geWorkbench

Overview

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geWorkbench is an open-source bioinformatics platform that offers a comprehensive and extendible collection of tools for the management, analysis, visualization and annotation of biomedical data.

Benefits include:

• Integration with existing bioinformatics modules for analysis and visualization.
• Support for a variety of genomic data including microarrays, sequences, pathways, networks, alignments and phenotypes.
• Access to remote servers and clusters for the performance of computationally intensive calculations.
• Accesses analyses with biological annotations from the National Cancer Institute.
• Flexible import options: Allows user to merge files from various sources.
• Community: decribe this aspect
• Insert developer benefit ( plugin)

Tutorials

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Getting Started

• Starting the application
• GUI elements
  • Panels
  • Navigation

Loading Data

• Data formats

Working with Marker and Phenotype Panels

Creating panels

We can now assign phenotypes to each chip. We will place the phenotypes in the default group, however you can create new phenotype groups by pushing the New button on the Phenotype Panel at lower left.

Here we select and label arrays in the Phenotype Panel which contain samples from the congestive cardiomyopathy disease state...

Next, we can similarly label the remaining arrays as "Normal". We have also checked boxes to indicate that these groups of arrays are "Active". Various analysis and visualization components can be set to only use/display activated arrays or markers.

For statistical tests such as the t-test the Case and Control groups can be specified. This is done by left-clicking on the thumb-tack icon in front of the phenotype name. Here we are specifying the disease arrays as the "Case". The remaining "Normal" arrays are by default labeled control.

A red thumbtack indicates the arrays have been specified as "Case".

Visualize Panels

Here we select the relative display type.

Returning to the Open File dialog as we before by right-clicking on the project entry, we will select the "cardiomyopathy.exp" file we previously saved...

Resulting in the following colorful display of the array data for the first array....

Clustering Gene Expression Data

• Hierarchical Clustering
• Self Organizing Map (SOM)

Differential Expression

• T Test
• Multi Test
  • Volcano Plot
  • Color Mosaic

Regulatory Network

• Reverse Engineering
• Cytoscape

Enrichment Analysis

• Go Term
  • Go Miner

Sequence Analysis

• Sequence Retrieval
• Sequence Homology Analysis
  • Blast
  • Other

Pattern Discovery

• Position Histogram