GE Healthcare Introduces DeCyder 2D 7.0 Differential Analysis Software

27 Nov 2008

GE Healthcare has introduced DeCyder™ 2D 7.0, a new software package for analysis of 2-D Fluorescence Difference Gel Electrophoresis (2-D DIGE) experiments. 2-D DIGE is a powerful tool for detection and quantitation of real biological differences in protein abundance between different samples. DeCyder 2D 7.0 applies a gel comparison method that introduces zero statistical error, offering reliable data and analysis for 2-D DIGE experiments.

The DeCyder 2D software from GE Healthcare significantly increases throughput by accurately addressing measurement of protein differences with superior statistical confidence. DeCyder 2D 7.0 introduces fewer false positives/negatives due to its unique co-detection algorithm. The new software also reduces hands-on time from days to minutes, with minimal user-to-user variation. Advanced statistical analysis and visualization tools, allow deep understanding of the contribution of variation to the data. In addition, linking of data from two or more datasets is enabled, increasing sample population to allow better differentiation between real and experimental variation.

DeCyder 2-D Differential Analysis Software has been specifically developed for 2-D DIGE and is a key component of the Ettan™ DIGE system. The 2-D DIGE system offers significant cost and time-saving benefits in comparison with classical 2-D electrophoresis. Protein mixtures are labeled prior to electrophoresis with size and charge-matched, spectrally resolvable Cy™ Dye fluors, allowing the simultaneous separation and comparative analysis of up to three samples on a single 2-D gel. Following 2-D electrophoresis, the results may be scanned with the Typhoon™ Variable Mode Imager or Ettan DIGE Imager. DeCyder Differential Analysis Software automatically locates and analyzes protein spots, assigning statistical confidence to every difference between the samples. As the system ensures that each protein spot has its own internal standard, gel variation issues are eliminated, significantly increasing accuracy and reproducibility and reducing the number of gels that need to be run.

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