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GeneSpring: cutting-edge tools for expression analysis

GeneSpring is widely regarded as the gold standard for expression data analysis. When you use GeneSpring, you join thousands of elite scientists worldwide who depend on its sophisticated analysis techniques to advance their research. Designed to meet the needs of the individual researcher, GeneSpring seamlessly interfaces with Silicon Genetics’ Signet software, which provides a highly scalable platform for enterprise- level genomic research.

GeneSpring Key Features

Advanced Statistical Tools
GeneSpring provides a host of tools to ask detailed questions about complex data sets. These include t-tests, 2-way ANOVA tests and 1-way post-hoc tests for reliably identifying differentially expressed genes. In addition, GeneSpring's class prediction tools can identify genes capable of discriminating between one or more experimental parameters or sample phenotypes. Groups of genes identified by expression profiling can be further characterized by performing sequence searches for potential regulatory elements.

 

Data Clustering
GeneSpring provides sophisticated clustering methods to uncover patterns of gene expression data and the relationships between these patterns. Researchers can use one or a combination of clustering options to characterize their data: gene trees (hierarchical clustering), experiment trees, self-organizing maps, k-means, Principal Components Analysis (PCA) and QT clustering. QT clustering is an unsupervised technique that allows you to specify both the minimum size and maximum correlation coefficient of each cluster. Principal Components Analysis (PCA), allows you to reduce the complexity of your data by discovering a number of principal components that define most of the data variability.
● Make use of the latest clustering techniques
● Reduce the complexity of your data
● Discover genes that are primarily responsible for the variation
 

Visual Filtering
GeneSpring offers visually intuitive filtering tools for both entry-level and advanced users. All visual filtering windows generate graphs of results in real-time. These filters allow researchers to exclude particular conditions, set minimum and maximum values and choose specific gene lists to filter. GeneSpring also has an advanced filtering window designed for power users. The advanced filtering window allows you to create complex Boolean expressions to identify genes with a highly specific expression pattern. Once these filters are created they can be saved and shared with other researchers via Signet.
Easy filters for entry-level users, advanced filtering for power users
• Real-time visual inspection of filtering results
• Automates complex tasks
• Standardizes important experimental SOPs
 

3D Data Visualization
The 3D scatter plot tool provides in-depth and interactive representations of highly complex data. Expression data values or analysis results can be placed on any of the 3 user-defined axes to create a powerful medium for array data presentation. Average expression values for each classification cluster in a scatter plot can be plotted to reduce noise and quickly identify patterns in the expression profiles.
 

Data Normalization
Sixteen transformations are available for creating powerful and flexible normalization scenarios. Normalization steps can be applied in virtually any order and include operations such as dye swapping experiments and median polishing. Scenarios can be saved and applied in other experiments.
 

Pathway Views
With the pathway viewer, genes and their expression patterns can be visually characterized based on their location within a cellular pathway. Users can design their own pathway diagrams or directly import publicly available pathway maps. Users can predict genes associated with discrete steps in the pathway of interest.

  • Visualize KEGG general and organism-specific pathways
  • Support for a large number of GenMAPP pathways
 

 

Search for Similar Samples
GeneSpring allows you to compare the expression profile of a given sample to all of the other samples in Signet or GeneSpring, even if they are derived from different experiments or aren't associated with any experiment at all.

 

Support for MIAME Compliance
MIAME is a standard that describes the minimal information that is needed to fully describe a gene expression experiment and being MIAME compliant is fast becoming a prerequisite for publishing your data. GeneSpring makes it easy to become MIAME-compliant. Customize MIAME-compliant attributes from an easy-to-use window in GeneSpring. Ensuring that your team adheres to MIAME guidelines is simpler than ever before.

  • Complies with new publication requirements
  • More powerful editing capabilities
  • More flexible and intuitive attribute creation window

 

Scripting
GeneSpring comes with comprehensive script building and editing capabilities. Researchers can create custom scripts to automate repetitive analytical tasks, ensure consistency in the analysis process and simplify data analysis management. Using this tool, researchers can design scripts that automatically upload results to Signet or combine scripts with basic functions to perform more complex analyses.
 - a ready-made collection of scripts that span the complete analysis process. It includes scripts for automating a broad range of quality control, statistical analysis, and biological data query tasks.

MAGE-ML Export
Export your experiments in MAGE-ML, the Micro Array Gene Expression Markup Language. With experiments in MAGE-ML format you can easily prepare submissions to public gene expression repositories. Many journals have recently adopted a requirement that all authors describing gene expression data must submit the data into a public gene expression database. GeneSpring makes it easy to prepare these submissions.

  • Easily prepare submissions to public gene expression databases
  • Satisfy the new rules for gene expression journal articles