Princeton University Office of Technology Licensing and Intellectual Property

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                                                Post Office Box 36

Princeton , New Jersey 08544-0036

                                                Phone: (609) 258-6762

                                                FAX: (609) 258-1159

Efficient and Scalable Algorithm for Surveying Relative Abundance of Biologically Important Compounds in High Resolution LC-MS/MS Data

Princeton University Invention # 08-2458

Princeton researchers in the Computer Science Department and the Lewis Sigler Institute for Integrative Genomics have developed an efficient and accurate algorithm that processes large volumes of high resolution mass spectrometry data. The technology enables unprecedented surveys of protein, metabolite, and lipid abundances, and thus, promises to aid in the discovery of new biology and commercially important pharmaceuticals and disease bio-markers.

Cells and tissues contain a mixture of three important classes of compounds: proteins, metabolites, and lipids. The abundance of these compounds reveals the nature of the internal state of these cells and tissues. Unbiased quantitative surveys of these compounds across experimental conditions and controls promises to reveal new biology and enable the discovery of new drugs and biomarkers. High resolution liquid chromatography- tandem mass spectrometry (LC-MS/MS) is the prevailing experimental technique for identifying and measuring the relative abundance of proteins, metabolites, and lipids in cells and tissues across experimental conditions.  This experimental technique generates large amounts of data. Consequently, we developed an efficient algorithm that enables visualization of this data and enables the accurate measurement relative protein, metabolite, and lipid abundances.  We integrated this algorithm into cross platform (Mac, Linux, and Windows) software. The software was used to demonstrate that the algorithm handles large data sets that span hundreds of experimental conditions and replicates on a conventional computer, vastly exceeding the performance of existing algorithms. 

Princeton is currently seeking industrial collaborators to further develop and commercialize this technology. Patent protection is pending.

For more information on Princeton University invention # 08-2458 please contact:

                        Laurie Tzodikov

                        Office of Technology Licensing and Intellectual Property

Princeton University

                        4 New South Building

Princeton , NJ 08544-0036

                        (609) 258-7256

                        (609) 258-1159 fax

                        tzodikov@princeton.edu