., timepoints in a biological experiment or series of similar treatments). ExpSets are associated with a Project, which is specific to the hypothesis being tested (i.e., related experiments published together in a single paper). The Raw Data module is the staging area for raw data storage and preprocessing of the data for integration into the Production Data module, which is specific to the technology platform and involves data filtering, normalization, scaling, etc., and conversion of the data to the common format employed for Production Data. Uploading data from the Upload Data module into the Raw Data section requires the user to select from the Project Management section a preexisting Project/ExpSet/Experiment or create new ones as desired; in this way the higher level information is only entered once and does not need to be added every time Replicate microarrays are uploaded. The LIMS and genome annotation are associated with the Replicate arrays. The Print Process Management and Array Design modules are specific to the microarray technology platform and are used to manage and store the information associated with the microarray production process.

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The general structure of the web-based OUMDB and relationships between components are shown as follows.

Relationship diagrams showing structure of web-based OUMDB

(Figures are adopted from “Enhancing Web-based Microarray Database Functionalities with Bioconductor and R packages” )

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FRONT END TO DATABASE The Presentation module, that is, the Graphic User Interface was designed from the user’s perspective and is described in more detail here:

We accommodate users entering E. coli GenExp DB with one of four goals in mind: 1) they want to query the DB for their favorite gene(s); 2) they want to examine data associated with a published project; 3) they want mine data using tools available on our site; or 4) download the data for their own purposes. We are in the process of revising the “wrapper” on all of our web pages. The menu structure will soon be changed to reflect the user-based philosophy described here. 1) The user enters the gene of their choice in the query box and the gene-specific data for all available experiments is displayed in a heat map format red is induced, green is repressed and black indicates no change.

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On mouse-over, the experiment specific information is shown and the user has the choice of displaying the entire experiment data as a ratio graph. Gene-specific information from the annotation tables and meta-links to external databases are shown below. Mouse-over on the ratio plot reveals data for other genes and clicking executes a query for that gene:

Or the data can be displayed as an overlay on the E. coli metabolic pathway chart, using Pathway Tools (v9.0) written by EcoCyc programmers.

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2) The project list is displayed below the gene-information section. Here the user is presented with several options for displaying experiment sets.

Users can plot the data according to statistical confidence intervals determined from the data:

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Users can display a hierarchical cluster of the chosen experiment set:

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Users can display the data in a heatmap format that displays all genes by their order on the genome, in blocks of 200 genes and each experiment in the Experiment Set on consecutive rows. Mouse-over of the heatmap displays the b-number, gene, ratio value, and Experiment for the selected data point. Clicking on the data point retrieves the gene-specific annotation information.

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Users can display the date in Gene Expression Genome View, also a heatmap format, which accommodates all genes in very large experiment sets to fit on a single page. The genes are shown on mouse-over and clicking opens a new window (insert) containing a detailed heatmap for the 20 nearest genes:

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Users can display large Experiment Sets in log ratio vs. time point plots with each line representing an individual gene The data shown here includes 4289 genes and 17 time points. The plot can be redrawn according to user-defined parameters.

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3) Users can go into the tool box and select Experiments and/or Experiment Sets of their choice for display in the format of their choice. (See displays above.) 4) The user can download the data for analysis in their favorite software package: Users can down load data by clicking on the info option:

Users can filter the data by any of the available criteria…

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And display the numerical data according to their filtering strategy. They can also download the data in this format.

PRODUCTION DATA The Production Data module is a database table designed to accommodate the most commonly accessed data fields, i.e., microarray intensity, ratio values, and confidence intervals, regardless of the technology platform used. Thus the OUMDB has the potential to handle proteome and metabolome data, provided that Preprocess Data, Raw Data, and Upload Data modules are programmed to interface with the specific application. The purpose for the Production Data module is two-fold. First, the data are stored in a common format that is standardized for the tools that can be accessed in the Presentation module. Second, accessing the data from the scaled-down Production Data module with its minimum of data fields makes the tools operate much faster. These are huge advantages for displaying transcriptome data, as most databases do not truly integrate data generated on different platforms and are notoriously slow in display time because they access data from very large raw data tables. The schema for the Production Data module is shown below:

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Production Data Schema

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PROJECT MANAGEMENT The Project Management section consists of four modules that serve to organize microarray replicates, experiments, and experiment sets by project, user, etc. User Management: users are assigned to different levels with different privileges. Only one user is assigned the role of administrator. The administrator can create new users and assign privileges to other users. Users may have two different privileges, that is, super user or normal user. Users may change their own information including their password through the web interface. Project Management: The Project Management section is associated with LIMS information as shown here. The modules are described in greater detail below. Project Organization LIMS Organization PROJECT: top level of data container. The project is equivalent to a scientific paper in containing experiment sets associated with a single hypothesis or related sub-hypotheses. EXPSET: second level of data container. The Experiment Set is equivalent to a biological experiment that consists of several measurements corresponding to a series of time points or treatments. These are equivalent to a figure in a scientific paper. EXPERIMENT: third level of data container. The Experiment is equivalent to a single time point or treatment that is typically replicated for validation of the result. REPLICATE: fourth level of data container. Replicates are normalized and averaged to make up a single experiment, i.e., time point or treatment.

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LABORATORY INFORMATION MANAGEMENT SYSTEM (LIMS): The LIMS section of the database is used for managing information about microarray Replicates (experiments) and for recording all information related to the biological experiment, such as what organism is used and how samples are treated. BIOMATERIAL AND HYBRIDIZATION

Biosample Source: general information about biosample source such as

organism, strain, genotype, etc.

Biosample: biological laboratory experiment from which RNA sample is extracted, i.e., bacterial culture or tissue culture

Extracted Biosample: extracted RNA representing gene expression in

biological experiment

Labeled Extracted Biosample: labeled RNA used to hybridize to the probes on the array

Hybridization: labeled RNA hybridized to the array

PROTOCOL

Biosample Protocol: information about how biosamples were treated, i.e., culture conditions, etc.

Extracted Biosample Protocol: information about how RNA samples were

extracted from biosamples

Labeled Extracted Biosample Protocol: information about how the RNA samples were labeled with dyes, radioisotopes, biotin, etc.

Hybridization Protocol: information about how the labeled samples were

hybridized to the arrays (i.e., microarray slides).

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AUTOMATION OF PROJECT MANAGEMENT PROCESS In order to keep the user from having to enter information into the database more than once, an automated process was created for adding Replicate arrays. This process associates each Replicate array with the Experiment, Experiment Set and Project of choice and encapsulates LIMS information, including biosample, extracted biosample, labeled extracted biosample and hybridization into Replicate entities. In the case that a Replicate is the first to be associated with a new Project, the option is offered to create all entities of the Project Management and LIMS database fields.

Flow chart of the Project Management section

Automated process of creating Replicates

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TRACKING PROJECT INFORMATION Here we show the relationship between Replicate information (i.e., raw data and image) and LIMS information. Again, existing information for Project Management modules and LIMS may be reused by selecting from dropdown lists or created if not present.

Hybridization Array Layout Array image Data

Labeled sample Hybridization protocol Diagnosis plots

Extracted sample Label protocol

Biosample Extract protocol

Biosource Biosample protocol

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WELL ORGANIZED DATA STRUCTURE The Project Management section is organized in a logical, hierarchical format, making it easy for users to find Projects, Experiment Sets, Experiments, Replicates, and LIMS information, as well as the genome annotation data. One such table used for looking up experiments is shown here.

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UPLOAD DATA Each technology platform has a specific raw data format associated with it. The Upload Data module brings data from a specific platform into the Raw Data module where it is staged for preprocessing. The Preprocessed Data module transforms the data according to a platform-specific protocol by normalization, filtering, and parsing into the format of the Production Data module. For membrane arrays, phosphorimaging produces a TIFF image file that is further processed in ArrayVision™ ver 5.1, Imaging Research, Inc. software. The data are normalized by a global normalization strategy and replicates are chosen for calculation of expression averages, ratios, and statistical confidence. Affymetrix GeneChip arrays are scanned on a platform specific system and the image is processed by using a platform-specific software package. The data are normalized and scaled, then replicates are chosen for calculation of on/off threshold, expression averages, ratios, and statistical confidence. These platforms are not described further. The focus here is on microarrays. Replicates (Microarray Raw Data) and the associated array image (generated using scanner platform-specific image processing software such as GenePix) are uploaded to the Raw Data module. All image processing information such as scan power and laser power are also uploaded, as shown here. The user associates the Replicate with the proper Project/ExpSet/Experiment and browses to the image and raw data files. Clicking on the upload button places the Replicate files into the Raw Data module.

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PREPROCESSED DATA Raw Data must be carefully evaluated before being sent through the Preprocessed Data “Pipeline”. The following OUMDB tools allow the user to determine the quality of microarray Replicates and decide whether or not to enter them into Production Data. Individual Spot image and information Users need to see the microarray image during data analysis to ascertain that the replicate behaved properly in the biological experiment and at any time the results for a particular probe are called into question. The user can readily “click” back and forth between the image, the data file, and probe-specific information, as shown here. This information is accessible from any stage of the analysis/display process within the database.

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DATA ANALYSIS: The data analysis tools for analysis of individual Replicates are powered by R and Bioconductor. R is a widely used open source, high-level language and environment for statistical computing and graphics. Bioconductor provides tools for the analysis and comprehension of microarray data (bioinformatics). Data analysis tools in OUMDB can perform (but are not limited to) the following functions.

Pre-filtering data Pre-filtering of the data allows the user to flag or remove data points from consideration on the basis of several parameters, including null spots in the array, spot quality (bad spots flagged during scanning or by spot-data analysis), signal to noise ratio, and background subtraction for gene on/off calls. Pre-filtering is also used for multi-species microarrays (which we use routinely) to consider only those microarray probes that are specific for genes represented in the Biosource and biosample.

Pre-filtering data

Normalization Users are offered a choice of the following normalization methods.

• Within-print-tip-group intensity dependent location normalization (Lowess) followed by within-print-tip-group scale normalization using the median absolute deviation (scale print-tip). Our Upload Data “pipeline” uses this normalization protocol, with background subtraction.

• Global median location normalization (global median) • Global intensity or A-dependent location normalization using loess (global

loess) • 2D spatial location normalization using loess (2D) within-print-tip-group

intensity dependent location normalization using loess (print-tip) • Total intensity

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Normalization options

REPLICATE DATA VISUALIZATION AND EVALUATION Diagnostic plots offer the user a means for evaluating the quality of individual array experiments.

Background and foreground image

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Boxplot Histogram

Cy3-Cy5 plot log (Cy3)-log (Cy5) plot

M-A plots are the standard for presenting microarray data from individual or replicate microarray experiments. This plot is offered for raw data and for data sets which have been filtered and/or normalized, and/or replicated. The M-A plot is “clickable” taking the user to annotation and metalink information for individual genes.

M-A plot Gene annotation

Click

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• Student’s t-test statistic analysis

t-test result

t-statistical analysis results The student’s t-test is used for comparison of Replicates during the quality control process for deciding whether or not to include the result in an Experiment.

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View sorted data displayed in JAVA Applet

• Linear Model and Empirical Bayes Method

Replicates correlation analysis: The quality of replicate experiments can be evaluated by determining the simple correlation between replicate arrays.

Sorted by b num

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PREPROCESSED DATA “PIPELINE” The Data Analysis Pipeline interface allows the user to create Experiments from Replicates and to group Experiments into Experiment Sets for upload into the Production Data module. The “pipeline” not only allows novice users to Preprocess the data for entry into the Production Data module using standardized filtering and normalization protocols, but also sophisticated users can quickly enter a large number of Replicates.

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The user selects an option, i.e., Create Experiment, and chooses Replicates to be pre-processed to create an Experiment, which is associated with a Project and ExpSet.

Clicking the Submit button sends the data into the Pipeline, where they are filtered and normalized during the pre-processing step, the Replicates are averaged and the statistical metrics are calculated. The Preprocessed Data are automatically entered into Production Data module.

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In cases where it is important to select “responders” in a particular microarray experiment/comparison, the following statistical procedures are useful. Statistical analysis Users are offered several standard statistical methods for evaluating experimental noise and for determining whether or not genes are “responders” in their experiments. That is, genes with significant, differential expression between experimental conditions in pair-wise comparisons. Simple noise determination: The standard deviation of replicate spots for multiple replicate arrays is determined to provide the user with a measure of noise for particular spots (genes) in their replicate experiments. Differential gene expression analysis for two experiment sets: Users are offered several choices of methods for evaluating whether individual genes are statistically significant “responders” in their experiments.

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Differentially expressed genes using Linear Model The “V” plot is clickable for user convenience and provides the user with a means for changing the color of statistically significant “responders”.

Differentially expressed genes Gene annotation

click

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EXPORT DATA Results of data analysis can be downloaded in the tab delimited text file format and can be easily imported to other data analysis software such as Spotfire.

Data analysis result

Downloaded analysis result

Export downloaded data to Spotfire

Export to Spotfire (M-A plot with spots colored by gene function groups contained in downloaded file are visualized in Spotfire)

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PRINT PROCESS MANAGEMENT: the database keeps track of essential information, including the array printer configuration and probe/plate source information annotation (used to print arrays). This module generates the Array Design that is retained in the Array Design module. For membrane and GeneChip arrays, the Array Design is provided by the manufacturer and uploaded into the database at this level.

Array Production List Array Layout

List of plates used for the array print run

Individual Plate information

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Entity-Relationship (ER) diagram of OUMDB modules specific to the Print Process Management through Preprocessed Data fore microarrays is shown as follows:

OUMDB ER diagram