Syngen's Dymension software for automated 2D protein gel analysis now includes common spot outlines, a revolutionary algorithm for detecting presence or absence of proteins in large data sets
Dymension's new common spot outlines feature saves time with proteomics workflow by generating data, with minimal manual post-editing which can be statistically analysed to confidently identify the correct proteins for costly downstream research.
The unique algorithm in Dymension that produces common spot outlines firstly warps all the gel images in an experiment together to create an averaged gel image and then performs detection on this image.
These common spot outlines are overlaid onto each gel in the experiment's series.
With Dymension's method of producing common spot outlines, each spot exists on every gel so results are less affected by noise and are therefore more representative of the common features of the underlying data.
This means scientists can run less duplicate experiments, thus saving money on consumables and gel handling time.
Laura Sullivan, Syngene's divisional manager stated: "The resources required to analyse proteins identified from 2D gels can be considerable so scientists want to ensure they are working with the right proteins from the outset and this is where Dymension can help.
"We are pleased with this method of common spot outlines in Dymension because it contributes to gaining statistical confidence in 2D gel results from even large data sets.
"Using Common Spot Outlines, we are confident researchers can save time in up and downstream laboratory processing, which will ultimately increase the productivity of their proteomics research."
