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University at Buffalo selects new platform

The pharmaceutical sciences research group at the State University of New York at Buffalo, USA, has chosen the Sage-N Research Sorcerer platform to assist with its advanced proteomics research. Providing sophisticated algorithms and server capabilities, Sorcerer will replace the current supercomputers that are used within the University’s laboratory to characterise and identify proteins and handle the large amounts of data generated from high throughput mass spectrometers.

Sorcerer is a ‘plug and play’ platform for life sciences that is designed to support multiple software programs and is suited to researchers in high-throughput laboratories, core facilities and corporate environments. It provides a combination of optimised hardware and can be easily customised with different post-processing tools. The platform also offers high-level server-based solutions for storage, back-up and rapid processing of the complex data-sets generated by high-end mass spectrometers.

The Lab of Clinical Proteomics and Pharmaceutical Analysis, led by Dr Jun Qu, assistant professor at The State University of New York at Buffalo and the chief scientist in Bioanalysis in CEBLS, has chosen Sage-N Research’s Sequest 3G search engine and Matrix Science’s Mascot – both hosted on the Sorcerer platform – to study a variety of applications including diagnostics and biomarker discovery for cardiovascular diseases; colon, pancreatic and prostate cancer; cocaine addiction; retina degeneration; COPD; and HIV.

‘It has been great to be able to finally deploy both Mascot and Sequest 3G on the Sage-N Research Sorcerer platform and experience the benefits of two very powerful search engines for important protein ID applications,’ commented Dr Jun Qu. ‘Most members of the industry are still using standalone PC servers which can be time consuming to configure and would consume more power to process the same amount of data. For example, 60,000 to 80,000 spectra were generated by a single nano-LC/MS run of a clinical sample; in our lab, one typical clinical project will involve the analysis of two to three millions of spectra, which is a heavy burden even for a supercomputer.’ 


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