Thanks for visiting Scientific Computing World.

You're trying to access an editorial feature that is only available to logged in, registered users of Scientific Computing World. Registering is completely free, so why not sign up with us?

By registering, as well as being able to browse all content on the site without further interruption, you'll also have the option to receive our magazine (multiple times a year) and our email newsletters.

Biomarker development support Japanese research centre's cancer research effort

Share this on social media:

Japanese research foundation RIKEN has chosen Genedata Expressionist to support biomarker development for cancer research. The government foundation is using proteome analysis and mass spectrometry (MS) to identify biomarkers to aid in the development of serum-based diagnostic methods.

'We generate huge volumes of MS data in our proteomics facility,' explained Dr Koji Ueda, a project leader at the Laboratory for Biomarker Development at RIKEN. 'Data analysis has been a major bottleneck and Genedata Expressionist is the only solution we found that can meet our high throughput needs.'

RIKEN's biomarker development group is using the Refiner MS and Analyst modules from the Genedata Expressionist system for high throughput LC-MS-based proteomics data processing and analysis. Refiner MS processes thousands of samples and terabytes of raw MS data and combines intuitive visualisation with sophisticated algorithms for peak intensity and alignment correction, detection, quantification and matching.

'In the field of Clinical Proteomics, there has been a great need to quantitatively analyse as many clinical samples as possible because of individual variations, and we had problems with the alignment of such a huge amount of MS data. However, Refiner MS solved them for us,' continued Ueda. 'Now, we can confidently quantify and identify peaks, which has improved our overall biomarker development. In addition, using Analyst we can focus on sophisticated statistical analysis tasks, which allow data-mining from all perspectives and help us find the best predictive biomarkers in human sera.'